Future Ready CFO webinar series

Krista Fuller
Welcome everyone, and thank you for joining us today. I'm thrilled to kick off a conversation about something every finance leader is thinking about right now, what it takes to be a future-ready CFO in the age of AI. Here's the reality that came through loud and clear in our 2026 Future Ready CFO survey, which is reported here. Finance leaders are no longer preparing for transformation, they're operating inside it. We'll draw on research, real-world perspectives, all from our experts, and we will explore how the CFO role is evolving as AI becomes mainstream, volatility becomes structural, and CFO accountability becomes clear. Today's webinar is the first in a series on the Future Ready CFO sponsored by CCH Tagetik. CCH Tagetik is the leading unified finance platform to power close, planning, and reporting with expert AI so you can lead today, shape tomorrow. I am delighted to be joined by Deloitte Expert, a partner who brings deep finance transformation expertise and a point of view shaped by working with CFOs around the world. Here's what you can expect from us today. Real-world perspective from finance leaders and a practical look at how AI is moving from promise to proof in the office of the CFO. Before going deeper into this webinar, let me briefly introduce you to the speakers. I am Krista Fuller, Global Head of Product Marketing here at CCH Tagetik. And today I have the pleasure to be joined by my colleague, Mike. Over to you, Mike. 

Mike Shuker 
Hey, Krista. Yeah, Mike Shuker here. So I run our solution consulting practice within CCH Tagetik. And I'm hoping that during the course of this interesting webinar, I can kind of draw on what I'm hearing from group CFOs that I'm working with, their transformation priorities, how that filters down to kind of transformation projects they're undertaking in AI and we can bring all of that together. We're also joined by Payal, so I think Payal, I'll hand to you to introduce yourself. 

Payal Shah 
Yeah, hi. Thanks, Mike and Krista. Very happy to be here today. I'm a partner in finance transformation in Deloitte based in the UK, but been doing this for the last 20 to 22 years, working with CFOs and the teams in really driving transformation through the organization. 

Krista Fuller
Thanks to you both. Our goal today is to explore what's changing for finance leaders in the AI era, why it matters, and what finance leaders can do next. So here's how we'll spend our time together. We'll start by exploring why transformation today looks fundamentally different from even a few years ago. From there, we'll walk through the inflection point that finance leaders are facing and what the research describes as a shift from a readiness mindset to a post-readiness reality. We'll then walk through the key findings from the Future Ready CFO report, and we'll capture how CFO priorities and responsibilities are changing. As we begin today, I want you to know where the insights came from as you listen to the views of our expert panelists today. Our 2026 Future Ready CFO report is built on a global survey of more than 1,600 senior finance leaders across 20 countries inside large organizations, typically more than 1,000 employees and over half a billion dollars in revenue. So when we share what CFOs are thinking about today, we are listening at scale. And here's what I want to be very clear about. This is not another AI trend report. We set out to answer a different question. How are CFOs actually operating right now in a world where disruption is not just an event? It's the current reality. That is the lens that we'll talk about today. Let's take a look at who actually answered these questions. You are looking at CFOs, chief accounting officers, VPs of finance, and heads of finance. across North America, Europe and Asia, representing different economies, different regulations, different pressures. And here's why that matters. When you hear a finding, it might be easy to say that that's an issue for a particular market, but you can't say that here. When finance leaders across 20 countries, across industries, across company sizes are telling us the same thing, that is more than a trend. That is systemic. So with that foundation set, let's kick things off. So I have the pleasure of turning it over to Mike. 

Mike Shuker
Thank you, Krista. I think one of the clearest conclusions that we can draw from the research that we've just been discussing is that we're now at what we call the CFO inflection point. Finance is no longer preparing for transformation, it's now operating within it. And I think for many years, Finance transformation was framed as a journey towards readiness, modernizing your systems and processes, building stronger connections between finance, operation strategy. And while those efforts are still important, we think that many finance organizations have already crossed the important threshold. And the inflection point comes really from the realization that readiness is no longer enough. According to the Future Ready CFO report we're referencing, Finance leaders are now operating in a post-readiness era. So transformation is no longer an initiative with a start and end date, as you've traditionally seen, but more of a continuous operating condition that we're experiencing. And that shift is being driven by convergence, I think, of multiple simultaneous pressures, things like persistent market volatility that disrupts initial assumptions, rapid expansion of AI adoption, especially when looking at evaluating forecasts, scenarios, investments, You've got the expanding regulatory and compliance demands that organizations are suffering from. And then I think capital constraints is another big one that we'll dig into during the course of this webinar. All of these forces require CFOs to be, I think, responding in real time and typically without perfect information. So that's where accountability is starting to change. And as our research shows, CFOs are increasingly being judged not on how well they prepare for disruption, but on how well their teams are performing when disruption is already taking place. And that is the inflection point we refer to. CFOs moving from managing financial outcomes and the sort of stewardship position that they traditionally had to supporting transformation and ultimately, I think, orchestrating enterprise performance. Now Pial, I want to bring you into the discussion at this point and I'm interested to hear from your perspective in your work with CFOs and finance leaders for that matter, are you seeing that same shift from preparing for transformation to actually operating within it? And alongside that, if you can give us a view on what the biggest pressure point you're seeing in terms of what leaders are naming right now and if they're becoming that enterprise wide performance orchestrator? 

Payal Shah 
Thanks, Mike. I mean, I really feel for the CFOs for today. They're dealing with multiple constantly evolving priorities. In our last quarterly Q4 2025 CFO survey, just as the CFOs were finding that business confidence and risk confidence are actually picking up, we've all had another geopolitical problem over the last eight weeks. So geopolitics does remain the biggest risk factor for the CFOs today, and they just cannot let the organization break. So this is really testing their ability to be able to build organizational agility, flexibility, and responsiveness throughout the organization. I agree with you, Mike. I think the role of the CFO over the last 8 to 10 years has really been expanding. I mean, when I started my career, it was very much about working on finance transformation programs for CFOs, but now I see them as a new sponsors or the co-sponsors of enterprise-wide transformation programs and as a business partner to growth. In terms of your question regarding priorities, cost management continues to remain a top priority for all CFOs, and we're seeing this ever since the COVID period of 2020. In the consumer industry where I'm spending most of my time in, M&A, disposables, investment in technology, ERP, best of breed applications, AI, speed of data, are all the conversations that I'm having with them and probably that sums up all the work that I've been doing over the last 18 months as well. Over the last year, I had the chance to really design a complete end-to-end program, architecture, platform, processes, data for a new company, 8 billion euro company. And the test for us was really building a foundation and capability that gives them a platform for data and process integrity, a platform for growth, but also enables AI and facilitates A leaner and agile organization and operating model of the future. I just think that the role of the CFO is no longer restricted to finance. It's really powering the whole enterprise. 

Mike Shuker 
Yeah, thanks, Payal. And I think actually that kind of links in quite nicely to, I think, a couple of scenarios that I'll dig into later. So let's kind of, we can get into a few of the findings now and I think we can draw back on that CFO role and how that's evolving. So as we move forward with that context in mind, what I'd like us to do now is start to walk through four key findings from the Future of the CFO report, and that when you combine these, they ultimately define this post readiness error that we're discussing for finance leaders. So each of the findings highlights, I think, a different pressure point that finance leaders are dealing with. And they're all connected by 1 central theme, ultimately, the accountability for outcomes in an environment of ongoing uncertainty. So we'll start with the first and most foundational finding, one that underpins everything else that we'll be discussing during the course of this webinar today. So I'm going to hand you back to Krista to talk to us about how the transformation is ultimately now central to the CFO mandate. 

Krista Fuller
Thank you, Mike. Here's the first finding. And in many ways, as Mike said, it frames everything else we discuss. Transformation has moved to the center of the mandate. When we asked finance leaders to describe their priorities, digital initiatives, process modernization, and technology leadership, more than half of these leaders told us these priorities are already part of their day-to-day role. Now here's the part that I think is the real story. nothing has been taken off the CFO's plate. Financial oversight, compliance, risk management, they're all still there. And in many cases, they're harder than ever before. What we saw that has changed is what's been added on top of what is already a heavy load. So our research showed clearly that today the CFO is expected to lead modernization, guide AI adoption, shape enterprise strategy, and still deliver accuracy, control and compliance every quarter, quarter after quarter without fail. And so if we pause and think about that for a moment, we're asking one leader to be the steward of the business and the architect of the future at the same time. And what we're finding through our research is that it's not just a workload issue, it's a fundamental redesign of the role. And that pressure, this dual mandate is a thread that runs through everything we're discussing. So I will hand it back to you, Mike. 

