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Good day, and welcome to Accenture's Third Quarter Fiscal 2026 Earnings Conference Call.

[Operator Instructions]

Please note, today's event is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Executive Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our third quarter 2026 earnings announcement. As the operator just mentioned, I'm Alexia Quadrani, Executive Director, Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the earnings release, which we issued a short time ago.

Let me quickly outline the agenda for today's call. Julie will begin with an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the third quarter. Julie will then provide a brief update on the market positioning before Angie provides our business outlook for the fourth quarter and full year fiscal 2026. We will then take your questions before Julie provides a wrap-up at the end of the call. We're also pleased to announce that we will host our Investor Day in New York City on October 14, more details to come. Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and, as such, are subject to known and unknown risks and uncertainties, including but not limited to, those factors set forth in today's news release and discussed in our annual report on Form 10-K and quarterly reports on Form 10-Q and other SEC filings.

These risks and uncertainties could cause actual results to differ materially from those expressed in this call. During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations of these non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this conference call. Now let me turn the call over to Julie.

Thank you, Alexia, and everyone joining us this morning, and thank you to our more than 798,000 people for your extraordinary work. Before Angie takes you through the detailed numbers, I will give you some color on the quarter and on the progress we're making on our growth strategy. In Q3, we delivered strong results with broad-based revenue growth across geographic markets industry groups and types of work and once again took significant market share, underscoring the relevance of our services and our strong competitive position. To put our performance in context, we added approximately $1 billion in revenue in Q3 over FY '25 and $3.4 billion year-to-date over the same period last year.

We also delivered strong margin expansion, EPS growth and free cash flow while continuing to invest in our business and our people. This quarter, we had 30 clients with quarterly bookings over $100 million bringing us to 104 of such bookings year-to-date, a 13% increase over the same period last year. This is 1 of the best indicators of the depth of our client relationships and the scale of the reinvention programs we are helping deliver. I also want to give you context on 2 factors that impacted our results this quarter. First, we were impacted by the conflict in the Middle East. We saw a revenue impact of approximately $100 million compared to our expectations, which is all consulting type of work. split evenly between the direct impact on our Middle East business and indirect effects outside of the region. In the last few weeks of the quarter, we saw this indirect impact globally in products and to a lesser degree, in resources, mostly in discretionary spend. In addition, sales in the Middle East were impacted by approximately $400 million and also in EMEA due to longer decision-making.

Second, a couple of our large managed services opportunities moved into FY '27 for company-specific reasons. Now on acquisitions, because of the exciting OT cybersecurity acquisitions we announced today, which I'll talk about in a moment, we now expect to deploy approximately $9 billion of capital this year based on the anticipated closing dates of the acquisitions. We are also adding important capabilities in other strategic areas of growth, including our recently announced acquisitions of Alpha Health a service-led digital health platform in Italy and Whaler, a leading creator and social agency in the Americas. I am also thrilled to congratulate our approximately 124,000 people who were promoted this fiscal year, a 30% increase over last year, including more than 900 who were promoted to Managing Director. Our people make the difference in our ability to deliver our results and value to our clients.

All in all, we are pleased with how we're executing in this environment. Now let's turn to how we're executing our growth strategy to be the reinvention partner of choice for our clients and the leader in the widespread adoption of AI and I want to provide a few examples of how we're capturing new areas of demand in the age of AI, what we're doing to expand our total addressable market and the areas where we are shifting to more non-FTE commercial models over time. We believe that AI will be a tailwind for us and our industry as it scales because it is a catalyst for reinvention and is creating new opportunities to put growth and efficiency for our clients and for us. We are building a stronger foundation every quarter for us to win as AI adoption scales. Let me walk you through some examples. We're starting to see clients who have more advanced digital core has moved to larger AI transformation programs. You can see this demand in several significant AI-focused wins across multiple industries and markets which we publicly announced with companies like British Telecom Group, Mitsubishi Chemical, NSK, Piraeus , Stellantis, TEPCO, Vodafone and the Women's Tennis Association. The major theme of all of these programs is that we are moving clients from using AI to running on AI.

We're also seeing more clients move from pilots to production and all of this is happening even as AI is still in the early innings. This quarter, we saw another 100 clients initiate advanced AI projects with us. we have announced a number of expansions of partnerships with our top 10 ecosystem partners in AI and data, and our revenue growth from these partners continues to outpace our overall growth. We are also on track to more than double our bookings from our key emerging AI and data partners compared with FY '25, including Anthropic, Databricks, GEMINI, Mystral AI, NVIDIA, OpenAI, Palantir and Snowflake. We are deepening these partnerships around specific areas of opportunity where we can combine their technology with Accenture's industry, functional and delivery expertise. Now let's talk about our big move in OT security to create a platform-led growth business with a non-FTE commercial model. This morning, we announced that we are acquiring a majority stake in Dragos a leading platform for operational technology or OT cybersecurity, and all have runZero, a leading vulnerability and exposure assessment firm and Net Rise, a leader in device security. Together, these acquisitions will create a first-of-its-kind OT security platform that lets clients see threats, find vulnerabilities and fix them before it becomes a crisis.

Cyber is a key enabler for AI. We cannot have an AI revolution without critical infrastructure, and you cannot have those without OT security, which is where today the world is most vulnerable. The urgency is real. AI and geopolitical risks are accelerating the need for cybersecurity adoption for the operational technology that underpins critical infrastructure and industrial operations, such as power grids, pipelines, manufacturing, distribution facilities and data centers. Dragos which is the anchor of our strategy, has strong ecosystem relationships with our top ecosystem partners, including AWS, CrowdStrike, Microsoft, Palo Alto and ServiceNow, which we will leverage to scale. Our expansion into the OT cyber platform business builds on our strong foundation of cybersecurity services including OT, we have grown our services organically and inorganically over the last decade from roughly $700 million in FY '16 to $10 billion in fiscal 2025. And a 35% CAGR over the period, 4x that of Accenture's over the same period. This investment more than triples our total addressable market in OT security, which is growing double digit.

We are also expanding our total addressable market by going after a new exciting customer segment, the mid-market. We estimate that the mid-market, which we look at as companies with between $300 million and $3 billion of revenue, is a $240 billion addressable market for us, growing high single digits. That is why we are launching a new business next suite called Accenture Edge. This business will embed Accenture's large enterprise expertise and ecosystem relationships in business solutions designed specifically for the mid-market. We see that companies in this segment face many of the same technology, data, AI, cybersecurity and productivity challenges at large enterprises. But they often need solutions that are faster to deploy, more repeatable and rightsized for their scale. This segment is also an important priority for our ecosystem partners which see strong demand and want to partner with us because we can bring scale, consistency and delivery quality to a fragmented services market. Accenture Edge will also include seamless integration with Accenture's joint venture with Microsoft, Avanade.

Avanade will continue to serve as the provider of Microsoft platform services to mid-market clients bringing deep cloud and security and AI expertise to help companies adopt AI at speed and scale. Together, these actions show how we are building a strong foundation for us to win in expanding our addressable market across new growth areas and client segments and evolving towards more non-FTE revenue over time. We believe this positions Accenture well for our next phase of growth. Over to you, Angie.

Thank you, Julie, and thanks to all of you for taking the time to join us on today's call. We are pleased with our third quarter results with revenue above the midpoint of our guided range with strong profitability and robust free cash flow. We delivered these results while continuing to invest in long-term market leadership and returning significant cash to shareholders. Based upon these results, we are on track to deliver or exceed all aspects of our guidance provided in September.

Let me summarize a few highlights in the quarter. Revenues grew 3% in local currency with growth across geographic markets, industry groups and types of work. Excluding the 1% impact from our federal business, revenues grew about 4% and we continue to take significant market share on a rolling 4-quarter basis against our basket of our closest global publicly traded competitors, which is how we calculate market share. Operating margin expanded 20 basis points to 17% compared to Q3 results last year. This was achieved while making significant investments in our people and our business. EPS grew 9% in the quarter to $3.80 compared to EPS last year. Finally, we delivered free cash flow of $3.6 billion and returned $2.2 billion to shareholders through repurchases and dividends. Nine months into the fiscal year, we invested $3 billion primarily in 13 acquisitions.

With those high-level comments, let me turn to some of the details, starting with new bookings. New bookings were $19.3 billion for the quarter, a 2% decrease in U.S. dollars and 3% in local currency with an overall book-to-bill of 1.0. In Q3, consulting bookings were $10.3 billion with a book-to-bill of 1.1. And in Managed Services bookings were $9.1 billion with a book-to-bill of 1.0. Turning now to revenues. Revenues for the quarter were $18.7 billion, a 6% increase in U.S. dollars and 3% in local currency. Consulting revenues for the quarter were $9.3 billion, up 4% in U.S. dollars and 1% in local currency. Managed Services revenues were $9.4 billion, up 8% in U.S. dollars and 5% in local currency driven by mid-single-digit growth in technology managed services, which include application managed services and infrastructure managed services and high single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenues grew 1% in local currency. Growth was led by software and platforms, high tech and industrial, partially offset by a decline in public service. Revenue growth was driven by the United States. Excluding the about 1.5% impact from our federal business, Americas grew approximately 3%. In EMEA, we delivered 4% growth in local currency, led by growth in public service and software and platforms. Revenue growth was driven by the United Kingdom and Italy, partially offset by a decline in Germany and in the Middle East. In Asia Pacific, revenue grew 8% in local currency, driven by growth in public service, banking and capital markets and insurance. Revenue growth was driven by Japan, Australia and Singapore. Moving down the income statement. Gross margin for the quarter was 32.8% compared to 32.9% for the third quarter last year. Sales and marketing expense for the quarter was 9.7% compared with 9.9% for the third quarter last year. General and administrative expense was 6.1% compared to 6.1% for the same quarter last year.

Operating income was $3.2 billion in the third quarter, reflecting a 17% operating margin, a 20 basis point increase from operating margin in Q3 last year. Our effective tax rate for the quarter was 24.2% compared with an effective tax rate of 24% for the third quarter last year. Diluted earnings per share grew 9% to $3.80 compared with diluted EPS of $3.49 in the third quarter last year. Days services outstanding were 48 days compared to 46 days last quarter and 47 days in the third quarter of last year. Free cash flow for the quarter was $3.6 billion, resulting from cash generated by operating activities of $3.8 billion, net of property and equipment additions of $186 million. Our cash balance at May 31 was $10.2 billion compared with $11.5 billion at August 31. With regards to our ongoing objective to return cash to shareholders. In the third quarter, we continued to accelerate our share buybacks and repurchased or redeemed 6 million shares for $1.2 billion at an average price of $198.84 per share. As of May 31, we had approximately $3.2 billion of share repurchase authority remaining. Also in May, we paid a quarterly cash dividend of $1.63 per share for a total of $1 billion.

This represented a 10% increase over last year. And our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on August 14, a 10% increase over last year. And year-to-date, we have returned $8.2 billion in cash to shareholders, which is $1.3 billion more than the same time last year, demonstrating our commitment to shareholder returns. In closing, we remain focused on executing our business and capturing new opportunities for growth while continuing to invest to strengthen our relevance in the age of AI for long-term market leadership. And now let me turn it back to Julie.

Thank you, Angie. When I look at the breadth of work we signed just this quarter across industries and parts of the enterprise, it is staggering. The reason is that we are truly the only company that can cover at scale, everything from the AI and technology foundation to reinventing nearly every part of the enterprise. We bring a track record of delivering results for decades and deep, trusted relationships as seen in the fact that 195 of our top 200 clients have been clients for more than 10 years.

We are working with McDonald's, one of the world's most iconic restaurant brands, serving more than 70 million customers every day as we support key elements of their ongoing transformation. Together, we partnered across the enterprise with a particular focus on finance and people modernization and customer loyalty. This foundation is helping McDonald's become faster, more innovative and efficient as they continue to grow and stay competitive. Stay tuned for the next reinvented with Accenture episode on CNBC in the coming weeks featuring McDonald's Chairman and CEO, Chris Kempczinski; and how McDonald's is continuing to reinvent for the future. Now let's double-click on demand in the quarter. We saw a few major themes. Clients continue to invest in the foundations needed to scale AI. This includes strengthening their digital core through cloud, data, security and operating model transformation. A lot of our reinvention work today is helping clients get ready for AI and data remains a critical enabler with at least 1 out of every 2 advanced AI projects continuing to lead to a data project.

Second, clients continue to look to reinvent faster, leverage our proprietary platforms and expertise and achieve greater efficiencies and growth, including through managed services across the enterprise. We're seeing the nature of these programs with managed services evolve with clients asking from our consulting and AI expertise within them, exactly the shift we have been positioning for. Bath & Body Works is a great example of our work in managed services. A global leader in personal care and home fragrance, Bath & Body Works is one of America's most iconic retail brands. They have a strong growth agenda built around their core product lines, brand modernization and expanded distribution. Delivering on that requires a smarter, more scalable operating model underneath it. We're expanding our partnership to make that possible, consolidating fragmented operations across critical business functions into a unified managed services model with Agentic-AI embedded throughout and humans in the lead. The result is automation replacing manual effort, faster speed to market and significant cost savings and productivity gains that Bath & Body Works can reinvest directly into growth.

Another area of strong demand are the AI enablers we've been investing in from capital projects to data centers to learn manage to cybersecurity 1 of our largest AI enablers. As I mentioned earlier, OT security is one of the hottest areas driven by AI cyber threats and geopolitical risk. We are seeing that demand in our services business as clients look to protect the physical infrastructure that keeps their operations and communities running. For example, we're helping one of the largest electric utilities in North America secure its electrical grid, protecting power infrastructure that serves more than 10 million people. As utilities modernize and connect more devices across the grid, the cyber threat landscape is expanding rapidly. Operational technology environments like substations and transmission networks have historically had little visibility into cyber activity, making that harder to detect and slower to resolve. Building on a decade-long cybersecurity partnership, we are extending our work beyond traditional IT into the physical infrastructure.

Sensors will be embedded at substations, connecting them to a centralized security operations center for continuous monitoring and automation will turn raw data into actionable insights. The result is a more secure, resilient grid, protecting the homes, hospitals and businesses that depend on it every day. Finally, clients with more advanced digital cores are starting to take on larger AI programs, exciting green shoots. These large-scale AI programs are complex. And to make advanced AI work, deep industry and functional knowledge is needed in addition to technology and AI expertise. A great example is BT Group. One of our U.K. clients where our long-standing relationship is expanding into a new AI partnership for BT business, the division which provides the connectivity backbone for U.K. businesses and public services. BT business manages networks have massive scale and a threat environment that is evolving faster than traditional operating models can keep up with. So we're embedding AI directly into the core of how they operate, building on their existing network intelligence, customer data and service management platform.

AIOps capabilities with autonomous agents will detect route and resolve incidents with cell healing that accelerates how quickly issues are resolved. The result will be fewer disruptions, faster resolution and a more resilient network positioning BT business to lead the next generation of AI-powered managed services to its customers. As we look at the opportunity to scale AI, we're a partner of choice because we are delivering tangible results. For example, Fox Communications, the largest private broadband company in the United States worked with us to drive growth and efficiency across marketing, sales and service. Together with a leading large language model provider and hyperscaler, we built an AI engine that validates and enrich it's leads, generates personalized campaign content and automates brand and legal validations. In B2B sales and marketing, conversion rates increased and drove net new revenue. Lead accuracy jumped from 13% to 97%. Campaigns speed to market improved by 55% and marketing content teams are 40% more productive with capacity free to drive further growth. This is what AI ROI looks like in practice. Not a pilot, but a production-grade commercial engine delivering results at scale.

Banco Bradesco, one of Brazil's largest financial institutions is another great example of delivering tangible ROI from AI. Competing in the country's biggest lending market, new and used vehicle financing, their ambition was to grow deliberately at scale without sacrificing risk discipline. To fragmented platforms meant slower decisions inconsistent experiences and limited ability to compete at speed in a market where dealers and customers expect instant answers. Together, we've built a single unified platform that orchestrates the entire journey from the dealer portal and customer origination through government database checks, credit validation and loan processing. All of it works seamlessly in real time across many integrations with and for the bank. Bradesco grew its vehicle financing portfolio 7.3% quarter-over-quarter with a unified platform, a key enabler of that performance. With that, over to you, Angie.

Thanks, Julie. Now let me turn to our business outlook. Given the macro uncertainty, we expect more of the guided range to be in play for Q4. For the fourth quarter of fiscal 2016, we expect revenues to be in the range of $17.75 billion to $18.4 billion. This assumes the impact of FX will be approximately negative 0.5% compared to the fourth quarter of fiscal '25 and reflects an estimated 1% to 5% growth in local currency. And as it relates to our federal business, we expect to anniversary the headwind and get back to growth in the fourth quarter.

Moving to full fiscal year '26. Based upon how the rates have been trending over the last few weeks, we assume the impact of FX on our results in U.S. dollars will be positive 2% compared to fiscal '25. For the full fiscal '26, we now expect our revenue to be in the range of 3% to 4% growth in local currency over fiscal '25, including an estimated 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 4% to 5%. We continue to expect an inorganic contribution of about 1.5%. And with our exciting announcement to expand into the OT security software market that we have just made, assuming those transactions close this fiscal year, we now expect to invest approximately $9 billion in acquisitions this fiscal year and we continue to have a pipeline of attractive acquisitions for FY '27.

For adjusted operating margin, we now expect fiscal year '26 to be 15.8%, a 20 basis point expansion over adjusted fiscal '25 results. We now expect our annual adjusted effective tax rate to be in the range of 24% to 25%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We now expect our full year diluted adjusted earnings per share for fiscal '26 to be in the range of $13.78 to $13.90 or 7% to 8% growth over adjusted fiscal '25 results. For the full fiscal '26, we continue to expect operating cash flow to be in the range of $11.5 billion to $12.2 billion property and equipment additions to be approximately $700 million and free cash flow to be in the range of $10.8 billion to $11.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.3. We now expect to return at least $9.5 billion through dividends and share repurchases and as we continue to return a substantial portion of cash to our shareholders.

Finally, as part of our routine review of our capital structure, including taking into account our elevated D&A outlook for FY '26 and we expect to access the long-term debt market to increase our liquidity for B&A spend and general corporate purposes as we look to optimize our capital structure and reduce our cost of capital. In connection with that, we expect to maintain a strong investment-grade credit rating with a low net leverage ratio. With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. I would ask that each of you keep 1 question and a follow-up to allow as many participants as possible to ask a question. Operator, will you provide instructions for those on the call, please?

[Operator Instructions]

Today's first question comes from Bryan Keane at Citi.

It looks like the Middle East conflict should be alleviating given the recent agreement with Iran in the U.S. How does that impact you think the Middle East weakness you saw the $100 million in weakness you saw this quarter as we get into the fourth quarter because you guys are still highlighting some macro uncertainty in the guide?

Yes. Thanks, Brian. Because the indirect impact really started in the last few weeks and mostly in discretionary spend, we do think that there will be more impact in Q4, which is why we're saying that more of the range is in play. In these areas, it's not clear how fast, right? Things will change, particularly because some of the industries are dealing with kind of longer-term issues. So think about automotive, where we have a large presence they were already challenged and now with the higher gas prices that's added to it.

So it's difficult, of course, to predict and even exactly how it's all going to play out. But we -- because we started seeing it really in the last few weeks on the indirect impact we do think that more of the ranges in play. The impact in the Middle East, again, it just depends on how quickly people start to focus on kind of growing and that. So Q4, that's how we're seeing Q4.

Got it. And then the pushout on managed services, on our calculation, it was a little over $2 billion lower than we expected. Does that bump Q4 totals? Do you get that? Has that already been signed? Is that bump Q4 totals by an additional $2 billion or so? Just trying to quantify the impact there?

Sure. No. So what we saw was a couple of deals pushing out FY '27 with that and not into Q4. So we did see some slippage overall in EMEA, right, that we're to try to make up for in Q4. But the bigger deals. And remember, managed services, like when you're doing a deal, let's say, $300 million, $400 million, $500 million, it can have a big swing, right? And so we're seeing -- we've seen a couple of the big lumpy bigger ones move out for company-specific reasons to FY '27. So I wouldn't think about it as massively increasing Q4 at all because it's pushed further out.

Our next question comes from Tien-tsin Huang with JPMorgan.

Julia I was hoping, can you give us a little bit more on the thesis of acquiring the OT security assets. I understand the great growth spaces within security, but just there's a lot of layers to this. It sounds like you're adding more non-FTE content, but is there above average risk here because you're stitching together the 3 assets. And it sounds like there's going to be some initial dilution. So I just want to better understand the risk there. And then finally, just why prioritize security as an enabler for AI versus other areas to win in AI. We obviously trust what you guys have done in the past. I'm just trying to better understand because this seems more strategic than about adding revenue per se?

Thanks, Tien-Tsin. Exactly. This is about long-term growth and really a massive market when you start to think about how it's not about even assets, everything is going to the physical world, right? Physical AI is coming. Everything is going to be connected. And so you can't have an AI revolution unless you have critical infrastructure, and unless you secure -- when you start moving into physical AI, and you can't have that without OT security. 95% spend in the past has been about IT security and OT security is a much bigger market and critical need. And we're starting from a $10 billion cybersecurity services business that we've built over the last 10 years organically and inorganically, a 35% CAGR. And we've been in OT security all along.

And so one of the things that we do really well is to understand where the technology is going to create demand in our clients. In terms of the platform itself, Dragos has an excellent platform. The addition of Net Ryzen 10 is just enhancing an already strong program, platform. And what today do is they have a bunch of fragment, they have to like contract here and they have the counter and they have to stitch it together. So day 1, just the first thing is it's one contract, right? And then we'll enhance the platform, which Dragos has a ton of experience because they've been building that platform. So we don't see risk at all in terms of stitching it. And day 1, we're already making companies a lot happier because they can have 1 buy, not the -- this is a -- we view really a massive opportunity because it's meeting such a critical need. And it is simply you cannot succeed in AI unless you've got security.

Interesting. trying to learn more about it. That's why I want to ask. Just as my follow-up then, maybe for Angie, just thinking if the entire range in play to your answer to Bryan's question there. Just trying to think about the bottom line visibility there. What are you doing to protect the bottom line to the extent that you see the maybe if we'd start to lean more towards the bottom end of the range, for example, what are you doing to protect the bottom line? Is there flexibility there given all the investments that's going on?

Yes. And I think for us, we did because of the uncertainty that we experienced, particularly in the last 3 -- the last few weeks of the quarter, we did want to make sure that you understood that more of the ranges in play. Tien-tsin, I think one of the things that you're trying to get underneath is really around our exit rate and what that looks like going forward, right? So and I know that, that's top of mind for you guys because you use Q4 at that basis. But I went in to make sure that they get a few points out for you to consider because this is what we're thinking about as well.

So if you think about the acquisitions that we have announced today and the expected closing, we do expect to enter FY '27 slightly below 2% of inorganic growth. Secondly is our AFS headwind will sunset this quarter, and we expect that it will return to growth this quarter. The third is related to the managed services opportunities that Julie mentioned and when those actually -- when they close in '27. And then, of course, this conflict that Julie already mentioned in discussing with Brian, that's a variable, and we'll see how that evolves. But at the same time, we are executing in new areas, including demand in AI and expanding our TAM. So these are the elements as we think about in terms of how we look forward into '27. And importantly, within our range for revenue of 1% to 5% for the quarter, margin and EPS, we expect strong overall margin and EPS expansion for the year.

And our next question today comes from Jason Kupferberg with Wells Fargo.

So the consulting bookings growth was actually pretty strong in the quarter. And even on an LTM basis, it's pretty solid. But the constant currency revenue growth in consulting has obviously been a bit more tepid. So I'm curious what may be causing a bit of that disconnect? Are there issues with backlog conversion? Has the mix of renewals increase? Other factors? I mean, obviously, you talked about the Middle East, but not sure that would necessarily be affecting the revenues more than the bookings in this quarter specifically. And again, if we look at it on an LTM basis, it just seems like there's a little bit of a disconnect there. So would love any thoughts.

Yes, let me just give you a little bit of context here. So in terms of consulting type of work, we did see it tick down, and it was the result of the indirect and the direct impact of the Middle East. The $100 million that we called out was all in consulting type of work. We do expect in Q4, moving forward into Q4, AFS will return to growth, and we do expect a ticket, which is comprised of both the AFS returning to growth. as well as we had 4 consecutive quarters, as you call out of consulting business growth.

Yes. And Jason, the consulting bookings growth is very much driven towards the fundamentals that we continue to see, which is clients saying really actually have to reinvent meaning we're not just in our managed services deals, looking for just efficiency. We've seen a 3-quarter trend now of more consulting work in those large programs for managed services because our clients are asking us to help them use AI and change the processes to do more change management to really embed new ways of working. And so we're seeing that consulting grow in a lot of these larger deals that also include managed services, and it's a direct result of our strategy that says this is not a technology play. It's a business play.

Understood. Okay. And then just a follow-up on Q4. So at the midpoint of the 1% to 5% range, are you assuming a similar $100 million headwind as you saw in Q3? And if you can just parse out your expectations on consulting promoting services growth for Q4, that would be great.

So, overall, so the impact that we saw was really at the latter part of the last few weeks of the quarter. And so we expect that to continue for the full of Q4. So that is factored into what we expect. And then overall for the year, we expect consulting to be in the low single digits and managed services to continue to be in the mid-single digits.

Our next question comes from Kevin McVeigh, UBS.

With the $9 billion M&A, that's obviously up from $5 billion, is there any way to think about how that settles in 2027 in terms of the dollar contribution of that? And then what type of growth is associated with those acquisitions?

Kevin, so overall, with our overall, with the $9 billion of acquisition spend as we look forward into based upon the timing of when these close and the profile of the acquisitions themselves, we look to enter FY '27 with slightly under 2% of inorganic contribution from these deals.

Yes. And Kevin, in terms of just the profile of that revenue, what you're seeing is that we are moving into higher growth areas. So we're really excited about the cybersecurity acquisitions that we just announced, that's $208 million ARR, growing at 48%. So that's just an example of how we're using the acquisition to move into higher growth areas and they have a different profile in terms of their commercial model. So one of the things that I've said consistently is that things that our clients have been buying and services for a long time, it's going to take a while to like change the buying patterns which is why we're making -- but it's much easier to go into new categories or to provide new kinds of value and switch to non-FTE models.

And so you've seen that with what we just did with cybersecurity. You saw that with Ookla. We announced Alpha Health this week in Italy. That's also a services and platform combination. And so we're going to continue to move ourselves into non-FTE in part by these acquisitions that will then drive organic growth.

That's helpful. And then, I guess, with the shift to Accenture Edge, any thoughts as to go to market? Is it similar to kind of the traditional -- should we expect similar mix of consulting versus managed services? And does that impact the Microsoft relationship at all?

No. In fact, it should amplify that because we're really going to go after. We've done a great job with Microsoft and we now are even putting more resources into focusing on the mid-market. So there's going to be a lot of pull-through across the board. So Avanade has been super successful. We are -- have been making some acquisitions in this area. The way we go to market there is really ecosystem led. But what we can now do is like say we're in -- with one system partner and there's another opportunity, it's much easier to pull them in, which is why we think it will amplify what we're doing with Avanade and there's a seamless integration to make sure that that's what we're doing.

But we're also doing it in a much more efficient and focused way because our clients in the mid-market don't need the same client coverage model that we use for larger enterprises. And that's what's really exciting about this. And when you think about it, the industry has had this challenge now for a few years on discretionary spend, which is really smaller deals. And going into the mid-market, in a big way is going to allow us to structurally kind of offset the challenge on the discretionary spend for large enterprises through this. So we're really excited. It's obviously early days, but we've got a great track record with Avanade, and we think it will amplify Microsoft and the other ecosystem partners that the mid-market needs.

And our next question today comes from Jim Schneider at Goldman Sachs.

I was wondering if you could maybe comment broadly on the and budgetary impact you're seeing from AI infrastructure spending and token spending specifically in terms of upward pressure on their budgets. And what impact are you seeing on sort of what you view as to the -- your addressable TAM in terms of services and even software? And are you seeing any kind of change that would kind of drive some moderation in that infrastructure setting to benefit you in the coming quarters?

Yes. So Jim, the one of the things we're clearly seeing, in fact, we're-- we have a practice that we're starting to grow now is on how to help clients optimize their use of tokens. It feels a lot like the cloud scenarios. Remember when people were moving to the cloud, and then they were like, oh, wait a minute, we're not -- we're spending a lot more on the cloud than we thought, and we built a whole FinOps practice on helping optimize cloud.

So we definitely think that we're seeing that with the clients, and they're coming to us because we're doing a really good job ourselves of being able to know how you use the tokens, which models you use for which problems and that's something we've been focused on since the very beginning. It's also helping because we have delivered real ROI and our clients are seeing the spend, but they're struggling with the ROI and so it's helping us there. And at the same time, there's like only -- there's a certain amount of spending that's going to happen. And so we're not seeing it be material to impact the spend on services today. And if anything, we think it's going to drive more to use services and that's how we're seeing it develop.

And one of the things that we're really focused on is expanding our TAM in other ways, right, because the budgets haven't been even with AI, they're spending it differently, but they haven't been increasing. And that's why moving into cyber security platform business, triple -- more than triple our total addressable market in OT security. The mid-market is a massive TAM that we're now going to -- and that's not been a focus of ours other than generally. So we are really focused on expanding our TAM while we're capturing more of the AI spend.

That's helpful. And then maybe just as a follow-up. In terms of your M&A strategy, clearly, you've been pivoting towards more product-based acquisitions, both with the cyber assets just announced today as well as Ookla, would you expect given software valuations are where they are today, you'll be more aggressive going into fiscal '27 and be even harder for software acquisitions in the next few quarters?

Yes. So we are definitely seeing at our clients, this convergence between services and software and specifically areas where it requires domain knowledge quite deep understanding of the enterprise. So OT security is perfect. It integrates with all of our other tech ecosystem partners, but it really requires supply chain and engineering and so many skills beyond security. So we are going to continue to look at those opportunities because particularly as the technology and AI changes so much, clients are looking for more and more opportunities to not have to build things, to not have to sort of try to figure it out themselves.

And these are areas where we're embedding expertise and data. And that's kind of like our focus in that real expertise and so in addition to looking at it for acquisitions, we're also going to be building more and more and we've already started with our ecosystem partners where we're basically going to have IP together that create solutions that also drive then our services. So -- and we're building them ourselves, like so for example, our [indiscernible] platform internally, we're now taking to clients, and that's a platform we built to optimize tokens. So the strategy is around acquiring and partnering and building. And you should expect that because those are the opportunities we see in the market for growth that we'll continue to focus there. as well as AI enablers, right? So cyber security, capital projects, data centers.

And our next question today comes from Dave Koning at Baird.

