How resilient CFOs lead in uncertain times

Hello everyone and welcome to today's webinar, How Resilient CFOs Lead in Uncertain Times. My name is Prash Strategy and I'm a Global Director of Planning and Analysis of OneStream. I'm thrilled to be joined by two of my outstanding colleagues from Accenture, Dhruv Jain and Jose Melendez.

Together we'll explore what resilience really means for today's finance leaders, how they can navigate volatility, leverage AI and analytics for faster insights and build more agile and connected organizations. Our goal today for you guys is to share practical examples learned from clients and what's next for finance in an increasingly unpredictable world. So with that, before we get started, I'd like to give Dhruv and Jose a chance to introduce themselves. Dhruv, I'll start with you. Hi, good afternoon, good evening, good morning. Not sure where you may be in the world.

Thank you for joining this webinar. My name is Dhruv Jain and I'm a Principal Director with Accenture. I spend a lot of my time working with finance teams and seeing how they can use AI in all forms within the function. And most recently I've been working with our own finance team on using the Gentic AI to drive some process improvements for ourselves. So super excited to be here and sharing some of our learnings with you all.

Thanks Dhruv. And Jose? Yes. Hello, Praz. Thank you. My name is Jose Melendez with Accenture. I'm part of our strategy and consulting firm focused on CFO and enterprise value. I spend the majority of my time working with CFO and finance groups transforming not only their business processes, but also the technology that supports them. I have over 15 years in the financial planning, budgeting, and forecasting space. And looking forward to the conversation today and how we introduce that into the AI and the age of AI. Great. Thanks so much, Jose. So let's move on.

Let's start by grounding ourselves in the reality CFOs are facing today. According to Accenture's research, eight in 10 CFOs expect external disruptions to increase over the next two years. Whether that's from geopolitical risk, inflation, supply chain shifts, or technology change. And because of what we're seeing across our customers in OneStream, that volatility isn't an exception anymore. It's the baseline.

CFOs can't control disruption, but they can control how they respond and how prepared their organizations are to pivot quickly. What's also fascinating is that 72% of CFOs say their transformations now span three or more functions. So finance is no longer transforming in isolation. However, it's orchestrating change across operations, HR, technology. We're seeing CFOs lead enterprise -wide initiatives in data integration, AI adoption, and performance management, really positioning finance as a connective tissue between strategy and execution.

And the third insight is 84% of CFOs say interconnectivity of strategic decisions has increased. I think to me, that's a key signal that traditional siloed decision-making just doesn't work anymore. Every decision, whether it's pricing, workforce investment, or capital allocation, has ripples and effects across the enterprise. So that's where the modern CFO playbook comes in. You're really connecting data, people, and technology. So decisions are faster, more coordinated, and grounded in reality.

Now what really stands out in the data is just how much pressure CFOs are under. 88% of organizations are facing disruptions from three or more external factors. That means the world CFOs operate in constantly moving areas such as interest rates, geopolitical shifts, regulatory changes, supply chain volatility, and of course, the technology transformations. That's all hitting at once. And yet the outcomes of transformations aren't equal. If you look at Accenture's data on transformation impact, there's a massive spread.

Organizations are operating in a few different areas. Those with almost a 50, you know, some are operating in areas where 15% are operating a low level of transformation ambition, 11% are at a high level. But what's good to see at least is 74% are operating at a moderate level at this point. So what that tells me is that how you transform and not if you transform makes all the difference. And leaders aren't just automating processes. They're re-imagining finance as connected, data-driven, insight-led organizations.

They're using AI to move from hindsight to foresight, from manual tasks to really data-driven decision-making. So with that, Dhruv, you and your team have done incredible work defining what differentiates high transformation finance organizations. Can you, you know, share what sets those leaders apart, what they're doing differently, and how they use data and technology and operating models to, you know, drive that performance jump? No need to get into deep details. We're going to cover a lot more of this as we go on.

Thanks, Prash. The question I think you're asking is what differentiates a CFO? Yeah, in terms of their, you know, high transformation organizations that you see, like best in class, really.

Yeah. So I think the CFOs which are doing really well, which are transforming and transforming at pace, I think they get a few things correct. One is they understand what is the value, what is the value of the transformation to their business. They're able to bring the team together because when we go on the journey with AI or in any form of AI, it's a true team sport. It cannot be done on by its own. So you have to bring, whether it is your IT function, your data function, supply chain, you have to bring them all together. And then they are always thinking about the user experience.

