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 wanna go through a couple housekeeping items. The you know, 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. 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'll do our very, very best. With that, I'm gonna quickly introduce myself and then pass it to my, like I said, esteemed guests to introduce themselves. Like I said, I lead Corporates & AI for data and research. So very simply put, that is CapIQ and the data feed delivery of all of that great content. I have had the pleasure of working with the three 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. I'm excited to talk about that today. Before we do that, Francis, I will pass it over to you for an intro. Bonjour, 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 called Growth and Strategy, in charge of supporting the development and the implementation of a 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. Supporting, you know, Justine and team thinking about inorganic growth, but also our, you know, ratings, energy, the rest of our MI division, our index division, as we think about how to grow the company. We spend a lot of time with, you know, emerging companies in the space, thinking about how they're applying new technologies to, you know, our clients' workflows and our workflows. I'm excited to be here today. Awesome. Thank you. 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's Alaina Tosatti. I lead 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 CapIQ and our unique data and IP, along with other offerings in software and services, really focused on serving into the capital markets. 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, 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 fact about themselves on their intro, so sorry you all missed that portion. With that, you know, 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, you know, 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. The blog that I authored was really based around kind of a couple key trends that I saw. I'll hit on a couple of those and just to get the conversation started and then get some thoughts from Francis and team. I would say a couple of the key trends. I'm not gonna 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. If you feel like it's evolving or there's something new happening every day, you are not alone. That is the number one trend I'm seeing, no matter the size, the sophistication, the market cap of the organization. I think following that, there's, you know, a real push today more than ever to really determine and measure the ROI and the impact of that GenAI initiative, right? I think we shifted from a time of a lot of POCs, 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. I actually did a little research based on our earnings call transcript, 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. This is for our whole corpus of earnings calls, so very broad swath of global companies. 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. Nothing totally drastic there. What we noticed is over that same timeframe, there was a 57% increase in mentions of AI cost savings and positive sentiment around cost savings related to AI. You can see how that trend and that, you know, management expectation and street expectation of ROI on this investment is really starting to play out across all industries, all sectors, all geographies. 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's this all gonna go, but at the end of the day, money talks, and money's still flowing. In 2025 alone, according to Capital IQ, there was $95 billion raised across 143 funding rounds. That's for, you know, AI-specific firms. This takes out chip manufacturers. This takes out data center providers, right? This talks solely about that. Nearly tripling from 2024. Money's 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. You know, we do a ton of partnerships at S&P Global. You know, the AI firms are partnering with each other in different ways. We'll talk a bit more about that later. That continues to be a big trend. Two last points I'll make before I open it up. I'll say is that, you know, the risk tolerance has really evolved. I would say when I first started meeting with a lot of clients, there was probably two big camps. One camp of, we're all in, we're opening it up, we're letting, you know, our employees or our organization use AI, use whatever tool they want. Obviously, you can imagine some of that has tightened as there's concern around IP and data protection of internal information, et cetera. 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. Last but not least, I have to say this, but it's true. It's all about the data. Data's the foundation. Whether it's, you know, 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. Those are a couple 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. How many AI tools has your team trialed or adopted in the past 12-18 months? We'd love to see, is it 1-2, 2-4, 5+, or are you guys still early on your journey? I won't scoop the audience 'cause you all might have a different perspective. 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 well, and that's creating a lot of optionality across organizations. Let's see what you all said. Let's move to the next. All right. Pretty well-versed here. No surprise. You know, most are between one and four point. That's actually probably what I've seen, right? 1-4 could be maybe one's homegrown or something internal mixed with, you know, a third-party application or even using GenAI tooling in a solution or tool that you already have adopted and used for many, many years. A great example of that might be CapIQ. 5+. Interesting to see almost 19% of that. That's still a lot of different tools to trial and experiment. Definitely what we're seeing. With that, enough from me. Francis, what did I miss? What are you seeing? What do you think is also happening in the market? Yeah. I didn't know if I could answer the survey, Justine, so I did not. Just in full transparency, I would have been in the 5+ category, so the 19%. Because we're testing 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. 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. 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. ROI of investment in AI has been a source of questions for the past three years. It's definitely one that we're 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 say the verticalization happening, meaning development of AI as part of a what is really specific to industries, part of a core value chain. That means that beyond the cost savings, there are opportunities as well for a revenue growth for companies. 