And maybe just -- there's so many crosscurrents right now, macro, I just AI impacts, et cetera. When you kind of strip everything out around macro, et cetera, are you seeing -- do you believe that there's actually underlying kind of fundamental building of the AI demand? And would you expect, even though we see numbers decelerating a bit that the actual underlying demand for newer activity is building and can drive acceleration in coming quarters and years?

Absolutely, David. And we see that building every quarter. So think about our clients with $100 million of bookings of more. It's $104 million, 9 months in, that's 13% more than last year at this time. we called out that we're starting to see the AI like large enterprise programs where they're not just use cases, but really embedding it. And British Telecom Group is a great example. Stellantis is a great example. Stellantis is across their manufacturing, British Telecom is across their operations. And when we look at the AI projects themselves, right, while still small, there's been a steady increase in the average size.

So you're seeing that, and you're seeing that we're starting to have those green shoots of where clients have matured -- much more mature digital cores where we've helped them than the next step of these bigger AI programs. And that fundamental building of every quarter is continuing. The demand is the same, right, getting ready for AI and then deploying AI. So we're really optimistic because we believe AI is going to be a tailwind as it scales for us in the industry.

And one just a numbers question for Angie. Fiscal '27 margins, should we expect anything different than the normal 10 to 30 bps? I know with the new acquisitions coming on and investments, et cetera. But is the underlying margin expansion still expected to be about the same as normal?

Yes. And David, we'll give you updates on our overall FY '27 outlook, but our goal is always to continue to drive improved gross margins, improved SG&A while we invest significantly in our business.

And our next question today comes from James Faucette with Morgan Stanley.

I wanted to just spend a moment to sensitize us to the fourth quarter. I hear you very loud and clear that there's a wider potential range. At the same time, we're getting almost 2% of inorganic contribution plus recovery in federal services. So should I interpret perhaps at the low end of your range, ongoing deterioration that you seem to have indicated, it started to materialize in the latter part of the quarter versus if to get to the upper part of the range, it would be some improvement there and maybe middle of stabilization. I'm just trying to understand the scenario that you're trying to sensitize us to for the fourth quarter.

James, yes, that's correct.

Okay. And then turning to the acquisitions, really intriguing and then, obviously, a lot of questions this morning. I'm wondering how we should think about the -- this type of product-driven acquisitions? Is this really where we should think about Accenture being focused a go-forward basis? And what are the implications that we should have in our minds about the impact to long-term margins, margin trajectory, et cetera?

Sure, James. So first of all, our acquisitions are going to continue to be a mix of services, services and products and products, right? We are skewing, however, toward where we see the demand, which is in areas where we uniquely have the domain expertise, the clients needed, it's usually triggered by AI. And that's what we're seeing. So we're skewing our acquisitions to where we see the biggest growth opportunities right now and where we see the biggest growth opportunities. On the product side, are in these areas that are being triggered by AI. And I'll talk about valuations in a minute. But we're also continuing to do services acquisitions.

But again, in the higher growth areas, and we have seen higher valuations even for the services acquisitions like the data centers, but it's also paying off for growth, right? So if you think about what we did with DLB associates, that acquisition, they're growing very high single -- very high double digits, right? So think about our acquisitions as getting into higher growth areas leading where the biggest opportunity is and shifting us to more non-FTE. So the valuations on the services we're seeing are ticking up because our end demand. And then we have different valuations for software and product. And we're being very transparent so that you can understand. And by the way, that Angie never gives you something early for '27. And we wanted to give you that on the acquisitions to kind of help you think about the impact.

Operator, we have time for one more question, and Julie will wrap up the call.

Our final question comes from Jamie Friedman at Susquehanna.

Thank you for the additional disclosures here, especially around security. I had a question about consulting. The consulting book-to-bill was solid again, it was over 1.1% this quarter. It was 1.3% last quarter. I know there could be some FX in there. But that's despite some of the headwinds you called out, Angie. So I was wondering if you could unpack some of the consulting by type of work. For example, technology consulting strategy consulting, you mentioned change management, Julie. Yes, it must be a dynamic time to be a consultant. So I was wondering what parts of consulting you're seeing particular demand for?

Sure, Jamie. So first of all, our clients really do buy solutions. And so we don't think of it as like strategy or this kind of. And so -- but I think what you're getting at is sort of like what are the types of areas. So for example, digital manufacturing, cybersecurity, change management embedded and bigger programs, marketing because it's driving growth. And so think about what's driving this is where clients can get more growth and for efficiency.

So of course, our cost facility is also going up, like how do you look across the enterprise. So it's pretty broad-based in terms of like the functions, but it's all places where you can really get an ROI. So clients are very focused on whatever kind of work, whether it's AI or not, intangible results. And look, that's why clients come to us, right? When you have clients doing $100 million bookings, you don't sign those kinds of deals unless you have confidence that you're going to get results. And that's a real theme in our competitive differentiation and why we're taking market share is across the board. Clients are focused whatever kind of work on clear ROI.

And then for my follow-up, last quarter, Q2, you had a disclosure about 2025 fixed price at 60% of work. Can you talk about the evolution of fixed price? Is that type of work in particular demand and how the margin characteristics of fixed price may compare to the other dimensions of the company?

Jamie, let me take that. We continue to see our fixed price work be over 60% and continuing to increase. There's no real difference as we look at it by type of work. It's in the similar zone for both consulting as well as managed services. And obviously, you see that play out in our margins overall as well. So margin is not a big difference that I would call out relative to fixed price versus the other commercial constructs. But it is embedded in our 20 basis points of expansion for the year.

So thank you, everyone. In closing, I want to thank all of our shareholders for your continued trust and support and I want to thank all of our inventors every day for our clients and our communities. We'll talk to you next quarter.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines, and have a wonderful day.

Hello, and welcome to today's Carnegie Mellon University Software Engineering Institute's webcast, Rethinking and Maturing AI Adoption. My name is Ipek Ozkaya, and I'm the Technical Director of AI Native Software Engineering at the SEI. And I've had the incredible pleasure of leading this project focused on AI adoption maturity with our team at the SEI and the incredible team at Accenture.

We want to make today's conversation as attractive as possible. So please feel free to put your questions into the YouTube chat area. And we've already received close to 200 questions. There is no way we'll be able to get through any of them in completeness, but we'll try to get to them as much as possible afterwards.

It is no surprise today that businesses are -- across all sectors are redefining themselves and going through a structural shift through AI solutions. And they are trying to redefine their operational relevance, their operational workflows as well as get ahead of the businesses through ROI. Software-driven organizations are also going through the same challenge. In fact, the software as a discipline is being redefined through AI, looking into efficiency, productivity and of course, some of the risks that come with it.

And clearly, all the organizations that deliver us the frontier models, OpenAI, Google, Microsoft and Anthropic are developing improved capabilities around the cloud, and we're receiving these capabilities around a lot faster. If we look into 2 years ago, the early generative AI models could barely solve some of the cybersecurity tasks. But today, we know the Mythos and GPT 5.5 could actually execute some of the complicated multistage attacks on vulnerable networks. So this is a very challenging act to get ahead.

And lasting AI value and ROI is at the top of every executive, every engineer and any worker or across any of the sectors. And intentionally to finance systematically manage AI practice maturity is not now a luxury. It's a key differentiator for organizations to get ahead. However, doing more AI does not define maturity. Scaling AI to every single engineer in your organization does not define maturity.

So that's actually where the challenge is in today's conversation. True AI maturity is measured by how you are able to deploy with engineering rigor, how you're able to develop resilient and transport the capabilities, how do you bring together governance approaches where it's needed and be able to evolve the technologies and catch up with the evolving technologies.

So of course, it was not an easy task to develop an AI maturity model amidst one of the most fastest changing technologies, and that presented two challenges for our collective teams. One challenge was how do you provide structure in a rapidly changing environment. And then the other structure is to really focus on maturity rather than compliance and checking the box. And we'll try to get to some of those and how we address them.

Our approach has been in [ price ], focused on the data and the reality and running some of the early adopter programs. So the results we are going to share with you, and you also have the open model available to you, that reflects the collective insights of the research, academics, industry and our private partners throughout different organizations.

So today, I am joined by my colleagues, who I have the incredible pleasure to have shared the journey. And I won't be able to do justice to their background, so I'm going to allow them to introduce themselves. So thanks a lot for joining me today. And to get us started, could each of you please briefly introduce yourself and share the lens through which you view AI adoption and AI maturity? And I'd like to start with RP from Accenture.

Thank you very much. It's been a pleasure working with Software Engineering Institute and CMU while we build the AI maturity model. My name is Rajendra Prasad, I go as RP. I'm the global technology lead for Accenture.

One of the key business challenges when I speak to a lot of leaders in the industry today is how do we measure the business benefits of AI and how we scale AI implementation across the enterprise that can deliver the promised business outcomes. For that, one of the key aspect is to kind of approach the implementation of and the benefits in most measurable, structured disciplined, methodical way. And the maturity model that we work together will help us to accomplish that thereby delivering business leaders business benefits and then scaling the implementation. Thank you for that.

Thank you. Anita?

Okay. Great. Well, first, it's great to be here with my colleagues from Accenture and SEI. It's been very exciting to work on the AI adoption maturity model with our 2 organizations. As far as introductions, my entire professional career has been about defining, researching and advancing modern software engineering practice. And of course, now that includes AI.

I currently direct the software engineering portfolio of work at the Software Engineering Institute at Carnegie Mellon University, where I lead efforts to make software a strategic advantage, especially for national security and national defense. We do that by advancing trustworthy and resilient software-intensive systems. The lens that I view AI adoption and AI maturity is to build on the many lessons that we learned when we worked with [ Watts Humphrey ] on the capability maturity model for software.

The CMM and CMMI, CMM for software and CMMI, was a structured framework to help organizations assess their software development practices, also improve their software development processes and then most importantly, improve project outcomes. At a time when there is increasing hype and pressure to take advantage of AI for business transformation and for cost improvement, we can use the AI adoption maturity model in a very similar way to assess AI capabilities and build a road map to strengthen the practices to achieve AI goals.

Thanks a lot. And we also have some voices from the trenches. Tony, could you introduce yourself for us?

My name is Tony Leraris. I'm the Accenture CIO. And I'd also just like to thank you for letting me be part of the conversation and being part of the work that's been done in the past few months. It's been a real pleasure.

For me, I'm responsible for enabling the 750,000 people of Accenture with the right technology and innovations to be able to serve their clients as well as the technology that we need to run our business. And when I look at AI, I tend to look at it from a very practical lens. And so for me, it's how do we scale that across an enterprise of this size in a way that is sustainable and responsible.

It is easy to deploy generative AI technologies. It is hard to operationalize it at scale. And so what I'm looking for is how do we do this in a sustainable, responsible way to generate value for our company and for our clients.

Thank you. John?

First, I'd like to say thank you so much for our Accenture Partners. My name is John Haller. I've been at the SEI for about 10 years in total, doing applied research around operational resilience, right? How can organizations sustain their critical services, what's the balance between protective controls and sustainment controls? And now, what are the AI implications of that? I also spent about 8 years in a large financial institution, helping with information security and the join between information security and resilience and operational resilience.

So when I think about AI adoption, I'm really looking at it from the perspective of technology risk management and how that impacts the other risk areas and also the cybersecurity perspectives, which we'll talk a little bit more about.

Thank you. Let's go to Majd.

Thank you, Ipek. It's a pleasure to be with you from Accenture's beautiful New York City offices overlooking Manhattan. My name is Majd Sakr. I'm the Chief Learning and Research Officer at Accenture. That means I worry about our workforce and their capability to make sure Accenture remains competitive. I'm in the same role at Accenture [ Learn Vantage ], which is the external facing workforce training arm of Accenture, where we train millions of our enterprise clients, those workforces.

I'm also a computer science faculty member at Carnegie Mellon University. I'm delighted to be with my SEI colleagues here, where I've been for the last 20 years. And I'm part of a large research lab that runs a lot of studies on workforce training.

From my perspective, Ipek, I spend -- from my Accenture roles, I spent multi-days with the C-suite and with the board talking about AI and workforce training and the evolution transformation that needs to happen. And a lot of the questions that we get is, are we doing this right? And where should our big bets go? And what are the right transformations that we need to think about and we need to strategize around?

And so there's a big need for a scientifically proven AI maturity model that helps guide the enterprise and all of these leaders to recognize where they are, where they're headed that is anchored in a lot of fundamental research that enables them to feel confident in terms of the outcomes that are going to come out of that and then the next steps that they can take so that they can move their organization in the right direction.

Thank you for that. And Kaveh, please.

Ipek, thank you for having me here. I have spent my entire professional life in healthcare. I serve as Senior Adviser to Accenture for the healthcare sector. I practice medicine. I spent early part of my career as an operator and delivery system and funders. But the last 2 decades, I've been focused on the intersection of technology and healthcare delivery, how do you make healthcare affordable and accessible and effective.

I have been very concerned about a particular issue that we see globally in all rich countries, which is that the demand for healthcare is rising because of an aging population while the workforce available to serve that demand is actually in absolute decline. And so the mismatch is putting every developed country in the same place, which is they're going to run out of caregivers.

And there were no good answers. And I became interested in AI primarily because I saw no other path for us to extend human capacity. If we couldn't have a technology that could take some of the tasks away from humans, give them time back so they can take care of other people. And generative AI, in particular, because of its ability to take over language skills, was really the first technology we had seen.

What was interesting to me about that and why this maturity model is so interesting is because the benefits of society did not -- do not come strictly from the existence of technology. It's necessary, but not sufficient. The societal benefits actually come from how the work has changed. And what's important about this maturity model is how it emphasizes that maturity is not just data and tech. That's 2 of 8 elements, but there are other elements, to include work and workforce-related elements, that are just as critical to AI maturity. And I think that is a really critical understanding that needs -- that organizations need to have.

Thank you all for that. And for our audience, if you realize it's by design that we have different perspectives because AI today is what in terms of industries, in terms of disciplines, and it's all around in many of the work we do.

So let's then go through -- as we were going through our data collection, we've come across a lot of misconceptions of AI adoption and maturity. So that's what I would like to ask to my colleagues here. If you have to identify the single biggest misconception organizations have about AI adoption maturity today, what would it be? And maybe Anita, if you would get a start [indiscernible] we see at the SEI?

Well, I'm going to build on all the points that you just made because I think they're right on. I think a big misconception and one that I think derails many AI programs are just the things that you talked about. People say AI maturity is primarily about tech. Okay? It's specifically about having better models, more data, all things like that. This belief really can lead organizations to investing money in some of the wrong places, millions in fancy, large language models or ML Ops platforms before they really clarify what business problems actually need AI.

Secondly, hiring top-tier data scientists while ignoring that the frontline staff really lack the training or incentives to be able to use AI outputs. Another issue, building models that work in notebook, so working in the small, but really fail in production because operational workflows, data pipelines and/or governance aren't quite ready. And one more, confusing experimentation. So maybe 1 successful use case works.

But with maturity, we're talking about something else, right? We're seeing it sustained, scalable, accountable value delivery. So these are at least some of the misconceptions that build on what Kaveh had to say.

And RP, you see organizations of all sizes and all sectors. So what are some of the misconceptions that you observed?

I think the fundamental -- I do say that more than misconception, the belief that the technology can transform in a large enterprise where people are the most critical aspect of the change is where we need to balance both. The change management, all the aspects that Anita talked about are very critical for enterprise. How does the change management and a maturity framework can help enterprises, the critical success factor for managing change is quantify and show the benefits to the people that the implementation of technology, in this case, AI, be it systems, be it software, is delivering the promised business outcomes.

So the measurement aspect, change management aspect and managing that very, very [ thinly ] is the key fundamental premises on which enterprises has to operate as we move forward. That's kind of what I get when I speak to my leaders in the field.

True. And I guess 1 of the misconceptions related to that, as we saw through is like bringing tools does not mean AI adoption if you're not measuring and if you're not building that change. And Kaveh, you mentioned it from the perspective of healthcare. So what are some of the misconceptions in one industry sector, maybe?

Because healthcare is probably a sector where you're not going to be having technology doing whole replacement, it's just going to be partial. What you're actually going to do is have to remodel the jobs. And to emphasize that, there's 3 layers that we're seeing organizations in healthcare have to deal with. Because AI maturity is not about does the AI work, it's, does the organization get a benefit.

So first layer is individuals need skills because technology is a coworker. You're now spreading the tasks out between you and technology and you need certain skills. The second one is at a more organizational level. I'll give you a hypothetical, and we model this. If I have 10 nurses in a hospital medical surgical floor doing the same thing today, the future state for the same workload is 6 nurses plus technology reallocating the tasks. And those 6 nurses aren't all doing the same thing. Some are doing highly specialized things that come and go as needed, and some are doing the same thing all the time.

So as a manager, instead of having 10 people showing up all doing the same thing, I have 3 people showing up doing the same thing, 3 people showing up doing specialized thing, plus technology. And as a leader, my work is reorchestrating all of that. That is a different job for the people and for the manager.

And then the last one is going from here to there is an operating problem. A CEO of a very large health system -- and I had this conversation. And he completely accepted the premise of the nature of the problem. And his statement to me was, "I don't know who to give this problem to. It doesn't fit in our operating model today. It's not the job of HR, it's not the job of IT, and none of my operators actually know how to take us through this journey." So we're seeing all those levels in play if you are going to get the benefit of the technology.

And it's so true because as we were developing the model, one of the areas that we focus significantly on is workflow reengineering because it's really rethinking workflows with AI, and that's actually a reflection of maturity. John, we've seen a lot of those. So what are some of the misconceptions that you and I have observed?

Well, I've heard organizations talk about reinventing cybersecurity and risk processes, right, as if something completely new is needed to safely manage AI adoption, right? And I think there's some nuances and caveats there. I mean many of the things or most of the things that your organization is probably already doing are very applicable to managing AI technology and managing those risks, which we'll get into a little bit more detail about that.

So the cybersecurity practices you have now are the good basis for safely adopting AI. Notably, this conversation is not specifically about Mythos and AI sort of in the attack, which we could probably have a separate webinar about, right? But start with the cybersecurity practices you already have. And then when you think about risk, right, it's not a separate risk function. It needs to be baked into the risk activities that you already do.

From an enterprise risk perspective, it's really about this idea of cross-domain risk, right? Like if you have a large enterprise risk function and you already have certain domains, technology, information security reputation, like those conversations between risk areas are really important. They've always been important for technology, but now they're really important, and they support the kind of things that Kaveh is talking about, how do you really bring all the stakeholders on board? How do you understand maybe what impacts and other areas might be?

I think it's very important because we're not AI does not mean you throw away all the good of the discipline of any of the engineering software and cyber. And that is, in fact, a misconception that a lot of organizations go through. Majd, I find your dual role very interesting, and you see things probably from different perspectives. So anything else to add?

Sure. I'll mention two quick misconceptions. One is this laser focus of AI as an efficiency and productivity opportunity rather than seeing it as -- yes, productivity and efficiency alongside growth and potential reinvention of what you do. Products and services are all now up for being disrupted, being thought of differently and how do you offer these things has to be thought through.

The other one is something that was already mentioned, which is if you get the tools and you train people on the tools, then you should see like where is the productivity or where is the innovation. And we recognize that there are many different things that need to happen along AI literacy building and then hands-on training.

And then you have to start going to where Tony was speaking earlier on how do you integrate and operationalize AI in the day job of the individual. So you have to start thinking about role-based training, and you have to start thinking about domain, whether it's healthcare or financial services. That's where you need to take the training. And then you will start to see that people will integrate AI in their day-to-day work.

So we -- I'm following some of the audience comments, as we like there's an audience comment that summarizes the conversation as skill sets, tool sets and mindset. And I think that's a very nice way to bring it to summary.

So Tony, I'd like to go to you next because as the CIO of a very large operation on the global scale, you have a perspective from the trenches that probably changes by the minute. So what keeps you awake at night [indiscernible]?

Yes. I mean the -- the challenge is not for me how to give people access to AI. What I really think about is how do you scale this and operationalize it. So what am I concerned about? I'm concerned about governance. As John mentioned, I think a lot about risk. And then I want to make sure that we have accountability for the outcomes that we should be driving from AI. And it's not just productivity, as Majd mentioned.

And so for me, I really do think about how do we operationalize that. And I think that for me, AI maturity is not just how we deploy this. It's how do we do it responsibly, sustainably and end up with measurable business outcomes. If we do that, like that's what I'm worried about is how do we do those things, not how do we give people the technology.

Some of your responses actually are very relevant to some of the questions we got. So here, I'm going to pull an audience question that we got during the registration. And John, this has all your name written on it. The question for goal is, what are the cybersecurity challenges when adopting AI?

No, I think it depends on where you're at in your journey. And there are certainly risks, and there are certainly cybersecurity challenges, right? But if you're starting out and you're in the exploration or experimentation stage, you need to have the basic blocking and tackling of cybersecurity, right? Because there is there is certainly a data protection risk here, right? If you say go forward and experiment, the proper controls need to be in place so that your data is essentially not going out the door and being used to train models and things like that. So that basic blocking and tackling needs to be in place.

And then as you're adopting more and more AI and your organization has a dependence on kind of the stability and the AI services and the trustworthiness, there are some risks that we already manage in cybersecurity, but that become a little bit more important, right? Like the integrity of data, the trustworthiness of data, controlling access and things like prompt injection and so forth. When you think about kind of towards the future ready and particularly agentic workflows, right, then we get more into how do you manage all those agents and how do you manage their access and how do you manage their entitlements, right?

And this is something that a lot of organizations that I've seen, due to varying degrees like service account management, for example, but it just becomes particularly relevant as you go towards agentic. But the message really is do the blocking and tackling, think about the additional risk as you implement and put those controls in place. And then as you go to agentic, there are plenty of tools and resources out there to help people.

The other thing I should mention with respect to the model, the model is the super set of practices for adopting AI. And there is cybersecurity content in the model and the specific kind of slice that is adoption-specific. But for anyone who's wondering it's not everything cybersecurity for AI, right? For example, you're not going to see specific content about vulnerability management. Why is that? Because you need to manage your vulnerabilities anyway, right? So it's the things you need to focus on that we think are very -- there's practices and processes that we think are very relevant to adopting AI.

And that's actually an important point because you need to have the practices to start with and how you're augmenting for AI and what are the AI-specific ones rather than just reinventing the wheel and some of the aspects. Unless there's anything else to add, I think it's time to start focusing on talking about the model and aspects of it. But I'd like to start with asking RP and Majd because what happened is about 1.5 years ago, RP and Majd came to the SEI with the challenge of developing a maturity model for AI adoption, so given the reality of the [ state ]. So why -- what triggered that, RP, if you would like to give us a little bit of historic background to why we started?

It's a very important and very interesting question. Thank you for asking. I think Majd will add to it. One of the key discussion point at the time when we thought about implementing AI, AI and the technology is changing at a rapid pace. The speed at which it changes, I call it as 1 hour is equal to 1 day, 1 day is equal to a month, 1 month is equal to a year of speed are you changing.

So the speed at which AI technology is changing, enterprises needs a methodology, a mechanism, a process discipline to kind of go from point A to point B. I come from what Anita mentioned is what's [ Hamre School ], where if you want to go from point A to point B, you first need to know where you are on the map. This is what's famous [ court ], right?

So we wanted to understand how do we get this methodology and maturity spectrum designed and built. Then who is better than SEI because you guys have the research, the experience and the expertise to look at systems, software engineering, multiple disciplines like security, configuration management that required project management, measurement area where Anita's expertise in there. So if you look at all of them, including the continuous evolution of the technology, we thought we should work along with the SEI. And thank you for the partnership. And that's the trigger, giving industry and enterprises model and the mechanism and the methodology to drive and implement the rapid pace of change of technology in a very structured way. That's kind of the context.

And that was actually a fun challenge for us to tackle. Majd?

Well, I mean, we've gone through many inflection points, and every one of them requires us to rethink how we do things and how we're going to approach to solve certain problems. And what are things that we need to challenge ourselves on so that we can do them better?

And one of these things that AI is challenging is how does the enterprise rethink itself, what it offers and all of these things. And when you look at what was out there in terms of AI maturity model, unfortunately, there was a lot of initial efforts that were not -- that were narrowly focused, and they were not evidenced in research. And so we wanted to head in the direction where we can substantiate the work that we're going to put out there for the world to benefit from.

And so we were driven by a lot of the agendas and strategies that were set by organizations and governments across the globe. So it was important for us that the model will be a global AI model, not necessarily North America-centric or something like that. And we wanted to make sure that it tackles all the different enterprises and organizations.

And to double down on what RP said, coming to the SEI was a no-brainer. The global credibility that you hold and the history that -- of what you have accomplished in the past and how much you have benefited the world in improving their adoption of technology and software development gives a lot of credibility to us now taking all of that and enriching it with a lot of what Accenture brings to the table, which is process reinvention, how do we think about the workforce, how do we train the workforce. So it seems like a perfect partnership. And so far, it has been. So that's how the story came together.

Well, thanks a lot for that background. So let's share with the audience, a little summary of the maturity model, AI adoption maturity model that we've developed. And we've also gone through a number of early adopter programs. But before I get started and talking about the model with Anita and John, I would also like to pull two questions from the audience that came earlier on because this is important and we're going to address them. And one of them is from Leonardo, how much data is behind this framework and what companies have been surveyed, given how novel AI adoption is? Incredibly valid and very fair question. And the other one is for Marco, who asks, most maturity models assume a fixed end stage you climb toward. What model capability is improving faster than most organizations can execute a single phase of their road map? How do you build a maturity model that doesn't go stale? And where do you draw the line between sequencing adoption deliberately versus just staying adaptive because the ground keeps moving?

Excellent questions. And in fact, we did not take those questions lightly. We ask those questions to ourselves from day 1 because there has been quite a number of successful initiatives in maturity modeling as an instrument. As we sit across these two organizations, SEI has been very fortunate and has changed some of those techniques, and we still use some of those like CMNC.

So the way we approach this was how do you rethink AI adoption and maturity. That was the challenge that we ask ourselves. Our #1 activity was to look around. Because if there were already successful models, why not use them? So we did a very extensive literature search as well as survey and executive interviews in terms of looking in the challenges and what instruments they use.

Of course, there are quite a number of techniques out there that look into AI readiness and maturities, some of those [ maleate ]. And within the next couple of weeks, we'll actually release that study. There are 136 examples. Some of them are only research efforts. Some of them are organizational techniques that are mostly internally focused, but there are other publicly available models as well. What we did not observe was the evidence of how they were developed or how they were used.

So with that, and we also looked into a number of organizations as well as industry -- large-scale industry in terms of where their challenges are. So the way we rethought the AI adoption is, first of all, how do you focus on AI relevant capability? Because at the end of the day, AI is software. As the Software Engineering Institute, we very strongly believe that. And because AI is software, if you're not disciplined in your software, guess what, your AI adoption journey will fail. Same can be said for cybersecurity.

So we try to focus on that delta that is focused on AI and AI adoption maturity. And how -- where will you provide value. Given that the model that we've developed is focused on AI-relevant capabilities, because a lot of organizations will [ die select ] to move fast. And there are a number of components that we inserted into the structure of the model that will allow us to adapt. One of them, we call context attributes, which will allow organizations to adjust where they focus as they are looking into understanding their maturity. And these context attributes will also allow us to develop industry-specific or sector-specific or particular workflow-specific aspects of the maturity.

And the other aspect is the goal is not to define and identify a level. The goal is to really get to a road map. So there is the structure of the model at the end, which allows us to recommend where you would like to start where quick wins could be or where you really need to invest to get ahead of the curve. So that was the background that we developed it. And all of that data will -- we have, and we're going to share it through other means as we go through the next couple of weeks and months.

So -- but of course, any maturity is a progression, and we did think through the progression as well. And Anita, will you please talk us through some of that levels as well?

Sure. Do you want to go to the next slide?

Sorry. There you go.

Okay. There you go. Thank you. So first, today, many organizations are pursuing AI adoption, but it's with a really vague AI everywhere mindset rather than a clearly defined way or a clearly defined strategy for approaching AI adoption. So when you look at this diagram, the AI adoption maturity model is really intended to help organizations determine the level of AI maturity to pursue and then also help them to guide management of its implementation road map.

So when you think about maturity and you see these maturity levels, it's determined by the presence of intentional, repeatable, actionable and governed approaches. There need to be clear objectives standardized practices, and Ipek, you've talked about it, measurable outcomes and continuous improvement.

Yesterday, I think Manish Sharma, he's the Chief Strategy and Services Officer at Accenture, he had made a post in LinkedIn. And he said that this is about how we create value differently. And that's what we're trying to do here is create value by applying AI, but getting to a very different set of outcomes. I think, Majd, you said yesterday, it should be a part of the new DNA of the organization. So that's another way to think about this.

But since we talked a little bit about CMM or CMMI, this is unlike CMMI in that it is not about organizational certification. So the work that we've done here doesn't go into certification purposes.

And John, would you like to talk to some of the dimensions and the capabilities that we have?

Sure, maybe we'll go to the next slide.

Yes, we'll go to the next slide.

Just to kind of segue off what Anita said. It's not a certification. And what I've seen a little bit of my professional life is sometimes when you put a maturity model in front of technology executives who are in front of, I'll just say, type A people who want to achieve, they want to get to that level. They want to get to green. Well, it's not about getting to green. It's about identifying your gaps and areas for improvement and sort of challenging yourself, right? RP referred to it as like a catalyst guidance and catalyst in the forward to the document here. And that's really important. It should be that catalyst for just for where do you want to go and what do you need to get there.

So with respect to the different dimensions, they're really divided into organizational change management and then engineering life cycle. I'm certainly not going to drill into the slide, but it's all of the areas considered essential for adopting AI, from workflow reengineering, to strategy, including how do you identify technology partners and people that can help your organization, to cybersecurity and basically to architectural aspects of adopting AI. One of the things that you don't see on the slide but that's in here as well is what we call maturity indicators. And for anyone who's familiar with CMM or CMMI and the generic practices, this is the modern or, I'll say, tailored version of maturity indicators for the model. How do you know these things will actually persist and you're really adopting that culture? Do you fund them correctly? Do you measure them correctly? Do you think about the risks of what you're doing well or not doing well in a certain area, right?

So again, we could spend a lot of time looking at each of these. But this is all of the areas in the model that are essential for adopting AI and figuring out where you need to go.

And the model is publicly available, and we're happy to answer questions online as well.

Ipek, I wanted to build on some -- I think it was a question that you received from someone named Marco. Also, we are working really hard to keep this as a dynamic model. So as we are working with early adopter organizations and we have lessons learned, we are incorporating those lessons learned back into the model. So Version 1 of this is available, but we are going to keep it dynamic and keep it current by constantly refreshing with the lessons learned and the early adopter experiences that we have.