How do we improve the user experience, whether it is for our vendors, our customers, or internal employees? You have to think about it. And those are the three key things which they get right. And they're able to articulate all of that to the board to get sponsorship at the highest level.

Awesome. Thank you so much. So with that, let's move on to the crux of where we are today. The evolving role of the CFO. Now, what we know is CFOs are now strategic leaders. They're not just financial stewards. You know, from stewardship to strategy, they're our transformation leader. And they've got to do a bunch of things like balancing cost controls with innovation and growth.

So what we really need to focus on is how CFOs are building resilience in volatile markets. So, Jose, starting with you, what have you seen with regards to how the CFOs role has changed dramatically in the last few years?

Yeah, thanks, Praz. This is a very interesting question and really comes from kind of the crux of that research you mentioned. You know, ultimately, the research is being done directly with CFOs and finance leaders.

It's a set of research we've been hosting for over two decades now in the eighth iteration, as you've mentioned and quoted some stats from. So we're hearing these things directly from those individuals. We really categorize three key roles or attributes of the CFOs role. One is being an economic guardian. What this ultimately means is CFOs must lead an efficient and effective finance function focused on predictive insights in a volatile and uncertain world.

So Drew kind of mentioned that earlier in some of his summary. The second is they have to become architects of business value. CFOs take the vital role of business value architect across the entire enterprise, working with the C-suite to drive strategic change. So they're no longer seen as bookkeepers or reporting of the results. And you become at the forefront defining that business value and then driving the enterprise forward.

And the third is an interesting one. They're becoming catalyst of the digital strategy. So CFOs really have growing accountability beyond their traditional role, focusing on the value creation from that digital strategy and transformation. And the reason for that, and we'll hear about some of that later, is the role of finance, especially when you think about FP&A and defining forecast and future-looking projections. They need access to all the data. They need the enterprise to be connected to be able to guide the broader enterprise.

So that's great. And where do you see the greatest opportunities for CFOs to influence enterprise strategy?

Yes, I think one of them is around breaking down data silos once and for all. So as we think about access to that information, a lot of the data and information that's in the enterprise, the CFO in the office of the CFO does not formally own it. It might be supply chain function. It might be a marketing function or sales. So we need to break down data silos and gain access to that information once and for all. I think the second is really using advanced technology not only to process financial data but to unlock predictive forecasting.

And the third is empowering the finance professionals to build new skills and take on broader responsibilities. So as we think about the individuals that support the finance function, they're no longer the traditional roles. They need to become AI-savvy data scientists and et cetera to ultimately harness the power of that information.

That's interesting you brought that up, Jose, because I recall my days in finance. I was really conditioned and trained to look back on yesterday, to talk about what happened yesterday versus thinking about tomorrow. And obviously I aspire to talk about tomorrow, but I never got to that point. And do you see modern and best-in-class finance organizations achieving that slowly where they're able to report on what happened yesterday very quickly so they can focus a bit more on tomorrow and using modern technology and AI to really help propel that?

Yeah, we are. I think it's still a challenge for a lot of organizations, but it's definitely the aspiration, right? We want to automate and streamline the more transactional and traditional tasks. And we want to have the finance users really focused on the human element of analysis and projections and analytics as well. Excellent. And lastly, what new expectations are CEOs and boards placing on the CFOs today?

Yes, Adrib, do you want to take this one? Sure. Yeah, absolutely. So, you know, Pras, what we're seeing is that the pace of change has just magnified, has, you know, amplified and is happening a lot quicker. Business models are changing, markets are changing.

And so what the board and the CEO are asking the CFO is they want insights faster. They want to ensure that any decision they make, that the risks associated with making decisions to move in a new market or a new product line is minimized, mitigated, right? So they want to, they're putting pressure, they're expecting the CFO to come better informed with better insights.

And they want it to be data driven. And no longer can you wait for two and three weeks to get the data and the insights. It has to be at the speed of business, right? So it is how do we make the decisions more effectively, more efficiently, more quickly.

And I think that is where the CFOs are being asked to do more. So, Dhruv, there's some questions I want to ask you. Based on your research, where are most organizations today on their AI journey?