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 two months ago. We were asking some of these CXOs whether you know, you have two choices to make. Is AI more an opportunity to revenue, to grow the revenue or more an opportunity to reduce the cost? Actually, 78% put the emphasis on revenue growth in the coming years. To us, that's an illustration of the fact that some of this 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 thought leadership that we published actually yesterday with the World Economic Forum on the organizational transformation in the age of AI. 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. We can see there that in the drug discovery in R&D, which is very specific to life science, AI can actually help not only to accelerate the discovery, but actually change the discovery process itself. 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 the other trends as well. I keep that for later on, but I like to ponder on data, so maybe we can come back to that later on if that's of interest to your audience later. Yeah, no, thank you. I think everyone loves to hear real examples, so the life sciences example makes complete sense. Obviously in our world, we service kind of the range from life sciences all the way to financial institutions. We see obviously a ton of our use cases really around that financial services use case. You know, 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 have we evolved 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, Justine Iverson, taking a step back first, just thinking about the market and, you know, 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, you know, incredible, frenetic is a word that gets used quite often. Just to add to that piece. You know, you think to the launch of Claude Cowork earlier this year, and that's really furthering this expansion into enterprise use cases, right? It's clear now that agentic workflows, you know, can and are being applied today to real work for our customers within our teams at Market Intelligence, and they're capable of completing these really kind of complicated tasks autonomously. This is real. It's happening right now, and the pace is just an unbelievable adoption level. For businesses like ours, I think that really shifts that expectation of how we're going to use AI. Well, you know, a year or two 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've thought about it within Market Intelligence, we've really leaned on two 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. This will vary by use case. Right? 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. That'll really help them with alpha generation, which is their ultimate end goal. Again, as you talked about data, that really resonates because certainly what we... 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. Then the second lens that we've spent a lot of time thinking about, of course, has been sort of transforming within our own four walls. How do we rethink our workflows within Market Intelligence to leverage AI better? You know, 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. 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. 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. 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. 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, you know, from an S&P perspective, our brand in the market for decades. 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. You mentioned Claude Cowork and, you know, how quickly some of these, you know, tools have come to market and how quickly clients are interested in exploring. With that, I'm actually gonna go to my second poll question because I would love to see out of our group, you know, what has 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, you know, 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 home-built internal solution, or are you not using anything yet? You're still kind of in this experimentation phase. 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. 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. 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 emergent winners there, with Claude really making a lot of adoption within that, you know, investment banking, financial services space. Again, these workflow-specific tools, we're seeing it. You know, I think a big point that we talked about, and we'll talk about this a little more, I'm seeing some questions come in that play to this, is, you know, there's a lot of home-built 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. 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. That's something we've 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 gonna get great results. But if you do, you know, some technical work such as some Python wrapping. You know, you apply some business logic to understand, you can start to get really, really powerful results out of that. As you mentioned, Alaina, you know, getting information out of vast amounts of textual data, for example, very, very quickly is a great example of that. With that, Jesse, I wanna take a little bit of a pivot here. I know you spend a ton of time observing the market, trying to see who's gonna be a winner, who's not. I'm gonna ask a little bit of a cheeky question. What's your prediction for the IPO market in 2026? 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. You know, I think heading into 2026, I think people all thought it was gonna be a pretty robust IPO market. I think what's happened is in patches, you know, there's been maybe less confidence in that market and, you know, in many markets in general because, you know, kind of what is valuable about companies is starting to change. 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, you know, very important tasks in our economy. You know, I think that, you know, probably is true to some extent, perhaps not as true as, you know, every kind of equity research analyst, you know, believes or worries for the companies in their portfolio. Particularly around, you know, kind of more traditional line software companies, even data analytics companies like ours, you know, I think valuations have been a little bit more uncertain. I think, you know, for parties who can't sort of show real, you know, kinda AI traction, you know, there's been sort of weakening of valuations, and I think it will make it harder for companies like that to go public. Some of those I think were in, you know, kind of the queue to potentially IPO. At the same time, there's a set of companies that are only stronger because of this. You know, the large sort of frontier labs I think are kind of queuing up to try to go public at the end of the year. You know, there are companies that are sitting on the data that powers AI, sitting on the, you know, kinda data warehousing and cleansing that really supports this set of companies that are, you know, building that last mile of, you know, potential application layers on, you know, the AI models. I think, you know, they will be kind of a robust sort of set of potential IPO candidates, whether that happens this year or happens, you know, in the next couple of years, I think is, you know, an open debate. I think we will start to see, you know, a few of those companies go public, and, you know, that'll require a strengthening, I think, of governance, you know, 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. You know, if one does, you know, these things, you know, tend to come in trends, and so you could see a number of others kind of flowing from that. I love it. I'm gonna ask my last polling question for the audience and, based on your response. Of the audience, who do you believe will IPO first? Anthropic, OpenAI, or you don't think either will? I know those were. We've seen 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 two. All right, let's see what everyone thinks. Oh, mixed bag. All right. This is probably how I feel about it. It's pretty mixed. About 27% think Anthropic, 39% think OpenAI, and then, you know, a third also thinks neither. I guess time will tell. If we had a magic ball here, it would be great. 