Payal Shah 
So Krista, if I can add, right? I mean, I was doing a transition lab for a CFO of a business unit of a large global consumer company. And I was also talking to the CEO to really think through the role of the CFO. And what we're realizing, specifically in the consumer industry, as I say, that the DNA of the finance leaders is really changing. So while you absolutely expect them to have their what should I say, the accounting and the financial disciplines, increasingly people are looking at the CFO having had deep experience in the market, in brand, channels, products, etc. So it's like very surprising, like you're looking for a very market marketing focused CFO, but with deep financial discipline. So it's just evolving in the current times. 

Krista Fuller
Thank you for that great insight.

Mike Shuker
Yeah, that's really, really interesting. I think it actually will come back to that point, hold that thought, because I've got some insights, especially in CPG as well, which is probably one we can call on a second. So the second finding we wanted to cover, I think it builds on the narrative we've seen with the first. It helps to explain ultimately how transformation is unfolding with AI now a key force reshaping the CFO's role and the CFO's organization for that matter. So we're not just talking here about AI as a future trend anymore or the fact that it's ultimately a pilot experiment confined to an innovation team. Nearly half of the finance leaders that we surveyed identified AI adoption and implementation as the trend that was having the biggest impact on their organization today. What's most interesting, I think, is how CFOs now perceive AI. Historically, it was often associated with efficiency. So faster closing cycles, reduced manual work, lower costs within their organization. And that's still important. But ultimately, finance leaders are also increasingly view AI as a strategic capability now. helping them with financial modeling, forecasting accuracy, scenario planning, and even capital allocation. So AI is not just about automating back office. It's influencing, I think, how organizations decide where they invest, how they manage risk, and how to respond to some of those changing conditions that we've already been discussing. So all of those areas sit much closer, I think, to enterprise strategy as we've been discussing. And I think from my experience that connects quite well, PR, with your description of the modern CFO, let's say, and the profile we're seeing. I worked with a couple of FTSE 100 organizations recently, and their group CFOs and divisional CFOs, for them, there was a big level of priority given to understanding how external factors were influencing their organization. So CPI, inflation, raw material costs, how that was impacting their products, their sales and product lines and their costs. And what was that kind of what was the direct impact of the bottom line? So they understood there was a transformation opportunity from the discussion we were having, but their focus was exactly that. It was on product, it was on brand, it was on channel, it was on those external factors and how those things came together to give them, you know, potentially a more accurate forecast. So that was the discussion we were having alongside what AI could bring to their business. And that for them was actually, you know, they wanted to get the right data foundation, but to be honest, that more accurate forecast, understanding what was driving all of their different product lines, was the key decision factor in that change. So I wanted to bring you back in again at this point, Piao, and just understand, from a practitioner's standpoint, which AI use cases you see are delivering measurable impact for finance teams right now and where you see organizations maybe overestimating the maturity that's required?

Payal Shah
Yeah, thanks, Mike. So I mean, I look at it in two ways, right? So when we think about finance for finance function sake, I am still seeing most of these cases are very much driven by cost savings and productivity. I mean, just a conversation I'm preparing for the finance function for tomorrow is very much about how do we use AI to drive down the cost of finance over the next 12 to 18 months. But having said that, I think there are three areas that we see AI being used in finance. One is very much typically what you would expect, just transactional finance and reporting processes. This is where the use of rule sets, machine learning for automating and creating tasks is being leveraged pretty heavily, but of course supported then by human review and sign-offs. I think the second area is exactly what you said, Mike, in the area of forecasting, scenario, modeling, et cetera. But there are two things we have seen, that the scalability of that hasn't yet happened, is below expectations that we've all had over the last two to three years. And the second is the acceptance of that. Again, you mentioned about drivers, et cetera. I think what we're hearing is that when you have that model and it's been created, say, somewhere in Europe, and then you're leveraging it for the US, Actually, you find the finance professionals not really believing in that because they haven't, they don't feel the data and it feels invisible to them. And finance professionals being finance professionals almost want to do that in parallel to build trust. So trust is a little bit slow in this space. And the third one is we do see AI being used a lot in specialist areas such as treasury, like cash flow forecasting and then I actually, we also see it a lot in controls, in controls, automation, monitoring, etc. Having said that, in Deloitte, we've actually developed, given the number of conversations we're having in the market and with finance organizations in the space, we've developed an agentic AI blueprint, which now supports organizations to really go through a very structured approach of process transformation and identifying use cases either as part of the ERP transformation or in isolation. And it really helps the organization in identifying, prioritizing and sequencing them based of course on the maturity of the organization. But the one thing I'd like to call out that none of these can happen in isolation without thinking about what does it do from an impact of the operating model and the roles. So process transformation, use of AI immediately then links to the conversation in terms of what's the agentic AI organization of the future. But again, coming back to the role of CFOs, right? CFOs aren't looking at just finance as a function for the use of AI, agentic AI. In fact, I think they're really well placed to really take a cross-functional view because you look at it from an end-to-end process transformation rather than not. And I think coming back to your Coming to your question regarding maturity and what next, I think this is going to be quite important in terms of how can finance actually be the agents of change going forward, breaking down silos. I think that's where I think maturity sometimes differs.

Mike Shuker
I'm interested in that insight because I think it also leads us on quite nicely to talk about adoption because we've just discussed a few different types of use cases, at a transactional level, leveraging agentic AI, scenario planning, scenario modeling, cash flow forecasting, which we also see from our side. But it leads me into the next area, which I think is absolutely important and critical when you talk about breaking down silos, which is, as we talk about maturity, we also need to think about adoption rates, adoption rates and capability gaps, I would say, you know, where that sits within the organization and what we're seeing in the market. So I wanted to kind of take us into our third finding based on that. And it's about this critical tension, I think, that runs through the entire report. The ambition that organizations have to transform is high, but progress is often slowed down by these capability gaps that we're seeing. So when we talk to finance leaders about what are the biggest barriers you see to transformation, the answer that stood out most clearly was the resistance to change and cultural readiness. And that, I think, is an interesting insight when you've got many organizations, they've already overcome some of the technical barriers, let's say, but the bottlenecks they're seeing is more human and organizational within their businesses. And so the report itself highlights 2 critical gaps that I want to highlight. One is digital fluency and one is AI literacy. And I think in the context of the use cases, Payal. you mentioned, That's important. And since AI is, I would say, becoming embedded in core finance activities now, as part of the operating model, finance teams are being asked to engage with technology in more sophisticated ways, let's say, than they have traditionally. They've got to understand AI outputs. They've got to start to dig into assumptions that are being used in models. They've got to understand the underlying data and any potential limitations there might be in those. and then to understand how to translate the insights that they're getting from any AI model into decision making rather than just analysis. So for many, this is, I would say, a fairly significant shift in the skills that we're asking of those finance teams and their mindset. And I think that's evident again with only 18% of finance leaders saying that their organizations are digitally advanced enough to operate confidently when assumptions are breaking and it leads us to a bit of an execution gap in the organizations that we talk to. So for example, you know, some are just looking at, if you think about the practical use cases and what they're coming to the market and what we're discussing with our clients, Some are just looking at AI as really a simple way to democratize data. So move away from this concept that their finance systems teams are the only ones that can build reports and get access to key data within the organization. So it's about leveraging a chatbot, for example, to assist their users to be able to create narratives or retrieve dashboards from the data that they hold. And if you think about the knowledge required to do that within finance, it's little to no knowledge. It's simple prompting. But then I think at the other end of the scale in the organizations that I've talked to, you've got those who have maybe already advanced, set up COEs within their organization and they are looking to leverage that internal skill set. So they might already be using tools like Claude or Claude Code, and they want to be able to integrate that knowledge that they have with their enterprise digital platforms via gateways. So it's kind of, I'm seeing really two ends of the scale, right? Those that just want to leverage kind of generative AI and chatbots to kind of give their users more access to data, and those that want to do real kind of enterprise-based agent work using tools like Claude. So again, I'm interested to see from your perspective what you think the kind of biggest capability gaps there are in finance when people are adopting AI and how leaders realistically can look to close any of those gaps.