That's an interesting one, Pras. And we've seen it everywhere from where there are companies, early adopters who have, you know, gone on the AI journey. They've already embedded AI in finance processes and are realizing value.

And then there are others that are still, you know, running small test experiments, what we call proof of concepts to see what the value is and how to get about it. But, you know, I think what we're seeing uniformly across all the client conversations, all the research shows, is everyone believes that there is going to be a benefit from embedding AI across the finance value chain, irrespective of where they are, irrespective of the function, there is value.

Where teams and organizations are a little hesitant where they, where we are seeing they're, you know, not sure on how to start is there are a few questions that make some pause.

One is there is an awareness that without data, AI or any form is not useful. So, they're trying to understand is, is there data ready? And it's not just their data, but it's all the data from adjacent systems functions that are, is that ready for them to use? So, there's a lack of clarity around that.

And the other one is, should, where companies are saying is, should we wait for these capabilities to be provided to us by our platform provider? So, should we wait for one stream to give us some agents? Should we wait for something to come from SAP, which is an embedded AI or Oracle or Workday? Or should we build, right?

And if we should build, when should we build? How should we build? So, those are the questions which trip up or cause a lot, some of the hesitation with finance teams as, you know, getting started on the journey. But everyone believes that there is value to be delivered. Well, that's interesting you mentioned about waiting or starting on the journey on your own.

Do you find that finance organizations are, you know, some of them at least are taking the journey to do something on their own, whether it's building their own predictive models or starting their own AI journey without the help of providers such as us? And if so, what do you advise them and how best should they proceed?

Yeah. And so, again, we've got examples of clients on both sides of the spectrum, right, working with an automotive company right now. And they are using one stream actually for consolidation, right, as a consolidation tool.

And they are dabbling and they're trying to see how they can use one stream as an example for their forecasting, right, as a budgeting and forecasting piece.

What that does mean is they have to change their processes. They have to rethink their processes, right? But they're willing to do that. They're ready to do that because they've got an investment, right? There are also cases where there's another large chemical company which, you know, is looking at – they've grown through acquisitions. They've become big and because of which their problems have just got magnified and become a lot bigger.

And they're on a journey to upgrade their ERP, right? They are – but that journey is five years. It's a five-year journey to upgrade their ERP. They'll start – I think they start in 2026, second half of 2026.

And they cannot wait for five years to start using AI as invoice processing or for collections. So they realize that, you know, they're going to do some work today, which may be a throwaway work in five years when their ERP is up and running.

But the value on the table for them is too large to leave it – not address it today. So that's why they say we'll get started on it today. We'll start delivering value. And when the ERP is up and running, then at that time we'll make a decision whether we should shift to the ERP or whether we should continue with our capability. So, again, it depends entirely on how an organization defines value.

Makes sense. So, you know, before we actually started the webinar, you and I, I think, offline, we talked about the stat we have here, 95% failure rate for enterprises, AI solutions. That's something that MIT came up with. And I think that's – we've all seen that. That's been in the media. That's been everywhere. And it's been somewhat alarming for all of us. And obviously, none of us want our customers and the customers we're going to interact with to come to go down that road.

But part of that also means that succeeding can happen in a big bang manner or it can happen in a pilot mode and you just keep focusing on that pilot mode. So what separates organizations that are stuck in this pilot mode or just spinning their wheels on AI versus those that are actually achieving something? Yeah,

I think the key factor there, and I keep coming back to this as value, is define what is value. So I think it is important first define what is the North Star. Say where you want to go, put a stake in the ground, right?

Then by saying that – by putting the stake in the ground, you say, we want to go here because this is the value we deliver.

And the value doesn't have to just be cost takeout, right? It could be, am I going to be able to deliver business outcomes? Can I reduce – can I improve my working capital, as an example, right? Can I forecast with greater accuracy so that when I report the street, there is less variability, right? I'm more predictable. So define the value, right? And once you've defined the value, then it becomes an easy conversation to have with your leadership to say, this is what we want to do and this is why we want to do, right?

Because like a lot of transformation projects, think of an AI journey as a transformation project, a lifestyle change. And you have to get leadership sponsorship, board sponsorship. And it cannot be a one and done because this is a lifestyle change. This has to be an ongoing. So you have to commit and you have to be all in. And the only way you get sponsorship for that is if you can demonstrate enduring value, right?

And these are the companies who've got this right. They will – they have progressed further along on the journey.