And then the question about climbing towards a fixed state is actually a very good question because one of the constant debates we had as we were both going through our early adopter program as well as developing some of the practices was how do you measure maturity and what does measuring maturity mean. And we actually have a dimension called maturity indicators. And John, maybe if you could say a couple of words about the maturity indicators as well? Because that's essential because the goal is not to climb to a state, but the goal is to be able to understand how those maturity indicators guide you through the process.

Sure. So they really revolve around accountability for the practices that are in place, measurement of the practices and then how you're actually resourcing those practices. So at lower levels, you're basically assigning accountable individuals, you're making sure that your policies and procedures and all of those things are fully in place to support adoption.

And then this will sound familiar to people who are familiar with maturity models generally, right? As you go up the scale, you're thinking about the risks of gaps you may have in certain areas, and you're actually building that into your risk management process, right? It's not just we have some risks and we get together. It's we have risk, we formally recognize those. We figure out how important they are. As you go further up to scale, you're really assigning metrics, key risk indicators, key performance indicators and making sure that people have kind of the same picture.

And then as you go further up to scale into future ready, this is where your organization is really striving to be a leader in the space to influence the community, right? I would say, this is where you're really kind of, I would say, spreading your wings, it's built into your business. So we use all of these -- we use words like repeatable and predictable. But at the higher maturity levels, it's not -- you don't really have to think about adopting AI. It is how you do business, right? But the -- I mean with that, that doesn't mean the model is only for organizations that want to get to that level. The model is for organizations who are trying to improve and who are trying to get on the path and get on the journey.

That's very true, and it's also driven by what you would like AI to accomplish. Kaveh says that all the time. It's not what AI can do, it's what AI should do, and that really defines the guidance.

For this next question, RP, I'm going to put you on the spot because AI is a very technology-driven endeavor, and we're bringing a maturity modeling approach. So given some of your experience from the past, why is a maturity model approach is the right approach to this problem that organizations are facing with the fear of missing out versus the resources spend and the value to get out of it?

Oh, that's a lot of things in there. For example, one of the topic we discussed in the last 30 minutes is AI is a software and systems engineering. I always say, all the rules of software engineering and systems engineering applies still.

I think if you go with that premises, and the way John mentioned in the model, we have -- what are the two typical aspects of implementation of any technology? One is the engineering aspect. Another one is people aspect, which is the change management aspect. As you go through the maturity adoption of implementation of the technology, your risk management, your change management, your expectations and business alignment continues to evolve, unchanged dynamically. That's the word that Anita was speaking, right? We need to get the dynamic inputs of implementation, both from the chain management aspect and engineering aspect of the technology that we implement.

And that's the critical reason why a maturity framework, I call it as a guidebook to move forward for enterprises, to look at something industry-relevant, business aligned and that covers all the aspects of the software engineering and people aspect to drive the implementation and adoption in a true sense. That's why the aspect of maturity models is critical for me.

And we've definitely seen the value as we went through some of our applications. So I'll take another question from the questions that came previously because it's very relevant to how we thought through the -- both the development as well as the implementation of the model.

And the question from Richard is, using your AI maturity model, how did you balance quick wins against long-term government staff capability building and sustainable institutional change? Is there a road map that can guarantee successful implementation while sustaining momentum?

First of all, nothing can guarantee outcome. If you're not a disciplined organization, that's a people problem. With that, John?

Well, I mean, so with respect to quick wins. The question, I think, is quick wins versus how do we know this is going to last? How do we know this is going to be sustainable, right? So within the model and the approach is the idea of context attributes, right, that, first of all, context attributes to me really means what are the inherent risks and what are the important things to the organization. Right?

If you're in a highly regulated organization, I spent a lot of time in finance, right, your approach to these areas might be a little bit different, right? So that, along with, frankly, an assessment, helps you identify your quick wins or the things that you need to do now or in the next 6 months, right? The long term is really where we get into like the maturity indicators and kind of going up to scale. How do you know that these practices are really going to be around in a year or 18 months and that you're actually going to be able to, we kind of say, emerging property, right? But that idea that you can just -- that you can do these things and you have to think a little bit less about it. That's what I would say about it.

For our purposes, quick win is not necessarily just check the boxes for the next practice. It's really prioritizing in terms of that road mapping approach. And it actually is very true. There's an audience comment, which I will share with my colleagues. AI is advancing rapidly, suggesting that models are also rapidly modernizing. In this scenario, the company has adopted AI. But if it doesn't keep evolving, it tends to [ allude ] competitiveness over time. Absolutely. And this is, in fact, some of the areas the capability is, in fact, focused on how you evolve and how you think about evolution, modernization and to be able to keep up with the space. So it's not a state in time, but it's really how you have practices to be able to continue to reinvent, as Majd says, as well with the evolving technology.

So I would like to make sure we spend some time talking about our early adopter experience. And I'm going to turn to Tony here because Accenture Global IT graciously stepped up, and we had to convince them that it was worth their time and allowed us to do our very first early adopter pilot with their organization. So Tony, why did Accenture Global IP step up and try the AI maturity assessment out? And what was the journey and what has happened since then?

Yes. So I think for many -- just like many other enterprises, we knew that generative AI technology was going to be part of our landscape. We know and knew the kinds of outcomes that we expected to get to. But like for many organizations, you have to figure out what path you're going to take to get there.

For us, the opportunity to participate in the research-based assessments seemed like an incredible opportunity to help us assess where we are and where we need to go. We found some of the strengths that we have in terms of like technical capabilities. We also identified some areas that we needed to focus on, like governance, value measurement, workforce management, workforce transformation.

And coming out of there, I think we are able to use the assessment based on the model to create a road map for us so that we know where we're going to go. And we're using that to help guide our future now, especially with like our workforce transformation and some of the work that we're doing.

And for me, it's pretty obvious. I talked to many other CIOs in my role. They're all facing sort of similar things, but I feel like we have a head start on that because of this assessment we took and because of the things that laid out and where we wanted to focus going forward. And that is driving our road map.

Thank you. So before I turn to Kaveh, I'm going to read the question so that there's a backdrop to this because I think this will resonate with as well. But it's -- so the question is, how do you distinguish between an organization that's exceptionally mature at automating all processes versus one that's mature at using AI to enable fundamentally new and establishing stakeholders?

And I think you see this a lot within the healthcare and some of those that you actually look at that.

Great question. It actually highlights in many ways, what effectively the maturity model highlights, if you go from less mature and more mature. Because in the beginning, you're largely probing, experimenting, learning, dropping AI into existing processes and seeking optimization. When you get to more mature, the organization has made a commitment to change what it does fundamentally to get a benefit that doesn't exist today. And for that, you have to make a strategic decision that, that's actually an objective.

And what's interesting in healthcare sector specifically is that healthcare organizations, particularly, let's -- I'm going to focus on delivery system rather than insurance, which is a little bit more financial in its orientation. At the end of the day, delivery system wants to do what it's always done. It wants to provide safe care, effective care, affordable care. It doesn't necessarily want to be in a different business.

And so some of the commitments to change that you'll often see an organization -- so let's -- an analogy might be if you're an automotive company and you want an autonomous vehicle, you're not getting there without AI because it doesn't exist. There is no healthcare analog to that right now. But the nature of that commitment doesn't actually exist. And therefore, the kinds of organizational changes that you need to do aren't really an imperative.

So what you'll see is a lot of organizations will use the maturity model, but once they realize what are the attributes of the more mature, they have to make a decision as to whether that really relates to where they're at. And I think it's important to not take the position that it's axiomatic that you must change. But to the extent that you have that as an outcome, you are going to have to change things that are very fundamental to your organization.

Another way to think of it is, if -- the quick wins thing is interesting. The way I think about it is most healthcare organizations. What they say by a quick win is I'm going to drop the technology, and 80% of my benefit will be tech and 20% will be changing processes. That's quick. But most of the things that happen to have in healthcare is 20% tech and 80% work. There is no quick win in that kind of a change. And that, I think, is really a sector-specific, problem-specific decision that has to be made.

And it's also -- I think we've observed in some of the specific assessments, the assessment, in a way, helps them make that hard choice, right, that...

It illustrates to them the choices that must be made, and these are strategic choices.

Yes. Exactly. And John, any other points to a from other early adopter experiences that we've had?

I thought I would talk a little bit about scoping, and we experienced this during another early adopter experience. Right? And we could talk for a long time about scoping. But it's important to think about the problem you're trying to solve and how you're going to apply the model, right? A lot of times, enterprise leaders want to apply a tool like this at the enterprise level. After all, they're responsible for the enterprise.

There are lots of different ways that can be used. You can do organizational unit. Of course, you can do an enterprise assessment. You can think about what is your maturity relative to a specific service that you're trying to support with AI.

One of our early adopter experiences was in a safety critical industry. And it was not enterprise. We essentially assessed and appraised 2 organizational units, both of which were adopting AI for the purpose of software development. And one of the -- some of the findings where we noticed differences in their understanding of risk and also their understanding of the corporate process around risk, they were doing a great job, but they were managing risk in slightly different ways that I think was kind of a surprise to the people who were involved. So it was useful.

Some of the things that we also observed through these experiences is if you are already using AI for your product development and have that from some of the traditional machine learning AI, you actually adopt a little easier because you know some of the uncertainty aspects. And that really speaks to existing discipline in software, cybersecurity and of the other aspects which we've seen in some of the early adopter programs.

So I'd like to take a couple of questions that we had come in because some of them are very interesting and also speak to the measurement and the challenge aspect. So this one is probably at the top of everybody's mind. How is token cost management factored into maturity? So any takers? Maybe I'll start with Tony because that's probably what keeps you awake at night that you don't share with us.

I was waiting for this question. I think that this is a great example of how you have to have maturity across your enterprise and as you move token economics overall and token cost management. And I think as you move from this world of experimentation to sort of visibility and control, you're going to have to have incredible discipline to understand the right places to implement this in your organization. You're going to have to have the discipline to be able to really do value measurement. And ultimately, this discipline is going to be required in order to build this into your overall budget, to your overall business strategy, how you price your services to what your costs are. And this ultimately is what you're going to use to calculate sort of the trade-off between cost and value for what's right for your business. And so my viewpoint in general is the discipline that you're going to get from having a strong model is what you're going to need to be able to do token economics throughout your enterprise to find the right value and the right amount of AI to use.

And John, maybe if we could answer from the perspective of the model, where we do focus on hard questions like that, that might help our audience?

Right. So just to be clear, you're not going to see a section in the model that says token costs. You're not going to see a section in the model that says maturity is less than a certain dollar amount. But what you will see when you read the model is there are a handful of practices and capability areas that very clearly address token cost and how to manage token cost.

We're looking at the ROI behind automating workflows, the monitoring and some of the technology infrastructure practices. There's a slice or a down select, or for people who are very familiar with models, model scoping, right, that gets you to the question of token cost. There's not a section that says token cost, but it's certainly in there.

Absolutely. And it's a combination of where you're focusing and how you're actually evolving these. So Tony, there is interest from our audience in terms of understanding why Accenture IT has stepped up. So I'll read the question, and maybe you could -- some of it might be a repetition, but let's make sure we address it. So the question is -- right, here is the question. Seems Accenture IT did an assessment against the model. Yes, they did. And what was the objective of the assessment? And was it done at the start and our current state to determine progress and how and where the results used and useful? You did somehow talk to this, but maybe if you could...

Yes. I mean, I can confirm, we absolutely did that early stages. Our goals were both to assess ourselves and to give feedback and because we think that there's real value. I did comment that we think it helped us understand what some of our strengths are. We do think it helped us understand some areas where we need to focus.

And so I want to make sure I get the question because I feel like I'm repeating myself. But we absolutely used it to lay out our road map for where we want to go because we think that it's hard to assess yourself, right? And it's hard to innovate from -- within your own company. This kind of assessment was very valuable for us to figure out and lay out the road map for where we want to go.

And also to emphasize there were dual goals there. One was to use this as an opportunity to see how the global IT was faring in their adoption journey, but it was also very important for the model team for the SEI and Accenture team to really see whether the model works. That was, in fact, one of the biggest goal, and we actually call it Pilot 0 because we got some feedback, for example, there are repetitive areas. Do we really need to spend as much time in those areas? And we folded that feedback into the model before we released the fabric, and there were -- and before we tried the actual early adopter assessments with actual organization. So there was that big goal in addition to the assessment goal as well. So it had multiple goals there.

And we will continue to do that.

And we will continue to do that because the technology evolves organizations evolve and our understanding of what AI adoption means evolve. In fact, when we started this journey, agentic AI was not what is agentic AI today. That's like -- and this is only we're talking about 12 months.

So we have a question, and Majd, maybe this is something that you may want to chime in on. The question is, is the company's maturity in adopting AI linked to the number of models or the complexity of the problem being addressed also? I think this is a very good question. What do you say?

Can you say that one more time?

So the question is, is the company's maturity in adopting AI linked to the number of models or the complexity of the problem being addressed?

Well, it definitely has to do with what the organization is trying to accomplish and what are the processes that enable the organization to get there. There are many things that the model gets at. And one of them is what is your -- what is your strategy towards AI adoption and AI transformation. Another one is do we have the right governance, do we have the right data, platforms and access to the models. Doesn't have to do with the number of models that you're currently running internally.

Of course, when you are managing a large number of models, then there's complexity in the economics and the budgets that you need to be able to roll them out. And you need to maintain them and you need to make sure that you have a constant flow of the data so that you can update these models and make sure that their accuracy is still relevant to you. One key component that maybe we didn't touch on is the workforce aspect of this. And the other one is the workflow aspect of this.

So in an organization, you're probably visiting all of your processes and saying, how do we reinvent these processes end to end. And based on how we reinvent these processes end to end, the work changes. And now as the work changes, what kind of workforce do I need to be able to deliver on this work.

And then -- so when the work evolves, based on all of the changes that you're thinking about, then you have to say, what are the roles inside my organization that I need to have? And then how do I train people for the roles that they are in? And as we've all been saying, evolution is ongoing. So this is not a one and done. This is something that will be constantly happening.

So the power of the model is in assessing where you are as an organization. What are the strategic bets that you want to make because they are low-hanging fruit for your organization to make and to develop the capability to say, we know how to do this. More importantly, after you've done some early wins, you can start to have some medium- and long-term plans as well. Those are things that you have to think about and plan for. And you have to think about these things from the core thinking of the model so that you do them in a good way.

So another thing that we don't want to forget, and I'm sure we're going to revisit this at closing, is this is an ongoing process for any organization as they're adopting AI. And so the model will help you recognize where you are, but this is an ongoing process that you will continue to assess, you'll continue to evaluate and you'll continue to evolve your processes, your governance, your people, just to make sure that you remain relevant and competitive in this market.

And for the number of models perspective, the word model is overloaded here. Number of generative AI models that you have within your organization are yours. This is, in fact, lock-in hedge betting that a lot of organizations are going to because their capabilities evolve so rapidly, but that is really depending on how you're actually mapping to your goals, as you said. So that's not necessarily the determinant.

I would like to get to as many audience questions as possible, and we have a number of them coming. But there is one that we also received early on from Stefan. And the question is, I work with Department of War programs. And many want more AI capability. Who doesn't want more AI capability? Especially large language models, but struggle to bring them into the environment where they can keep them AI gapped and isolated. Do you see this problem getting solved sooner than later? Any additional thoughts in this topic will be appreciated. John?

Well, I think the -- I mean, there are architectures where you can do that, involving cloud tenants and so forth to operate AI in -- it's not fundamentally a different problem in terms of architecture and knowing where your data is going. So there are resources out there to solve this problem. And the DoD DOW is developing those capabilities.

So we're headed there. I think there are some various administrative hurdles and things like that. But this is a solvable problem, I think. Both -- certainly in industry and certainly in the defense establishment as well.

Yes.

And we're already seeing that the DoW, they are building their own models. And they're able to deploy them, and these things are portable. And as John said, you can host them on your internal cloud infrastructure that is [ air gap ]. So definitely these things are evolving pretty rapidly and the DoW is leading the way there.

And in fact, we see in our engagements a lot of the organizations are trying to bring some of them within controlled environments to the extent possible to help with their software engineering and software development capabilities to provide them to their software developers, which is a usage scenario. They are still not looking at from the perspective of how does AI change the work, how does AI change some of the mission aspects, but we have initiatives within the Software Engineering Institute looking into those as well. So I think we'll see an acceleration as you mentioned, John, but there are different scenarios that are already happening.

Ipek, we're seeing a lot of programs apply things in -- well, like in dual environments. They'll use the traditional ways of doing things, but also start applying some of the new AI models. So we're trying -- we're seeing a lot of the dual ways of accomplishing the missions.

True. There's a share of that. I'll take audience -- another audience question. We have a number of interesting questions coming in. So the question is, what signs are expected that AI is indeed driving evolution within the company? It's a good one. Any want to take?

When you don't think about AI.

It's just the way that you do your business.

The way that you do your business. Any other?

I think if we can -- when an enterprise is seeing the positive trajectory of the business growth -- and like any technology in the past, if technology is industrialized, institutionalized in an environment, and this is the way people do it every day, like what Majd said. It is the way of working. And thereby, the exponential growth of business, when you see that, that means your technology is in action and the maturity has come in. And the spectrum of maturity changes, and that's the way of thinking. That's how I see it.

One other dimension is that there are either strategy or tactics that simply wouldn't be possible without AI. You couldn't remove it anymore. In many cases, when you ask that question to organizations before they -- in the early stages of maturity, you ask them if there's any part of their strategy that cannot be accomplished without AI. They'll often say, no, anything can be done. A, I would make it better, but I don't need it.

When you're on the other side of that, there are things the business won't exist without AI. It's a fundamental requirement of the decision that you've made, and I think that would be another way to know.

Yes. And a progression that is also shared in literature is whether you're augmenting, adapting and autonomously changing. With augmenting, you might be just increasing already existing automation but using AI, but as Kaveh mentioned and automation, existing automation is just with AI. With adapt, you maybe have a little bit more reliance, but with autonomous when this doesn't mean everything needs to be autonomous, but you actually have really changed the process steps and the floor to it. So that's another way that you could use as a...

Ipek, there's another thought there that if you have many AI pilots and many early wins, that means your organization is AI ready. And that is a misconception that we also have to address, right? So the fact that you have several champions or several AI projects that are currently running does not mean that the organization as a whole can achieve this natural progression of an idea all the way to production in the process that RP just described.

So we have to make sure that we differentiate between these two and not force ourselves to start to say that we are AI ready and we are an AI-first organization just because look at the number of projects we have going. You really have to think about all of the different dimensions of the model that Anita and John described.

And this is actually a very important point, Majd. And thanks for bringing that up because we do see and we did see a lot in terms of when we ask, for example, how do you measure success. Or is the number of people using whatever is your favorite AI-based tool. Or we have a number of use cases that are with AI, which is an aspect of the experimentation, but it is not adoption or it's not necessarily evolution.

We have a lot of very good questions. So there is 1 that we received early on, which I would like to focus that from there. And the question is, what section of the maturity model do you see as being highly important, but most widely missing in enterprises today? So I'll look at Kaveh, maybe?

No, I think the work in the workforce. I think that's -- because it's the hardest and it's often viewed as -- this is still viewed as a technology problem. At least that's my observation.

But Ipek, to build on Kaveh's point. It's a cultural problem also. So when you talk about workforce and people, you're talking about changing the culture as well.

So if I may amplify what was just said there. All organizations are going to have access to the best AI out there. So if you think by just enabling that capability inside your organization, you're going to be able to offer differentiated products and services, you're missing the point.

So it is the people that are going to help. So it's their ideas, their creativity, their approach, their strategy. So back to where we started and how you open, Ipek, it's what can your people do with this capability. You are integrating that capability within your organization, but then how do you unlock that what your people can do with it, and that's going to be what shows up in the market against your competitors.

Absolutely.

I would also the workflow and people aspect, absolutely the most important part of the model and the differentiator. Purely from a technology management perspective, a lot of the model content around ecosystem, right? How do you actually integrate with your legacy environment and scale is sort of in the upper part of the model or the "higher maturity," very important from how do you really scale this in the long term. I mean, without a doubt, it's people first. But there are technology management pieces of the model that I think will be very beneficial.

And in fact, if you're not building the right partnerships, you're not going to be able to succeed. And that is how the AI ecosystem is evolving, whether we like it or not.

I'd like to channel Tony here a little bit and say governance requires attention. It sometimes is seen as a burden, but it's really a major unlock if you have the right governance mechanisms in place. Tony, any thoughts on that?

Yes. I mean I agree, Majd. I mean what was resonating with me when you made those comments is like the easiest thing nowadays is just giving people the technology. And when you're really thinking about what's going to help enterprises being successful. I mean, you start with like did we achieve the outcomes that we wanted to achieve.

And then you go back and you can look at -- did the technology help us to get there. And if you don't enable the workforce and you don't do the things you guys were commenting on, you're just not going to get there. So you start with the outcomes, think about the workforce, how you're going to enable people, what's going to be differentiating, completely agree. And if you don't underpin that with governance, I don't think you could be successful.

Can I amplify 1 issue? That -- I think people don't realize until they're in the middle of it. The technology arm has a technology and has a data piece. And actually, we find a lot of the organizations, their data is not AI-ready at all.

And so I'll go healthcare specifically, we have all kinds of standards around transport and other attributes. But the problem with data for AI is that it needs to be accessible in a semantically and contextually useful way. And it is a myth that language models can solve that problem. And we're discovering that right now, and it's becoming a huge source of frustration.

There was a theory that you could take dirty data, throw an LLM at it, and it will work. That is absolutely not true. And because it's not true, all the data is sitting around not being useful, and you have to go back and think about this in a completely different way. And because the semantic standards that you need will never be solved by forcing the creators to create it in a standard way, there's a lot of heavy lifting that goes between what you have and what you need to actually ingest into any form of AI.

So there is an interesting question which selfishly, I would like to take because this is something that John and I and my colleagues have had endless conversations. The question is, there's local optimization, business units, individuals, and organizational optimization. From an organizational performance perspective, how does the AI maturity model measure this? Which is a scoping question. Right, John?

What's -- so it can do both.

Well, yes.

But there's a lot to be said for having a defined scope, right? And really thinking about -- I mean, let's say, you're a multinational or a nationwide company. Yes, you could do an enterprise assessment, and that can be very valuable. And frankly, all of these activities cost money at some level. So there's a certain sense to that.

But there's also a sense that says, I really want to have an exemplar within the organization. I really think this is the area where I should focus. So there's 2 ways to approach it. But I would -- I mean if you're in a large organization, I would really counsel people to think about that sort of model speak subcomponent piece of the organization that you really want to focus on. Because you can get things that are more actionable sometimes when you really focus on what you're trying to achieve.

And the context attributes aspect of the model also help you understand whether you're focusing on the business unit or if you need to focus on the enterprise, how do you need to approach that? That might require you to do the assessment with -- especially if you're a large global organization, you might need to do that with multiple business units to be able to get at enterprise level understanding, but the way we've set the model up is you could actually get an enterprise level understanding, especially of your gaps as easily as well. And I invite the person who asked the question to look at the booklet that we shared of the model and look into some of the experiences that we'll be sharing as we go on.

So we cannot talk about AI adoption without talking about humans. So the next question is something that we've received during the registration. So most everything we hear nowadays suggests humans are expandable and will become eventually AI's waste product. On the surface, AI CEOs are starting to backpedal on this messaging, but there's some truth to it. How does CMU SEI see this whole thing going? Where are the opportunities for human in the loop? We call it human in the lead. I guess that's Anita? I have to ask that to you.

That's a lot to unpack, but I did see RP cringe when you asked that question. Yes, I mean, I guess, the SEI's work really emphasizes human-centered AI engineering, not human replacement. And for example, humans aren't just approving AI outputs, they're codesigning guardrails, detecting drift, recalibrating systems that are in production. So there's a lot of different roles there that work in tandem.

SEI's research and decision explainability helps human spot bias, hallucination or mission drift before harm can occur. So that's also an important area of research. We've also talked about humans in the loop. It's -- they're feedback loops, and we can use that as training data. So it's not just oversight. It's continuous learning. Humans label edge cases, correct model assumptions and signal when the context has shifted.

So again, there's a lot of different and important roles that work in tandem. We also have some very active work active learning pipelines where there are domain experts that curate high-impact samples to retrain the models themselves. So making the loop efficient, not just human in the loop as a check box, but really making it an efficient part of the process.

So for us, some of our research is if you're building or deploying AI, you've got a design for fallback. How does the human take over if the AI fails or stalls or is ambiguous. That's sort of one area. Measuring human in the loop efficacy, not just is there a human, but how effective is their input. Is it real time, can they improve the models as well.

And then the other thing that I thought about was upscaling teams in AI literacy. So operators need to understand model limitations, not just to code the models, but to question and even interrogate them. So there's a lot of really important roles that have to work together. So the future isn't humans versus AI, it's humans plus AI.

Right. Majd?

One thing to add here is that if we're not careful with this overpush of just use AI as is, we are also pushing human beings at some point to relinquishing cognitive capability when they're utilizing AI. And while working with our CHRO last week here, we were thinking about how do you -- as you're training your workforce and as you're evolving your workforce -- RP, you were there -- we were discussing on how do you retain cognitive ownership as well.

So as you are training your workforce to leverage this capability inside the organization, you have to be principled and intentional about retaining the cognitive ability of your workforce and what value they bring into the discussion that they're participating and when they're leveraging AI. So a little bit of a caution tail here that this reckless abandon of quick to use the tools and then the wrong incentives to amplify the use of the tools without thinking about where do we want humans, as Anita mentioned. And we want to make sure that they retain their cognitive ability and don't start just providing an average of this is what AI produced, and that's the best that we can produce. To maintain agency over all of that is critical as we do this.

Right. So there's a joke in healthcare right now that says all -- any doctor that can be replaced by AI should be replaced. And that's a real statement about...

I like that.

You're not really worth your salt if you can be replaced by AI because you're not doing enough important work anyway. I think about that through the lens of low complex cognitive test, high complex cognitive tests. And in healthcare delivery, specifically, there's also physical test. To take a job, AI can only do parts of those things. And in certain sectors like healthcare, we have a raw shortage of high complex cognitive capabilities. I need to liberate as much of that as possible by taking low cognitive abilities and moving them somewhere so I can give somebody more time. That, by the way, is rate limited because a person can't work at peak cognitive capacity all the time.

We have another issue in healthcare, in particular, which is who's responsible for the liability associated with an error. So think about the question about a patient having a direct interaction with a bot for a diagnosis. If it's wrong, whose problem is it? The patient? Is the patient going to have the liability for that? Is the tech company? Guarantee you, neither of them are. As a society, we have no liability construct that doesn't include a learn-it-intermediary. And that will be a big journey that will have nothing to do with the AI companies for us to ever get to that stage.

And it's also important that tools are increasingly having the AI capable -- the features. So that doesn't mean all of a sudden tools are taking over as well. You really need to understand it from the human's responsibility perspective. And at the end of the day, AI is a tool that actually is an exciting tool.

Well, we're fast coming to the end of the time, this really flew by. So I would like to give our -- my guess, an opportunity to close, and I very much appreciate all the exciting conversation, we could go on and on. So as organizations move from experimentation to enterprise scale AI adoption, which is still a journey, not a destination that anyone really have effectively reached, what do you believe will become the most critical differentiator for long-term success? And what is one recommendation you would have for our listeners? And Kaveh, why don't we get started with you?

Yes. Actually, I think 3 of them. One of them is you have to get the why before the how. That's a strategic question because that drives everything. The second one we're seeing is the CEO has to personally be the loudest voice with the most leaning in effort. It's not a task you hand to anybody else. I've seen this repeatedly. If it's struggling and the CEO doesn't be -- doesn't wait, lean in and put their own personal time and effort, that's a big one.

And then the third one is recognizing that the -- that you don't gain the organizational benefit without changing the work. You can prove the AI works, but you don't get the benefit until you change the work.

John?

A CEO or a leader who is unafraid to actually ask the why question. And then this is a really old-fashioned answer, but human teamwork, right? The leaders in the organization really having honest conversations and teaming together, if you're very siloed, that's not going to -- bad idea, right? It's the human teamwork that's really going to drive the vision forward for the organization.

Majd?

I would say that we have to think about AI as a capability that we are building inside the organization. And then that has to be coupled with a culture of experimentation. And what does the culture of experimentation mean given that Tony is in the audience is that we have to make sure that the token economy takes into account these kinds of things.

And the culture of experimentation is budgeting for time and money for throwaways so that you can explore and experiment into what actually is going to stick and move the needle in the right direction. The last thing I'll say is this is never a one and done. This is an ongoing process. So the organization needs to be in that mindset that we will continuously update ourselves and we'll continuously disrupt ourselves so that we can continue to lead in terms of what we can accomplish.

Tony?

Yes. I think for me, it's organizations that are going to have a structured maturity approach as they figure out how to bring AI through the organization. And you have to have balance in that structure. You need to have experimentation. You need to understand your costs, you need to think about your security. And you need to then think about the outcome, the business outcomes that you're going to try to achieve.

And a structured approach will allow you to balance all of those things. We read a lot that experimentation is very important, but you have to move past experimentation at some point. So how do you balance all 4 of those things with a structured approach as I think the organizations that master that are the ones that are going to be successful.

RP?

I think the model is out there, like you said. And there are 2 critical components that we discuss today. One is organizational competency and then engineering competency. That kind of summarizes how an enterprise need to move forward in adopting AI. I think it is simple, scale and sustainable. We talked a lot about scalability. I think it is 1 step after scalability is how do I continuously sustain the business value and the AI implementation that I put in. So I would like to see all the enterprises move from scale to sustaining the business value by leveraging organizational competencies and engineering competencies.

I also want to thank SEI team for partnering this with building the model, bringing our expertise. I think as we move forward, there's a lot to learn. We can continue to help enterprises to get trained on the model, get more expertise in the model. We can help enterprises to baseline their maturity processes and working along with the SEI to build the road map.

So there's a lot we can do. We can contribute to the industry as we move forward, and we will continue to get the feedback from the field and strengthen the model and organizational and engineering competencies, thereby we can scale and then such day. Thank you.

Thank you. And Anita, bring us home.

Okay. So I agree with all of mym colleagues. But I think I -- when you had talked about misconceptions very early on, I'll go back very quickly to that, that, I think, long-term success has to be around organizational alignment around it on AI as a strategic capability, not only a tech initiative. But everyone here talked about the learning organization. So not only do organizations have to be learning about how to adopt AI effectively, efficiently and with the AI adoption maturity model as a road map, but I think even for us as the general community to share data, to share experiences, so we can be a learning AI adoption maturity model community. So we can feed that back in to the road map and make it current and based on what actually works out in the community.