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? Yeah. Well, many things, but if I had to pick one, I would pick 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 polling 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 poll employees of large companies during the year, three or four 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 intellectual property, of responsibility, of ethics, and so on and so forth. For us, that's really the imperative for executives to actually, you know, 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. 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. There is a big mismatch there. We actually built an index with Wharton. For all of you, if you're interested, it's out there. It's on the Knowledge at Wharton, and you can look at your own skills compared to the market trends and what is asked by employers. Even we attempted to put a monetary value on some specific skills to measure the current mismatch. 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. 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 of the coming month 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, you know, 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, you know, taking notes. Halfway through the meeting we're like, "Why didn't we turn on Copilot to take notes for us," right? Like it's. You know, someone on the call said, "Yeah, my son would have not even thought twice about this. It would have already been on. It would have been part of the workflow." 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. There's that. 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, you know, very streamlined experience. I also believe change management and talent management is an area we're seeing a lot of focus on that needs to happen. How do we upskill our current employees? How do we prepare for the future? What does the future look like? I've asked, you know, CCOs at banks, you know, the junior banker, what does that look like to you? It's hard to predict what that's gonna look like because not only do they do certain jobs that can become more efficient with AI, but they're also the bench for the next job. 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? I completely agree on the change management point. We've gotten some questions on that. I think one of the biggest challenges, that was one of the questions I relayed 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 that'll look like? As we were saying, it's the one on everyone's mind. I'll take a stab at least my perspectives. In fact, picking up, Francis, actually on one of the points you were raising about Shadow AI and the idea that, you know, 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, you know, increase in sort of complexity and speed. 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. 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, you know, our own risk environment, in order to better serve our own employees, but also, you know, ultimately into the end customers. I mean, you think of the recent launch actually with Google Maps, right? How they've now integrated Gemini AI into their Maps application, and that's gonna transform how we interact there with new recommendations and suggestions in this immersive 360 experience, right? We're seeing it rapidly, and it's getting ahead of us on that consumer side. That is creating the right flywheel, I think, for the enterprise, as well. As a you know 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. Again in a 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. I think back to the, you know, question on some of those, you know, LLM providers and where those are going. I think they'll continue to evolve. I mean, when we've seen releases of new models, it's leaps and bounds each time. Again, this will continue, and we're gonna need to continue to kind of keep up. This is something we've obviously embraced and you've led for many of the discussions, Justine, on our side. 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 gonna do a quick lightning round, then we're gonna 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. Quick lightning round, and while I'm getting to it, feel free to answer the question on the screen here. You know, what is one prediction or outlook you have for the AI market and who do you think is gonna be the winner? Again, don't worry, we're not holding anyone to your opinion today, but we'd love to see what you're thinking today. Francis, why don't we start with you? Yeah, I'm not in the business of identifying winners directly. What I can tell you is what we can see growing. 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 a 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, you know, kinda different providers being good at different things and, you know, continuing to sort of leapfrog each other. I think that's gonna happen for a while. It seems unlikely to me that things will meaningfully, you know, converge to one provider. That's my read. Alaina, I know you answered this a little bit, but if there's anything you wanna add, you're welcome to. I was gonna say I crept into it a little bit in my last one, so apologies, maybe I just double down on the point. You know, I think we're in this, as they've kinda called it in industry, you know, era of specialization, right? No more general purpose AI. We're seeing specialized models, agents, skills being developed. These are solving very specific domain challenges for industries. I guess my prediction around this is that we just continue to see the rise of some of these more specialized models. They also, you know, 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. I'll put that on there. 