And what they've also done really well is they've brought their people along because there is a fear, there's an apprehension, there's a perception that AI is the big bad wolf which is going to come and eat our jobs, right?

I don't think that's the case because there is going to be a need for humans to take care of, make the decisions for controls.

So that's why it's absolutely important to manage the user experience both to allay the fears as well as to give people a reason to use the tool, right? And anecdotally, I'll tell you something which we did at Accenture is we built the AI capabilities first for our people, right? We said, how can we surface our policies? How can we make it easier for people to understand our operating procedures? So let's make us – let's make them use it. You know, how do people use Copilot or Gen AI or ChatGPD?

Let's get them comfortable where they see these tools are helping them and then at a personal level and then we can show them how to help them at a professional level. Then they'll believe that it's not going to come and take their jobs away.

I'm glad you mentioned that because that's something I think that's out there and it is a bit of a misconception. It's not taking your job away. It's making your job more efficient. It's helping you do all the value-added tasks. Like I tell people, finance folks spend a lot of time on their CPAs or MBAs or CMAs or CAs or whatnot on education. Now it's time to use that education to think forward versus just reporting what AI can do for you.

So, Jose, any thoughts on, you know, some of the success you've seen on organizations or in terms of early adoption and what they should proceed with to begin?

I think Drew's covered a lot of the core. I think the one piece that I would add is really around evaluation of where they're at and evaluating the maturity level. I think a lot of this relies on data. We've talked about that. I would take that further and say, you know, it's an evaluation of what I'll call the core. That could be the core finance ERP, supply chain, HRMS systems. Like there has to be an established scalable architecture that that data sits around.

And another interesting angle is always the operating model. You know, we talked earlier about the role of the CFO and kind of driving digital strategy. Well, how does that either conflict or complement the role of the CIO and others' organizations? So I think having an aligned operating model and strategy across the organization is really important so they're not conflicting with each other. And you have, you know, conflicting expectations or goals and objectives as well.

And, Pras, I'll just add something to what Jose said, right? Is so as you think about the operating model, it's absolutely critical to think about the interaction model and the ways of working. Because, like I mentioned earlier, this is a team sport. And, you know, traditionally, when we've looked at the operating model, we've looked at it with a finance view. We have to look broader. We have to connect for it to truly be scalable and sustainable. Agreed. Absolutely agree.

So let's spend some time now looking at, you know, how AI works in terms of, you know, being a unified works and really becoming a unified data platform and how that empowers CFOs to move from reactive to proactive. And what we can talk here is about why is unified data so critical before CFOs can truly harness AI. So, Dhruv, if you can discuss that, you know, why data is so critical in this exercise.

Sure. So I like to think of AI as a new joiner to your company, right? So I like to think is we're implementing AI, but it's really it's a new employee that I'm bringing on to the company.

And just like when I bring on a new employee to my company, I have to explain to them, you know, to do a particular task, where should they go to get data?

Which ERP should they run? Which planning system should they go and get data? I have to explain it to them, right? Plus, I have to tell them, you have to go to the SharePoint side because this is where your SOPs are. This is where desktop procedures are, right? So I've got to do that. And I can do that today one by one as I bring employees on.

But when I start bringing using AI, with AI, I have the ability to scale, right? And I have the ability to ramp up very quickly, go from one to a thousand employees, virtual employees, digital employees, almost instantaneously.

I, as a human, don't have the capacity to tell them all where to go and get the data. So that's why I have to make the data, bring it all in a place where these digital employees can access it. And again, it doesn't have to be, all the data doesn't have to be centralized. It can be virtual, but I have to make it all available in a seamless fashion. So these digital employees of mine, these agents or these AI tools know,

they don't spend the time cleaning up the data or massaging it, but they get the data. Because I, in finance, cannot afford any hallucination. I cannot say my balance, my revenue was 50, 50 billion when it was 55 billion or vice versa, right? I cannot afford those kind of, or I cannot pay a bill, a hundred dollars less, right? Or I cannot send out an invoice, which is off by a thousand dollars. So I cannot afford those mistakes. So that's why I have to bring my data in a way it can be used consistently.

And do you find that organizations that are adopting some of these early use cases are thinking about really things such as scenario modeling and doing, leveraging the power of AI and especially the clean data they have available to them to do things such as simulations, especially in these uncertain times.