And in fact, the successes of the past in using maturity approaches have been defining practice in a consistent way across organizations globally, and that's our hope is with this AI adoption, which is model up. Well, thank you to all of you. And it's only through collective experiences like these and collaborations between industry, government and academia will actually be able to solve this AI problem because the technology moves fast. It's exciting and it's scary all at the same time.

And our goal with the AI adoption maturity model is to help serve that purpose and shape some of the broader practices to come along. We -- while this is the first phase, established the maturity model, which you are able to read today, the second phase will include building the empirical data and the basis for different kinds of use cases. We will continue to share our experiences using the model as well as some of the learnings, and we'll fold it back into the evolution of the model. And we'll expect these experiences to provide critical insights to all of us as we understand what AI adoption really means.

There were questions there, which we couldn't get to, for example. Well, there are tools out there that change by the minute. How do you give guidance to tool usage? We've talked about whether scaling a particular tool really means maturity. So all of these questions are valid questions and our goal in developing the practices and the capability area as well that you would actually find answers as you're developing your AI strategy as you go through the AI adoption experience and define your organizational or team or business unit goals.

And we extend our deepest gratitude to everyone who contributed. There are the faces you see, but there are teams behind us, and we appreciate everybody's contribution. But also, we appreciate the contribution of our early adopters who trusted us with their problems, challenges and their data and everyone who talked to us and who shared their experiences and data with us along the way. And we're looking forward to working together with Accenture and all the partners and our collaborators. And we'll be sharing our findings and the data, and we welcome your feedback as well.

Anita, John, Majd, Kaveh, RP and Tony, thank you. I know I've spent those 90 minutes with the busiest people I know. So really, really appreciate it, and I know our audience appreciate it as well. So at the end of the webinar, we ask you that you complete our survey and the link will be posted on the YouTube chat area now. And we appreciate your feedback now or later. You can send all your questions to [email protected], and you'll be able to get the links to the references that we made throughout the conversation.

Thank you for spending your last 90 minutes with us, and looking forward to the conversation as we all understand what AI can do for our organizations for our workflows and make our lives better without introducing the risks. Thank you, everyone.

Good morning. Thank you for standing by. Welcome to Accenture's Second Quarter Fiscal 2026 Conference Call. [Operator Instructions] As a reminder, this conference is being recorded.

I would now like to turn the conference over to Alexia Quadrani, Managing Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our second quarter 2026 earnings announcement. As the operator just mentioned, I'm Alexia Quadrani, Executive Director and Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the news release we issued a short time ago.

Let me quickly outline the agenda for today's call. Julie will begin an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the second quarter. Julie will then provide a brief update on our market positioning before Angie provides our business outlook for the third quarter and full fiscal year 2026. We will then take your questions before Julie provides a wrap-up at the end of the call.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and as such, are subject to known and unknown risks and uncertainties, including, but not limited to, those factors set forth in today's news release and discussed in our annual report on Form 10-K and the quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed on this call.

During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations from non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this call.

Now let me turn the call over to Julie.

Thank you, Alexia, and everyone joining us this morning, and thank you to our more than 786,000 people for your extraordinary work. We delivered another strong quarter with $18 billion of revenue, growing 4% in local currency and once again taking significant market share. We had record bookings of $22.1 billion, bringing H1 bookings to a total of $43 billion. We had a record 41 clients with quarterly bookings greater than $100 million, bringing us to 74 of these bookings in the first half, 12 more than this time last year, demonstrating the continued demand for reinvention at scale.

We delivered 30 basis points of operating margin expansion with strong EPS growth year-over-year, generating significant free cash flow while investing significantly in our business. We closed 3 strategic acquisitions, deploying $1.6 billion of capital, and we now expect to deploy $5 billion in acquisitions this year with capacity to do more for the right opportunities.

In double cooking on our revenue. Our revenue growth was broad-based across geographic markets and types of work. Revenue from our top 10 ecosystem partners continues to outpace our overall growth, and we are expanding these partnerships. And we are on track in FY '26 to more than double our bookings over FY '25 from partnerships with our key emerging AI and data ecosystem partners. And we delivered these strong results through the disciplined execution of our growth strategy as our market remains roughly the same.

Our long-term growth strategy is to help our clients reinvent and to capture other new opportunities created by AI. To accelerate this strategy, we are using 2 key competitive advantages: our strong balance sheet and our long history of successful acquisitions. Our goal with acquisitions is to more rapidly expand into higher growth areas with attractive margins, which will fuel organic growth and increasingly help us grow non-FTE-related revenue. In H1, we invested in 4 areas: first, AI powered transformation. Last week, we closed the acquisition of Faculty, a leading U.K.-based AI native services company with the Decision Intelligence product business that provides a platform for us to expand into new areas of unmet AI demand with non-FTE revenue.

We also acquired 2 companies to accelerate our growth with Palantir, an emerging ecosystem partner. Decho in the U.K., which focuses particularly in defense and public sector markets and RANGR Data in the U.S., which works across industries. Second, AI enablers. AI enablers include data centers, cybersecurity, energy infrastructure and data. We acquired a 65% stake in DLB Associates, a data center engineering and consulting firm with high double-digit growth. We also acquired CyberCX, a leader in cybersecurity in Australia, and we announced the acquisition of Ookla, a global leader in network intelligence, competitive benchmarking and customer experience analytics. Ookla, with only 430 employees, generated $231 million of revenue in their calendar year 2025 through non-FTE subscription and licensing revenue models and an 8% year-over-year growth rate and with healthy margins accretive to Accenture.

Third, high-growth secular trends. These trends include capital projects, defense and public sector and education around AI, data and tech. We acquired Orlade Group, a French capital projects firm, which expands our presence in the energy, utilities, rail and aerospace sectors including nuclear power plants and power grids. This acquisition is also part of our focus on key AI enablers because AI requires significant expansion of energy infrastructure.

We also expanded LearnVantage, our business is capturing the education opportunity through the acquisition of Aidemy in Japan, a portion of our LearnVantage business, leveraging our proprietary platforms, operate with a non-FTE commercial model growing double digits. Finally, mid-market expansion. We made 2 mid-market acquisitions, NeuraFlash and Total eBiz Solutions and announced [indiscernible] to expand our presence in the mid-market, where we are experiencing higher revenue growth and a higher volume of smaller deal sizes that convert to revenue faster.

As we delivered this quarter, we also are executing at speed on our talent strategy for the age of AI. We now have over 85,000 AI and data professionals already exceeding our goal of 80,000 professionals by the end of fiscal 2026. Thanks to our intentional talent strategy, we will hire more entry-level reinventors in FY '26 than FY '25, which is important for our financial model, just as our clients must reinvent so must Accenture. Our inventors completed 13 million training hours this quarter alone and 192,000 completed our agentic AI fundamentals program, co-created with Stanford's Institute for human-centered AI. After significant investment in training starting this year, we have made the use of the AI tools and contributions to helping Accenture become the most AI-enabled company in the world, now a formal part of our performance evaluation.

Finally, we are pleased at the number of external recognitions of our broad-based strength that we have received in the last several months. Please check out these recognitions in our earnings presentation for the quarter.

Over to you, Angie.

Thank you, Julie, and thanks to all of you for joining us on today's call. We were very pleased with our second quarter results with record bookings for the quarter, revenue at the top end of our guided range, strong margin expansion and robust free cash flow. These results demonstrate the resilience and durability of our business and continued execution of our strategy to be the reinvention partner for our clients. We delivered these results while continuing to invest for long-term market leadership and returning significant cash to shareholders.

Now let me summarize a few highlights for the quarter. Revenues grew 4% in local currency and were broad-based across geographic markets and types of work, reflecting the diversity of our business as we continue to take market share. Operating margin was 13.8%, an increase of 30 basis points compared to Q2 results last year and continues to include significant investments in our business and our people.

We delivered EPS in the quarter of $2.93, which represents 4% growth compared to EPS last year. And finally, we delivered strong free cash flow of $3.7 billion and returned $2.7 billion to shareholders through repurchases and dividends this quarter. We also invested $1.6 billion primarily attributed to 3 acquisitions in the quarter.

With those high-level comments, let me turn to some of the details starting with new bookings. New bookings were a record $22.1 billion for the quarter, representing 6% growth in U.S. dollars and 1% growth in local currency, with an overall book-to-bill of 1.2. Consulting bookings were $11.3 billion with a book-to-bill of 1.3. Managed Services bookings were $10.8 billion with book-to-bill of 1.2.

Within bookings, the percentage of our work, which is fixed price continues to increase over 60% in FY '25, this reflects the rising importance of our proprietary platforms and clients' need for cost and delivery certainty where our scale, experience and financial strength matter.

Turning now to revenues. Revenues for the quarter were $18 billion, reflecting an 8% increase in U.S. dollars and 4% in local currency at the top end of our FX-adjusted guided range as the foreign exchange impact for the quarter was positive 4.4% compared with a positive 3.5% estimate provided last quarter. Consulting revenues for the quarter were $8.9 billion, up 7% in U.S. dollars and 3% in local currency. Managed Services revenues were $9.2 billion up 10% in U.S. dollars and 5% in local currency, driven by mid-single-digit growth in technology managed services, which include application managed services and infrastructure managed services and high single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenues grew 3% in local currency, led by growth in banking and capital markets, software and platforms and industrials, partially offset by a decline in public service, driven by our U.S. Federal business. Revenue growth was driven by the United States. Excluding the 2% impact from our federal business, Americas grew approximately 6% in local currency in the quarter. In EMEA, we delivered 2% growth in local currency, driven by growth in insurance, life sciences and public service. Revenue growth was driven by the United Kingdom and Italy.

In Asia Pacific, revenue grew 10% in local currency, driven by growth in banking and capital markets, communications and media and public service. Revenue growth was led by Japan and Australia.

Moving down the income statement. Gross margin for the quarter was 30.3% compared with 29.9% for the same period last year. Sales and marketing expense for the quarter was 9.7% compared with 10.1% for the second quarter last year. General and administrative expense was 6.7% compared to 6.3% for the same quarter last year. Operating income was $2.5 billion in the second quarter, reflecting a 13.8% operating margin, up 30 basis points compared with results in Q2 of last year.

Our effective tax rate for the quarter was 24.3% compared with an effective tax rate of 20.4% for the second quarter last year. Diluted earnings per share were $2.93 compared with diluted earnings per share of $2.82 in the second quarter last year, reflecting 4% growth. Days services outstanding were 46 days compared to 51 days last quarter and 48 days in the second quarter of last year.

Free cash flow for the quarter was $3.7 billion, driven by cash generated by operating activities of $3.8 billion, net of property and equipment additions of $150 million. Our cash balance at February 28 was $9.4 billion compared with $11.5 billion at August 31 with regards to our ongoing objective to return cash to shareholders. In the second quarter, we accelerated our share buybacks and repurchased or redeemed 6.8 million shares for $1.7 billion at an average price of $246.09 per share. This brings our year-to-date total to $4 billion in repurchase or redeemed shares, which is a significant step up from the same time last year. Also in February, we paid our second quarterly cash dividend of $1.63 per share, a 10% increase over last year for a total of $1 billion.

And now back to you, Julie.

Thank you, Angie. I will start with the demand environment and then turn to why we see AI as a tailwind, which we believe will shape our growth over the next few years. We saw again this quarter clients continuing to prioritize their most strategic and large-scale transformational programs, which positions us in the center of their reinvention agendas. As clients finalize their budgets going into calendar year 2026, we are seeing spending similar to 2025. Demand continues to be driven by a few major themes. First, clients are implementing foundational programs with our ecosystem partners to capture the full opportunity of AI. These typically involve cloud, security and data modernization, often combined with operating model and talent transformation.

We continue to see at least 1 out of every 2 advanced AI projects lead to a data project. Second, clients continue to look to reinvent faster, leverage our proprietary platforms and expertise and achieve greater efficiencies through managed services across the enterprise, and we see clients working with us to create more investment capacity to increase their spend in new areas. And third, clients with more advanced digital cores are starting to take on larger AI programs. We also are seeing more moving from proof of concept to production, while others are still at the beginning of their journey with another 100 clients or so initiating advanced AI projects with us this quarter.

Across many of these programs, AI and data are now central, sometimes as the destination and increasingly as part of the work from day 1. A good example of these demand trends is how we are partnering with the Estee Lauder Companies, a global prestige beauty company to advance its new one operating ecosystem and to drive a more connected, scalable and consumer-centric enterprise. Enabled by our platforms, we will collaborate with the company to transform how work gets done, leveraging AI and automation across the end-to-end value chain. Over time, this is designed to accelerate execution and enable teams to focus on driving innovation, consumer experience and brand desirability. This work supports the Estee Lauder companies and building the capabilities needed to activate new ways of working and aligning teams, technology and partners to enable the business to operate with greater agility.

We see AI as a tailwind because it's helping us win more today and take market share, and it is creating new opportunities for growth over time. We're continuing to take market share quarter after quarter because of the combination of our early leadership in advanced AI, our deep ecosystem partnerships with both established leaders and emerging players and our decades of investments to be both deep and tech and relevant across the entire enterprise from the back office to core operations to the front office.

We play a critical role in the AI ecosystem. Foundation models provide the intelligence and our role is helping clients understand what to deploy and when, how to integrate it into their systems, reimagine their processes, modernize their data and digital core help redesign their operating models and do effective change management and help build the capabilities and talent needed to scale across the enterprise.

As the technology changes even more quickly, our clients are turning us to help them navigate. They also want us to help them go faster, sometimes by building their capabilities and other times by leveraging ours. Take SaaS implementations overall. Clients are continuing to modernize their tech stacks with SaaS but they're now asking that new SaaS implementations be designed from day 1 to use processes that integrate both AI from the SaaS provider and other providers and clients are more and more willing to do end-to-end transformation, all of which requires leadership in AI and SaaS, but it also requires the industry and functional knowledge and the ability to work across the enterprise, not just in certain functions.

They look to us to bring our point of view and experience on what the new tech stack should be as well as on how their processes should change due to AI. We're seeing this across industries and across functions. For example, in retail, service is no longer just about fixing problems, customers expect a consistent experience every time even on a massive scale. We're working with retailers to reinvent contact centers and design the new processes with agentic AI. This means partnering with us to develop agentic enterprise strategies and architecture foundations which will serve as a platform to enable agentic solutions.

We aren't simply upgrading contact center technology, we're reimaging service at scale. The future workflow involves digital agents and human agents operating as one coordinated team to serve customers. We're starting to bring together the best of major technology ecosystems from day 1, such as a SaaS player and a hyperscaler and creating agents that work across both platforms, creating leading-edge, customized solutions. This simplifies work for employees, delivers faster and more personalized support for customers and creates a service engine designed to scale efficiently as the business grows.

Our combination of strength is helping us meet the needs of our clients today and AI is opening up new opportunities where these strengths position us for growth in the long term. We see a long funnel of work as we have the advantage of the biggest client base in our industry, all of which will need to reinvent.

Let's look at ERP. We have been the #1 partner to all the major ERP ecosystem partners for years. And over the last several years, we've deployed modern ERP systems across hundreds of clients. When those systems were implemented, advanced AI did not yet exist. Now those clients want to embed the new AI and data capabilities and transform their end-to-end processes. For example, with one of our largest oil and gas clients, we're seeing a clear pattern. First, we modernize their digital core. Over several years, we partnered on a major ERP transformation to implement a cloud-based platform that simplifies operations, standardizes processes and creates a single source of data across the enterprise. It was a significant multiyear investment. Now with that foundation in place, they're investing again, embedding AI directly into the systems that run the business. This is not a separate layer of technology. Its intelligence built into core workflows across finance, supply chain, asset maintenance and field operations. These capabilities analyze large volumes of data initiate routine actions and support better decisions in real time.

The impact is tangible, faster cycle times, fewer manual steps, lower operating costs and stronger operational resilience. We're beginning to see the same sequence more broadly, modernization of the core followed by AI-driven enhancement, enterprise systems are becoming the platform that allows AI to deliver value at scale. Because of the work required across every process and every industry, we are starting with early leaders who have advanced technology stacks and want to pioneer. We believe that over the next 12 months or so, this opportunity will gain momentum.

AI is helping us grow another strong business, cybersecurity. We see advanced AI as a catalyst to our cybersecurity business as the threat landscape expands and new tools emerge to protect and attack. AI is also unlocking opportunities in technology modernization, one of our key strengths. For example, for decades, parts of the technology stack like the mainframe have been considered too complex or too costly to modernize. Today, advanced AI and new hardware capabilities are making mainframe modernization feasible, which we believe will open as major services market.

We are also seeing significant opportunities in core operations where AI is enabling us to make today's impossible possible. This is one of the reasons why our custom systems integration work has been having a renaissance and is showing strong momentum. Core operations are where generally they're not as many SaaS providers because the needs are complex and industry specific. These are areas where a lot of digitizing still needs to happen, like moving to the cloud and building data foundations, but where advanced AI is going to be able to provide solutions that are not available to clients today or are too expensive. Examples include finance and risk, such as know your customer and banking, claims and insurance prior authorization in health care and manufacturing, to name just a few.

Advanced AI also is opening entirely new areas of growth for us because it is creating new opportunities for our clients and because making AI work requires entirely new capabilities. Take customer engagement. In Accenture Song, LLMs are driving the biggest revolution in retail since the advent of social media. While it is still very early, conversational and agentic commerce are changing how customers discover, evaluate and purchase products. We are seeing strong demand, and we are uniquely positioned to serve our clients because of our ecosystem relationships and expertise in marketing, sales and service.

Radisson Hotel Group, a global hospitality company is a clear example of marketing and commerce reinvention. Over the past several years, we modernized their digital and data foundation, uniting brands on a single global platform. This created a real-time 360-degree customer view to enable personalized marketing across more than 1,500 hotels and over 30 languages in markets. Now we're putting agentic AI at the center of how demand is created and converted with Accenture Media console where AI agents optimize content based on traveler intent, streamlined campaigns and dynamically allocate budgets.

We're also helping Radisson adapt to how Discovery is evolving with agentic commerce, connecting live inventory and rates directly into conversational platform so travelers can move seamlessly from discovery to bookings with an AI-driven journeys. Since our collaboration began, Radisson share of its direct bookings has tripled. This isn't incremental marketing improvement. It's AI-driven commerce and it shows how each wave of technology reshapes industries like travel, opening new growth opportunities for our clients, and therefore, for Accenture.

Our clients also are turning to us to help them build and provide the capabilities to use AI across the enterprise. For example, in one of our large clients, we're working with a leading language model provider to embed advanced models directly into how the company's IT team delivers their projects. Those models are accelerating software work across the board, new development to upgrades and some projects are already seeing delivery move 50% faster. Beyond engineering, the same models are automating complex documents, enabling real-time reporting and powering AI agents embedded directly into day-to-day business processes.

And for developers and next-generation agentic coding platform is changing how work gets done with teams building their own tools and AI agents at scale. This is what reinvention looks like, AI becoming central to how work is designed, delivered and run. We also have entirely new offerings, such as setting up dedicated AI services to either be their main execution arm sometimes in a build-operate-transfer model or to be like a factory augmenting a client's core AI team.

This quarter, Piraeus Bank SA and major bank in Greece partnered with us to set up a central AI hub to be their primary execution arm with an option to transfer to them in the future. They are leveraging our strong capabilities to help them more quickly capture value from AI and navigate the complex ecosystem environment.

Before I turn it over to Angie, I want to step back and give you our perspective on the future. AI as it stands right now, may turn out to be the most powerful technology breakthrough since electricity. We cannot even today describe all the ways in which we will use this technology, let alone the opportunities to come. But the one thing we know is the only way to realize the power of this technology is if companies can change dramatically to use it. And through every prior technology evolution in the last 5 decades, 1 constant has been that the companies have turned to Accenture to help them make these big changes, and that is why I am so confident in our future.

Over to you, Angie.

Thanks, Julie. Before I get into our business outlook, I want to share how the conflict in the Middle East is affecting our business and how we are reflecting it in our guidance. First, we have roughly 3,000 colleagues in the Middle East, a region which represented about 1% or $1 billion of revenue in FY '25. Our colleagues are safe, and we are providing them with all the support we can. Currently, we are not seeing any significant financial impact. While we know the environment is more uncertain given the conflict, we always call it like we see it. And based upon the information we have today, we are increasing key elements of our full year guidance.

Our range for Q3 and the full year reflect our best view today of the potential impact of the conflict in H2. It does not take into account a significant escalation or the occurrence of major economic disruption.

Now let me turn to our business outlook. For the third quarter of fiscal '26, we expect revenues to be in the range of $18.35 billion to $19 billion. This assumes the impact of FX will be approximately positive 2.5% compared to the third quarter of fiscal '25. Our Q3 guidance reflects an estimated 1% to 5% growth in local currency, including about a 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 2% to 6%.

For the full fiscal '26, based upon how rates have been trending over the last few weeks, we continue to assume the impact of FX on our results in U.S. dollars will be approximately positive 2% compared to fiscal '25. For the full fiscal '26, we now expect revenues to be in the range of 3% to 5% growth in local currency over fiscal '25, including an estimated 1% impact from our federal business. Excluding the impact of federal, our revenue is expected to be an estimated 4% to 6%.

This year, we continue to expect an inorganic contribution of about 1.5%. We have a strong pipeline of opportunities and now expect to invest about $5 billion in acquisitions this fiscal year. But as Julie said, we could do more if the opportunities present themselves.

For adjusted operating margin, we continue to expect fiscal year '26 to be 15.7% to 15.9%, a 10 to 30 basis point expansion over adjusted fiscal '25 results. We continue to expect our annual adjusted effective tax rate to be in the range of 23.5% to 25.5%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We now expect our full year adjusted diluted earnings per share for fiscal '26 to be in the range of $13.65 to $13.90 or 6% to 8% growth over adjusted fiscal '25 results.

For the full fiscal '26, we now expect operating cash flow to be in the range of $11.5 billion to $12.2 billion, property and equipment additions to now be approximately $700 million. We are raising our free cash flow guidance by $1 billion and now expect free cash flow to be in the range of $10.8 billion to $11.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.3.

We continue to expect to return at least $9.3 billion through dividends and share repurchases, an increase of $1 billion or 12% from fiscal '25 as we remain committed to returning a substantial portion of our cash generated to our shareholders. Our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on May 15, a 10% increase over last year.

As we move into the second half of the year, we remain focused on executing our strategy, investing for the future and managing our business with rigor and discipline.

With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. [Operator Instructions] Operator, would you provide instructions for those on the call, please?

[Operator Instructions] And today's first question comes from Jason Kupferberg with Wells Fargo.

Definitely appreciate all the AI-related commentary and the client examples. What kind of quantitative evidence should investors be looking at to help substantiate the view that Accenture is a net beneficiary of AI?

Thanks, Jason. I would just start with that at this point in our business, AI is permeating everything we do because it either is driving why clients are actually doing things like moving to the cloud. But when we're doing something that isn't specific AI, they are looking at our AI credentials because everything is aimed to get to AI. And then, of course, we have direct AI. And then our managed services business is being evaluated by how good our platforms are and their expectations of building AI.

And so like to start with like your first kind of way of looking at is how is our business performing relative to everyone else. And are we taking market share, right? That is the -- because at this point, it's not isolated, right? It really is why we're winning, and it's -- you have to have it to win -- you have to be a leader to win at the levels we're winning of like $22 billion.

And then we're going to give you metrics, Jason, over time, that will change to kind of tell you. And so today, we look at market share. We look at our overall growth. And then the metrics we're giving you are the ones we're using, which is because everything is so tied to the big ecosystem is our growth with that ecosystem outpacing overall growth? And then how are we doing with the emerging players.

And then we are looking at how many companies are initiating AI with us among our client base, which are the metrics we gave you today. So the metrics will change, right, but they reflect what we're looking at as we drive our business.

Understood. Understood. And maybe just one on the numbers. I mean the consulting bookings growth has been accelerating in the past few quarters. And then just looking at the modestly upsized revenue outlook for the year. I mean even if you only deliver the middle of the Q3 range in constant currency, I think you'll be at 4% year-to-date, you've obviously got the easier compare in the fourth quarter when you lap the U.S. federal headwind. So any reason to not think the upper part of the full year, 3% to 5% range is a pretty plausible outcome. I mean, unless obviously, if there's some major economic disruption from the Middle East.

Jason, the 3% to 5% guide for the full year, which is really 4% to 6%, excluding federal, is our best view based upon what we see today. We had bookings that were really, really strong at $22 billion, which were actually a record for us this quarter in our third consecutive quarter have $20 billion of bookings or more. And so for us, our guided range, we -- we do aim for the top. We'll see how things play out, but it's our best view. And you're right, we will anniversary AFS in the fourth quarter of the headwind from that, and we expect that to grow in the fourth quarter.

And our next question comes from Tien-Tsin Huang with JPMorgan.

Nice results here, especially on the free cash flow. I'll also ask on AI if that's okay. Julie, appreciate your comments there on why it's a tailwind, but I was just thinking with these frontier models that are improving so quickly it's driving a lot of news flow and a lot of debate. And are you seeing any correlation or are you tracking this how these models and how they're improving and their capabilities improving and how that might impact your bookings growth and conversion to revenue? I'm just trying to understand if there's some kind of correlation or pattern and how that might impact your numbers here going forward as the frontier models improve?

Thanks, Tien-Tsin. It's a great question. I think it goes to the heart of kind of what's different with models which is when you release functionality in a packaged solution that we've seen in the past, right, is the models are basically just a super powerful engine. So if you think about the car, right, you've got this great engine only if it's connected to everything. If it has wheels so you can actually make it run and the transmission to guard it.

And so when the models come out, there isn't a direct correlation to bookings or new work but what it does is create the next opportunity for us to look at what are the solutions that it's going to now create. And so if you think about in earlier days, a lot of the work was focused on things like amortization and content creation the better the models are, it's able to fuel things like moving into agentic where -- and we're starting to see that. So we're starting to see more experimentation and use of agents really workflows as the models get better.

So think of the release of the models as the beginning of creating new opportunities for us to take to our clients even as, right? There's a lot of work that we have -- that we're doing based on everything that has already happened. Does that help?

No. It really helps like analogy, and it's insightful. So -- maybe as my follow-up, just I get this question a lot, Julie and Angie, just thinking about the large deal you've won a lot that trend continues. But with respect to AI, how would you characterize the mix of advanced AI work between growth or revenue-generating use cases against the efficiency-led use cases, we hear a little bit of a pickup on the growth side, but I'd love to hear what you're seeing on the ground there on the mix shift?

Yes. So I think the first shift that's happening is the focus. It is not yet in the mix. So our latest survey that we do every quarter of the C-suite and how their view of AI. The latest survey had 78% now saying we think growth is going to be the biggest value. That's not yet translating on the ground to being the biggest driver mostly because of where the technology is. If you think about kind of the early days, a lot of it is about content, summarization, et cetera, that is really an efficiency play.

And as the capabilities improve, you start to see more ability to take it into the core business and to do more complex work. So we are absolutely seeing an uptick in growth, growth focused AI programs, but efficiency is still leading the way. I will tell you that the most exciting area right now on growth is conversational agentic commerce, demand is surging there. And I think as that -- and that's what we're investing a lot. I think as that takes off, you're going to start to see real results from -- on the growth side from these new developments. And that's a whole new market. And it's a whole new opportunity for us that we're super well positioned, of course, because of Song.

And our next question today comes from Kevin McVeigh at UBS.

Congratulations on the results. If I heard right, it sounds like the acquisitions for -- will be upwards of $5 billion. I think that was from $3 billion last quarter. Is that right? And then it doesn't seem like it's translating to the inorganic growth. So is that just the timing of the contribution from the inorganic? Is that just the timing of when they -- when those acquisitions come in?

Kevin, that's exactly right. So we currently see $5 billion and have the potential to do more, based upon the opportunities that are -- become available. In terms of the inorganic contribution, we still expect about 1.5%, and that really is on timing.

That's terrific. And then just the expanding AI -- the expanding bookings with the newer partners you have, is there any way to think about how that is relative to kind of the existing pool and how that scales over time? Because it seems like they're on pace to double, which is terrific. Just any way to dimensionalize that? And I guess, the new ones, I mean, with Anthropic data bricks and NVIDIA so on and so forth?

In terms of dimensionalizing it, what we're seeing is that really across the board, we've got really strong growth, both with our large ecosystem partners and with the emerging partners. And the way to think about that is that the -- as the -- in a little bit what I was talking about earlier is the models are improving the kind of ability to deploy them in the enterprise expands, right? So think about we're now doing work like in know your customer in the KYC area in banking because we don't have great package solutions of software there. As the models get better, it solves some of the problems that are there.

You're seeing in things like the mainframe is the models are helping us do the really not fund dirty work of converting code, we are now able to kind of open up that market where clients are now going to be more willing to touch the mainframe. So I would really think about both the large ecosystem partners and the emerging partners together because all of these solutions are really working across the ecosystem.

And our next question today comes from Darrin Peller at Wolfe Research.

All right. Can you just touch on your head count growth expectations? And maybe just higher level, your headcount strategy, has there been any change in linearity that maybe you're seeing, particularly related to AI, but more broadly in the environment right now would be great.

Darrin, so for us head count, we expect our headcount to increase in the second quarter based upon the demand that we see. And really, this is a continuation of the talent rotation that we discussed at the beginning of the fiscal year, and we expect our headcount to -- we expect to add headcount in the second quarter -- in the second half.

Okay. And guys, just when we think about linearity and how that's trending, given AI and implementation for either your own use cases or customers. I'm just curious if it's impacting the strategy going forward?

By linearity, if you mean the sort of revenue and headcount, I just would remind you that we really have not had a linear relationship since around 2015 when RPA when automation really came in. And so we would expect to continue to believe that disconnecting. And that's what's baked into our guidance.

Okay. All right. Just one more quick one is on visibility. Just sitting where we are today versus perhaps this time last year, how do you think about your visibility and confidence in the remainder of this year and even the next 12 months, just given all the conversations, are there any more uncertainties in clients now given AI or anything else for that matter, that geopolitics, et cetera?

Well, what I would just say is that, obviously, there's a lot of uncertainty right now in the environment which we're not baking into our guidance. But our guidance reflects our confidence in what we are seeing at the account. And we haven't seen -- at our clients, and we haven't seen anything yet being impacted by the war. But obviously, there is a big uncertainty because of the war, but we're very confident based on the information we have right now, on what we see for the next 2 quarters.

And that's why we were able to bring the bottom up of our guide and what we see as the large deals layering in. And the second is the anniversary of AFS. So we have visibility to that.

Our next question today comes from Bryan Bergin at TD Cowen.