Payal Shah
Yeah. So just reflecting and listening to what you're saying, Mike, I think from a maturity and what organizations are doing or what my clients are doing are quite similar to you because I have one client As you rightly say, I have many clients who have centers of excellence and they're on that path. And then I have other clients who are probably looking at two or three use cases every month and investing in them. And typically I do see that being sponsored by the CFO slash CIO or both of them together. But in terms of the maturity and the capability gaps, what am I seeing in the market? I think the first one being is that I still hear conversations in terms of tell me how and where can we use AI to bring our cost reduction down or to give us better data. I think it's almost going for using of AI for AI's sake. So there's a little bit of education and training in terms of really making people think about, you know, how can you use AI for the wider benefit of the organization and yourself. So really reimagining use cases more properly. I think the second thing that I'm seeing is that for AI to work, you really need a stable data platform and good quality and trusted data. And this often takes years to develop, and that creates a lot of frustration in the business when you're not able to scale the use cases as well. So I see the scalability being really lagging the intent and the excitement. And that also leads to a very important point, which is important to talk about, is a return on investment and how do people want to put, how much capital are people willing to put behind it? That again is something that is lagging behind. But I think the point that you spoke about and I mentioned earlier that I think finance is really well placed, just like they have been in the past because of the finance discipline that they have is they can actually break through silos and be change agents to drive adoption. So really looking at it one end-to-end process, irrespective of whether functions. So let's take order to cash as an example. Order generation actually The starting point of that is within your commercial organization. Then it goes into supply chain for fulfillment purposes. Then it comes to invoices, then it comes to cash, then it goes back to queries, right? So it's literally really breaking that silo to really think about it from an end-to-end perspective and thinking about three things. One is, where can you automate? What rule sets can you use? Where will the human interventions happen? And then how does a rule change? So what should your operating model be, including GPS? I think these are the things that we need to think about in organizations you think about as they move on this journey rapidly.

Mike Shuker
Yeah, and I think it's interesting that you raised a point around that data foundation and getting that right, because what we also see, I think, is We see finance leaders coming to us and some of the use cases they're yearning for ultimately come down to data management. How can we help them with AI even to cleanse their data on import, show them anomalies, show them variances, bring that stuff to their attention because they are struggling ultimately with their existing process to see within their data where that stuff stands. So I think we're also being asked to help not only with kind of democratizing data with having access to data to leverage it for reporting purposes, but also to cleanse data as another use case that we see a lot and get that data foundation right.

Payal Shah
Yeah. And Mike, it's also about building trust, right? So let's assume that you've got a machine learning and a modeling that generates A forecast. And whether you get it at 80% accuracy, 85% accuracy, 90% accuracy, the thing is no forecasts are 100% accurate, right? So it's really about how do you build that trust that anything that you have 80, 85% accuracy, then you have the humans to review it and bring that to the level that we're happy with. But I think that's taking its time in terms of building trust.

Mike Shuker
Yeah, and it's a question that we commonly get is around transparency of the models. So understanding how something's been calculated, the assumptions that have gone in, maybe the drivers. So giving that level of visibility helps to build that trust, I think that you're mentioning really. And I think that you talked also about capital allocation and the squeeze on allocation ROI, which is really the focus of our final finding. So as we step into finding 4, looking back at the research, capital allocation has become, I would say, one of the arguably one of the most challenging responsibilities for the modern CFO. Previously, you could see that investment decisions were framed as growth opportunities or discretionary spend. And I think today, capital allocation is increasingly being influenced by risk management and not just ROI optimization. So I think in our Future Ready CFO research, finance leaders pointed out three forces that are ultimately shaping where that capital is being deployed. The first is AI adoption and implementation, which we've been discussing as part of finding two and we've kind of dug into a little bit. Interest rate volatility, I think, is another key one that's impacting modern finance leaders. And then that regulatory and compliance complexity that we've touched on a couple of times, I think also has a major impact. And these are all those external pressures that require, I think, sustained investment, even when your resources are constrained, as you pointed out, PR. And I think AI investment is a good example that we can touch on. You know, on the one hand, there is, I think, uncertainty around ROI in AI that we've discussed. And that really does have a significant impact on finance leaders. But I think on the other hand, AI is probably one of the most significant investments finance leaders are prioritizing today. And if we look at the why of that particular point, I think this is because as we discussed, AI is no longer an optional innovation. is because there's that external pressure from external stakeholders and shareholders to invest in AI and embed it in your operating model. And also because if you don't do that, you risk falling behind your competition. You risk kind of, you know, the gains that can be had in forecasting capability like we talked about in decision speed. And that's, I think, at least as concerning as the risk of overspend. So there's ultimately, I think, definitely evidence when organizations come to market for finance transformation projects that AI impact and ROI is a huge business case driver for them. So it's evident, you know, in the line of questioning, in the types of RFPs we see, you know, that is truly evident in there. And it's a key, I think, a key driver for project prioritization and for what gets a green light within an organization, basically. So an example being, where AI investment, I think in capital allocation meets is where we're seeing organizations need to be more agile in running more scenarios and to keep pace with change in, changing supply chain situations, raw material cost change, interest rate change, geopolitics, as you pointed out, Payal. So we're seeing a lot of that kind of come to the fore. And I had a question for you, I think, actually, on this topic. When it comes to capital allocation, We understand it's getting tougher, but how do you see CFOs mounting AI investment when there's ROI uncertainty in the organizations that you're working with?

Payal Shah
Yeah, it is really tough, Mike, and I don't think that there is a single answer or anyone has nailed down in terms of the approach. I think your surveys are showing it, our surveys are showing it, all organizations saying that they are allocating capital towards the use of AI, right? But how does it then make its way into the governance processes differs quite a lot. So over the last, I would say, 12 to 18 months, most of my programs have been technology enabled transformation, right? And then there are a number of criteria that we talk about. If we're going to invest in AI, then surely the cost of the software or the tooling should be cheaper. Surely the cost of maintenance of that should be lower. Oh, and if we're using AI, surely the operating model from my FTE cost reduction should go down as well. Everything is correct, but we're also then saddled at this point in time with rising costs because of inflation and supply chain and all of that. So actually the cost of procurement is actually going up versus the typical thing that you would see. I think the other thing, if I make it a bit more tangible, is I haven't seen a single conversation in the last 18 months when a tool selection or a software selection is happening, when I'm not being asked about what is the AI capability of that tool, what is a roadmap that they have along that. And when it comes down, it's not just about the license costs or whether it's going to Am I going to look at a very technology enabled organization, but it's also about how are you going to implement it and what AI and automation are you going to use in terms of implementing that solution that you did three years ago? So I think these are all forming its way into any budget allocation that is then happening more tactically case by case basis.

Mike Shuker
I think in the discussions that we have, that's definitely evident. You can see that even when someone's looking to replace, let's say a legacy solution they've had in play for a long time, there's an opportunity there for them to, I think, set the foundation for the future in terms of digital transformation. But the number one driver is what are we going to get out of this from the context of AI use cases now and in 12 to 24 months. So we're absolutely seeing that as well. You know, what ROI is it going to bring? It's almost, it's justifying the capital allocation up front, I think. And it's a key driver. So we've gone through now a few of those findings. We've gone through four key findings of our CFO, finance CFO report. What I'd like to do at this stage is hand you back to Krista, who's going to kind of, I think, summarize some of the findings we've just walked through in terms of key takeaways and how we bring this all back together for actionable insights.