It doesn't have to be massive simulations. That might be just based on a scenario. But are simulations part of what might make AI adoption that much better at this point? Absolutely. And if you think about it, right, the early usage of AI was around in forecasting, right? Because forecasting by very nature is you're telling something in the future, right?

You're using data. Traditionally or historically, I won't say traditionally. Historically, it's been the belly button which has guided it, right? My gut tells me that's why I'm going to do a 5% increase. But now I can use data, which is in the enterprise, to do my forecasting. And also now with these advanced tools, I can bring in external drivers, external factors on, you know, automatically to say what is the impact. So, for example, you know, we know tariffs had an impact on a lot of businesses, right?

And if I could get an early signal that this is, you know, there's a tariff conversation happening between the U.S. and China as an example, right?

Can I take those early signals? Can I use it to have AI do some scenario planning to say, okay, your revenue forecast was a billion dollars. You were going to source inventory from X location. But because of tariffs, this could be the impact.

So, this is where we can see the native forecasting capability, which is available in a lot of the planning budget forecasting tools being augmented by unstructured big data coming from outside and really helping the decision, the insights. I won't say the decision making, but the insights generation quicker, right? You're not waiting for the analyst to say, okay, there was, there's a foreign exchange chain, you know, there's a dip in foreign exchange rates or there's a tariff or whatever that it is.

You're letting technology get you that information, build out a scenario, and then use as a professional to the point you're being impressed is, you know, we've got all these CPAs, et cetera. You can now use that to really have the business conversations to drive decision.

So, Dhruv, I think what you outlined is, you know, what I see as utopia for finance. Like, really being proactive, talking in that proactive manner where they're spinning up scenarios based on what they think might happen or whatnot. But what about being reactive as well? Like, as you said, tariffs, right?

Tariffs came on, you know, earlier on. There was that infamous April day or whatnot where, you know, all of a sudden tariffs were announced and everybody's spinning their wheels, seeing the impact of that. But I'm assuming with the power of AI and scenario modeling and scenario planning, all built into one on a proper data set where you can access that data. You have the ability to then simulate different scenarios based on, you know, if you're, you know, whether you absorb the cost of tariffs, you pass it on, you know, however way your business might handle it. Yeah, absolutely.

Reactive, to me, is reporting the news, right? It is, you know, you're using the information which you have. You're looking backwards in the mirror, in your rear view mirror, and you're making a decision of which way to go. Or, you know, there's a car following you and how do you avoid that car? Absolutely. I think those are table stakes, right?

And we've been doing that all the while. It is how do you now layer in? Because, yeah, it's good to know what happened. But my leaders, my CEO, my board wants to know more, is more interested in what will happen, right? Is if this happens, how are we going to react?

Right. And you can put in a lot more external factors as well. Like, I know I've heard of some of our customers incorporating weather patterns and external data that might not usually be available to you, or you might not think of in the common sense, in terms of driving forecasts as well and driving their simulations. So there's a lot more that one can play with.

Yeah. And, Brass, I'll give you another example, right? So building on what you said, we were working with the consumer goods company, right? And different markets have different trends, right? So they were making beverages, right, carbonated beverages.

They got early signals that there was a trend in buyers, in buying patterns where the younger people in Europe were looking for more healthy alternatives.

So as they started looking at their forecast for what should be their sales in the next quarter, the next two quarters, they were able to take that signal, which they wouldn't have earlier thought of, and use that to factor into their scenario modeling and forecasting in the out quarters.

Excellent. Excellent. So we're still talking about data here. And I'm curious as to how finance teams really balance speed with governance and trust in data, whether that's putting guardrails around their data.

And you mentioned hallucinations, how to avoid hallucinations as part of the forecasting. What can finance teams do to protect themselves in this? Yeah. Yeah. And I think the governance and trust goes hand in hand because if you don't have good governance, you're going to get numbers which are not expected, right, which is going to dilute the trust. And the governance is not just in the data, but it's also the definitions of the data. I'll give you my worst case scenario is I was working with a company which had 11 definitions of revenue, right?

I mean, something which you and I would think, you know, in finance, there's just one definition of revenue, one calculation of revenue. They had 11 calculations and definitions of revenue. So it was not a data issue, but it's also a definition issue, right? But now once we know what revenue is and we can define and share that definition with everyone and then identify what are the data sources so that it is complete lineage and traceability as to what are the different sources for that KPI, you know, it gets, it's a lot easier to trust that data.