I wanted to ask about some of the emerging and, I guess, evolving delivery model. So as we think about AI increasingly in the future, to what extent does the broader GSI and tech service model need to pivot to an FTE model, you had an interesting announcement yesterday with Microsoft. Just curious, like if we fast forward a few years, is the majority of tech service implementation likely to be in an FTE model or just more so a mix. And near term, any important financial considerations as you lean into this model with partners.

What I would say is it will definitely be a mix, and that's because the way that the FTE model today really gives value is when you are going in and solving problems that haven't been solved typically in mission-critical areas, where in order to get the AI to work, and these are usually like the spoke problems, at least initially, you have to have deep domain knowledge from the clients, the technology knowledge, and then what we bring to the table, right, the experience, the integration, the industry and functional knowledge and you work in teams to new problems.

And then the idea from there, of course, is that we will then replicate that over time at other clients. And so there's more and more agility and how we're delivering, but the actual kind of thing that people call FTE models is really about solving those problems. We do think that delivery overall, it is changing. We're already using both more technology, but also being able to like kind of use more of the team that have all of those functions, industry technology, functional expertise. And so we do think that the way we bring our teams together will change and some of that will be more upfront and like an FTE model. But what we are really differentiating in the market right now is that we have all of those skills at Accenture, and that's helping us win more.

Okay. That's helpful. And then on free cash flow, so you've got a strong generation here trailing 12 months of almost 30%. Can you comment on what you're doing differently here in net working capital or something tax related that's allowing for that and obviously, the raise for the year aside from lower CapEx? And is it sustainable as we think about future free cash flow conversion?

Thanks, Brian. Our free cash flow, we just recorded record free cash flow on a year-to-date basis of $5.2 billion, and it is really driven by efficiencies in our operations, as well as DSO. Our DSO was roughly about 5 days below last quarter, and it was 2 days better than the same time last year. That is just us continuing to focus on getting cash and operating more efficiently. So our raise this quarter for the full year, we were really pleased with, and it's a strong result and free cash flow to net income ratio of 1.3.

And our next question today comes from Jonathan Lee at Guggenheim Partners.

Julie, I want to build on a prior question around V&A. Can you help us understand what's driving the step-up in deployment and whether this reflects a shift in acquisition strategy towards larger or earlier-stage assets, higher multiple of the market or perhaps a pivot toward IP-led deals?

Sure. I'll let Angie, just talk about kind of how we're seeing valuations in a moment, but I want to talk a little bit just about your point about what the strategy is. So our strategy that we've executed over the last decade or so, has been to use V&A often to go into new areas that are higher growth. So we did that with Song. We've done that with Industry X. You saw us do that with capital projects over the last few years. And that is all to fuel organic growth, right? So it's increasing our total addressable market by going into new higher-growth areas. And that's, again, what you're seeing us do that. And we're doing that in key AI enablers. And so those are things like data centers, energy infrastructure. We're doing that in big secular trends like defense you've seen those acquisitions over the last couple of years and public sector is another one. Education is another one. So higher-growth areas, increasing our TAM. And then increasingly, we see an opportunity to meet unmet demand in the market where you don't have solutions where we can build products, either organically or by purchasing them. So our Faculty acquisition, for example, has a really unique decision intelligence product.

And then in addition, there are new commercial models where data is one of the key enablers of AI. And so you saw our Ookla acquisition, which is really about an incredible data set. And the way that then gets into our business is the network is really core to both communications and all enterprises and so AI having this kind of a data set is incredibly powerful, and it's a completely different commercial model, licensing and subscription base.

And so we're also using our balance sheet to get into these exciting new areas that also bring us new commercial models that are not linked to FTE. And that will then mean we kind of have a different mix in terms of the financial profile and valuations and Angie just give us a quick update on how to think about that.

Yes. And so in some cases, we are paying higher multiples than in the past. So the immediate uplift is lower in those instances than prior acquisitions. So that said, we are intentionally shifting towards higher growth, higher-margin assets that are going to fuel organic growth and strengthen our capabilities, and it's really to position us for long-term growth and returns.

Helpful color on the strategic pivot there. As a follow-up, one of your partners recently highlighted the ability to reduce SAP or premigration workloads to as little as 2 weeks using AI, how do you respond to concerns that AI tools are compressing project time lines relative rate cards and reducing the TAM for systems integration work? And are you seeing similar compression in your own engagement? And if so, how are you offsetting this through volume or new service offerings?

So in general, you should think about our strategy is always that the more that we can use technology to bring more value to clients faster, the better it is for our business. And that's the strategy you've seen us execute ever since RPA like burst on the scene in 2015, because when you can actually make -- especially the technical piece of it go faster, so much work, all the process change, all the change management, et cetera, that like the SAP deals are multiyear. And those often become gating items that they're not investing in other parts of the technology landscape or other parts of the business because they have these huge projects.

And so we see this as -- whether it's in ERP or mainframe, it helps because the actual technical piece is a small piece compared to the rest of the work, and it leads to more work. And so for example, just last week, we were at AI Palantir's Conference with SAP, Palantir and Accenture on stage saying we're going to develop those products. They're really not developed yet scale in any way. But we're working together because it will be a net benefit to our clients, which means it will be a net benefit to us. That's how we think about it.

And our next question today comes from Sean Kennedy at Mizuho.

So one of the themes across the sector currently is higher-margin AI services offsetting more competitive pricing in legacy services. So I was wondering if Accenture seeing similar trends helping with gross margin? And also how much of a productivity boost is Accenture seeing internally from these AI programs?

Sean, so let me just start with pricing. For us, this quarter, we -- pricing, which is the margin on the work that we sell. We saw improvements in some areas of our business, and we continue to operate in a highly competitive environment.

Yes. Internally, we think about applying AI in our delivery where -- we're continuing to improve our efficiencies in delivery, which has also helped fueling our growth. And then in how we operate Accenture, we're deploying all the services we give to clients to us. We're often the experimentation place as well, and we're really pleased and you can see that being reflected in the efficiencies we're getting that are reflected in ROI. .

Operator, we have time for one more question, and then Julie will wrap up the call.

And our final question today comes from Dave Koning with Baird.

It seems like you're doing very well with big clients, 41 with $100 million plus in quarterly bookings this quarter. That's been in the low 30s. So that number is up 20% plus. Do you think big clients, the big -- the huge companies that we all know well, are early to spend on AI and big transformational projects in the midsized companies are kind of a little more of a wait-and-see mode. And actually, you might be at the front end of a big swell of those picking up after the big clients are starting to spend on this. Just interested in your thoughts there.

Yes. It's a great question. I don't see it developing quite that way because actually some of the smaller companies are spending a fair amount, like that's where we had a lot of growth, and it's one of the reasons we're focusing even more on the mid-market and made a few acquisitions there to fuel that organic growth. The way I would think about the $100 million bookings or more is that in the large enterprises, is -- it just is a reflection of just how much reinvention they have to do. And that AI is the catalyst for that. And they have big states that have to be modernized a lot of change.

And so those -- that's really kind of a reflection more of how much they have to do. And at the same time, I believe we are early in what will be a large funnel of work because in both the big enterprises and the small enterprises, you just think about -- I talked a little bit about this in the script, like everybody who's put in modern ERP in the last few years, and we're the #1 partner there, none of -- no advanced AI was possible there, right?

So like that's a whole wave of work that has to get started, and then we're beginning to see early momentum on, right? And that's not even then -- we've done very little still in core operations because the advanced AI isn't quite there. Like physical AI is going to be coming. Agentic AI is still early. So we just see a lot of work, but the technology has got to get to the right level and of course, factors like the macro beat into that. So we're really excited about the long term because there's so much more value that clients are going to get from AI. And we're demonstrating every quarter that they're the one that they're coming to us for it. So thank you for the question.

And just a quick follow-up. I think Angie said federal spending would be up year-over-year again in fiscal Q4. I'm just wondering into next year, does that normalize that back to the higher level that it's been into next year?

David, we'll give you an update in September on that. So we feel really good about the fact that we are anniversarying the headwind, and we're getting back to growth in the fourth quarter with our Federal business.

Go Federal. All right. Thanks, everyone. I just want to thank all our shareholders for your continued trust and support. And all of our reinventors around the world that every day are delivering incredible value. So thanks for joining, and we'll talk to you next quarter.

Thank you.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines, and have a wonderful day.

Hello, everyone, and welcome to today's webinar. My name is Justine Iverson, and I look after the Corporate segment as well as the AI strategy for Data and Research within Market Intelligence. I'm thrilled for you to all join us today for our webinar, Reinventing AI Strategy for 2026.

Before I introduce or let our esteemed guests introduce themselves, I quickly just want to go through a couple of housekeeping items. The objective of today's session is for this to be interactive. You'll see there's no slides in this presentation. This is an open conversation amongst experts within the AI space to help you as you're thinking about your AI strategy. But at the bottom of your screen, you'll see some widgets where you can gain access to some blogs and some other resources as well as the ability to ask questions. We want to hear from you. We want your questions. So please enter those throughout the session. And we'll do our best to answer them all. We probably won't get to them all, but we will do our very, very best.

With that, I'm going to quickly introduce myself and then pass it to my -- like I said, esteemed guests to introduce themselves. Like I said, I lead Corporates and AI for Data and Research. So very simply put, that is Cap IQ and the data feed delivery of all of that great content. So I have had the pleasure of working with the 3 on this call, Francis, Jesse and Alaina in various different roles. And also get to spend a ton of time with clients and some of our partners in the space as it relates to AI. So I'm excited to talk about that today. But before we do that, Francis, I will pass it over to you for an intro.

Hello Justine, thank you for welcoming me. I'm Francis Hintermann. I'm the Global Lead of Research at Accenture, working from New York City in a team, which is growth in strategy in charge of supporting the development and the implementation of the strategy of Accenture.

Fantastic. Thank you. Jesse, over to you.

Awesome. Thanks, Justine. I'm Jesse Kramer. I look after M&A and investments at S&P Global for the company as a whole. So supporting Justine and team thinking about inorganic growth, but also our ratings, energy, addressable MI division, our Index division as we think about how to grow the company. We spent a lot of time with emerging companies in the space, thinking about how they're applying new technologies to our clients' workflows and our workflows. And so I'm excited to be here today.

Awesome. Thank you. And Alaina, last, but of course, not least, to you.

Thanks, Justine, and really excited to be here for this discussion today with you and Francis and Jesse. My name is Alaina Tosatti. I lead our -- what is the Strategy and Business Transformation team in S&P Global Market Intelligence. Market Intelligence is one of the business divisions of S&P. It's about a $5 billion business, and it houses, as Justine talked about, brands like Cap IQ and our unique data and IP, along with other offerings in software and services, really focused on serving into our -- into the capital markets. So looking forward to the discussion today. I spend a lot of my day and time thinking about growth opportunities, how we can be more efficient and how we balance that with risk across our portfolio in Market Intelligence.

Awesome. Thank you all so much. I promised everyone I wouldn't make them give a fun factor up themselves on their intros. So sorry, you all missed that portion. With that, the basis of this webinar really came out of the hundreds of client and partnership engagements that I mentioned earlier on. Candidly, it's the favorite part of my job is to be able to be in the market, see what's happening. It's such a fun space. I say this all the time. I've never worked in something moving so quickly, particularly as we think about you all on this call, right? You all work for banks, investment managers, corporations and I think you probably all are feeling the same that we are. It's moving quickly, and we're doing our best to meet you and serve you where you are and make sure you're getting the most out of our content and our offerings in this space.

So the blog that I offered was really based around kind of a couple of key trends that I saw. So I'll hit on a couple of those and just to get the conversation started and then get some thoughts from Francis and team. So I would say a couple of the key trends, I'm not going to go through them all, but the biggest trend, I will say that I'm seeing to be completely candid, is organizations are still figuring out their AI strategy. So if you feel like it's evolving or there's something new happening every day, you are not alone. That is the #1 trend I'm seeing no matter the size, sophistication, the market cap of the organization.

And I think following that, there's a real push today more than ever to really determine and measure the ROI and the impact of that Gen AI initiative, right? I think we shifted from a time of a lot of POCs and a lot of experimentation to now a real push for understanding that top bottom line impact that organizations are seeing. I was hearing this in my client conversations and working with our partners. And so I actually did a little research based on our earnings call transcripts that we have. I went and looked from Q3 -- Q4 2023 to the end of last year just to see how mentions evolved across earnings calls. Again, this is for our whole corpus of earnings calls. So very broad swath of global companies. And super interestingly, over that time, AI mentions, right, just the mentions of AI in these earnings calls was pretty steady, about 4.5% increase over that time. So nothing totally drastic there.

But what we noticed is over that same time frame, there was a 57% increase in mentions of AI cost savings and positive sentiment around cost savings related to AI. So you can see how that trend and that management expectation and Street expectation of ROI on this investment is really starting to play out across all industries, all sectors, all geographies. So that's one trend that I really started to see evolve over the past 12 months that I think will continue into 2026.

Another quick one. I think there's a bit of speculation or where is this all going to go? But at the end of the day, money talks and money is still flowing. In 2025 alone, according to Cap IQ, there was a $95 billion raised across 143 funding rounds. And that's for AI-specific firms. This takes out chip manufacturers. This takes out data center providers, right? This talks solely about that, nearly tripling from 2024. So money is still flowing. There's still investment. There's still a lot of interest in this. Jesse, I know we'll talk about this a little bit later from your perspective.

The other thing I'll say that I've seen a big evolution over the past 12 months is the partnership ecosystem continues to evolve. We do a ton of partnerships at S&P Global. The AI firms are partnering with each other in different ways. We'll talk a bit more about that later. So that continues to be a big trend.

Two last points I'll make before I open it up, I'll say, is that the risk tolerance has really evolved. I would say when I first started meeting with a lot of clients, there was probably 2 big camps. One camp of we're all in, we're opening it up. We're letting our employees or our organization use AI, use whatever tool they want. Obviously, you can imagine some of that is tightened as there's concerns around IP and data protection of internal information, et cetera. But on the other side, we've seen firms that were a bit more slow to adopt or a bit more conservative, really evolve to really change their posture and figure out how to bring that into their organization safely, soundly, securely to really help their employees.

And last but not least, I have to say this, but it's true, it's all about the data. Data is the foundation, whether it's our data using data like S&P Global's data, proprietary data on the client side or other data, it's all about how you are utilizing that data to get the most out of it.

So those are a couple of key trends I've seen. Before I pass it to you, Francis, I'd love to ask the audience if my first hypothesis or my first observation plays out. So how many AI tools has your team trialed or adopted in the past 12 to 18 months? Would love to see, is it 1 to 2, 2 to 4, 5 plus? Or are you guys still early on your journey? And I would say, I won't scoop the audience because you all put in perspective. But from my view, we've seen a ton of optionality in this space, right? There's the hyperscalers such as [ Claude ] and OpenAI really investing in this space all the way to these last mile solutions that do something really, really well. And that's creating a lot of optionality across organizations. So let's see what you all said.

So let's move to the next. All right. So pretty well versed here. No surprise. Most are between the 1 and 4 points. That's actually probably what I've seen, right? And 1 to 4 could be maybe one's homegrown or something internal, mixed with a third-party application or even using Gen AI tooling in a solution or tool that you already have adopted and used for many, many years. A great example of that might be Cap IQ. 5 plus, interesting to see almost 90% of that. That's still a lot of different tools to trial and experiment. So definitely what we're seeing. But with that, enough for me.

Francis, what have I missed? What are you seeing? What do you think is also happening in the market?

Yes. I don't know -- I didn't know if I could answer the survey just since I did not. But just in full transparency, I would have been in the 5-plus category, so the 19%. So because we're testing tools, new tools every day. And I think it's part of what is fascinating right today is that we've got new options coming nearly every day. So it's interesting to see where your participants are.

Thank you for asking me this question. I read you on a regular basis. I read your top 10 trends with interest. And out of what you mentioned earlier on, I'd like to pick up the one on ROI because, of course, as part of my remit at Accenture, I oversee all the thought leadership that we develop and we publish and we share with our clients. And ROI of investment in AI has been a source of questions for the past 3 years. So it's definitely one that we are extremely interested in. I agree with what you mentioned around cost saving, around use cases that you call administrative that we would call horizontal ones, right? Think about customer service, think about knowledge management, think about IT and the tech organization itself. Absolutely, yes. I think what's interesting though, in last year and even more this year is that we can see the verticalization, what we said about verticalization happening, meaning development of AI as part of what is really specific to industries, part of a core value chain. And that means that beyond the cost savings, there are opportunities as well for revenue growth for companies. And actually, we survey our clients and our partners on a regular basis at Accenture, and we published just our latest survey of CXOs at Davos, so 2 months ago. And we were asking some of the CXOs whether they are -- they have a choice to make, is AI more an opportunity to grow the revenue or more an opportunity to reduce the cost. And actually, 78% should be emphasis on revenue growth in the coming years.

And to us, that's an illustration of the fact that some of the AI implementation is moving to the core value chain of companies where they can actually create more value, create more opportunities to grow. We published some of these examples in a PhotoSheet that we published actually yesterday with the World Economic Forum on the organizational transformation in the edge of AI. And you will see there lots of case studies in different industries. If I just got to mention one to make it tangible to your audience, it's about the pharmaceutical industry, life sciences. And we can see there that in the drug discovery in R&D, which is very, very specific to life science, AI can actually help not only to accelerate the discovery, but actually change the discovery process itself. So for me, it's just one example. I could add more. But in the interest of time, I will stop here. Justine, I could react on other trends as well. I keep that for later on. But I like your point on data. So maybe we can come back to that later on if that's of interest to the audience later.

Yes. No, thank you. I always -- I think everyone wants to hear real examples. So the life science example makes complete sense. Obviously, in our world, we service kind of the range from life sciences all the way to financial institutions. So we see, obviously, a ton of our use cases really around that financial services use case. So Alaina, I'm curious from your view, like what are you seeing from your seat based on this? How is this impacting how a company like us, a major financial institution that services a vast array of clients? How are we thinking about our strategy and how we evolve that over this time as well?

Sure. Happy to jump in. Francis, really like your point about the shift to the focus on revenue growth and opportunity. I think that's something we're living and seeing kind of firsthand. Maybe taking a step back first, just thinking about the market and what have we been seeing, right? The pace is just the thing that stands out the most with this landscape, right? What we've seen from a development perspective has been incredible, frenetic is a word that gets used quite often, just to add to that piece. You think to the launch of Claude Cowork earlier this year, and that's really furthering this expansion into enterprise use cases, right? So it's clear now that Agentic workflows can and are being applied today to real work for our customers within our teams and Market Intelligence, and they're capable of completing these really kind of complicated tasks autonomously. So this is real. It's happening right now, and the pace is just an unbelievable adoption level.

And for businesses like ours, I think that really shifts that expectation of how we're going to use AI, whether a year or 2 ago, when you were talking about efficiency gains and optimization, now it's all about business transformation. What are we going to look like in this new age and how will we remain relevant and how will we pursue growth with this new paradigm. When we thought about it within Market Intelligence, we really leaned on 2 perspectives. First is really how the customers themselves for Market Intelligence are going to be using or are already using AI to transform their own work. And this will vary by use case, right? We giving a specific example, thinking about the buy-side customers, right, asset managers and hedge funds that we may work with, they're leveraging AI to kind of better ingest and synthesize and make sense of really large volumes of data that they're getting from multiple sources, including their own proprietary data that they want to keep quite safe and protected. And that will really help them with alpha generation, which is the ultimate end goal. So again, as you talked about data, that really resonates because certainly, one of the core tenets of what we offer into the market as a data partner is that providing of differentiated data to these firms and making sure that our data is ready and fit for purpose for whatever new tooling we may see within the customer segments.

And then the second lens that we've spent a lot of time thinking about, of course, has been sort of transforming within our own 4 walls. How do we rethink our workflows within Market Intelligence to leverage AI better. And we could talk about examples here within customer support teams, so better enabling some of those teams to get requests done quickly and efficiently so we can focus on the higher value and more of a white glove with customers or whether that be in our data operations groups who are automating a lot of the repetitive tasks and focusing on data ingestion and linking and normalization, which again speaks to how valuable that data will be once we can deliver it back to our customers.

So the real call to action there has been just embracing AI across everything that we do, thinking about not only how it will drive efficiencies, but how it can really drive value as we go forward. And maybe just one last point to add, and I think we can dive into this later if we need to. But really, the other element that we've thought a lot about and continues to remain critical is trust. And so again, as customers are adopting these tools, as we're using them more frequently, it's really critical to have the trust and the governance mechanisms in place to ensure that we keep that high quality and really what has defined from an S&P perspective, our brand in the market for decades.

So I'll stop there for now. Same with Francis, we could probably go for a long time on each and every one of these, but we'll stop there for now.

No, I love it. And you mentioned Claude Cowork and how quickly some of these tools have come to market and how quickly clients are interested in exploring. So with that, I'm actually going to go to my second poll question because I would love to see on this out of our group, what is your firm adopted internally? Are they using Claude, ChatGPT, a workflow-specific tool, right? There's a ton of these in the market that do a specific workflow very, very well. They're obsessed with solving that specific workflow, whether that's a Rogo in investment banking, a Harvey in the legal space. Are you using a homebuilt internal solution? Or are you not using anything yet? You're still kind of in this experimentation phase. So I would love to see what's been adopted on the side of all of our attendees.

All right. I think we should be able to see results now. And again, I think this plays to our previous one. There's a bit of a mix, right? I've seen a ton where there's actually a bit of a mix of tooling. So okay, interesting. ChatGPT, no surprise. I think we're seeing a lot of that. What I've seen candidly in my engagements across our corporate base, so think technology firms, consulting firms, we see ChatGPT as one of the early emerging winners there with [ Claude ] really making a lot of adoption within that investment banking financial services space. But again, these workflow-specific tools, we're seeing it. And I think a big point that we talked about, and we'll talk about this a little bit more, I'm seeing some questions come in that play to this is there's a lot of homebuilt solutions that are fantastic as well.

One of the big questions that came in and that we talked about a little bit was around organizing internal data. That is not a new challenge for organizations, right? That's one that we've seen in the market forever. I think AI just shines a light on that more than ever. And so something we've done at S&P is we've really tried to lend our expertise to help organizations with that. That's something we do, right? We're a data company. We take messy data, we make it organized and valuable. So that's something we spent a lot of time on is just thinking about how you do that. A great example, if you try to take tabular structured data and just put it into a large language model, you're not going to get great results. But if you do some technical work such as some python wrapping with -- you apply some business logic to understand, you can start to get really, really powerful results out of that. How you mentioned, Alaina, getting information out of vast amounts of textual data, for example, very, very quickly is a great example of that.

So with that, Jesse, I want to take a little bit of a pivot here. I know you spend a ton of time observing the market, trying to see who's going to be a winner, who's not. So I'm going to ask a little bit of a cheeky question. What's your prediction for the IPO market in 2026? And then we'll put up our fun poll question that might spur a bit more conversation from you, too.

Awesome. Well, Justine, I love a cheeky question on some of this. I think heading into '26, I think people all thought it was going to be a pretty robust IPO market. I think what's happened is in patches, there's been maybe less confidence in that market and MA markets in general because kind of what is valuable about companies is starting to change, and it's changing because of the fast pace of innovation and the thought that AI sort of can do everything or at least can do a number of very important tasks in our economy. And I think that probably is true to some extent, perhaps not as true as every kind of equity research analyst believes or worries for the companies in their portfolio, but particularly around kind of more traditional client software companies, even data analytics companies like ours, I think valuations have been a little bit more uncertain.

And I think for parties who can't sort of show real kind of AI traction, there's been sort of weakening of valuations. I think it will make it harder for companies like that to go public. Some of those, I think, were in kind of the queue to potentially IPO. At the same time, there's a set of companies that are only stronger because of this. The large sort of frontier labs, I think, are kind of queuing up to try to go public at the end of the year. And there are companies that are sitting on the data that powers AI, sitting on the kind of data warehousing and cleansing that really supports this a set of companies that are building that last mile of potential application layers on the AI models.

And I think they will be kind of a robust sort of set of potential IPO candidates. Whether that happens this year or happens in the next couple of years, I think, is an open debate. But I think we will start to see a few of those companies go public and that will require a strengthening, I think, of governance, sort of a change around some of the kind of circular economy phenomenon that's happening and probably a little bit more of a shift towards profitability in those businesses. But if one does, these things tend to come in trends. And so you can see a number of others kind of flowing from that.

I love it. And I'm going to ask my last polling question for the audience and based on your response. So of the audience, who do you believe will IPO first? Anthropic, OpenAI or you don't think either will? I know those were -- we see a lot of -- there's a lot of headlines about this. So curious what the audience thinks about this one. Give it another second or 2.

All right. Let's see what everyone thinks. Oh, mix bet. All right. This is probably how I feel about it. It's pretty mixed. So about 27% think Anthropic, 39% think OpenAI and then 1/3 also think it's neither. So I guess time will tell. If we had a magic ball here, it would be great, but we will see what happens throughout 2026 on this front. Awesome.

I think, Francis, I'd love to come back to you. What surprised you the most over the past 12 months? Or what's changed the most from your perspective on the AI front from your view?

Well, many things. But if I had to pick up one, I would pick up the one on work. I mean Alaina was mentioning some of it within your own company and what you do for clients. Definitely, we see a lot happening in the market. If we just start with a question of usage of AI tools as you were pulling the audience, Justine, we can see some shadow AI in place in companies, right? We've got executives sometimes telling us, well, employees are not so enthusiastic about it. That's not what we see. Again and again, as we pull employees of large companies during the year, 3 or 4 times a year, what we can see is lots of interest from employees to the point that when they have not access to the enterprise version of some of these tools, they actually use their own personal account to use these AI tools at home and then feed that back in the work, which obviously what we call shadow AI, which obviously is not good in terms of everything you can think of, of intellectual property of responsibility of ethics and so on and so forth. And so for us, that's really the imperative for executives to actually answer to the needs of their employees and understand where it's going on in terms of the job market and how they can help their employees in terms of upskilling and reskilling.

And in more general terms, we see an evolution towards what we call a skill-based economy, more and more defining the needs depending on the skill of employees. And there is a big mismatch there. We actually built an index with Wharton University for all of you, if you're interested, it's out there. It's on knowledge at Wharton. And you can look at your own skills compared to the market trends and what is asked by employers. And even we attempted to put a monetary value on some specific skills to measure the current mismatch.

And broadly speaking, what we can see is that this mismatch is at the core of AI adoption at scale and will impact the ROI that we were mentioning earlier on. So for us, that talent reinvention is really what we started to see in the past year and what we envision as being one of the major trends over the coming months and the coming years because it will take years, but that talent reinvention, making sure that employees have got the relevant skills to perform in this AI economy is going to be the critical factor to make it a success eventually.

I couldn't agree with that more, and I have a funny, maybe not funny story from just this week is a bunch of us internally were talking about some of our future AI work that we're doing, a ton of excitement around it, and we're taking notes. And halfway through the meeting, we're like, why didn't we turn on Copilot to take notes for us, right? Like it's a very -- and someone on the call said, yes, my son would have not even thought twice about this. It would have already been on, it would have been part of the workflow. And that's just funny because, to your point, there's a change management in the current workforce that needs to happen. We have these tools, but we need to start adopting them. We've worked a certain way for so long. There's human inertia in how you do your job. So there's that.

But then to your point, there's the next generation that this is inherent. This is part of their day-to-day. They've never not lived without this very streamlined experience. So I also believe change management and talent management is an area we're seeing a lot of focus on that, that needs to happen. How do we upskill our current employees, how do we prepare for the future? And what does the future look like? I've asked CCOs at banks, the junior banker, what does that look like to you? And it's hard to predict what that's going to look like because not only do they do certain jobs that can become more efficient with AI, but we're also the bench for the next job. So how do you balance what needs to be done today, how we can be more efficient today with building the bench to continue to grow that business overall. So I completely agree on the change management point. We've gotten some questions on that. So I think one of the biggest challenges, that was one of the questions as it relates to people is change management and integrating it into workflows, really understanding how that evolves the day-to-day. With that, we'll do a couple more questions, and then we'll go to Q&A because we have a ton coming in. Alaina, what do you think will change the most in the next 12 months? I know that's a tough question, but what do you think?

As we were saying, it's the one on everyone's mind. So I'll take a stab at, at least my perspectives. And in fact, picking up Francis perspective on one of the points you were raising about shadow AI and the idea that today, if we aren't moving fast enough in enterprises, it is on the consumer side, just, again, a pace of adoption that we have not seen in any other technology of late, and it will only kind of increase in sort of complexity and speed. So that was one of the points.

As I think about the next 12 months, I think consumer AI tools will continue to move even faster. And what does that mean on our side? Well, that just raises the expectations and the strong bottoms-up pressure that enterprises are feeling to keep pace, right? We have to be able to provide these tools in a safe and controlled and risk -- our own risk environment in order to better serve our own employees, but also ultimately into the end customers. I mean you think of the recent launch actually with Google Maps, right, and how they've now integrated Gemini AI into their maps application, and that's going to transform how we interact there with new recommendations and suggestions in this Immersive 3D experience, right? So we're seeing it rapidly and now and it's getting ahead of us on that consumer side. And so that is creating the right flywheel, I think, for the enterprise as well.

Maybe one other point then for the next 12 months is I think that high-impact enterprise use cases will continue to scale as we think about this next year, right? We think about investment banks who are already leveraging AI for step changes in how they generate pitch decks or investment memos and again, aggregate all this input and take things from days to minutes. That will continue. And again, in that poll and survey that talked about homegrown solutions that may be one of those unlock enablers, especially within some of the more regulated industries and intensive areas like banks that we work with.

And then I think back to the question on some of those LLM providers and where those are going, I think they'll continue to evolve. I mean when we've seen releases of new models that sweeps and bounds each time. So again, this will continue, and we're going to need to continue to kind of keep up. And I'd also expect a lot of them to -- and this is something we've obviously embraced and you've led for many of the discussions Justine on our side, but these continued partnerships between some of the more -- the data as well as the vertical solutions alongside these incredible models and the capabilities there just to better unlock very specific use cases for the customers.

I love it. We're going to do a quick lightning round, then we're going to open it up because we have received so many inbound questions. I would love to open it up to the audience to answer some of those. But a quick lightning round. And while I'm getting to it, feel free to answer the question on the screen here. What is one prediction or outlook you have for the AI market? And who -- or who do you think is going to be the winner? And again, don't worry, we're not holding anyone to your opinion today, but we would love to see what you're thinking today. Francis, why don't we start with you?

Yes, I'm not in the business of identifying winners directly. But what I can tell you is what we can see growing. And what we can see growing is the focus on what we call sovereign AI. I mean, what is happening in geopolitics. Obviously, we see it every day in the news. And it is impacting our clients. We, at Accenture work predominantly with large companies around the world. And this question of sovereign AI about what part of the stack has got to be localized, where you operate, how you develop the interoperability between the different layers and the different regions and still keep some agency in your strategic moves. For us, that's definitely a winning topic, if I can say it this way, Justine.