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 question 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? I do think throughout the year, we're gonna continue to see some of that kinda rubber hit the road, that realness, right? Again, it's kinda how I started. There's a push to really start to see the ROI, whether that's top line growth, bottom line impact. There's, I think, gonna be a bit more challenging from clients of all these tools. Like, we need to see that impact. We wanna see that. I think there's gonna be a continued push for that. I believe that there is a spot for both, you know, 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's vast, and I think there's gonna be room for them in 2026 at least. We'll see how that continues to evolve into the future from there. With that, we're gonna open it up to the audience. I'm gonna 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. 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. This kinda 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? I guess a couple of thoughts. You know, 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, you know, 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, you know, sponsoring pieces of infrastructure projects. You know, there is a question as to how much of that they've guaranteed and how much of it rolls up, you know, into, you know, their obligations. You know, in that world, I think really unpicking how much they're responsible for, are they, you know, kind of marketable from a public company perspective, you know, 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. I think that's sort of an important piece as well. Getting clean financials for these businesses is gonna be one of the big hurdles, I think, of, you know, taking them public. You know, 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 kinda grow revenue into that to kinda get to some kinda leverage ratio. I mean, today that almost makes no sense, right? Because they're not profitable. To get to some kind of leverage ratio, that starts to make sense. There's another question that said, "What is that gonna mean for all of us?" It's hard to see that not eventually meaning that the price of compute and these offerings will go up, particularly for enterprises. Like, we're in this moment now of, you know, the economics have been made so attractive for everybody that, you know, we can just use these tools for kind of everything. I don't think that's the world we'll be in forever. You know, particularly as these companies look to drive profitability, 'cause eventually they'll have to, you know, the use of AI may become a little bit different, and you'll need to be hyper-focused on efficiency, you know, to use it in a profitable way for your business. I love it. Thank you. Francis, I'm gonna 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. 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. If you were there, you may remember the prediction that were made up at the time by some that the consulting industry was, you know, going to go down because there would be no need of consultants anymore in the era of cloud. Fast. Even you had some prediction made by some researchers at Oxford University, which were saying that overall 47% of the jobs would be automated, and that would be certainly true as in the consulting industry at even to a larger extent. Well, if you fast-forward 12 years, you can see that the consulting industry is today actually larger than it was 12 years ago, and I believe that 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. Yeah, 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 questions on guardrails and how to, you know, 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. You know, 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. 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? We turn those guardrails up. We'd rather tell you we can't answer that than give you a bad answer or an answer that's not accurate. 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 and to all that we do and what we've always done, what we're founded on. I think that's an important lever. One, it's that foundational data layer being accurate, clean, complete. Two, it's really about turning up those guardrails as you're implementing that with LLMs and other tools. That's something that we've done here that has really helped us. 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. We always ground those answers in those results so that someone can check it. 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. 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. There's a couple questions on that throughout. Alaina, I'll pass- Can I- Oh, go ahead. Sorry. Jump in. Yeah, no, maybe just one word on that, Justine, because I was listening to you with interest and totally concur with what you were saying. Our own CEO, Julie Sweet, said that it's not about human in the loop. It's about human in the lead. That has a meaning because everything you said was about, you know, the responsibility stays with human in terms of setting the direction, setting the boundaries, making sure that the discipline is actually executed that it should be. It requires more leadership rather than less leadership, and that's why Julie has been coining that term again and again of human in the lead. I think what you were saying, Justine, is a very good illustration of that. I'm gonna adopt that. Human in the lead is gonna be my new catchphrase instead of human in the loop. I think that's spot on. Alaina, I'll pass this one to you just 'cause 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, the Different lenses for companies. As we've talked about, it has 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 in higher value, in new services for customers and new growth opportunities. Again, increasingly, we're pivoting into this new growth paradigm. Of course, those two things are what CFOs are constantly thinking about. 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? I think not only from both the role as in, you know, 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. 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? 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. Love it. Jesse, this question kind of plays off that a little bit, because these are things the CFO thinks about at an organization. But what is your thought on the current AI bubble conversation and how companies, you know, they're accelerating it, but CapEx expense is also increasing where maybe they're not seeing that return? You know, there was an MIT study that said, you know, 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 gonna be some correction at some point, just 'cause there's so much hype. Eventually, I think we'll see, you know, one or more of sort of the big name companies, you know, make sort of missteps that will make people question the value of all of this. I think the kind of overall kind of efficiency gains from, you know, the technology and the tools feel real to me. They feel real to me in sort of the, you know, just use of it in sort of our daily lives. You know, that is the underpinning of something that's not a bubble. In fact, it's sort of more economic output that sort of underlies all of this. Like, I think it's probably at a, you know, specific company level, there's gonna be stuff that's, you know, kind of overhyped. At a 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, you know, invest in the space right now. You know, it's definitely one that's sort of on my mind pretty consistently. Yeah. I think we've had a lot of conversation on this ourselves between the two of us, so I think a big topic that we'll just kinda have to see how it evolves and how valuations continue. Francis, I'm gonna come back to you, a bit different question or a bit 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 and training. There's internal firms creating it. You talked about firms like you're 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're actually presenting a new research at the NVIDIA event, GTC, this week on that. 