Krista Fuller
Thank you so much. As we listen to both of you discuss the situation in the market, it's very clear that whether it's the business shift, the AI shift, the market pressure, they all point in the same direction, that the CFO has a new role. And in the report, we call it the performance orchestrator. Here you all discussed it through the use cases as the change agent. It's strategically a very It's a growing opportunity and you can see it right here on the slide. So this slide was from the 1600 that were surveyed and they said that they see the modern CFO sitting here right at the center. All those things that were discussed at the center of the strategy, capital allocation, technology and risk, cross functional collaboration. I think you've heard use cases that perhaps. are well beyond what we would think of traditional finance use cases. And I think all those things clearly point to the fact that the role has shifted and the CFO doesn't just own these domains in isolation. That CFO is responsible for connecting them. And we've already shared those that responded said that their traditional role doesn't go away. It exists alongside all of these new needs within the organization. Stewardship, accuracy, control, compliance, they're all still essential. And as you would expect, the research showed that regulatory complexity and risk still continue to increase and are top of mind for these decision makers. What they are struggling with and deciding on is how to manage the breadth of the role, how to connect it all from data to insight and decisions. And so the majority of the finance leaders told us that they are collaborating with business leaders, CIOs, the rest of the C-suite so that they are not working in silos. This matters even more in the AI world. We just talked about data readiness, having a firm foundation. So when AI becomes central in shaping forecast, whether they're sales forecasts, market views, looking at risk and opportunity, when it's shaping plans, capital decisions, the CFO and the finance organization is no longer just the person who receives and drives analysis. They also are taking responsibility for making sure that the outputs that they are delivering are trusted, explainable, used consistently. That idea of data narratives is truer now than it's ever been. And as you've heard from the discussion, it's not about now running every decision through finance in order for a business to scale in this growing complexity, finance is in the role of providing clear insights and data foundations so that better decisions can happen anywhere and everywhere across the business. And so you've heard firsthand exactly how this is evolving. I hope as you listen to today, you're thinking about, well, what's next? What steps do we need to take within our organization to be ready for driving toward the future? So this webinar is the first of our four-part series in the Future Ready CFO series. In the next sessions, you'll figure out and learn how finance can get to the next level with AI as its ally. We'll talk about collaborating with other departments for enterprise transformation. And finally in the 4th webinar, transformation lessons learned from finance leaders in 2026. The practical guidance that I know you found so valuable today. So keep an eye on your inbox for updates and we invite you to join the next ones. And as we are drawing to a close today, let me point you to the report itself. I hope you found this information valuable. To everyone that has joined, thank you for your time. We're hoping we are leaving you with a sense of what is shaping the future and what future ready sounds like. If you've not already downloaded the full report, please scan the QR code on your screen. Everything we've touched on today and quite a bit more is there waiting for you. I want to thank Mike and Payal. Thank you so much for your perspective, your insights, the use cases that you shared. I know that the audience has a long list of topics that they'll take back to their internal boardrooms and their discussions with their teams. So thank you both for your contributions and we thank you for joining us today. Take care everyone. Thank you.  

Hans Gobin
Good morning, good afternoon, good evening, ladies and gentlemen, and thank you for joining us for today's FP&A Trends webinar, where today we'll be talking all things how to augment the finance function with AI. My name is Hans Gobin. I'll be your facilitator today. Just so you know a bit of stats and we are breaking the record today, I think we've got well over 1050 registration. So lots of people are going to join us and we have people from well over 60 countries as well. So let me start and let me introduce to you what we have on the agenda for you today. So augmenting finance function with AI. AI in finance today, where do we really stand? From pilot to scale, practical steps, making AI work. AI-powered innovation at Microsoft. So we've got a fantastic case study. Intelligent finance, boosting performance with AI and agents. And finally, we've got conclusions, recommendation, And let's not forget, we've got our fantastic Q&A session at the end as well. So please, ladies and gentlemen, the chat box is there open for you. Any questions you have all throughout this session, please send them to us. We will try and answer some of them today. Those that we can't answer because, of course, of time, we will answer directly to you on your e-mail. So please send us your question. Right, a good time now for me to introduce my speakers, my panelists. So ladies and gentlemen, panelists, if you can join us, please. And let me start by introducing Gopi first. So our first speaker today will be Gopi Singh, EPM director, FP&A leader at PWC US, based in LA. And today, Gopi will talk to us from pilots to scale, practical steps to making AI work. Gopi, fantastic to have you on the panel today. 

Gopi Singh
Looking forward to it, Hans. Thanks for having me.

Hans Gobin
Brilliant. Thank you, Gopi. Second, we have Christopher Wittman, Director of Finance at Microsoft, based in Seattle and Seattle FP&A board member as well. And today, Chris will talk to us about AI-powered innovation at Microsoft, of course, what he's going through and how he's doing it at Microsoft. Chris, looking forward to hear from you.

Christopher Wittman
Hi, Hans. Happy to be here.

Hans Gobin
Fantastic. Last but not least, we've got Antonio Rosa, who is Product Management Associate Director, Extended Planning and FP&A at CCH Tagetik, based in Milan in Italy, and today He will talk to us about intelligent finance, boosting performance with AI and agent. Antonio, fantastic to have you with us.

Antonio Rosa
That's a great pleasure to stay with you, all of you. Thank you.

Hans Gobin
Brilliant. Ladies and gentlemen, three fantastic panelists, brilliant insight and presentation, questions and answers and discussion question coming to you very, very shortly. But for now, I will ask my panelists to turn their camera off, go on mute, whilst I do, a little bit of housekeeping. So first, let me introduce to you our global sponsor, CCH Tagetik. We all know CCH Tagetik, embracing the future of finance with AI-based corporate performance management software, brilliant software. So thank you, CCH Tagetik. FP&A Trends Group need no mention. Most of you know we've got international FP&A board in 33 odd cities. 301 meetings we've done so far. The webinar today is 192nd one. Of course, we've got our FP&A Trends survey, which you participate in, research papers. You also part of our LinkedIn member and our website. Let's not forget we've got an AI committee as well that has started since March 2018. So ladies and gentlemen, if you're not part of the FP&A Friends group, please look at these different ways of joining us. Now a real housekeeping one hour webinar as you know your participation is very much required we've got four polling questions we will learn what the panel is doing as far as AI is concerned shortly but we also want to know what you guys are doing so please do vote. Do send us your questions via the chat box you can download the presentation right now in the handout section of the tool otherwise you will still get a copy of the recording link and presentation after the meeting. Let me just quickly remind you when I close the meeting, you will get a feedback pop-up session that comes up. So do spend 30 seconds, a minute, give us some feedback, tell us how we did, but also tell us what you would like to hear from us in the future. So very quickly, let me introduce what we will be talking to you about today. So this webinar has been brought to you by CCH Tagetik. It's a number two in a four series webinar, which we've called a 2026 Future Ready CFO webinar series. At the end, there will be a QR code that you can scan to get the report as well. So let me share some stats with you very, very quickly, which comes from that report. So AI is now a real finance priority. We know that. It's not something new. It's something that has been there and will carry on. So AI adoption, 47%, AI adoption and implementation is the global trend with the greatest impact on their organization, 47% people tell us. 85% of the people poll say AI will reshape the finance leadership role over the next year. So it's here to stay. It's not going anywhere. Where are people using it? So use cases in financial modeling, 63%, financial reporting, 62%, capital allocation, 62%, budgeting, forecasting, performance analysis, 62%, scenario planning, 60%. So very well used as well. However, scaling is harder than starting. 33% see AI as a top opportunity to drive impact. 32% see it as a top disruption. 18% digitally advanced team. 69% are in early steps or established stages. Why the problems? 41% implementation costs versus ROI, difficult to pin down. Let's think about regulatory and complying risk. 40% talk about that. And 37% say data quality is one of the biggest issues. Now, finally, the future-ready finance model needs new skills and a stronger operating model. So we've got to change the way we work with AI coming in. So what does the poll tell us? 51% identify data analytics and digital fluency as the most important skills for future success. They also cite AI automation and literacy as essential, 46%. And finally, 53% already have responsibility for digital initiatives and process transformation. So ladies and gentlemen, just a few stats from that report that you can download towards the end of this. So let me now very quickly launch first polling question of the day. So ladies and gentlemen, if you can please vote. Right, so over the past 12 months, where has the CFO's influence increased the most in your organization? Enterprise performance and strategy, AI and digital investment decision, cross-functional planning, risk and regulatory oversight, traditional finance operation. So if you can tell us Over the past 12 months, where has the CFO influence increased the most in your organization? Enterprise performance and strategy, AI and digital investment, cross-functional planning, and then finally, sorry, risk and regulatory oversight, and then traditional finance operation. I'm now going to end the poll and share the results. So what can we see? 44% enterprise performance and strategy. Very much what we expected. 16% AI and digital investment decision. 14% cross-functional planning, 10% risk and regulatory, and then finally traditional finance operations, 16%. So very much where we thought we were going to be. Ladies and gentlemen, thank you very much for voting. Let me close this and let us now move to our first presenter. So our first presenter today, as mentioned before, is Gopi Singh, EPM director and FP&A leader at PWC in the US. And today, it will take us from pilot to scale practical steps to making AI work. Over to you, Gopi, whenever you're ready.