Love it. Jesse, what about you?

I think the status quo is probably likely to continue with kind of different providers being good at different things and continuing to sort of leapfrog each other. I think that's going to happen for a while. It seems unlikely to me that things will meaningfully converge to one provider, my read.

Alaina, I know you answered this a little bit, but if there's anything you want to add, your welcome.

I was going to say I crept into it a little bit in my last one, so apologies. But maybe I just double down on the point. I think we're in this -- as they kind of called it an industry era of specialization, right? No more general purpose AI. We're seeing specialized models, agent skills being developed. These are solving very specific domain challenges for industries. And so I guess my prediction around this is that we just continue to see the rise of some of these more specialized models, they also offer the economic benefit and the right fit for a lot of the use cases that need to be deployed against and ultimately can help us deliver more trusted outcomes. So a little bit on that.

Love it. My answer actually plays a little bit into one of the questions we got. One of the questions we got, so I'll answer it with my prediction. One of the questions we got is there's a lot of hype, right? There's a lot of press releases. There's a lot of noise out there. Like how do we know it's real. And I do think throughout the year, we're going to continue to see some of that kind of rubber hit the road, that realness, right? There's -- again, it's kind of how I started. There's a push to really start to see the ROI, whether that's top line growth, bottom line impact. And so there's, I think, going to be a bit more challenging from clients of all these tools, like we need to see that impact. We want to see that. So I think there's going to be a continued push for that. I think I believe that there is a spot for both, for all different types of solutions, whether that's a hyperscaler like Claude Code doing something, whether that's a last mile provider doing something really well. I think the market is vast, and I think there's going to be room for them in 2026, at least we'll see how that continues to evolve into the future from there.

So with that, we're going to open it up to the audience. So I'm going to go through some questions. We've had almost 100 questions already come in. So we probably will not get to all of them, but we will do our best to answer some of these out of the gate. So I think one of the first ones I have and Jesse, maybe we'll direct this one to you. Let me just scroll to it, sorry, we have lots in here. How -- this kind of plays to what you were saying earlier on the markets, but how does the amount of debt taken on by these companies impact their IPO chances? OpenAI is heavily levered. So does that mean they would need to IPO soon to continue the funds? What are your thoughts on this?

So I guess a couple of thoughts. The first is, I think the debt markets and private markets will continue to fund these businesses as long as they keep innovating and growing. I think in order to become a public company, these businesses will need to go through sort of a more rigorous audit and SEC process. A lot of the debt, at least as I understand it, that OpenAI has taken out has been sponsoring pieces of infrastructure projects. And there is a question as to how much of that they've guaranteed and how much of it rolls up into their obligations. And in that world, I think really unpicking how much they're responsible for, are they kind of marketable from a public company perspective is a really good question. There's also sort of a related question of how much of their revenue today relates to related party transactions. So I think that's sort of an important piece as well. But getting clean financials for these businesses is going to be one of the big hurdles, I think, of taking them public.

And then if they are responsible for all the debt they've taken on to build these data center projects and sort of the infrastructure that the models need, I think there's a real question as to whether they can be public this year or whether they have to kind of grow revenue into that to kind of get to some kind of leverage ratio. I mean, today, that almost makes no sense, right, because they're not profitable. But to get to some kind of leverage ratio that starts to make sense.

There's another question that said, what is that going to mean for all of us? And it's hard to see that not eventually meaning that the price of compute and these offerings will go up, particularly for enterprises. And like we're in this moment now of the economics have been made so attractive for everybody that we can just use these tools for kind of everything. I don't think that's the world we'll be in forever. And particularly as these companies look to drive profitability because eventually they'll have to, the use of AI may become a little bit different, and you'll need to be hyper focused on efficiency to use it in a profitable way for your businesses.

I love it. Thank you. Francis, I'm going to pass this next one to you. I think it's a great question given your role. What do you foresee as the future of management and strategic consulting firms in the era of AI? And how do you see that evolving?

It's a great question, of course. And when we look back, 12 years ago, some of you may remember, not all of you, I guess, but that's a privilege of having gray hair is that I was already there 12 years ago. When cloud started to expand greatly, some -- if you were there, you may remember the prediction that we made at the time by some that the consulting industry was going to go down because there would be no need of consultants anymore in the era of cloud. Fast -- and even you had some prediction made by some researchers at Oxford University, which were saying that overall, 47% of the jobs will be automated, and that would be certainly true in the consulting industry even to a larger extent. But if you fast forward then 12 years, you can see that the consulting industry is today actually larger than it was 12 years ago. And I believe that's something of that kind, which is going to happen with that current transformation. If I believe what analysts are saying about that current transformation is that there is a need for companies to get the help of consultants to go through that transformation.

I love it. Yes, agreed. I think it goes back to what we were talking about earlier with change management, right? That's a great area where there's so much support and necessary need from that industry.

I'll answer a couple. We've gotten a couple of questions on guardrails and how to avoid hallucinations. I always joke, I never said the word hallucination at work until the past 18 months, and now it comes up in almost every client meeting. So not a word I thought I would say at work very often. And I can talk about what we've done here at S&P because I think, again, we know our clients, you all make million, billion dollar market moving decisions on the back of our data and on the back of your own expertise and analysis. So the way we really think about that is when you're using an LLM, right, you have your guardrails that you can instill on those. And the way I always explain this very simply is we turn that knob all the way up, right? So we turn those guardrails up. We'd rather tell you we can't answer that than give you a bad answer and answer that's not accurate. And all of our answers are grounded in our leading data. At the end of the day, it's about data. It's about accuracy, quality, completeness of our data, and that remains core to all that we do and what we've always done, what we're founded on. But I think that's an important lever. So one, it's that foundational data layer being accurate, clean, complete.

And then two, it's really about turning up those guardrails as you're implementing that with LLMs and other tools. So that's something that we've done here that has really helped us. And like I said, we are happy to say we can't answer that or we can't do that for you versus giving you an incorrect answer. And then we always ground those answers in those results so that someone can check it. And I think that is something really important as we talk about training and upskilling workforce is teaching that validation step, right, not taking maybe the answer that you're getting as truth without kind of digging into it. We've all seen it. We've all seen the headlines of a fake court case that makes its way into some work or things like that. And so I think as you think about how to avoid that risk, it's also a human element of checking that, auditing those responses as you are taking those answers from AI. So there's a couple of questions on that throughout. Alaina I'll pass...

Can I say?

Oh, go ahead. Sorry, jump in.

Yes. Maybe just one word on that, Justine, because I was listening to you with interest and totally concur with what you were saying. And our own CEO, Julie Sweet, said that it's not about human in the loop, it's about human in the lead. And that has a meaning because everything you said was about the responsibility stays with human in terms of setting the direction, setting the boundaries, making sure that the discipline is actually executed as it should be. So it requires more leadership rather than less leadership. And that's why Julie has been coining that time and again and again of human in the lead. And I think what you were saying, Justine, is a very good illustration of that.

I'm going to adopt that. Human in the lead is going to be my new catch phrases of human loop. I think that's spot on.

Alaina, we're going to -- I'll pass this on to you just because I know you spend a lot of time with our CFO and our team members. What is your view? Are CFOs now being asked to understand AI and the need for strategic framework? Like how does that change how you think about strategic roles and the role of someone like a CFO at an organization?

Yes. I mean, short answer, absolutely. I think as you can imagine, this is so critical from different lenses for companies. As we've talked about, it is -- has been for a long time, been talked about from an efficiency perspective and how can we optimize what we are doing today, how can we free up resources, how can we reinvest that in other places and higher value in new services for customers and new growth opportunities. And again, increasingly, we're pivoting into this new growth paradigm. And so that, of course, those 2 things are what CFOs are constantly thinking about. And so understanding what we are doing in AI and challenging us to continue to do that is definitely a big part of that piece. There's also, of course, an element about how just CFOs and really any teams within organizations, but just picking on that as part of this question, are using it within their own teams today, right? And so again, I think not only from both the role as in representing and thinking about where we're going from a financial profile for our business, but also thinking how we can really improve efficiencies across a lot of the team members that we have today and whether that be in finance or any other supporting team in the organizations. We talked earlier about this incredible need for change management. And it has to be in every team, and we do see that actually today. We see a lot of interest from colleagues across the organization. So we want to embrace that and actually encourage everybody to be innovating a bit in what they're doing. How could -- you take one tool tomorrow and just optimize a little bit of what you've been doing for several years? How can you improve that going forward? So there's some very basic building block and incremental elements that we can all be thinking about. And also, of course, at the bigger picture for the organizations, CFOs and strategy teams are hyper focused on this area.

Jesse, this question kind of plays off that a little bit because these are things the CFO thinks about out of an organization. But what is your thought on the current AI bubble conversation and how companies -- they're accelerating it, but CapEx expense is also increasing where maybe they're not seeing that return. There was an MIT study that said companies are chasing this AI, but they're not seeing that. What are your thoughts on that? How do you see it from your perspective?

Look, I think it's hard not to think there's going to be some correction at some point just because there's so much hype. And eventually, I think we'll see one or more of sort of the big named companies make sort of missteps that will make people question the value of all of this. But I think the kind of overall kind of efficiency gains from the technology and the tools feel real to me. They feel real to me in sort of the -- just use of it in sort of our daily lives. And that is the underpinning of something that's not a bubble impact. It's sort of more economic output that sort of underlies all of this. And so I think it's probably at a specific company level, there's going to be stuff that's kind of overhyped. But at an overall economy level, I think probably grow into a lot of the valuations that we've seen over time. And that makes it kind of tough to invest in the space right now. But it's definitely one that's sort of on my mind pretty consistently.

Yes. I think we've had a lot of conversation on this ourselves between the 2 of us. So I think a big topic that we'll just kind of have to see how it evolves and how valuations continue.

Francis, I'm going to come back to a bit different question or a different type of question goes to it. What's your advice or what have you seen successful as it relates to AI training internally, right? All of these hyperscalers have tools in training. There's internal firms creating it. You talked about firms like your supporting on this. What have you seen work in this space? And what's your advice for the audience?

Try it. That's the advice is that we should all try it. I don't think there is one silver bullet. I think there is an enormous appetite to actually get to some learning, and that learning comes by trying it. We call that the era of co-intelligence. We actually presenting a new research at the NVIDIA event, GTC this week on that. And we say co-intelligence because it's a complete change in the sense that we all can build AI agents. These AI agents are going to learn from you about how to best serve you and you're going to learn from the agent as well. So that's what we call co-intelligence because it's learning from the agent and educating the agent at the same time. And I think for all of us, you mentioned different tools earlier on. That's an opportunity to enhance our own job. And to the point that at Accenture, we created a line of service dedicated to training executives in this area of AI that we didn't have before. We even bought a company called Udacity doing courses because we saw that the appetite of executives across the board was enormous, and we wanted to be there to serve them. But so for me, at the individual level, it's about that. If you don't have your agent yet, build it and you will have fun. Some of it will be extremely helpful in your job.

I agree. I think one of the biggest advice I give is the same. Like you got to start somewhere. I think where we've seen the most advanced adoption is where people really think through their workflow. And instead of just trying to pick anything, they find one spot and really focus on that and then build from there. So I think that's other advice I'd give is really think about your day-to-day or your team's day-to-day or your organization's day-to-day, where is there that constant bottleneck of time and then try it. So I think that's great advice there.

Another question we got and one, candidly, I've answered a lot amongst our client base, et cetera, the question we got was specifically around maybe some of the more traditional legal providers such as LexisNexis, et cetera, and how that will be impacted. I'm not going to speak specifically to them, but I'll speak to traditional offerings or offerings that have been around. Maybe Cap IQ is a great example of that. What does that mean? What does the future look like?

And I'll give my honest view on this. I think it's evolve or die. We have to continue to evolve and meet our users where they are and how they want to work. So the example I always give with Cap IQ, for example, it was built on the foundation of making it easier for people to do their job. I think a marketing slogan early on was get you out at 10 instead of 2 a.m. okay? Maybe our marketing slogan now is get you out at 6 instead of 10. So I think that evolves the foundational basis of what these organizations do has not changed, right? It's to make it easier for our end clients or their end users do their job, but how that gets done has changed. So a big thing we've really focused on is bringing the best of that Gen AI technology to the tools you already rely on. I think we've talked about it today, governance, getting new tools in-house, like that's work, right? That's a lot of effort. You have to go through procurement. You have to go through testing and making sure these tools are accurate, et cetera. So great, let's help our users out by bringing the best to the tools that they've used in their workflows. And so I think it's all about evolution. It's all about meeting users where they are, et cetera. So that's an answer I'll see on one of those type of questions of how does it evolve.

And maybe I can add just one word on that, Justine, you didn't ask me the question, but let me just add one word on that because we've been on that journey of developing partnership with data providers ourselves for our own tools for 3 years now. And kudos to S&P, you were part of the very first companies to actually be out there to develop these new tools with us and to provide lots of data in our own tools through APIs. And we are heavy users, of course, of Capital IQ and SAP Trucost as well for ESG data and very thankful to your company to be there. And really, you were part of the early ones to be in that game.

I love it. I promise I did not plug Francis to say that, that was on his own accord. Alaina, I think you want to add something?

I was going to add because we almost made it through this webinar without using one of the most common things, MCP. And maybe just to put a point on one of the areas that you've that we've discussed and certainly has been a differentiator and as we go forward as part of the strategy as well. But as we think about unlocking, especially as a large -- a company that sits on a large and very differentiated data estate, MCP is one of the ways we're going to enable and are enabling customers today to interact with that data. And this will be key in really helping unlock those agentic workflows for the customers. So back to your point, wherever they are doing their work, whether that be Claude in the future, whether that be their own homegrown systems, as they evolve with that, we evolve and are there in advance, hopefully, and also alongside them to bring that journey together. So we're seeing that, obviously, increasingly from a demand perspective from the customer side and are meeting that too with that just as one example of a way that we're going to modernize from a distribution lens.

Yes. Thank you. We would have been pretty remiss to not mention one of our focus areas. And honestly, what we're hearing from the market, that was a point, right? MCP was something that wasn't a word in anyone's vocabulary 18 months ago, and now it's the biggest topic or one of the most innovative areas that we're seeing. And if I were to make another prediction, I suspect there'll be some new technology or word that comes out in the next 12 months that drives how we're all thinking about this and utilizing AI as well. I know we're almost at time. So I'm going to kind of wrap here. Sorry, we didn't get to everyone's questions. Like I said, we appreciate the engagement. There was more than we could have handled on this. So we'll work to get responses back to folks accordingly.

Is there any closing remarks from anyone? Anything anyone wants to say in departure before we wrap in the next couple of minutes. Francis, we can start with you if there's anything you want to add.

Well, one of the most exciting things right now for me is about AI simulation. And in the research world, for those who are interested in research, we're developing lots of AI simulation in very interested to continue the discussion for those who are interesting on Tech Stack, so we can interact there. And in terms of the business, we can see the emergence of agentic commerce. And for us, that's going to be a very interesting area to follow in the coming few months because we can see that its doubling up in that space.

Love it. Jesse, what about you?

Look, I'm going to echo something Francis said earlier, which is try it. And it kind of goes to the fact that we're in this moment of the adoption curve, which is to say that like it's kind of being subsidized by the big companies and by investors to try to increase adoption. And so it's a moment to experiment, a bit more than normal, try it yourself, get it for your teams. Obviously, the right governance has to be put around it. But even experimenting in your personal life is definitely sort of a needle mover, I think, and it helps you and those who work for you kind of learn about how to use the tools better. And I think that's a big boon right now.

Love it. Alaina?

I'll go back to just my original kind of sentiment around pace and taking a moment to reflect, again, over the last few years of what we have seen and how disruptive it has been and remembering that sometimes early disruption can look a little incomplete and inferior in some cases and make you question whether it's the right direction of travel. This is, as we have been talking about and we can all see now a few years into this, it's very real and very applicable to so many different parts of the industry. So the reference back to the cloud migration years ago, the disruption of BlackBerry with iPhone, many companies have underestimated the speed at which these kind of transitions can take place. So don't be in that camp. And as Jesse said and Francis referenced, try these tools and apply them to what you're doing today.

Awesome. And my closing, I don't have too much more to add other than what the 3 of you have seemed to have said and covered. I think I'll just hit on the last point of this space is moving so quickly, and there's such different knowledge gap -- knowledge areas with people, lean in, right? Find a partner. We're here to do that. Accenture is here to do that. There's so much going on out there that I think it really creates an interesting time just in the business world, in the markets on how you can rethink your business and rethink how you can partner and drive productivity for your firms. And again, at the end of the day, it's that top and bottom line growth. How can we do that? Well, not bottom line growth, top line growth. How can we drive that? How can we continue to push that forward? So reach out. We're here to help. And thank you all so much for joining today. Like I said, you will receive a recording of this. And if you are listening to the replay, thank you, and we look forward to continuing on the AI journey with you all.

Thank you, Justine.

Thank you, Justine.

Good morning. Thank you for standing by. Welcome to Accenture's First Quarter Fiscal 2026 Conference Call. [Operator Instructions] As a reminder, this conference is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Managing Director and Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our first quarter 2026 earnings announcement. As the operator mentioned, I'm Alexia Quadrani, Executive Director, Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the news release we issued a short time ago. Let me quickly outline the agenda for today's call. Julie will begin with an overview of our results. Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the first quarter. Julie will then provide a brief update on our market positioning before Angie provides our business outlook for the second quarter and full year fiscal 2026. We will then take your questions before Julie provides a wrap-up at the end of the call.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and as such, are subject to known and unknown risks and uncertainties including, but not limited to, those factors set forth in today's news release and discussed in our annual report and Form 10-K and quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed on the call. During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We will include reconciliations of non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section of our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this call. Now let me turn the call over to Julie.

Thank you, Alexia, and everyone for joining this morning. Apologies in advance for my voice. I am getting over a seasonal cold, and my voice is not quite cooperating. And really wanted to start today by thanking our nearly 784,000 people around the world for your extraordinary work and your commitment to our clients, which enabled us to deliver another strong quarter.

Let me begin by sharing that we are very proud to have earned the #4 spot on the Great Place to Work list of the World's Best Workplaces, our highest ever ranking on this prestigious list. This recognition reflects our strategy to be the most client-focused AI-enabled great place to work for reinventors. It is especially meaningful because it is based on feedback from our people worldwide. Our ability to attract and retain great talent is one of our most important competitive advantages.

Before handing over to Angie, I will briefly highlight the value we delivered this quarter, the importance of our partnership strategy and this quarter's strategic acquisitions. We are very pleased with our results as we continue executing our strategy to help our clients reinvent every part of their enterprise reflected in our bookings of $20.9 billion, including 33 clients with quarterly bookings greater than $100 million. We delivered revenue of $18.7 billion, growing 5% in local currency at the top of our guided range with broad-based growth across markets and both types of work.

And we continue to strengthen our competitive position by taking significant market share on a rolling 4-quarter basis against our basket of our closest global publicly traded competitors, which is how we calculate market share. Adjusted operating margin expanded by 30 basis points year-over-year, and we delivered adjusted EPS growth of 10% compared to Q1 last year. We continue to invest significantly to execute our talent strategy to rotate our workforce.

We have nearly reached our goal of 80,000 AI and data professionals and our people participated in approximately 8 million training hours this quarter with a significant focus on building advanced AI technology and industry skills. Advanced AI is increasingly embedded in our large transformation programs, either enabling future enterprise use or being implemented directly as part of our solutions. Our strong leadership in advanced AI is a clear competitive advantage as clients select us to help them capture the value of this technology now and over time and to build the readiness required to adopt it effectively across the enterprise. Momentum in the adoption of enterprise AI continues. Our advanced AI bookings this quarter were $2.2 billion, nearly doubling from Q1 last year and also up from Q4. Revenue reached another milestone this quarter at approximately $1.1 billion.

As we think about the advanced AI opportunity ahead, as you know, we were the first in our industry to share our bookings and revenue from advanced AI, which we define as GenAI, Agentic AI and Physical AI and does not include data, classical AI or RPA. We introduced the metrics in Q3 FY '23, just months after GenAI burst onto the scene initially to size the reality of the opportunity and to demonstrate our early leadership. At that time, bookings were about $100 million across roughly 100 projects and revenue was immaterial. We have measured it consistently since that time. To date, we have now delivered approximately $11.5 billion in bookings across 11,000 projects with revenue of $4.8 billion. This will be the last quarter in which we share these specific metrics. The demand for AI is both real and rapidly maturing.

We've now reached a point where advanced AI is being embedded in some way across nearly everything we do, and many of our clients are focusing on moving beyond stand-alone proof of concept or initiatives. We're shifting to more scaled end-to-end solutions that integrate multiple forms of AI, and it has become less meaningful to isolate the data specifically for advanced AI as it does not reflect how the demand is evolving on the ground, the scope -- the full scope of our AI work for the value we're creating.

Now turning to our partnership strategy. Our partnership strategy is grounded in client demand. Demand for reinvention remains strong with our clients continuing to prioritize larger transformational programs focused on building their digital core and driving both efficiency and growth. Technology is front and center for every client, and the 60% of our revenue in Q1 from work we do with our top 10 ecosystem partners continue to outpace our overall growth.

Given the importance of the broader technology ecosystem to our clients, we plan to continue providing insight into the role our top partners play in our growth by maintaining the metric we introduced at the end of FY '25, the percentage of our revenue tied to work with our top 10 ecosystem partners and its growth relative to our overall growth as it provides a clear view into our largest, most important partnerships. We also plan to continue to share our partnership strategy and how we're growing new businesses with an expanded group of partners. Most of our clients operate with a network of ecosystem partners to meet their enterprise needs. They rely on us to help integrate those partners and expect us to be the leader with the most relevant players across their enterprises, including new and emerging players.

As a result, it is important that in addition to our top 10, we work with a broad set of partners that play important roles across enterprises. Many of these support specific functions such as digital manufacturing, product engineering, core banking and insurance, supply chain and finance, while others are helping clients advance their AI and data capabilities. Our partnerships are critical to our clients reinventing all parts of their enterprises. Together, they represent meaningful opportunities for growth and further strengthen our ability to deliver comprehensive end-to-end solutions.

Over the past year in response to client demand, we've been expanding and, in some cases, forming new partnerships with emerging AI and data companies. And these -- and we will play a key role in helping our clients use these technologies, including creating new solutions and integrating and leveraging the synergies with their existing ecosystems. These evolving partnerships, which are laid out in our earnings presentation are a significant competitive advantage for us.

Turning now to our strategic acquisitions. Earlier this week, we announced an agreement to acquire a 65% majority stake in DLB Associates, a U.S.-based leader in AI, data center engineering and consulting in the rapidly growing data center professional services market, an estimated $12 billion addressable market expected to double by 2030. Along with our FY '25 acquisition of U.K.-based Soben, this meaningfully expands our capital projects, capabilities and presence in the high-growth data center consulting market.

It also positions us to capture growth not only through the work we do with our -- helping our clients use AI, our primary business, but also in the opportunity created by the companies building the infrastructure to power AI. And this quarter, we also invested $374 million primarily in 6 strategic acquisitions. We're scaling our capabilities with SIPAL, integrated product support business in Italy, which brings deep defense and aerospace engineering experience -- expertise for mission-critical programs and Total eBiz Solutions in Southeast Asia, which adds AI, cloud and digital workplace innovation that strengthens Avanade's position in the region.

And we're scaling new growth areas with NeuraFlash in the U.S., a Salesforce and advanced AI leader whose Agentic solutions expand our reach into the mid-market. Aidemy in Japan which enhances LearnVantage with AI learning and reskilling capabilities to help clients build AI-ready workforces, and Decho in the U.K. and RANGR Data in the U.S. which strengthen our Palantir and advanced AI capabilities. In summary, we are pleased with how we delivered the quarter and continue to strengthen our foundation for long-term growth. Over to you, Angie.

Thank you, Julie, and thanks to all of you for joining us on today's call. We are very pleased with our first quarter result with revenue at the top of our guided range as well as strong adjusted margin expansion, adjusted EPS growth and free cash flow. These results reflect the execution of our strategy to be the reinvention partner for our clients. We continue to invest for long-term market leadership while delivering significant value for our shareholders.

Now let me summarize a few highlights for the quarter. Revenues grew 5% in local currency, reflecting nearly 4% organic growth and were broad-based across geographic markets and types of work. Excluding the 1% impact from our federal business, our revenues grew approximately 6% in local currency in Q1. Adjusted operating margin was 17%, an increase of 30 basis points compared to Q1 results last year and continues to include significant investments in our business and our people.

We delivered adjusted EPS in the quarter of $3.94, which represents 10% growth compared to EPS last year. And finally, we delivered free cash flow of $1.5 billion and returned $3.3 billion to shareholders through accelerated repurchases and dividends this quarter. We also invested $374 million, primarily attributed to the 6 acquisitions in the quarter. With those high-level comments, let me turn to some of the details, starting with new bookings. New bookings were $20.9 billion for the quarter, representing 12% growth in U.S. dollars and 10% growth in local currency with an overall book-to-bill of 1.1. Consulting bookings were $9.9 billion with a book-to-bill of 1.0. Managed services' bookings were $11.1 billion with a book-to-bill of 1.2.

Turning now to revenues. Revenues for the quarter were $18.7 billion at the top of our guided range, reflecting a 6% increase in U.S. dollars and 5% in local currency and a foreign exchange impact of 1.4%. Consulting revenues for the quarter were $9.4 billion, up 4% in U.S. dollars and 3% in local currency. Managed services' revenues were $9.3 billion, up 8% in U.S. dollars and 7% in local currency, driven by high single-digit growth in technology managed services which include application managed services and infrastructure managed services and mid-single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenue grew 4% in local currency. Excluding the 2% impact from our federal business, Americas grew 6% in local currency in the quarter. Growth was led by banking and capital markets, industrial and software platforms, partially offset by a decline in public service. Revenue growth was driven by the United States. In EMEA, we delivered 4% growth in local currency, led by growth in banking and capital markets, insurance and life sciences. Revenue growth was driven by the United Kingdom and Italy. In Asia Pacific, revenue grew 9% in local currency, led by growth in banking and capital markets, communications and media and public service. Revenue growth was led by Japan and Australia.

Before I move on, I want to briefly update you on the business optimization actions we initiated last quarter and completed in Q1 as part of executing our talent strategy. This year, we -- this quarter, we recorded $308 million in costs primarily related to employee severance, bringing the total for these actions over the past 6 months to $923 million. Our business optimization cost impacted operating margin, tax rate and EPS. The following comparisons exclude these impacts and reflect adjusted results.

Now moving down the income statement. Gross margin for the quarter was 33.1% compared with 32.9% for the same period last year. Sales and marketing expense for the quarter was 10% compared with 10.2% for the first quarter last year. General and administrative expense was 6.1% compared to 6% for the same quarter last year. Adjusted operating income was $3.2 billion in the first quarter, reflecting a 17% adjusted operating margin at 30 basis points compared with results in Q1 last year. Our effective -- our adjusted effective tax rate for the quarter was 23.9% compared with an effective tax rate of 21.6% for the first quarter last year.

Adjusted diluted earnings per share were $3.94 compared with diluted EPS of $3.59 in the first quarter last year, reflecting 10% growth. Days services outstanding were 51 days compared to 47 days last quarter and 50 days in the first quarter of last year. Free cash flow for the quarter was $1.5 billion, resulting from cash generated by operating activities of $1.7 billion, net of property and equipment additions of $157 million. Our cash balance at November 30 was $9.6 billion compared with $11.5 billion at August 31.

With regard to our ongoing objective to return cash to shareholders, in the first quarter, we accelerated our share buybacks and repurchased or redeemed 9.5 million shares for $2.3 billion at an average price of $245.32 per share. Also in November, we paid a quarterly cash dividend of $1.63 per share, a 10% increase over last year for a total of $1 billion. So in summary, we are very pleased with our Q1 results, and we are focused on delivering Q2 and the year.

Before I turn it back to Julie, let me provide an update on our commercial models. Our large base of fixed price work continues to grow and is a strong foundation for how we believe our commercial models will continue to evolve. In FY '25, about 60% of our work was fixed price, which is up about 10 points over the last 3 years. This reflects the increasing role of our proprietary platforms over a long period of time and clients wanting greater certainty in cost and delivery. This is where our scale, experience and strong financials matter. And now back to you, Julie.

Thank you, Angie. Starting with the demand environment, clients continue to prioritize their most strategic and large-scale transformational programs, which convert to revenue more slowly but position us at the center of the reinvention agendas. The pace of overall spending and discretionary spend in our market is at the same levels we have seen over the last year. We are delivering strong results and taking market share in this environment because reinvention is critical to our clients and our clients know we deliver real reinvention with real outcomes.

Let me turn to 4 strategic growth areas that are essential for enterprises to use technology, AI and data to achieve these outcomes. First, the digital core. Cloud, data and platform modernization remains foundational to every reinvention. When companies tell us they want to use AI, they quickly realize that AI is only as powerful as the data underneath it.

Most organizations have mountains of data spread across systems, stored in different formats, often unreliable or incomplete. Before AI can create value, underlying data and the processes connected to it need to be simplified, cleaned, connected and properly governed. We help clients manage all their data wherever it may be and turn it into something they can access and use to make decisions, train models and uncover insights.

We modernize their data platforms and make sure the data flows securely and consistently across the business so people can trust it and use it with confidence. We also use AI to improve data quality at scale. In the age of AI, data isn't just an input, it's the advantage. That's why we continue to see at least 1 out of every 2 advanced AI projects lead to a data project, and we're the partner that helps our clients unlock it.

For example, Essity, a global leader in hygiene and health, is making advanced AI, including Agentic AI, core to how they run their business, starting with procurement and finance, setting the foundation for company-wide reinvention. We are helping Essity build a cloud-based data and AI platform that combines Accenture's deep industry and functional expertise with our ability to scale advanced AI. We're starting in high-volume parts of the business, processing hundreds of thousands of purchase orders a year, the opportunity for double-digit productivity gains is strong. This foundation positions Essity to move decisively beyond pilots and reinvent end-to-end processes. unlocking new pathways to value and long-term growth.

Security remains one of our fastest-growing businesses, growing very strong double digits this quarter. As cloud, data and AI connect more of the enterprise, the threat landscape expands quickly. We are using AI to detect threats earlier, respond faster and simplify complex environments. Companies cannot scale AI unless they can do so safely, and this continues to be an important growth engine. Building on our long-standing relationship, we are partnering with one of Saudi Arabia's leading financial institutions to build a robust internal cyber defense capability that is designed to protect the bank, meet rising regulatory expectations and enable the launch of modern, sustainable digital services. We helped the bank move from limited visibility to a far more advanced security position, expanding threat detection, reducing incident response times and are helping to improve their national cyber maturity scores. We are also helping the bank achieve the full audit -- regulatory audit readiness, a critical requirement for trust and future growth.