Also, what the finance companies that are doing well, what they've realized is they're not trying to solve the problem for all the data that finance needs, right? IT is never going to be able to get all the data. And by the time they get all the data right, it's going to be outdated. So it is, again, working hand in glove with the IT team and the business owners to understand for a particular capability, particular use case, what is the data I need?

Who's responsible for the data at the source level? Who's going to be responsible for the data at the consumption level, right? And then building the data pipelines so that that chain of custody is established. And again, I would never recommend that, say, I'm going to get all my data and build a data platform with all of that because that's a never-ending story. Start with where there's value and move forward with it. Excellent.

So now let's spend some time talking about, you know, our customers. You know, obviously, you know, everybody on this webinar right now, they're excited to hear about some real-world finance transformations with AI and some of the tangible values customers realize as well. So with that in mind, Dhruv, do you have a customer story you can share with us? And maybe, Jose, afterwards you can speak to some ROI and metrics that you've seen the customers have achieved? Yeah. So, Prasad, I'll share the story of our own reinvention, right, what we are doing for ourselves.

So about five years ago, we started on this journey where we said we've got to take out almost a billion dollars in costs from our finance team, right? So we went on that journey. We, you know, we did the standards. We standard processes, shared services, right? We did all of that. We've got to a point where we were fairly efficient. And this is all the while we were growing. So we grew from, I think we grew almost 50% in revenue during the same time frame.

And now, you know, we said we've got to do more with even less, right? We've got to continue to evolve and see how we can drive value. So what we did was, and this is just about the onset of the agentic conversation.

So we said, let's look at our own finance function and see whether we can use agents and an agentic workflow to drive value. So working with our CFO, we said, okay, if we were to do this, what would be value for you? And she was like, we have to be more efficient.

We have to reduce our DSO. And she gave us a target. She said, let's reduce our DSO by three days, right? On a $64 billion turnover, that's a fairly significant amount. She said, we have to reduce our SOX audits by 80%. So that meant we have to have controls by design built into our processes. And she said, I want 100% of our finance professionals to be trained in AI.

Because all the new employees who are coming in, they are very comfortable with AI. But we want all our existing workforce to be trained with them. So she defined what the value was for her vision. And so we went down that process. And we just deployed our first pilot because we didn't do a Big Bang deployment. We said we will deploy in phases and waves. We just deployed our first pilot.

And what we are seeing is we will be able to unleash at least another 30% in capacity unlock. We are going to be able to meet a target of reducing our DSO by three days. And we have a plan where we are going to have all our employees trained in AI.

And we are on track to see our customer satisfaction scores go up, I think, by half a percent. Half a percent, half a score. So go from like something like four to four and a half. So we're on target for that.

That's amazing. I mean, hey, there's no better case study than yourself, especially now you can go share that externally as well. And really the results.

Jose, any thoughts on this? Any stats or anything that you can give us on, you know, with regards to ROI and things that, you know, organizations might look towards?

Yeah, Pres. I think what I would share is kind of, you know, the other ways that we think about value and ROI. And Drew kind of touched on this earlier, right? Cost efficiency obviously is one aspect of it. But compliance controls that we just talked about with, you know, lowering material misstatements, reduction in control issues like the SOX examples for Accenture.

I think the data element, right? I think the data element, right, is kind of a little bit of like hard to put a value on. But the fact of having unified data like we talked earlier is really critical and that unlocks a lot of value through various channels.

The customer and talent experience, increasing retention, right, of your employees, right? As you bring these new tools and capabilities and expectation of skill sets, right, you kind of increase that internal talent experience and that retention.

And people feel a little more empowered about the role and the contribution they're making, which, you know, we've seen a lot of turnover in the years prior with various elements of COVID and other things, right? People were moving around. So I think keeping that not only internal retention, but also then as you think about your customer satisfaction scores and the way that the company is able to look externally, we see a lot of increasing in CSAT scores and the DSO that we talked about here.

And then obviously the insight generation, right, is a given, right? Increased profitability, revenue growth, improving cash flow, and being able to manage that in a way that you see the cause and effect and understand and can explain it.