Gopi Singh
Great. Thanks, Hans. And thanks, folks, for joining us, as Hans mentioned, from all over the world. It's very exciting. We have a lot of great topics for you today. So we'll start off with this page. And I want to acknowledge all the elements that Hans mentioned around this is an area that is changing from week to week. And dare I even say, day-to-day, based on new features being released, new technologies, you name it. And we're seeing a lot of our conversations with clients really go from, should I be using AI, where should I be using AI, which obviously there's use cases that are pretty standard within the finance function, right, which we'll get to as we get to the Chris's part, but really to more of how should AI and humans really should be working together. And the reason the conversation has shifted from there is that adoption is very clear, right? Everybody is using AI in some degree, shape or form. And the Now it's really about like how do we go ahead and leverage those individual kind of use cases that folks are doing, whether it's in a particular team, a particular user, and you're getting some productivity gains to true transformation. And what I mean by that is from those individual use cases that are deployed in those teams or by individuals, how do we go ahead and expand to the process so that in addition to adoption, now we're truly getting transformed by the process, or sorry, transformed by AI, the entire function or the process itself. So that's a lot of the conversations that we're seeing go to next, right, is how do we truly get away from value that may be trapped in those individual use cases to more impact across the finance function, right? And as we kind of see this particular conversation evolving, there's and clients are going to design what the operating model between agents and humans look like, right? Or AI and humans look like. There's a few things that, if this is not designed correctly, ends up in issues, right? And I'll focus on the right-hand slide and I come back to what the strategic choice is. Number one is really, as we're kind of seeing companies go implement more and more AI cases, it's really a bunch of pilots that are out there, right? We know, as I mentioned from the use cases, it could be anywhere from analytics, predictive modeling, or things like around anomaly detection, et cetera, everyone's proven the value. As I mentioned, the adoption is clearly there, but it's just staying in that pilot state, right? How do we go ahead and now take it into our processes? How do we go ahead and embed it into the organization? So we're all kind of benefiting and truly being transformed by AI, right? And one of the other elements is really around the accountability portions of it, right? So you have the pilot, but the value is proved in the pilot itself, but when we think about the overall kind of gains to finance organization, you need to be able to update the AI. There needs to be human in the loop kind of components. So that accountability is not really defined. That pilot just stays a pilot, right? And you need to obviously go ahead and have the machine feed I'm sorry, the humans feed the inputs of the machines and vice versa to make sure it's continuously improving, right? And then the third element is really around controls when this partnership is not kind of designed correctly. And I'm not talking about auditability and traceability. A lot of your agentic workflows will have that kind of naturally embedded. That's the first question that finance users will ask in those discussions. But it's really At what points do humans really go ahead and check the outputs of the machine and make sure, okay, or the AI and say, okay, this is correct, this is not. So layering in that judgment component and having that as part of the actual AI use case. And then as I kind of alluded a little bit to this around value being trapped in pilots, even if they are being taken to production from all the pilots that are out there, how do we go ahead and eventually make sure the entire process is being transformed, right? So it's not just one person, one particular team. It's the entire function as well as the process with roles and governance defined, which I'll touch upon on the next slide a little bit over here, right? And there's three data points that, as Hans kind of already mentioned, that that explain or kind of tie very nicely into what we're seeing on the ground, these four elements, right? The ROI portion is still a little bit unclear, right, in terms of 41% seeing that as a barrier, right, of the respondents. That's primarily probably due to the number of pilots that are out there that don't get taken into production, or if they do get taken into production, they don't truly transform the function or the process itself. So you're able to get that end-to-end benefit. And number two is, in addition to audibility and traceability, how do humans stay in the loop in terms of making sure there's a consistent feedback point to where the AI, and if it's an algorithm, not hallucinating too much to a point where it's giving some erroneous results. The third component is the The third component is the data portion being a barrier. But we have seen a lot of clients work or companies kind of work. We have seen a lot of companies work around this, right? There's data harmonization projects that can be relatively time balanced. You're not spending too much time on this, right? So to kind of overcome all of this, it's a very strategic choice, right? And an intentional choice, I should say, is how do you go ahead and optimize the partnership between AI as well as humans to make sure that you're able to maximize the gains that are meant from this particular type of technologies. So let's touch a little bit about what companies are doing on the ground here around that intentional partnership. So Hans, if we can go to the next page really quickly. Okay, I know this is a little bit of a busy slide, folks, so I'll just kind of touch upon the main items, right? So again, right, this is not really AI is just an upgrade to your technology. It's really the end-to-end workflow that humans and AI are executing slowly being rewritten, right? So there's kind of three components of companies that are doing this really well and intentionally. One is around designing the partnership of who's doing what, right, in terms of AI, it's meant to replicate human behavior. So it's very good at large volume. Sorry, it's a it's meant to replicate human behavior, but it's specifically good at, you know, large volume calculations, which a lot of humans can do, but it can do like, you know, very quickly. It's able to recognize patterns very quickly. And then humans, of course, are very good at from a judgment exception handling perspective. So designing of how who's kind of doing what and then when is when is that When exactly are they kind of doing it in terms of what's human led, what's agent assisted, as in humans can go ahead and create, for example, the first version of a budget or a plan, and then agent can go ahead and quickly point out what the anomalies may be, what some of the patterns may be, et cetera. So really being intentional around that design. We see this a lot when we see predictive flows, sorry, predictive forecasting flows, where the data is there, you're basically having the sort The human will go ahead and create the plan. The agent is actually churning out the actual details, the first rev of it, if you will, from a forecast perspective. And because it's all linked together, you have very low run times. And we see companies achieving very accurate results with that and unlocking some significant working capital. So it's basically who should be kind of doing what and designing that very intentionally. And then number two is where should that really live? To this day, we don't see companies look at their EPM platforms and I'll say agnostic of technology of this being an obstacle or anything like that. It's the one place where the agents as well as the humans are really converging, right? Again, we can get, there's a whole different topic, right, about what should be done in an EPM platform, right? AI native versus external. And that's all something that needs to be figured out. But ultimately, the plan, the forecast, and other components, it really come together here. And this acts as a true governance layer. And if designed correctly, a lot of that effort that is teams will go ahead and experience today around manual forecasting, et cetera, really gets automated here. And that effort is shifting towards the AI agent, if you will, and then humans are really just kind of looking at the first pass until it goes to leadership, right? And then the third component and the final piece is the how, right, the governing of the actual partnership. So you have who's kind of doing what, where is it really being done, and then how is it being done? So if you look at things from the partnership perspective around AI doing something around anomaly detection, right? You have to define where are the escalation points and when are the escalation points, right? For example, if it's low, medium, high anomaly detection, if you're looking at JEs or you're looking at other kind of transactions in your systems, low risk, does human really need to be involved in that? Medium risk, maybe it's either manager or director level that gets reviewed and then at a high risk that's maybe even at a controller level that needs to get reviewed, right? So what does that governance really look like so that the humans are continuously in a loop, right? I won't cover the example at the bottom because I've already kind of covered that as part of the anomaly detection. But companies that really do this well as they're designing, again, that intention, they're being very intentional about what that partnership looks like and all these various components, whether it's your platform, the partnership around the AI, as well as the data flows and that orchestrated ecosystem come together to make this come together to make this a seamless and really an advanced kind of process, right? So with that, I'll take a pause. Hans, I'll turn it back to you for some questions.