Now with Accenture's deep cybersecurity expertise, we will bring in specialized talent, strengthen governance and help with upskilling to accelerate their progress. With a stronger foundation and the internal capability to maintain it, the bank can now introduce new services with far greater confidence and is well positioned for its next phase of innovation. Accenture Song grew mid-single digits this quarter. Song continues to help B2B and B2C clients drive growth by improving how they connect with and shape the customers' experience, the marketing that first reaches them, the website or store where they buy, the service when they need help and the digital products they use every day by bringing together design, creative, data, technology and industry expertise to reinvent marketing, commerce, service and digital products.

One example is our partnership with Virgin Media O2, one of the U.K.'s leading telecom providers, where we didn't just modernize technology, we worked together to transform the entire customer experience and how work gets done. By rebuilding their digital core and embedding advanced AI, nearly 10,500 service agents now work on a unified cloud platform with connected data and workflows. More than 300 customer journeys have been redesigned, simplifying processes and bringing the full customer context into a single view. These changes are helping agents resolve issues faster and more accurately, contributing to a 35% increase in Net Promoter Scores in some areas and same-day resolution, improving from approximately 65% 2 years ago to nearly 90% today.

A cultural shift is also underway, upskilling teams to enhance their customer-first mindset, turning service calls into opportunities to improve customer loyalty and trust. This kind of change takes strategy, process and talent working in sync with Accenture Song bringing it all together to design experiences that resonate at scale so that Virgin Media O2 can drive innovation and set a new benchmark for customer service excellence.

We also continue to grow in the core value chain of many industries through our Industry X offerings, growing mid-single digits this quarter. Manufacturing and engineering remain early in their digital transformation journeys. Digital twins, predictive analytics, robotics and other AI-enabled technologies are creating new levels of efficiency and resilience. Those same strengths are now propelling our capital projects work where clients need us to design, build and commission critical infrastructure and extend Accenture deeper into their core value chains. Take North America's transit sector, agencies are facing mounting pressure to modernize aging infrastructure and meet growing ridership needs, efforts that require delivering multiyear, multibillion-dollar capital programs.

Partnering with one of the largest public transit agencies, we're applying our infrastructure and capital projects expertise to help transform how these critical programs are managed, spanning a vast network of subway stations, maintenance facilities, bus garages and administrative offices. By bringing together multiple data sources across their construction portfolio, we are helping to enable more informed decision-making, supported by rigorous project controls, advanced scheduling, cost and risk management, and safety and financial oversight to improve efficiency, transparency and forecasting, just as we do in manufacturing. As a result, the agency is strengthening its daily transit operations and supporting safer, more reliable service for millions of riders.

Now let me share how we're seeing demand evolve with the work we're doing in advanced AI. It is early innings, which means there is significant opportunity ahead. Technology is rapidly evolving. While enterprise adoption at scale is nascent, demand continues to grow, and IDC estimates that the total addressable market for advanced AI is expected to grow more than 40% through 2029 from roughly $20 billion today to over $70 billion. We are seeing a steady increase in demand. Over the last 9 quarters, we've seen about 100 incremental clients initiate advanced AI projects with us each quarter, but most have a lot of work to do before they will be able to scale across the enterprise, and it is still a relatively small part of our client base, over 1,300 clients to date out of 9,000. So we see lots of opportunity to help those who have initiated and to expand in our existing clients as well as attract new clients.

Clients increasingly understand that advanced AI is not a quick fix. Adopting it successfully requires foundational work to deliver P&L impact and other critical outcomes. This is why our clients, and the broader ecosystem are turning to us to help bridge the gap between powerful technology and achieving real, measurable results. The real opportunity is not proving AI works, it is making it work everywhere. Scaling AI means working with all forms of AI and means embedding it across critical processes, so it transforms outcomes.

For example, we are partnering with Bristol-Myers Squibb, a global biopharmaceutical leader, to transform how therapies move from discovery to market by embedding AI at scale across the organization. Drawing on its deep life sciences experience, BMS is using AI to accelerate innovation and expand its impact for patients. We are also establishing early leadership in Agentic AI with the scale of our deployments working across the ecosystem. We have built an extensive library of over 3,000 reusable agents reflecting our deep industry and functional expertise. These agents have been used in real client environments, giving us a unique foundation of proven solutions to help clients move faster and with more confidence. Over to you, Angie.

Thanks, Julie. Now let me turn to our business outlook. For the second quarter of fiscal '26, we expect revenues to be in the range of $17.35 billion to $18 billion. This assumes the impact of FX will be approximately positive 3.5% compared to the second quarter of fiscal '25. Our Q2 guidance reflects an estimated 1% to 5% growth in local currency, including about a 1% impact from our federal business. For the full fiscal '26, based upon how the rates have been trending over the last few weeks, we continue to assume that the impact of FX on our results in U.S. dollars will be approximately positive 2% compared to fiscal '25. For the full fiscal '26, we continue to expect revenue to be in the range of 2% to 5% growth in local currency over fiscal '25, including an estimated 1% impact from our Federal business.

Excluding the impact of Federal, our revenue is expected to be an estimated 3% to 6%. This year, we continue to expect an inorganic contribution of about 1.5% and we continue to expect to invest about $3 billion in acquisitions this fiscal year with the potential to do more. For adjusted operating margin, we continue to expect fiscal year '26 to be 15.7% to 15.9%, a 10 to 30 basis point expansion over adjusted fiscal '25 results. We continue to expect our annual adjusted effective tax rate to be in the range of 23.5% to 25.5%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We continue to expect our full year adjusted diluted earnings per share for fiscal '26 to be in the range of $13.52 to $13.90, or 5% to 8% growth over adjusted fiscal '25 results. Due to slightly higher business optimization costs in the quarter, which were $58 million above our original Q1 estimates, we now expect GAAP EPS of $13.12 to $13.50.

For the full fiscal '26, we continue to expect operating cash flow to be in the range of $10.8 billion to $11.5 billion, property and equipment additions to be approximately $1 billion, and free cash flow to be in the range of $9.8 billion to $10.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.2. We continue to expect to return at least $9.3 billion through dividends and share repurchases, an increase of $1 billion or 12% from fiscal '25. Our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on February 13, a 10% increase over last year. We remain committed to returning a substantial portion of our cash generated to our shareholders. With that, let's open it up so that we can take your questions. Alexia?

Thanks, Angie. I would ask that each speak to one question and one follow-up to allow for as many participants as possible to ask a question. Operator, would you provide instructions for those on the call, please?

[Operator Instructions] And today's first question comes from Tien-Tsin Huang with JPMorgan.

Julie, I appreciate your comments on scaling AI and how it's not a quick fix. So I did want to ask or maybe comment that we've noticed a bit of a shift in how people view the consulting industry's role in AI. Do you agree with this, Julie? And if so, why now? What's driving the change? And are you seeing any impact on business activity as a result of that?

Thanks, Tien-Tsin. Yes, we're absolutely seeing the shift. And it's really because of what we've been saying for a while, right? Enterprise AI is fundamentally different than consumer AI. Consumer AI adoption is instant, right? In the enterprise, you can't adopt it unless you have the right security. You've done the right work around processes and most companies have fragmented and siloed processes. You have to have the right data, and most companies have mountains of data with a lot of issues in the data, and we call it, they have process debt, they have data debt. And of course, they need a modern digital core. And that's why so many companies are still early in the journey. Clients -- our clients are convinced AI is going to be a very important part of their future, and it's going to allow them to unlock brand-new value. And that's why they're coming to us because now they want to actually get there. And that's the foundational work that's driving our business.

And then when you look at our bigger deals over the last quarter, for example, you see that advanced AI is a bigger part of those deals, but you also see that it's both growth and cost because clients are not only fixated on the productivity side, you cannot cut your way to growth. And in this market, they need to find more growth. And this is where our strength really comes in, and that's why we're delivering strong quarters, right? We have great momentum this quarter because we can help them on the growth agenda and the cost agenda. And we're so critical with our ecosystem partners. We understand where the technology is and where it's going. And you saw that again this quarter with our growth, with our top 10 ecosystem partners outpacing overall and then, of course, some exciting new partnerships.

All right. Perfect. And sort of a related question, you mentioned the partners. I'll ask around these AI partnerships. You announced a bunch, Anthropic, OpenAI, Snowflake, et cetera. Just how might these partnerships on the AI front be different than other tech ecosystem partnerships in terms of we think about time to productivity or investments to certify personnel to scale it up, et cetera. Just your thoughts on that and how quickly might these AI partners get to the top 10 on the partner front?

Well, first of all, the partnerships really demonstrate our talent advantage. So we've got decades of experience in being able to learn new technologies, skill people, who else could have hundreds of thousands of people, helping our biggest partnerships continue to grow incredibly well and be able to dedicate and commit 30,000 people here, et cetera. So I think that's a really important part of what you're seeing with us in our growth and our ability to grow into new areas is that we've got this foundation of great talent and the ability to upskill. And look, we're expanding in these partnerships because of what we see in client demand, but our clients have an ecosystem of partners. And the role that we play is to be -- we really try to be #1 with all of the partners so that we can help our clients integrate and use these new technologies with their existing ecosystem, which is absolutely critical to them. So lots of excitement. The market is expanding, and we're going to grow with that market.

And our next question today comes from -- sorry about that. And our next question today comes from Jason Kupferberg with Wells Fargo.

I actually wanted to pick up where Tien-Tsin just left off. I was curious as a follow-on regarding these big partnerships in the AI world, when do they start moving the needle on revenue in your view? It's obviously critical to lay the foundation right now, but is this a next 12-month dynamic where we can start to see it showing up in the P&L perhaps? Or is it longer dated? And then I have a follow-up.

Jason, what I would say is these partnerships are part of an ecosystem for our clients. And so it's I think more about the market itself, right? So our enterprise adoption is dependent on clients -- these partnerships are all about enterprise adoption. So I would focus more on how the market and enterprise adoption is going as opposed to specifically -- because we can scale as fast as needed. And that is -- our expectations there are reflected in our guidance.

Okay. Understood. And then I wanted to pick up on your comments about the increased adoption of the fixed price work because I think it ties in with a trend we've been seeing in your numbers where revenue growth is outpacing headcount growth pretty materially and consistently. So do you feel like that trend is sustainable? And what's really driving that? Because we've all been talking about nonlinear revenue growth for a very long time, and it seems like Accenture is now actually starting to see it.

Jason, it's Angie. Let me take that. We're really pleased with our revenue per person this quarter, which did grow 7%, which is really primarily driven by our talent rotation. We're now hiring -- as we shared with you last quarter, we're hiring for the new skill. And so we expect that revenue per person growth to moderate over the course of the year. And that'll go up and down really based upon when we bring people in.

And Jason, just to add on though, you're right though, the sort of revenue and headcount, that sort of breakage has been going on though for a long time. It really goes back all the way to the introduction of RPA. So we'd expect that to continue, but it's not tied exactly, as Angie said, to like what's happening quarter-to-quarter.

And our next question today comes from James Faucette with Morgan Stanley.

Want to follow-up on a couple of things that have been said by Tien-Tsin and Jason. First, back on AI and those bookings. I appreciate that those are -- AI is becoming integral to all of your engagements and so maybe define specific bookings, et cetera, on a go-forward basis makes sense. But how should we think about like the mix between what we could think of as proof-of-concept type engagements versus going into full production? And any color you can give on the types of projects that are moving to production, whether that be by industry or type, et cetera?

Sure. Great question. So first of all, one of the things that I think is important to understand is that people have moved ahead away from just thinking about models, right? It's about models embedded in solutions, and most of the solutions involve different kinds of AI, right? So you have -- if you have classical AI or RPA that is 100% accurate, right, depending on what you're using, you really have to understand all of that. And that's what the clients are looking to us for, is to bring them more solutions, which is why our partnerships are really important, and our understanding of the industry and the function is so important. So if you're doing something in banking, which is one of the industries where there's a lot going on at advanced AI, know your customer and compliance.

The solution they're looking for, you have to understand the actual compliance, how it works across the bank. And that's really the importance -- now of you're seeing that people get that these are about solutions, it's about really understanding operations, and that's what we're bringing. If you look at sort of where things are scaling into production, customer -- so a lot of customer service, and I gave one of the examples today on the call with the Virgin Media O2, you're also seeing areas like finance and procurement. And so these are areas where you've got good technology readiness, we have a lot of depth of understanding here, and you can move relatively quickly because it's using a lot of usually pretty good data.

We see a lot of value coming and being embedded in the core value chain, the grid and utilities, right, pharma and R&D. But those are the harder areas to crack. And so it's still pretty early in terms of scaling, but that kind of gives you a flavor for it. And so any of the industries that have a lot of customer service are there. Banking has been one of the lighthouses, insurance to some extent already. But one of the things I tell my clients is that in every industry, unlike prior waves of technology, there are leaders in every industry who already had strong digital cores who are leapfrogging. It's very different than cloud where you had some industries like, say, energy lagging behind for quite some time. It's quite different this time. And that really plays to our strengths because we have that diversity of industry expertise, and our clients look to us to really bring it across the board.

That's great. And then as a follow-up, you mentioned and talked a little bit about the revenue per head and the evolution there. One of the questions that we get from investors a lot is around pricing, particularly on a like-for-like basis, and how that may be evolving and how we should think about the puts and takes there, especially as it relates to your margins and margin growth objectives.

Yes. I think -- so as it relates to pricing, so just overall, I think you have to look at it in totality. And so as you think about our pricing, one of the things, and we've been seeing, and it's early, but we're seeing pricing improve in several parts of our business. And as you look, one of the things that we're super pleased about is that our contract profitability, we're starting to see some of that improved pricing show up in the P&L and we saw that this quarter. So we were really pleased with that, and it's really about balancing those components.

And our next question today comes from Bryan Keane at Citi.

Wanted to ask about discretionary spend. We're all waiting around for a while here for that to come back. I'd just be curious to know how you're thinking about that heading into next year, conversations with clients and should we be hopeful that discretionary spend comes back in the turn of the calendar year?

All right, Bryan, I'm not waiting around for it to come back, okay? So just to be clear, we have not been waiting around for it to come back. So I know you guys are, but we're delivering our results despite it because we really -- at this point, we haven't seen a change in the market. And when you look around, like we're all reading the same thing. We saw what came out in the U.S. this week on jobs. If -- there isn't some catalyst out there where we're saying that's the catalyst that's going to change confidence or change industries. And look, we work across industries. Every industry right now has got a different set of challenges with a lot of these big macro trends.

And so we're not having conversations today that would suggest that there's a big -- going to be a change in discretionary spending. But what the conversations we are having, though, is CEOs who are just like Accenture, they're very resolute that they have to deliver results despite the market. And that's why we're focused on pivoting the way they're spending. We're focused on doing the large transformational deals and then being at the center so that when we hopefully do get tailwinds, we're there to really benefit from them. But we're not seeing a catalyst. We'd love to hear it if you guys are in the external environment and our conversations haven't changed, but people are -- they're going to deliver despite that.

Got it. Got it. And then just as a follow-up, that 60% of work being fixed price is above industry norms and you guys have been working on that forever, and that's pushed up 10 points in the last 3 years. How do we think about the -- as AI becomes a bigger piece of that, does that -- can that number get up to 70% or 80%? And just a little bit of how that pricing works with productivity, how do you pass on some savings and keep it yourselves?

Yes. Well, one of the things that I think as you think about our positioning in the market is that these fixed price deals really are about our clients having confidence that we can deliver outcomes. And to do that, you've got to have our scale, our experience, our strong financials. And so we see that it is a real competitive advantage in this market where clients cannot simply experiment, they can't take a flyer. They have to know that when they're investing with a partner, they're going to deliver results. And that's why we feel the market now continue -- we're taking market share because of that. So I can't predict exactly, I mean, I think commercial models are going to continue.

We believe they're going to continue to evolve, and we've got a really strong foundation for that. And we are starting to see more focus on trying to get to outcome-based. So I think more to come in the models, but it really strength -- it speaks to the underlying strength of our business. And then with respect to pricing and passing it along to our clients, remember, this has been a business model for the industry really all the way going back to the introduction of technology and RPA, right, where we're signing contracts that depend on our use of more technology over time to provide productivity. And so that's still the commercial model of the industry right now.

And our next question today comes from Bryan Bergin at TD Cowen.

Wanted to ask on the growth side, so just really the moving parts as you consider the fiscal '26 growth outlook from here. So you -- from the 2% to 5% after a solid 1Q at the top end, 2Q looks largely as expected, yet the Federal headwind is actually a bit less than the lower -- than the prior range. So it sounds like demand is broadly consistent. So the question is, what may have precluded a low-end raise just considering that low end raise had deterioration assumed before? Just curious, is there anything incremental there or just ongoing uncertainty and just prudent approach just given this early in the fiscal year?

Bryan, so look, as we think about -- we just had a really strong print in Q1. We had strong bookings 2 quarters in a row. We can see the backlog from our large deals. We've got a solid pipe. And so our 2% to 5% really reflects what we see going for the remainder of the fiscal year. We got 3 quarters left, and it's our best view. And we were really pleased that Federal came in a bit better than what we had anticipated which is the strength of the work that they do.

Okay. Understood. And my follow-up is on Song. So I heard the mid-single-digit growth that sounds consistent here, which is good. I wanted to dig in, and I appreciate the detail you gave in the slides. Can you just talk about the implications for Song growth? Just looking ahead, just you consider recent advances in models like SOAR and there's a perception that enterprises can do more of this themselves or just the cost of such services will see material deflation. So how do you navigate that type of a backdrop going forward?

SOAR is a great example of something we embrace and are helping our clients embrace because what SOAR does is help them accelerate production. And so it's like a really good tool, but it's just a tool, right? So what Accenture does is say, how do you use these tools to actually get productivity differently to, more importantly, create the right new products and the new experience. And that's why one of the things I was mentioning earlier was that if you look kind of at our largest deals this quarter, a big proportion of them have customer and customer service in it because clients need growth.

And the tools are just a productivity piece. They're not what you can do to -- how do you actually respond to social media sentiment in an hour, right, which is what we can do in our operations around marketing, for example. So we see Song as critical because again, can't cut your way to growth. The market's not getting better overall for our clients, and they're really turning to us to find the new ways and to help them use these tools in the meantime to get more productivity.

And our next question today comes from Darrin Peller at Wolfe Research.

Julie, what does the revenue opportunity look like at a client that has done all of the digital core work required to be effectively leveraging AI as much as you'd like to see? And then maybe just a quick follow-on would be just around managed service opportunity and what that looks like, really trying to get a sense if you have any customers that are really at that stage that you can give us examples of whether it's a net increase to revenue and the opportunities you have and different services that could help a client out that has the digital core ready.

Darrin, it's a great question and what we're seeing is it's really expanding our work. So think about you've built a digital core. Now of course, just keep in mind, there's still a lot to do in the digital core because there's so much new opportunities and new ways of thinking about data, for example, than if you built your data foundation a few years ago. So there's still a fair amount of work to do even if you've got a pretty modernized digital core. But the real work, and this is why I think it's so important to understand how you adopt AI, is that you have to then change the processes, you have to upskill your talent, right?

One of the things I talk to CEOs a lot about is that if someone comes to you and says, here's how we do something today, now we're going to use AI and there isn't a big change, then they're not going to get value. And most of the work today has been around sort of isolated areas. It hasn't been across the enterprise. And so what you're seeing is we talked a little bit also about this last quarter, this inflection point where you've got now clients saying to us, okay, we have to do this across the enterprise. How do we think differently? Like how do we put marketing and sales and service together and they used to be in different functions. What does that mean then for the use of AI?

So the actual rewiring is a huge amount of work. And remember, the technology today, like it's great in some parts of the enterprise. But like if you think about manufacturing or engineering where we've had been investing for decades, it's still early in the digitization journey even for those who have a core. So we have lots of great examples, and that's why I talk about to our clients. Look, where people have already invested a lot, they're investing with us now to try to leapfrog. And that's where we are so different because we're tech, but we have the industry and the functional and the process and the change management, which is what unlocks the enterprise AI. So we really see a big opportunity over the next decade.

That's great. That's really helpful. Just very quickly, following the business optimization, what is your headcount? What should we expect from headcount or your headcount strategy for the remainder of the year now? Happy Holidays.

Thanks, Darrin.

Thanks, Darrin. I'll take that. So as it relates to our headcount, look, we expect to -- we're doing our talent rotation, and we expect to increase our headcount throughout the year in the U.S. and in Europe. So you should see that come through for the remainder of the year.

And our next question today comes from Kevin McVeigh at UBS.

Congratulations on the results. I think you had mentioned that advanced AI is 1,300 of your 9,000 clients and that implies about 14% versus the 6% revenue and 9% bookings. Should we use that in terms of a leading indicator or goalpost of what the revenue and bookings should scale? And if that's the case, any sense of that 14%, how that scales over time, just as we're thinking about the adoption rate?

Yes. The way I would really just think about it, so it's not meant to be some new metric in that way. What it's really showing you is just how rapidly it's moving, 100 clients initiating a quarter and that it's at the same time, really, really early, right, when you think of our whole client base. But I wouldn't start now creating like kind of a new metric and that, it's just too early to start drawing those correlations. But I'm trying to give some insight into like kind of where is the market right now.

It's super helpful. And then just real quick, the 17.3% margin, I mean, I went back, I think that's the highest Q1 you've ever had. Is that a function of the efficiencies and again, the fixed pricing? And how should we think about the margin trajectory of the business maybe a little bit longer term to the extent you can comment on that?

Yes. Kevin, we're really pleased with the 30 basis points of op margin expansion that we posted this quarter, which was in line with our expectations. And certainly, our operating margin is affected by the level of investments that we do throughout the year. So we're really pleased that we're reconfirming the 10 to 30 basis points for the full year. As it relates to EPS, I do want to make one point, really pleased with the 10% growth that we posted this quarter, strong operational results, op margin expansion as well as gains on investments.

But there's one thing that I do -- there's 2 things that I want to call out as it relates to Q2 specifically for your awareness and how we're thinking about it. First is that we expect our tax rate in Q2 to be above our full year guided range due to the tax impact of equity compensation. And then second is that we had higher gains on investments in Q2 of last year, and we don't expect the same for this year. But importantly, there's no change to our overall adjusted -- our full year guidance for adjusted op margin, tax or EPS. This is really timing.

And our final question comes from Dave Koning at Baird.

The health and public services growth was sequentially, I think, up 7%, the strongest, or I think the second strongest in 12 years, so very, very good. Is a lot of that just the federal spending headwind dissipating from Q4 into Q1? Was that a lot of it? Or is it just underlying health and federal spending just getting better than normal?

David, so as it relates to our health and public service, really, we saw strength in -- we told you about federal, and we know that and that came in better than we expected. And then the second component is the strength that we're seeing in EMEA and Asia Pacific, really strong, well positioned in terms of that business for us.

And keep in mind, that's where we've been investing over the last few years, including in acquisitions, and you're seeing that -- those investments pay off.

So thank you, everyone. In closing, I want to thank our shareholders for your continued trust and support. I want to thank all of the people who do this work every day, all of our reinventors around the world. I hope everyone has a very safe and happy holiday season. Thank you for joining today.

Thank you. That concludes today's conference call. We thank you all for attending today's presentation. You may now disconnect your lines and have a wonderful day.

Good day, and welcome to Accenture's Fourth Quarter Fiscal Year 2025 Earnings Call. [Operator Instructions] Please note today's event is being recorded. I'd now like to turn the conference over to Alexia Quadrani, Executive Director, Head of Investor Relations. Please go ahead.

Thank you, operator, and thanks, everyone, for joining us today on our fourth quarter and full year fiscal 2025 earnings announcement.

As the operator just mentioned, I'm Alexia Quadrani, Executive Director and Head of Investor Relations. On today's call, you will hear from Julie Sweet, our Chair and Chief Executive Officer; and Angie Park, our Chief Financial Officer. We hope you've had an opportunity to review the news release we issued a short time ago, and we also have an earnings presentation, which will be made available on our website after the call.

Let me quickly outline the agenda for today's call. Julie will begin with an overview of our results; Angie will take you through the financial details, including the income statement and balance sheet, along with some key operational metrics for the fourth quarter and fiscal year. Julie will then provide a brief update on the market position before Angie provides our business outlook for the first quarter and full year fiscal 2026. We will then take your questions before Julie provides a wrap-up at the end of the call.

Some of the matters we'll discuss on this call, including our business outlook, are forward-looking and, as such, are subject to known and unknown risks and uncertainties, including, but not limited to, those factors set forth in today's news release and discussed in our annual report on Form 10-K and quarterly reports on Form 10-Q and other SEC filings. These risks and uncertainties could cause actual results to differ materially from those expressed in this call.

During our call today, we will reference certain non-GAAP financial measures, which we believe provide useful information for investors. We include reconciliations from non-GAAP financial measures where appropriate to GAAP in our news release or in the Investor Relations section on our website at accenture.com. As always, Accenture assumes no obligation to update the information presented on this conference call.

Now let me turn the call over to Julie.

Thank you, Alexia, and to everyone joining this morning, and thank you to our more than 779,000 reinventors around the world for your extraordinary work and commitment to our clients.

In fiscal year 2025, we delivered a strong year financially. We significantly elevated our competitive positioning, and we took our next big steps to position us for growth in the age of AI. We grew 7% last year, which was adding $5 billion in revenue with over $80 billion in bookings, and we did so again to back economic -- macroeconomic backdrop that did not improve over FY '24. And of that 7% growth, the majority was organic, and the growth was broad-based across markets, industries and types of work.

We also delivered strong earnings per share growth and generated strong free cash flow, both above our guidance on an adjusted basis and we returned a significant amount of cash to shareholders, an increase of 7% over FY '24. And we took share at more than 5x our investable basket. How did we do it?

We built on the rapid shift in our business we made by the end of FY '24 to address the challenging market conditions. We then took action to fully capitalize on the competitive advantages we have built over a long period of time to deliver these results. These advantages include our ecosystem partnerships, our breadth of capabilities, our deep and trusted client relationships, our track record of investing in new skills and rotating our business with successive technology revolutions, and of course, our ability to invest.

Our strategy for more than a decade has been to be the #1 partner for the tech ecosystem, and it's serving us well. Technology is front and center for every client and in FY '25, we continued to be the #1 partner for all of our top 10 ecosystem partners buy revenue. 60% of our revenue is from work that we do with these partners which grew 9%, outpacing our overall revenue growth in FY '25.

These partners are the world's largest technology tech companies by revenue and they're seeking deeper and deeper partnerships with us as they look for help to turn their technology into business outcomes and scale the adoption of AI. We continue to be the reinvention partner of choice for our clients. Our deep and long-standing relationships means we know our clients and their industries inside out.

Our global footprint and breadth of capabilities means we can serve more of our clients' needs for large scale transformations than any other player in the industry. We added 37 clients with quarterly bookings greater than $100 million in Q4 alone, bringing us to a record of 129 such bookings for the year, and we finished the year with 305 Diamond clients, our largest relationship.

Our early and decisive decision in FY '23 to invest significantly to become the leader in GenAI with a $3 billion multiyear investment is clearly paying off, as we capture this new area of spend for our clients. In FY '25, we've tripled our revenue over FY '24 from GenAI and increasingly agentic AI to $2.7 billion, and we nearly doubled our GenAI bookings to $5.9 billion.

And as a reminder, these numbers only reflect revenue and bookings specifically related to advanced AI which is GenAI, agentic AI and physical AI and do not include data, classical AI or AIUs in delivery of our services.

We are now going to use the term advanced AI as it encompasses the latest developments that are starting to gain traction. In addition to all we're doing around advanced AI: for over a decade, we have made disciplined inorganic investments to expand our market and fuel organic growth. For example, our capital projects business, which was initially built through several acquisitions around the world is now a $1.2 billion business for us.

And in FY '25, it grew 49% year-on-year, largely organically. While delivering these results, we also took the next big steps in our reinvention for the age of AI. We are reinventing what we sell, how we deliver, how we partner and how we operate Accenture. In short, on the ground, advanced AI is becoming a part of everything we do.

Let's review our reinvention to date. By definition, every new wave of technology has a time where you have to train and retool. Accenture's core competency is to do that at scale. Our clients cannot possibly build all of the expertise they need on their own. They need us to go first and fast.

In FY '23, we had 40,000 AI and data professionals with roughly 30 people working on a handful of Geni projects with negligible revenue. Today, we have 77,000 AI and data professionals. We've worked on more than 6,000 advanced AI projects just this year, and we delivered meaningful revenue in FY '25. We're also in the process of recouping all of our reinventors with the latest AI skills.

Over 550,000 of our reinventors are already trained in the fundamentals of GenAI. We've already significantly embedded advanced AI into key platforms like so that we are now delivering differently for our clients. And we've reinvented our corporate functions to create additional investment capacity among other benefits and will now increasingly use advanced AI in the next chapter.

In FY '25, we focused our new actions on the ecosystem, our talent strategy and our growth model. We expanded our partnerships beyond the top 10 in AI and data and created new ones with companies that are becoming critical to many of our clients who also want to work with us to help scale them scale their relationships and our revenue is growing in double digits with many of these partners.

In FY '26, we expect to increase our headcount overall across our 3 markets, including in the U.S. and Europe, reflecting the demand we see in our business. In addition to continuing to hire world-class talent, in FY '25, we developed and are implementing a refreshed robust 3-pronged talent strategy to rotate our workforce. We are investing in upskilling our reinventor which is our primary strategy.

We are exiting on a compressed time line. People were reskilling based on our experience is not a viable path for the skills we need [indiscernible] in areas of how we operate Accenture to drive more efficiencies, including through AI in order to create more investment capacity.

Finally, our growth model. On September 1, we launched reinvention services, which brings all of Accenture's capabilities into a single unit. Nearly 80% of our large deals are multi-service. The model, as we fully roll it out, will make it faster and simpler to sell and deliver everything Accenture offers and to rotate our offering to embed more AI and data and equip our people.

In summary, I am pleased with our strong results in FY '25 and are positioning for FY '26 and beyond. Over to you, Angie.

Thank you, Julie, and thanks to all of you for joining us on today's call. We were very pleased [indiscernible] at the top of our guided range and completed another strong year for Accenture. Our results reflect a relentless focus to consistently deliver on our shareholder value proposition while investing for long-term market leadership and reinforce our role as a trusted reinvention partner for our clients and a leader in AI.

Now let me summarize a [indiscernible] grew 4.5% in local currency. Excluding the [indiscernible] 1.5% impact from our federal business, our revenues grew 6% in Q4, and we continue to take significant market share at more than 5x reflecting the relevance of our services and the strength of our diversified portfolio and execution.