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. That's what we call co-intelligence, because it's learning from the agent and educating the agent at the same time. I think for all of us, you know, you mentioned different tools earlier on, that's an opportunity to enhance our own job. To the point that at Accenture, we created a line of service dedicated to training executives in this era 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. For me, at the individual level, it's about that. You know, 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 gotta 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. 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? Then try it. I think that's great advice there. Another question we got, and one candidly I've answered a lot among 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 gonna speak specifically to them, but I'll speak to traditional offerings or offerings that, you know, have been around. Maybe Capital IQ is a great example of that. What does that mean? What does the future look like? 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 wanna work. The example I always give with Capital 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, or maybe our marketing slogan now is, "Get you out at 6 instead of 10." 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 to do their job. How that gets done has changed. A big thing we've really focused on is bringing the best of that GenAI 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. Great, let's help our users out by bringing the best to the tools that they've used in their workflows. I think it's all about evolution. It's all about meeting users where they are, et cetera. That's an answer I'll see on one of those type questions of how does it evolve. Maybe I can. Justine- Just one word on that, Justine. You didn't ask me a question, but let me just add one word on that because- Please We've been on that journey of developing partnership with data providers ourselves for our own tools for three years now. 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. We are heavy users, of course, of Capital IQ and S&P Trucost as well for ESG data. Very thankful to your company to be there, and really you were part of the early ones to be in that game. Love it. I promise I did not plug Francis to say that was on his own accord. Alaina, I think you wanted to add something there too. I was gonna add, 'cause we almost made it through this webinar without you know, using one of the most common things, the S&P MCP. Maybe just to put a point on one of the areas that you've you know that we've discussed and certainly has been a differentiator and as we go forward as part of the strategy as well. You know, as we think about unlocking, especially as a large company that sits on a large and very differentiated data estate, you know, MCP is one of the ways we're gonna enable and are enabling customers today to interact with that data. This will be key in really helping unlock those agentic workflows for the customers. 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. We're seeing that, you know, obviously increasingly from a demand perspective, from the customer side and are meeting that too with. That is just as one example of a way that we're gonna modernize from a distribution lens. Yes. Thank you. We would've been pretty remiss to not mention one of our big 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. 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 gonna kinda 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 minutes? Francis, we can start with you if there's anything you wanna add. Well, one of the most exciting things right now for me is about AI simulation. In the research world, for those who are interested in research, we're developing lots of AI simulation and very interested to continue the discussion for those who are interested on Substack, so we can interact there. In terms of the business, we can see the emergence of agentic commerce. For us, that's going to be a very interesting area to follow in the coming few months because we can see that it's bubbling up in that space. Love it. Jesse, what about you? Look, I'm gonna echo something Francis said earlier, which is try it. You know, it kinda goes to the fact 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. It's a moment to experiment a bit more than normal. Try it yourself. Get it for your teams. You know, obviously the right governance has to be put around it. You know, even, like, experimenting in your personal life, you know, is definitely sort of a needle mover, I think, and it, you know, helps you and those who work for you kind of learn about how to use the tools better, and I think that's, you know, a big boon right now. Love it.? I'll go back to just my original kinda sentiment around pace and taking a moment to reflect, you know, again, over the last few years of what we have seen and how disruptive it has been, and remembering that, you know, 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 kind of parts of the industry. So the reference back to the cloud migration years ago, the, you know, disruption of BlackBerry with iPhone, you know, many companies have underestimated the speed at which these kind of transitions can take place. You know, don't be in that camp and, you know, as Jesse said and Francis referenced, you know, try these tools and apply them to what you're doing today. Awesome. My closing, I don't have too much more to add other than what the three of you esteemed 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 areas with people. Lean in, right? Find a partner. We're here to do that. Accenture's 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. 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? Reach out. We're here to help. Thank you all so much for joining today. Like I said, you will receive a recording of this, and if you know you are listening to the replay, thank you. We look forward to continuing on the AI journey with you all. Thank you, Justine. Thanks, all. Thank you, Justine.