Hans Gobin
Fantastic. Fantastic, Gopi, brilliant presentation. Love the last slide, not to say the first slide was fantastic as well. I have heard of the value trapped in pilots, the one plus one equal one and a half instead of being equal to five or six. So thank you for sharing that and thank you for sharing this last slide as to what can we do, how can we do, but also the value cases there and the value in practice which you very well defined. Now that you know what we should be doing, let us ask the audience, what are they doing as far as AI is concerned? Their level of maturity. So let me launch this polling question. So how would you define the current level of AI maturity in your finance? So ladies and gentlemen, if you can vote, please. Developing, established, advanced, leading, not too sure. So please, if you can vote, how would you define the current level of AI maturity in your finance function? Developing, establish, advance, leading, or not very sure. So I'll give it another few seconds. We have quite a few people voted already. Thank you very much for that. Keep voting, please. I will give it 5 seconds and I'm now going to end and share the results. So amazingly, of course, developing is 77%. Established is 7%, which is fantastic. Advanced 3% leading. So we've got 10, 11% who are well established. Not sure about 13%. So very much what we're expecting, I guess. Developing, a lot of people are looking at it and should soon be putting things into place. So let me just very quickly close this and let's move on to our discussion question. So panelists, if you can join us now, Gopi's got this question that he would like to ask the other panelists. So Antonio and Chris, if you can join us, thank you. Who do you think typically owns the decision of how humans and agent work together and who actually should own this. Chris, can I start by yourself, please? Give us your opinion.

Christopher Wittman
Yeah, sure. I mean, the most effective model that I've seen really is a shared governance model where you have risk, legal and compliance and governance that sets the boundaries of and the policies of what is right. Technology and AI teams enable solutions and provide platforms, but it's really the employees and the managers that help shape the operating model, so it has to be a shared model.

Hans Gobin
Fantastic answer there. Thank you very much. A good thought there. Antonio, your thoughts, please?

Antonio Rosa
Yeah. So obviously I agree with Chris. So this type of technology AI agents, now we're talking about agents. should be seen as a support to the finance team, but across all the organizations. So the human must be at the center of every process and human can be supported by this technology. But we cannot forget that human obviously is the focus and the last decision maker in this process for sure.

Hans Gobin
Yeah, absolutely spot on. Thank you very much, Antonio. Gopi, do you want to wrap it up and give us your thought?

Gopi Singh
So nail on the head, Chris and Antonio, right? We do typically see, right, that maybe IT may drive some of this, but it's definitely a cross platform or cross functional consideration around the governance model associated with this to truly get this right and have humans in the center of it.

Hans Gobin
Yeah, no, brilliant. Thank you all very much. Let us now move on to our next session, which will be delivered by Chris. And of course, Chris is going to talk to us about his own case study. So, Chris, over to you whenever you're ready.

Christopher Wittman
All right, so welcome everyone. Today I'm going to talk about how AI is transforming the way we run finance at Microsoft. It's moving us away from automation towards autonomous decision support across financial planning, analysis, and reporting. What we're doing really is we're moving from tools that assist to agents that act on finance's behalf. So let's start with why this matters. And so what we see here really is that finance leaders worldwide see AI as a defining force in reshaping the function and their role. And if I believe that AI will reshape their role for the next year, 62 expected to transform budgeting, forecasting and analysis. Now, the sample size here used is large and global. So what that tells me is that this really is a structural shift in the profession and not just a passing trend. But the real story really isn't that most leaders expect change. It's how quickly the horizon has collapsed from someday to this fiscal year. And so Microsoft's own history is proof of this concept. Over the last two decades, right, finance has supported nearly triple the revenue with barely 1/3 more headcount. So that decoupling of growth from cost is exactly what this technology accelerates. And so what I'm going to talk about here really is stuff that is proven and in production today. It's not pilots, it's not demos. So agentic AI is already delivering measurable impact across core financial operations. So there are four tiles here. You can read the tiles, but let me give you the story behind each one. Let's start with treasury and ingestion first. So the hard problem here was never the math. It was an unstructured mess that was feeding it with remittances trapped in PDS and email. We turned that ERP exhaust into a streamlined and automated process, and that is what led people to stop copying and pasting and start reviewing. So the time saved is the headline, but the real win is the shift in what teams were doing, right, from transactional to strategic. Let's move over top right, credit and collection is next. The insight that drove this was that our collectors were spending about 40% of the time preparing for calls rather than talking to customers. So we gave them agents that assembled the full picture and recommended what to do next. Across the broader collections rollout, this saved something on the order of 41,000 hours that were handed back, changing capacity and effectiveness. Again, what you're working on to drive the business. Let's move bottom left, cash application. The differentiator here really is that the agents learn, right? And they were getting better at matching remittances to payments, the more history that they see, so that the gains compound instead of plateau. So this same revenue processing capability that supports a team clearing about $130 billion in transactions also trimmed handling time and saved millions in savings. And the bottom right, validation and reconciliation, I framed this one really as a bridge to audit. So the same engine that checks incentive calculations against business rules transferred directly over into closed validations and also SOX controls testing. And that is our next frontier, which is why this box matters well beyond the example shown. So the key takeaway from this slide is that there are four different finance teams under one operating model where growth drives efficiency and insight, but not cost. But I wanna stress here that none of this really works without a clean and common data layer. We use Fabric, which collapsed legacy cubes and fragmented data sets into a single platform that handles billions of rows a month. This discipline core, I'll talk about that in a bit, is the foundation behind every result on this slide. Next slide, please. So what we talked about in the earlier slide really was reactive, right? We have a problem and we're going to address it. And so this next wave is moving finance from reactive to proactive and continuous intelligence. There are four capabilities I really want to highlight. Conversational FPNA analytics, and this is a conversational layer that lets you ask questions and for insight in common language, and you get a narrative back and not just a shark. Continuous forecasting is a shift that really is powered by the building of hundreds of robust models through chat. So that forecasting self becoming a quarterly drill and becomes kind of a common daily occurrence. Proactive anomaly detection. This is really great. This really flips the close from a backward view to an always on watch that services any variances before they surprise leadership. We don't like surprises in finance. And lastly, budget planning and automation. This really reduces the iteration cycles that we do every month end and quarter end. but also it helps with the creation of web dashboards. I know that Antonio will give a great dashboard presentation, but this really is going to be a winner. Next slide, please. So what I want to stress really is that we are continuing to compress the time from numbers to narrative until the inside becomes continuous rather than periodic. All of this really is built off of a repeatable architecture five pattern basically that form the foundation of finance operation, where ingestion of data leads decision support, which is then checked by control agents, explained by narrative agents, and then tied together by orchestration. Each layer compounds the one beneath it, and that is what makes this scale. And the iteration of it, again, makes your agent and like your agentic stack much, much, much better. So the governing principle at Microsoft really is a discipline core with a flexible edge. It's disciplined over over core data, taxonomy, and security so that we stay governed. And that is paired with freedom at the edge for individuals to innovate. And it's this balance that lets us move. from a co-pilot that assists towards agents that execute without ever losing control of our books. Next slide, please. So this didn't happen through a big bang transformation. It started really by, you know, small, quick wins, capturing what we learned and only the scaling patterns that worked. But five key takeaways that I want to really kind of hit on. Agentic AI is already live in finance. We've already seen this happen. I'm sure many of you are already using agentic AI. ROI is measurable and immediate. We can see that cycle times are reducing from hours to minutes, saving both time and also money. Emergency capabilities will refine FP&A. We are becoming less of a transactional reporting function and more of an influencer with a bigger seat at the table. And lastly, the path board is a layered agentic stack. And that really is the structure of how you go from data to orchestration, right? So in closing, I really want to emphasize that the technology is ready. And the real differentiator really is going to be the operating model that you use and your mindset around it.

Hans Gobin
Fantastic. Thank you very much, Chris. Brilliant presentation. And I love the fact you closed with saying it did not happen overnight. You know, it takes a long time, hard work. But of course, we can see through all the use cases how much time, how much resources, how much money is being saved through doing it well. So brilliant there. Thank you very much for sharing your case study. Let us very quickly now go and look at our third polling question and see what our audience is doing as far as AI is concerned. So just bear with me. Let me run this.

Christopher Wittman
So I want to ask the other panelists. What are the biggest challenges facing your organization in implementing AI across finance and workflows?

Gopi Singh
Chris, happy to take this one. Of course, you know, as a consulting firm, we do many, many AI use cases. But what I see in organizations is that last point that you touched on, right? The technologies are ready. People are curious. They want to go ahead and use this, but it's really the scaling element of it, right? How do we take from pilots to eventually process implementation to where AI is working with us? I know it's a little bit of what I kind of mentioned in my presentation, but how do we go ahead and layer that operating model, think about it and design and actually implement it?

Christopher Wittman
Thanks, Gopi. Antonio?