As a reminder, we assess market growth against our investable basket, which is roughly 2 dozen of our closest public global public competitors, which represents about 1/3 of our addressable market. We use a consistent methodology to compare our financial results to dof theirs adjusted to exclude the impact of significant acquisitions through the date of their last publicly available results on a rolling 4-quarter basis.

Adjusted operating margin was 15.1%, an increase of 10 basis points compared to adjusted Q4 results last year. We continue to drive margin expansion while making significant investments in our business and our people. We delivered adjusted EPS in the quarter of $3.03, which represents 9% growth compared to adjusted EPS last year.

And finally, we delivered free cash flow of $3.8 billion and returned $1.4 billion to shareholders through repurchases and dividends this quarter. Before I move on to the details of the quarter, I want to spend a moment on the 6-month business optimization program we initiated in Q4, for which we recorded a charge of $615 million and expect to record an additional approximately $250 million in Q1 for a total of approximately $865 million over the period.

The business optimization program has 2 parts. One related to rapid talent rotation that Julie mentioned, which reflects severance associated with headcount reductions that we are making in a compressed time line; and second, related to the divestiture of 2 acquisitions that are no longer aligned with our strategic priorities. These actions will result in cost savings, which will be reinvested in our people and our business.

In FY '26, we expect to increase our headcount overall across all 3 markets, including in the U.S. and in Europe, reflecting the demand we see in our business.

Now let me turn to some of the details. New bookings were $21.3 billion for the quarter, representing 6% growth in U.S. dollars and 3% growth in local currency, which is on top of the 24% growth in Q4 of last year. Overall book-to-bill was 1.2.

Consulting bookings were $8.9 billion with a book-to-bill of 1.0. Managed services bookings were $12.4 billion with a book-to-bill of 1.4.

Turning now to revenues. Revenues for the quarter were $17.6 billion at the top of our guided range, reflecting a 7% increase in U.S. dollars and 4.5% in local currency.

Consulting revenues for the quarter were $8.8 billion, up 6% in U.S. dollars and 3% in local currency. Managed Services revenues were [indiscernible] in U.S. dollars and 6% in local currency, driven by high single-digit growth in technology managed services, which includes application managed services and infrastructure managed services [indiscernible] and mid-single-digit growth in operations.

Turning to our geographic markets. In the Americas, revenue grew 5% in local currency, led by growth in banking and capital markets, industrials and software and platforms, partially offset by a decline in public service. Revenue growth was driven by the United States.

Excluding the 3% impact from our federal business, Americas grew 8% in local currency in the quarter. In EMEA, we delivered 3% growth in local currency, led by growth in insurance, life sciences, utilities and consumer goods, retail and travel services. partially offset by a decline in public service. Revenue growth was driven by the United Kingdom and Spain, partially offset by a decline in Italy.

In Asia Pacific, revenues grew 6% in local currency, driven by growth in banking and capital markets, public service and utilities, partially offset by a decline in energy. Revenue growth was led by Japan and Australia.

Moving down the income statement. Gross margin for the quarter was 31.9% compared with 32.5% for the same period last year. Sales and marketing expense for the quarter was 10.2% compared with 10.7% for the fourth quarter last year. General and administrative expense was 6.6% compared to 6.8% for the same quarter last year.

Before I continue, I want to note that results in Q4 FY '25 and Q4 FY '24 include costs associated with business optimization actions, which impacted operating margin, tax rate and EPS. The following comparisons exclude these impacts and reflect adjusted results.

Adjusted operating income was $2.7 billion in the fourth quarter, reflecting a 15.1% adjusted operating margin, up 10 basis points compared with adjusted results in Q4 last year. Our adjusted effective tax rate for the quarter was 27.9% compared with an adjusted effective tax rate of 26.2% for the fourth quarter last year.

Adjusted diluted earnings per share were $3.03 compared with adjusted EPS of $2.79 in the fourth quarter last year, reflecting 9% growth. Days services outstanding were 47 days compared to 47 days last quarter and 46 days in the fourth quarter of last year.

Free cash flow for the quarter was $3.8 billion, resulting from cash generated by operating activities of $3.9 billion, net of property and equipment additions of $108 million.

Our cash balance at August 31 was $11.5 billion compared with $5 billion at August 31 last year. With regards to our ongoing objective to return cash to shareholders, in the fourth quarter, we repurchased or redeemed 1.6 million shares for $474 million at an average price of $295.45 per share. Also in August, we paid our fourth quarterly cash dividend of $1.48 per share for a total of $922 million.

Now I'd like to take a moment to summarize the year as we successfully executed our business to deliver or exceed all aspects of our original guidance that we provided by September on an adjusted basis. We delivered bookings at $80.6 billion with a record 129 quarterly client bookings over $100 million and a book-to-bill of 1.2. Revenues of $69.7 billion for the year reflects growth of 7% in local currency with nearly $5 billion in incremental revenue added this year.

Our federal business was a 20 basis point headwind to our overall growth for the year. Consulting revenues were $35.1 billion, up 6% in U.S. dollars and 5% in local currency. Managed Services revenues were $34.6 billion, up 9% in both U.S. dollars and in local currency, driven by 10% growth in Technology [indiscernible] Services and 6% growth in Operations.

The following comparisons exclude the impact of business optimization actions I noted earlier and reflect adjusted results. Adjusted operating margin of 15.6% was a 10 basis point expansion over our adjusted FY '24 results. Adjusted earnings per share were $12.93, reflecting 8% growth over adjusted FY '24 EPS.

Free cash flow of $10.9 billion was up 26% year-over-year, reflecting a very strong free cash flow to net income ratio of 1.4. And with regards to our ongoing objective to return cash to shareholders, we returned $8.3 billion of cash to shareholders while investing approximately $1.5 billion across 23 acquisitions.

In closing, we feel good about how we manage our business while navigating the macro environment in '25. And now we are laser-focused on executing and delivering fiscal '26.

Back to you, Julie.

Thank you, Angie. Today, we work across every major market with more than 9,000 clients, including the world's largest companies, 3/4 of the Fortune Global 100 and 500. And as we look at the market, we have not seen any meaningful change, positive or negative in the overall market. We are focused on delivering results regardless of the market conditions by being the most relevant to our clients.

And relevance today requires leadership in AI. We're working with companies early in their journey to use AI, which want our help to get them AI ready and to leverage our assets and platforms to accelerate their ability to deploy AI as well as to help them do what they can now to use AI even when they're not fully ready across the enterprise.

We also are working with companies far along their journey to be AI ready and wanting to be the first to change the game with AI even as this potential is still emerging. The technology itself is new and rapidly changing. So across companies, they need help in understanding the tech landscape. This is where we are in age of AI.

It is very early innings, however you look at it, which means there is massive opportunity ahead for our clients, our ecosystem partners and us. It is well recognized that advanced AI has taken the mind share of CEOs, the C-suite and Board faster than any technology development we've seen in the past 2 decades.

At the same time, as reported widely, value realization has been underwhelming for many, and enterprise adoption at scale is slow other than with digital natives. This is why our clients are turning to us. We know that the gap between mind share and faster actual adoption is because the enterprise reinvention required to truly unlock the value of advance AI is hard and has significant costs.

There is a huge difference between how we're all using AI in our individual lives that is incredibly easy and what it takes to use it in an enterprise. The opportunity for AI is at the intersection of business strategy and tech and org readiness. For most companies, the biggest gap between mind share and adoption is tech and org readiness.

We're still in the thick of cloud, ERP and security modernization. Data preparedness is nascent at many companies and companies gravel with fragmented processes and siloed organizations. Generations of leaders need new skills to understand how AI should inform their business strategy.

The workforce needs new skills to use AI and new talent strategies and related competencies must be developed. Helping clients with all of this work is what is driving our growth and our pipeline of large-scale transformation continues to grow. We're also starting to see early signals of an inflection point. with more clients looking for true enterprise-wide plans and activation and seeking out our successful experience with scaling in enterprises and at Accenture.

Two years into this AI journey, we also are seeing a pattern in how AI can expand our opportunities with our clients. As some companies are making progress in creating AI readiness, it leads to even more work. Long-standing partnerships are deepening and the demand for transformation is accelerating. For example, take a major financial services client we've worked with for over a decade.

Their reinvention began with digital operations and cloud modernization. Now they've asked us to modernize their data estate, the foundation for scaling AI across the enterprise from the contact center and marketing to finance and the trading floor. As we begin to implement AI into many facets of their business, our relationship continues to grow as we retire legacy systems, transform core functions like HR and risk and build AI-centric capabilities to keep them ahead of shifting customer expectations.

This has meaningfully expanded the amount of work we do for this client. And in fact, over the past 5 years, the value of our contract has more than doubled. We're seeing more stories like this across our portfolio, where AI is extending across the enterprise and adjacent work is following.

Our contracts are expanding and our client relationships are compounding, creating a powerful, sustainable growth engine for Accenture. Building the digital core remains our biggest growth driver. Only now, our clients understand that Accenture is bringing even more capabilities because we understand how the digital core will enable them to use advanced AI, and advanced AI is now a new catalyst for doing the large-scale transformations of the digital core in the first place.

Taking an industry lens, let's look at banking. In banking, investment in digital core modernization remains strong with cloud adoption accelerating as AI demand grows. Here's what it looks like in practice. And I'm particularly proud of this work because the scale is frankly breathtaking, and we were trusted by this client with mission-critical work.

The Bank of England's real-time settlement service, which lies at the heart of most electronic payments in the U.K. was rebuilt on a modern digital core using private cloud and end-to-end automation. This upgrade improved security, reliability, speed and scale. The system now offers faster onboarding and secure APIs giving more financial institutions safe access.

It processes about $1 trillion in transactions every day. And in its first 5 months up to today has handled 22.5 million transactions worth $110 trillion. For people, that means big payments like buying a home, go through quickly and safely. This modernization strengthens a national platform, reducing risk and creating a trusted foundation for innovation. Now the system is ready for what's next, even the potential for the market adopting AI-driven payment services.

Now let's take a horizontal lens across industries with security. Security is essential to a digital core, which is reflected in our 16% growth for the year. We're seeing increased demand for advanced cyber protection and more integrated intelligent security solutions that can fully harness AI's potential and keep pace with emerging threats.

To further strengthen our position in the past quarter, we agreed to acquire CyberCX, our largest cybersecurity acquisition to date, which helps us in geographic expansion, bringing approximately 1,400 specialists in APAC and also bringing AI-powered security platforms, which are applicable globally.

We also acquired IM Concept, a Canadian identity security specialist, serving critical infrastructure, expanding the depth and regional reach of our managed security and identity capabilities that underpin secure AI adoption.

Now let's look through the lens of our unique industry functional process and talent and org capabilities. These, coupled with our technology expertise are making a difference to our clients. And these next 2 examples also demonstrate the pattern or see in expanding our relationships due to advanced AI.

Ecolab, a global sustainability leader has been a client for 15 years. Three years ago, we partnered with Ecolab to lay the foundation for their growth transformation, One Ecolab bringing the company together as 1 team to better serve customers, drive cross-sell and upsell and improve operational efficiency.

A year into that journey, we started working with Ecolab and its leadership to accelerate value with AI. Instead of executing one-off use cases, we redesigned the entire lead to cash process, the steps from generating a lead to collecting payment, using 9 scaled agentic AI agents.

These agents clean core data, resolve billing errors and automatically match customer payments to the right billing invoices. In cash application alone, work that used to be 100% manual is now about 60% automated, reducing errors and speeding up processes. By using AI to streamline operations, Ecolab is on path -- is on a path to deliver an estimated 5% to 7% sales growth and 20% operating income margin without increasing costs at the same pace.

Big picture, it supports the company's mission to deliver water, hygiene and infection prevention solutions to more customers worldwide. We're partnering with a leading energy company, which has been a client for nearly 2 decades, to reinvent field operations with cloud, data and GenAI. The challenge was scale, safety, cost and sustainability, running thousands of wells with fragmented data and a leaner field workforce.

We unified data from more than 25 legacy systems into a single cloud-based digital core. On top of that, we built AI-powered scaled digital twins that monitor, optimize and control the field in real time using our Accenture Industrial Intelligence Suite solution. That live view speed decisions and improved safety, often without sending a technician on-site, while emissions are continuously monitored for compliance.

This solution is expected to reduce lost production by up to 2% to 4%, increase productivity by up to 28% and decreased costs by up to 22%. Field exposure and unplanned visits are also reduced and emissions are expected to be lower. People can now focus on higher value work and the business can respond faster to a changing energy landscape.

Our scaled example sets the North Star. Here's an example of how our clients are starting to work with us for broader AI adoption across multiple areas to enable their business strategy. We've partnered with UOB, a leading bank in Acion for nearly 2 decades, on various initiatives, including multi-country application services rollouts to omnichannel enhancement.

Today, we're helping them scale GenAI and use agentic AI to transform customer experience and core operations. Using our AI refinery platform, we're supporting them empowering high-value use cases and customer engagement, risk management and workforce enablement. This transformation enables faster, more personalized service; strengthens decision-making with predictive insights; accelerates response times and enhances operational resilience. Together, we're positioning UOB to lead and create sustainable impact in the financial services industry.

Now an important part of our growth strategy is to be relevant to the core of our clients' industries such as digital manufacturing and to be relevant to their growth agenda. Industry X grew 10% and SON grew 8% in FY '25, both follow a similar pattern of meeting a strong digital core and reinvention.

The digitization of digital manufacturing and engineering and the use of AI and data to reinvent customer experience is still in the early days. We're seeing strong demand across both areas and continue to invest both organically and inorganically. For example, we recently acquired Momentum ABM in the U.K. and Superdigital in the U.S., extending our edge in B2B and social and influencer marketing.

Over to you, Angie.

Thanks, Julie. Now let me turn to our business outlook. For the first quarter of fiscal '26, we expect revenues to be in the range of $18.1 billion to $18.75 billion. This assumes the impact of FX will be approximately positive 1% compared to the first quarter of fiscal '25. Our Q1 guidance reflects an estimated 1% to 5% growth, including about a 1.5% impact from our federal business with AFS contracting mid-teens.

For the full fiscal year '26, based upon how the rates have been trending over the last few weeks, we currently assume the impact of FX on our results in U.S. dollars will be approximately positive 2% compared to fiscal '25. For the full fiscal '26, we expect revenue to be in the range of 2% to 5% growth in local currency over fiscal '25, including an estimated 1% to 1.5% impact from our federal business.

Excluding the impact of Federal, our revenue is expected to be an estimated 3% to 6%. This year, we expect an inorganic contribution of about 1.5%, and we expect to invest about $3 billion in acquisitions this fiscal year. For adjusted operating margin, we expect fiscal year '26 to be 15.7% to 15.9%, a 10 to 30 basis point expansion over adjusted fiscal '25 results.

We expect our annual adjusted tax rate -- effective tax rate to be in the range of 23.5% to 25.5%. This compares to an adjusted effective tax rate of 23.6% in fiscal '25. We expect our full year adjusted diluted earnings per share for fiscal '26 to be in the range of $13.52 to $13.90 or 5% to 8% growth over adjusted fiscal '25 results.

For the full fiscal '26, we expect operating cash flow to be in the range of $10.8 billion to $11.5 billion, property and equipment additions to be approximately $1 billion and free cash flow to be in the range of $9.8 billion to $10.5 billion. Our free cash flow guidance reflects a very strong free cash flow to net income ratio of 1.2.

We expect to return at least $9.3 billion through dividends and share repurchases, an increase of $1 billion or 12% from fiscal '25. Our Board of Directors declared a quarterly cash dividend of $1.63 per share to be paid on November 14, a 10% increase over last year and approved $5 billion of additional share repurchase authority. We remain committed to returning a substantial portion of our cash generated to shareholders.

With that, let's open it up so we can take your questions. Alexia?

Thanks, Angie. [Operator Instructions] Operator, would you please provide instructions for those on the call?

Absolutely. [Operator Instructions] Today's first question comes from Tien-Tsin Huang with JPMorgan.

Good presentation here. My first question, I'll ask on on visibility on revenue growth, if that's okay. Just would love to hear your thoughts on visibility compared to the last couple of years, given the backlog, which is quite big with large deals. You have the pipeline, of course. And then what you're seeing on discretionary spending given the economic backdrop as you see it?

Let me start with that. As we look at FY '26, we feel really good about our positioning. And so as you said, you saw our strong bookings of $80.6 billion in FY '25, that positions us for FY '26, we can see our backlog from the large deals and if you look at our pipeline and looking at our pipeline, it's solid overall, and we see strong demand for our large transformation deals.

From a discretionary perspective, what we've assumed is at the top end of the range, there's no change in discretionary spend; while at the bottom of the range, it allows for deterioration. And by the way, as you think about our guidance of 2 to 5 excluding AFS, we're at 3 to 6 for the year.

Okay. Just maybe, Julie, I'd like your AI remarks. Can you guess -- I want to dig in a little bit more, if you don't mind. Just give us your latest thoughts on AI-driven productivity and those gains and how they might unfold? I get that question quite a bit from investors. Do you see potential deflationary effects? And how might that impact Accenture services, both positively and negatively?

Great. Thanks, Tien-Tsin. So we don't see AI as deflationary. We do see and are seeing it as expansionary similar to every tech evolution we've been through. The move from an analog to digital, from on-prem to cloud and SaaS and -- as many of you who've been with us over the course of the years have known in every successive tech evolution, we've become stronger.

And so if you look at AI, we see the same thing. Yes, AI absolutely boosts efficiency in areas like coding or operations. But those savings don't disappear. They're being reinvested into new priorities. The list of what our clients want to do with technology is truly virtually unlimited.

And so when we can save the money by delivering our services with advanced AI that frees up their budget to do the next things on their list and that's what they're doing. They're always going to those next priorities, and we're best positioned then to help them. That is how we delivered our 7% growth last year. I mean 2 years in, we're seeing the pattern for how that journey to advanced AI is expanding our business.

And by the way, I will add that one of the most consistent things that I'm telling CEOs today is that their AI strategy has to focus on both growth and productivity. And almost every CEO that I've talked to says they pivoted way too far towards productivity and not enough to grow, which, of course, we are helping them list with things like Song.

And we give that advice really from our own experience and how we have successfully grown through every tech evolution, embracing the productivity on one side and then capturing the opportunity it creates on the other side by helping our clients.

And our next question today comes from Dave Koning with Baird.

Yes, great job and great to see GenAI bookings reaccelerate. A question, I guess, a little like Tien-Tsin's question. Just wanted to ask about the balance between GenAI and managed services. You do a ton of managed services work. You get to know client operations really well. .

You can probably go in and recommend GenAI work and gain a lot of share there, but then maybe display some managed services, and how does that really balance between consulting and managed services over time? And does the net of it all push revenue and margins higher?

So well, first, let me just kind of ground you in how we're thinking about consulting and managed services in FY '26, just so we all have like kind of the facts of how we're thinking about FY '26. And then I'll give you some more color on how we see those things actually work in the market. So Angie, why don't you just ground them in the FY '26...

So overall, for our guidance for FY '26, both Consulting and Managed Services are balanced. We see both of them in the low to mid-single-digit range. So that's the context.

And then as -- and how it actually works out on the ground, right, is that as you think about enterprise-wide strategies, A lot of times what we're talking to our clients about is where do you invest and build proprietary capabilities, where do you want to buy capabilities and where are you best situated to go faster because you're partnering and buying them through a managed service like Accenture.

And so what we're seeing a lot of is, for example, companies that are really behind, they're not as far along in their tech journey, they need managed services because they simply can't go fast -- it's not just a cost play. They want the cost takeout, but they want to use everything we've invested in our platforms to get them to the advanced AI. Similarly, in the core operations, things like digital manufacturing, supply chain, we're developing more and more managed services there in order to allow them to go faster.

And so we see this kind of continuing to develop as we have over the last several years where managed services have become very strategic, they're not just a cost play. And of course, the more we can save the money in the way that we deliver, right, so using advanced AI that allows them to then reinvest into the business.

And so very similar patterns, Managed Services really for the last 5 or 6 years has become a very important part to the strategy of companies and how to use advanced technology now with advanced AI faster.

Great. No, that's super helpful. And then maybe just a quick follow-up, are you expecting about a similar Q4 headwind through the first 3 quarters of this year and then anniversary it in Q4 and then kind of going forward, maybe not having much impact at all? Is that kind of how you're modeling it?

That's exactly right, we're -- we expect it to anniversary at the end of Q3.

And our next question today comes from James Faucette with Morgan Stanley.

I appreciate all the incremental color and DTL here from everybody. wanted to ask, we see, at least in the forecast a little bit of increase in CapEx, et cetera. Wondering if you can give us a little bit of detail where that investment is going and how we should expect that to play out further?

Sure. So Jim, on CapEx, we expect about $1 billion this year, which is about $400 million more than FY '25. And this is really about us expanding our real estate and leasehold improvements in certain geographies, certain major markets for us because we're bringing more people back to the office. So that's why.

Yes, kind of what I suspected. And then the second thing was just you mentioned and we saw the reacceleration in GenAI and bookings, et cetera, how is the pricing of [indiscernible] has the velocity of projects transitioning from proof of concept to production changed at all?

Yes. Let me just start on the pricing. And for our GenAI projects and the pure GenAI that we were -- or advanced AI that we've been talking about, we do see pricing that is accretive overall to Accenture's average.

Yes. And in terms of acceleration in terms of kind of moving from proof of concept to production, right, we're seeing more and more now move into production because it's just -- we're helping them with the proof of concept and then we're helping them scale.

But you also are just continuously seeing companies that weren't as fast out of the block now starting proof of concept. So it really is a cycle that many companies are going to go through. So -- and you've got leaders who are way ahead. You've got other companies that are just getting started.

And what I would say is rather than a reacceleration or deceleration, these things are going to be like everything. They're going to be lumpy, right, in terms of it. So -- but what we really look at is the overall trend of how much growth that we are getting and our share of this new spend.

And our next question comes from Jamie Friedman with Susquehanna.

I too appreciate your prepared remarks really thought-provoking. I wanted to ask, Julie, about the way you're defining advanced AI. And I think at the transcript [indiscernible] physical AI, I'm actually asking about why you're saying you won't -- you're not including data because we've sort of been train the data is foundational. So why is the data component not in the definition of advanced AI?

Because what we're trying to share with you is how we're taking spend in a new market. And by the way, data is absolutely critical. In fact, 1 out of every 2 projects in GenAI, agentic AI, physical AI is now has significant data pull-through. So our data business is on fire, right?

Like this is an absolutely critical area. Companies are just getting started. It's nascent in many places. It's part of the digital core that we're building. It's just that to date, we wanted to share with all of you transparently the really new areas. So data is part of the digital core that's growing.

We've shared with you that 60% of our revenue is from the ecosystem partners, that's including the data. And look, going forward, now that advanced AI is, in fact, in all of the work because it's either actual work or we're getting ready for the work, we'll think about how to share that. But just to date since it started for all of us, like really from negligible revenue, we wanted to share how we've been specifically accelerating in the new area of spend.

Got it. And then going further, will you say that every new wave of technology has a time where you have to train and retool and your core competence is to do that at scale. So I'm just wondering, relative to prior technology, and you alluded to some of this in prior architectures, but how do you think about that requirement, which you have tremendous mind share at, which is to do technology at scale, how do you think about this relative to some of the previous technological evolutions?

It's going faster. I mean there is so much demand and the technology is moving faster. So the more advanced skills and the new types of skills is coming faster. And that's why we're being very decisive, right? Upfront, we said we've got to start training everyone in the new skills. We're now saying we've got to move faster to that.

And also remember that when we went into this, we'd already trained about 500,000 of our people on classical AI because going back to FY '19, we said the next decade would be about tech cloud, data and AI. So we start with a very strong base. And this is definitely moving fast in terms of how fast the demand is coming and the importance of us really winning the talent rotation.

And our next question comes from Bryan Bergin with TD Cowen.

My first question on GenAI impact. Can you just speak about client behavior in seeking to use GenAI and agentic solutions more themselves? You mentioned the efficiencies from the tech in areas like software development. I'm curious if you're seeing more clients seeking to then benefit to do that more themselves versus with third parties?

And also curious if you've seen clients that thought they could do it themselves 6, 12 months ago and then realize they do need help and they return to you.

Yes. And in fact, especially early on because GenAI seems so simple, right? And then the reality is it's not the technology that is the biggest barrier, it is actually being able to get the mindset reorganize around how best to use it, the ability to do the change management, the process reinvention.

And if you think about your average company, their core competencies inside are not things like end-to-end process reinvention, right? You're hard-pressed to find a CEO that doesn't say, I feel like my organization is too siloed. I feel like we don't have the right way of managing our data.

And so we've had lots of clients who have started things on their own and then come to us who've got good proof of concept that their team was able to do, but then just can't scale it. I mean, I'm doing right now like just in the next few weeks, I'm personally leading a few different workshops with the entire C-suites of companies where the focus is, share with us how do we actually scale it and what can we really do now, right?

What is the -- because as -- we're a couple of years into this, like we have a number of solutions, which we're now doing repeatedly within industries and across industries, and our clients are looking for us to share that success so that they can stop just having their own team saying, "Well, I have this idea, this idea," and saying, "How can we actually get scale now??

Okay. That's helpful. And then a follow-up on the business optimization plan. Can you talk about maybe assumed savings you expect to achieve from this optimization plan and how it may help you evolve your operations? I'm specifically curious if you see that kind of combined with GI adoption internally, allowing you to operate at a sustainably higher utilization as that did tick up this quarter.

Bryan, I think that for overall, we expect savings of over $1 billion from our business optimization program, which we expect that we will reinvest in our business and in our people because it's so important for our future growth. And so we expect to reinvest that while still delivering mass margin expansion.

Yes. And then in terms of like kind of the connection, just making sure like this particular -- like these moves are primarily due to our talent strategy and then the other piece was an exit of a couple of nonstrategic acquisitions.

But on the talent strategy, it's more around well -- our #1 strategy is of skilling, given the skills we need, and we've had a lot of experience in upskilling, we're trying to in a very compressed time line where we don't have a viable path for skilling sort of exiting people so we can get more of the skills in we need.

That's really not related to kind of the utilization piece in terms of like it ticking up to 93%. We think it will stay in the zone, right, in the low 90s to that, and it will fluctuate a little bit. But to your point around sort of what can we do long term?

We are continuously looking at, as the technology matures, our new structure around reinvention services, we'll look to see are there ways that we can use the technology to deliver our services and operate Accenture in its core better. And that's one of the reasons why we have the new reinvention services to like really simplify how we're operating because that makes it much easier to start to use this AI. So more to come as we fully roll out that model and identify new opportunities.

Our next question comes from Darrin Peller, Wolfe Research.

It's good to hear from what it sounds like, the pace of procurement change has calmed down a bit from the government side such that you can forecast those. I guess, number one, just to verify, that's why you feel more confident around forecasting on it?

But then there's a lot of policy changes, just want to touch on a couple and ask your thoughts lately. Number one, now that we have a little more clarity on tariffs, do you see more capital investment by -- especially in areas like products?

Number two, maybe you could just comment on H-1B changes or potential changes. What are your thoughts around either wages or the pace of hiring of H-1Bs going forward? And how it may or may not impact the business? And then just a quick 1 on health care and The Big Beautiful Bill, if any impact you're seeing there?

Great. Well, thanks. So just quickly on Federal. We do see procurement is now starting to pick up, although it's still slower than it has been in the past. The demand in Federal is very much around modernization, consolidation, efficiency. Tech is at the center. So lots of demand around ERP and platforms.

Our position with the ecosystem is really key here and our strategy to expand to that partnership -- those partnerships is also important. We're really pleased with our new partnership with Palantir, which is really playing a critical role in federal. So we feel good about where we are in federal and are relevant to the administration's agenda, and that's what we're really focused on is being relevant to our clients. So that's Federal.

On capital investment, I would say it's still a little early, right? I mean we -- obviously, you've seen the improvement in with the cut in interest rates. We're a global company. So there's a lot of stuff going on around the world. And so I think it's just a little bit too early to call yet whether -- how much this is going to open up on the capital investment.

Of course, we're growing very significantly and taking advantage of the investments that are already happening, as you saw in our capital projects business. On H-1B visas, for us, this is really a nonissue because we only have about 5% of our people in the U.S. on H-1B visas and there are 4 really specialized experience and skills for our clients. So not something that is really a big impact on Accenture. And

then whether it's health care or a lot of the different policy changes, remember, our business thrives by helping our clients navigate change, right? So what we're seeing is that every time there's a policy changes, and this has been true for decades, right? That's why in our business, we have industry expertise, we have the functional expertise.

And -- so when you have new compliance rules, et cetera, like that usually drives more business for us. And so at this point, we see an opportunity to really stay close to our clients and help them navigate and take advantage and comply with new policy changes. And that's true in the health care, and it's really true across the board.

Operator, we have time for 1 more question, and then Julie will wrap up the call.

Absolutely. And our final question comes from Jim Schneider at Goldman Sachs.

Julie, I just wanted to follow-on on your comment that you expect headcount to grow during the course of the fiscal year across all regions? Can you maybe kind of frame for us the magnitude of that and the timing for it given the context of some of the other business optimization actions you're seeing, where would you expect headcount growth to land exiting the year perhaps?

I'll take that. What I would tell you is, look, we expect it to grow across all markets. We don't have a specific number that we're giving you. But based upon the demand that we see, we expect our headcount to grow.

Great. And then as a follow-up on that, if you can sort of maybe talk about the net impact of AI using internally to optimize your own work, your own business. Utilization, as I think you mentioned earlier, 93%, that's basically hitting a new record. But when would we expect that to see that either reflected in even higher utilization or potentially gross margins even though we don't -- you don't manage directly to that?

Yes. Remember, right now, our utilization is really a reflection of the kind of momentum and demand that we're seeing. You saw the bookings rate. And so our utilization, we would expect to continue to move around in the low 90s. So we don't have a structural change in utilization due to AI.

We are already embedding AI, particularly in our big platforms like Gen Wizard to drive efficiencies, and that's reflected in both our bookings and in our guide for the year. And we're going to continue to be the leader because that is what works, right? As you lead yourself. We're able to take that to our clients, we're able to show them how we're doing it and then help them do it in their business. So that's kind of how it's developing.

This concludes the question-and-answer session. I'd like to turn the conference back over to Julie Sweet for closing remarks.

Terrific. Thanks again, everyone, for joining. In closing, I just want to thank all of our shareholders for your continued trust and support. We are working every day to earn your trust and a huge thank you to all of our reinventors because you are why we are able to deliver these results. Thanks again.

Thank you. Today's conference has now concluded, and we thank you all for attending today's presentation. You may now disconnect your lines, and have a wonderful day.