Antonio Rosa
Yeah, I'm totally agree with you and also listen to your story, Chris. It's clear how the technology is ready. Now probably the main subject is to find the right use cases to be ready in terms of finance context, usability of the data process, and also obviously change management is part of this process. But for sure, technology is ready. It's just a matter of the office or the organization to be ready to leverage this opportunity.

Hans Gobin
Guys, can you hear me now?

Christopher Wittman
Yes.

Hans Gobin
Brilliant. Yeah. Sorry, carry on, Chris.

Christopher Wittman
Look, I just wanted to close that, like, you know, AI seems a little scary and a little dangerous, but there are two things that Microsoft uses that really help to scale. One is security and compliance, right? Making sure that as you design, that is part of like your core build, not a bolt on. Again, this goes back to the discipline core and then flexible edge. And the second thing really is is culture, right? And so if you have a culture that treats people as change agents instead of tax executors, it helps because it is those teams that are close to the process that are going to be the ones that build the solutions. So you have a core that helps to support what is important along with those that like help like build the solutions.

Hans Gobin
Fantastic answers, everyone. Thank you very much. Did we go around the house and everybody answered, right? Brilliant. And Chris, thank you for co-facilitating. Brilliant. Let us now move on very quickly to our last presentation and to deliver that we have got Antonio. So Antonio, if you don't mind coming on the camera, let me turn mine off and over to you whenever you're ready.

Antonio Rosa
Okay. So Good morning, good afternoon. So my name is Antonio Rosa and I'm part of product management for CCH Tagetik. So artificial intelligence is transforming the finance function from a traditional backward looking role into a more strategic business partner. So While finance already benefits from a strong availability of data, having data alone is not enough to be truly ready for AI. So what makes the difference is how the data is organized, managed, and turned into actionable insights. AI can automate repetitive tasks like data collection reporting while enabling more accurate forecasting, better risk management, and faster decision making. In addition, AI is not replacing finance. It is enhancing it, helping teams become more efficient, more insightful, and more impactful. So next slide, please. OK. The role of the CFO office today is evolving rapidly. Expectations are rising while the level of certainty is declined. What was once a structured calendar-driven planning cycle has now become a dynamic and strategic capability that must respond continuously to change. Leadership teams are no longer satisfied with periodic reports. They require real-time visibility and ongoing guidance. At the same time, compliance requirements are becoming more demanding, with the full transparency and notability expected across every number produced. So business stakeholders are also pushing for faster responsiveness, expecting scenario analysis and decision within days or sometimes also within hours. And while AI increasingly embedded in finance tool, confidence in its output is still catching up, making trust a critical factor for the adoption. Here we can see also How AI is a very center topic of our conversation. So I'm not here to tell you that AI is not transformative, for sure it is, but here What will concern me is when I talk to finance leaders like you, there's a rush to adopt AI tools that were never designed for the way finance actually works. So they don't understand consolidation logic. They can trace a recommendation back through an audit trail. They operate those snapshot data, not like data. And when something goes wrong, and in finance, it's not if, but it is when, there's no way to explain what happened or who approved it. As you can imagine, this is not innovation. If we think about in the next slide, the first indicator, seven out of 10 hours of your planning team worse this week wouldn't go towards analysis that move the business forward, but they'll go toward assembling and reconciling data. And here's what makes it worse. Even the organization that have adopted AI for planning aren't seeing results. So only 18%, one 8% report better forecast. So the question is why? Because AI is not Organizer is not explainable. It's not operating on trusted data. So you are in a position where doing nothing means falling behind. But adopting AI without the right foundation creates risk, you can see, until it's too late. And now we go better and deeper on three examples of how AI can support finance. So let's start from the machine learning, the first one. As a software vendor, as a tech company, we provide machine learning as a core AI capability across all financial processes, allowing finance teams to work on large and complex datasets to identify the real business drivers and anomalies of performance and connect financial and operational data in a consistent way for more accurate forecasts. The goal was very simple, to give a finance a clear understanding of what is happening and by allowing to connect the dots and turn the data volume into real value for the day-by-day activities and analysis. In the next example, when we talk about generative AI, here, next, okay. With generative AI, we wanted to simplify how people interact with finance data and systems. So using natural language to navigate faster across the data sets, to retrieve data, executing tasks directly inside the workflow. And we keep the AI to bring speed, less navigation, less manual work, less friction. But finance does not only need speed. So finance needs precision and precision does not come from the AI model as a standalone. It comes from the finance context. So that's why agents are grounded on a specific AI architecture that operates inside finance model and business logic. So outputs become reliable, explainable and usable. In the end of the three example that I shared with you, there is what we call the agents, what is the agents applied on our platform too. So in the next slide, yes please. Yeah. In the end, the CPM platforms enable AI capabilities that help the Office of Finance interact faster with systems and data. get work done more accurately and anticipate risk and opportunities across processes and business performance. So leveraging machine learning agents and an agentic experience where it matters most. So we can imagine that the CFO as a new team member that we can call planning sentinel. AI As a takeaway, AI is set to transform finance by moving beyond simple forecasting toward true optimization, enabling organizations to make faster and more informed decisions. However, the journey should not start with AI itself. It must begin with a strong data foundation and solid governance. The real value comes from focusing on high impact use cases, such as a forecasting scenario planning, where AI can deliver immediate benefits. At the same time, it is essential to build the AI literacy within finance teams so technology can be used effectively. And most importantly, trust remains critical. Ensuring explainability must come before automation if we want AI to truly scale within the finance function. And now we are really at the end of my presentation, and I would like to share with you what is something more of the future. So here we can see an example about how we apply and deliver the Agentic AI. So here we have a dashboard, and then you can call the Planning Sentinel just asking for the revenue about the product family, and you will receive in real-time responses identified the main drivers behind the request. At the same time, you can also perform, you can also run apps to the planning sentiment to generate a dashboard so you can have a better view and more deeper insight on your figures. So this is the situation today. Imagine what happened some days later. I receive a notification. This notification comes from my Sentinel, Planning Sentinel, and it is related to the revenue performance of the same product family I checked before. The alert highlights a significant variance between actual and sales forecast. So in the same time, the planning center will suggest possible action to realign performances between my Apple and my forecast. So the controller can select one of the proposed options and immediately will be possible to see the impact of the decision on my chart. A new dashboard will display this impact. And before I define my final action, will be possible to run all the necessary simulations, just doing all the simulations, choosing all the different options that the planning sentinel could suggest to you. This is, again, a real example about how this component and also the story and the experience shared by Chris and Gopi say to you that this is not more the future, this is the present. So thank you.

Hans Gobin
Fantastic. Antonio, great presentation and thank you for sharing that dashboard towards the end there. Let us now very quickly go into our last polling question. So I will now launch the polling question. So ladies and gentlemen, if you can vote, please. And this is what we have. So how much do you trust AI to support your financial decision making in your organization? We fully trust AI in certain decision. Mostly trust AI with validating before acting. Limited trust in AI is used but not relied on and do not trust AI enough for decision making. And finally, AI is not used in decision making. Wow. So can we please vote? How much do you trust AI to support financial decision making in your organization? Fully trust, mostly trust, limited trust. And then we have. Right, so I'm now going to end and share the poll with you guys. And the answer is fully trust 1%. mostly trust 25%, limited trust 34%, do not trust 14%, AI is not used in decision at all. So some really weird answers here, I guess, you know, we are using it, we trust it, limited trust 34%. So there's still a long long way for us to go. Now that we've heard this, let us move on, let us close this first of all, and let us move on to the similar sort of question to our panelists. So Antonio has kindly put the same question to our panelists. So how much do you think AI supports financial decision making in your organization today? So let's find out from Chris and I believe we know the answer that Chris is going to give us, but Chris, please Enlighten us.

Christopher Wittman
Sure. Look, AI already plays a very significant role in supporting financial decisions today, but it doesn't own the decision, right? So AI augments human decision making. It accelerates analysis. It frees finance professionals to basically do more strategic work rather than replacing human judgment. What I like to say is that AI generates options, but humans choose.

Hans Gobin
Fantastic answer there. Brilliant there. Thank you very much. Gopi, your thoughts?

Gopi Singh
Yeah, I agree, Antonio, Chris. I think the maturity definitely plays into perspective, right, in terms of how much have we already enabled on AI, right? And what's the maturity of my data? Is it certified? Is it, you know, source in a clean fashion, right? So I think that maturity dictates how much, and combined with how much technology, AI maturity do you have will play a factor of how much do you go ahead and ultimately trust in AI, right? I imagine it starts off on the lower end, it's like 25%, and then as more maturity kind of goes, you get into the upper bounds of 50, 60%, but the decision always lies with the humans themselves.

Hans Gobin
Yeah, absolutely spot on. Thank you very much, Gopi, for that. Antonio, your final thoughts and wrap up, please.

Antonio Rosa
Yeah, so I think that we can switch the word support in boost. If the organization will adopt this technology in the proper way, just if they follow what we say today with Chris and Gopi, just having a proper data set, having the governance of this process, they can switch the support in the boost. So they really can gain some advantages within competitors. So it's very important to have a full control of this component, having the data set, having the governance of this process.

Hans Gobin
Fantastic. Thank you very much for your answers, ladies and gentlemen. Brilliant question and great answers as well. And we've also heard from our attendees as well. So just before we move on to the key takeaways, let me remind everyone, ladies and gentlemen, keep sending your question. We will answer a few live now. The rest will get answered to you directly to your email. So please keep sending them. Before we move on to the Q&A, let us go and look at what will be the key takeaways from each of our panelists. So Gopi, can I start with yourself, please?

Gopi Singh
Yeah, absolutely. So I think the biggest thing for me is, AI is here, everyone. And people are not only using it, but they are now getting very intentional with what those particular use cases that benefit most of them are, right? And that will play into how much AI supports from a decision making perspective. And I imagine we're going to see more and more of this and it's our jobs to keep up with the technology best as we can and evolve with it.

Hans Gobin
Yeah, no, fantastic. Thank you very much for that. Chris, your conclusions, please.

Christopher Wittman
Yeah, no, as Gopi said, we are living in the age of AI, right? But the unlock really is going to be cultural before it is technical. So having an open to learn mindset and a bias towards experimentation and iteration is critical. If you apply that with good controls and governance, possibilities of what you can do with AI are limitless. And I've I've seen this spread beyond finance and have more of an impact within the daily operating cycle of a corporate professional. It is absolutely amazing what we're going to see over the next few years.

Hans Gobin
Thank you very much for that, Chris. Antonio, your comments, please, your conclusion.

Antonio Rosa
I'm very happy because all the people I met, CFO, all people that belong to finance office, I saw differences between what they would like to do in the past with the different maturities that they have now. So they are, again, the stakeholder, the company, organization. Now they have a clear idea about what they can do and they are totally open and ready to adopt this component for sure.

Hans Gobin
Fantastic. Fantastic. conclusions there. Thank you very much, ladies and gentlemen. Now, let us now move on to our Q&A session. We have got a lot of interesting questions, but today we will attempt only a few because of time. We're running out very, very quickly. So let me start with yourself, Gopi. Is the ROI for AI implementation clear given the cost of AI platform use. Do you expect this to remain steady or reduce over time as demand increases and market consolidates? What are you seeing? I mean, we all started with EPM, for example, where it used to cost a lot of money. Now the cost has gone down. What about AI and AI tools? What are you seeing out there?

Gopi Singh
Yeah, it's a good question, right? ROI in business case is probably the first conversation we have when it comes to AI use cases, right? So this is a very real conversation. And we do see, obviously, it's going to be use case dependent right now in terms of, hey, four of these five use cases or 10 use cases, what does the cost look like for that? What does the ROI look like for that? And the ROI may be an element of cost saving or it might be reduction or kind of re-shifting of staff somewhere else, whatever that kind of case may be, right? So all those components are considered. And we are seeing the cost components changing, right? There's some companies that are still using consumption-based or token-based pricing, but as you're scaling, as more and more technologies are coming, there's different elements of that which make it more affordable. But having the right kind of necessities, if you will, the clean data domains, right infrastructure where this can be accessed and all kind of come together in an interaction layer is going to make things more affordable, right? As companies think about that architecture and others are coming into play, finance, sales, et cetera, which obviously helps from an affordability perspective. So the ROI is clear and the costs are probably going to diminish as we think about the scaling of this.

Hans Gobin
Absolutely spot on. Very similar to what's happening in the EPM sort of world. Chris, the next question was to you. Your camera is turned off. Are you still there? Yeah. So a very interesting question here for you as well. What happens to the human finance professional displaced or replaced by AI in finance and related function? I mean, we all know answers to that, but I mean, give us an, what has happened to within Microsoft as in when we implemented

Christopher Wittman
AI? Yeah, I think I would reframe this as like, how does the skillset change, right? And so the way that we used to operate and the way that we used to work isn't going to apply in future cases, right? And so again, it's moving from the transactional to the strategic. And so skill set of finance will still be very valuable. What we do will still be very valuable, but how we do it will actually change. And so I mentioned about having a bigger seat at the table. And so this is a good skill set that we can develop within our finance team saying, hey, look, it's not just a number and it's not just data. What does that data tell you? And what is the really, really, and why do I care? What is the impact of this data? And so that is going to help finance really elevate the profession. So I don't think really it's a replacement of finance, but I think it's an upleveling of our profession.

Hans Gobin
Absolutely. I think, you know, as AI is here to stay and will move forward. So it's the skill set change. If you don't change and transform your skill set, you will be redundant. Let's not be at it. But of course, you know, yes, we have to scale upwards learn about AI, absolutely. And there will be no change to what happens to us. So thank you very much for that, Chris. Brilliant. I've got a great question here for Antonio as well. And Antonio, let's, you know, it's very much about tools here and we try and be vendor agnostic and everything else. But it's a really relevant question. So my leadership is pushing to utilize AI in FP&A processes, and specifically, a new AI solution is being marketed as an FP&A solution. Are people using AI solution as primary tool, or is it a supplemental tool to traditional solutions like your EPM platform, even though you've shared with us what the EPM platform can do? What are you seeing out there?

Antonio Rosa
As a software vendor, we move from, I hope the other panelists agree with me, we move from automation that it is seen also as something new to the new era that is the agentic AI. So now we are seeing more and more that in our solution, but also our competitors, to be honest. our platform allow user, allow finance team not only to automate some process, some steps in their workflow, but they support the finance team in the analysis understanding the root causes of this analysis, combining not only what happened inside the organization, but combining also external variable. For example, we had a retail vendor that in order to staff people in the shop, they also evaluated the weather condition, if there are some strikes and so on. So all those components allow the finance team to do better their job. So again, it's something beside the controller. It is not something where the controller has to fight against.

Hans Gobin
Yeah, absolutely spot on. And as we've just seen in that demo, the dashboard, the machine learning, the agentic AI, the gen AI, and everything is embedded in quite a lot of tools now, and software vendors are moving constantly to the next step. So yes, some people started off using different tools for AI, but of course you guys are including a lot of these tools within the EPM platforms as well. So thank you very much for that. Gopi, Chris and Antonio, brilliant. Thank you very much for answering those questions. Let us very quickly move to a few last closing slides that I've got. So upcoming webinars, we have three there. So 8th of July, we've got where AI meets FP&A reality. 9th of July, we've got designing FP&A operating model in the AI era. And then finally on the 16th of July, just before we shut down for summer, we've got from analysis to orchestration, how FP&A is evolving. in the AI era. So everything to do with AI, as we all know. Let me start by saying a big massive thank you to our panelists. So panelists, if you could join me on your camera as well, this would not have been possible for all the hard work that you guys put together over the weeks of prep, etc. So Gopi as well, if you can join us, thank you very much for all your hard work, putting all of this together, answering your question, and doing this for us. Let us also mention CCH Tagetik very quickly. A big massive thank you, ladies and gentlemen, attending the webinar as well. So a big massive thank you to you for, of course, attending, but also giving us what you're doing in terms of our votes. Before we go, This is that report I was talking to you about where the stats came from. There's a QR code here. You could scan it and download that report for yourself and go through it in much more detail. And finally, just before I close off the meeting, this is how you guys can keep in touch with us. So ladies and gentlemen, This is where I'm going to say goodbye. Thank you very much for attending. Just a reminder, when I close this, you will get a feedback form. If you can spend a bit of time, tell us how we did and tell us what you would like to hear from us in the future. But for now, thank you very much, everyone. Have a good morning, good afternoon, and good evening, wherever you are, and see you on the next one.