You know, I'm so glad you touched on customer retention because that is absolutely critical here because it's tied into the fact that it's not just everything you mentioned, you know, getting better insights, you know, understanding your productivity and delivering better profitability.

But having an engaged workforce, I mean, the reality is nobody skilled and a top-tier finance person wants to work on spreadsheets and spreadsheets only. Of course, Excel will always be a part of the equation, but complement that with, you know, high-scale finance transformation and everything we've touched on today, that's really going to lead organizations to be best in class.

That's fantastic. And they've got to increase the ROI on any AI initiative or transformation initiative they take. So if you had to give, either of you had to give, you know, a CFO one action to take in the next six months to future-proof their finance function, you know, what would you recommend?

Yes, President, I'll jump in first. I mean, I think it's, I would go back to the role of the CFO from earlier. Like, CFOs need to be a driver of change within the organization, right? They don't need to wait for it to happen to them, either through a technology organization or other functions. Like, they need to be a driver of that change and really not only be at the seat, but kind of be driving that agenda, given kind of the role of finance and the evolving role of the CFO. Yeah,

and just to add to that, I mean, you know, I was doing some CFO research today. I mean, in many, many organizations, IT is now reporting directly to the CFO. The CFO is really leading the change, as you said, Jose. So, you know, I think CFOs really need to push hard to get what they want from IT in this period of change, that they don't get locked into a design based on a skewed way of thinking from their IT partners. There is a world where they can get everything that they exactly need and want and aspire towards that.

Dhruv, any recommendations from you in terms of short -term action CFOs can take within the next six months to really future-proof their finance functions? Yeah, I think one is it's for them to be able to define what is value for them, define the vision, you know, put a stake in the ground, and then build the coalition around themselves, right, which includes their internal teams, right?

Get everyone from your order to cash, procure-to-pay, FP&A, everyone on that, bring them along on the journey, and bring IT along on the journey. Because, you know, there is a budgeting cycle, there's a planning cycle. So, earlier on, get IT, and I keep going back to this, without IT and data support, it is going to be difficult, if not impossible, to make progress. So, set the vision, get a coalition going, bring on people, understand who are going to be your champions, understand who are going to be your detractors, and start. Start, you know, there are going to be changes,

technologies changing very rapidly, things are going to evolve very quickly. Be prepared for that, but get started. Excellent. So,

it brings us towards the end of our presentation. So, we've touched on some really major things. We've touched on the fact that the CFO is clearly a catalyst for finance transformation, that there is a means to really, you know, choose your projects, choose wisely, measure the ROI impact on it, and then go ahead from there. And there's really, you know, and I think, Jose, I love the fact that you brought up, you know, finance needs to move forward so they can retain talent. I think that's an, you know, really under-emphasized piece of all this.

Before we open the floor up for any questions, any final thoughts from either of you? Maybe, Jose, I'll start with you.

Yeah, I think the thing I would want the audience to take away from this is, you know, don't think there's one way to do this, right? It's all obviously dependent on each industry, right? The situation of that company and the journey they're on, right? Drew gave a couple examples of a company that was going to be transforming their core. Well, that's a major kind of foundational element that's being taken out from under you. So don't feel like there's only one way to do this and you have to wait or have that perfect mold.

It's all about identifying the values Drew mentioned, identifying the right takeaways and actions that you want to get from that, and understanding where you are in that maturity curve that we talked about earlier, and trying to understand how you can then initiate that drive for change and obtain that value. So I think, you know, definitely evaluate, but don't feel like, you know, there's only one way or the client example we gave is the only way to be able to do it. There's multiple ways to do it.

Fantastic. So we have time now for a couple of questions. So I'm going to lead up with the first couple. We may not be able to get to all of them, but the first one basically mentions, in terms of finance skills for today's organization, what do I need to learn to be ready for tomorrow's world with AI? Jose, any recommendations on this that you would give guidance on?

Yeah. Yeah. I touched on it earlier. I mean, it's interesting when you start to look at the skills that we'll talk about that they're not going to sound like any traditional finance skills. And I think that's the point, right?

Finance practitioners already have the traditional accounting finance type background skills. The analytical, I think where it becomes a little more engaging is now when you start thinking about the ability to analyze data. So really like a data scientist, data analysis type skill sets, skills related to being able to talk with these new tools. And when I say talk, I mean prompt engineering type. How do you ask questions to a chat bot? How do you talk with AI teams that are building agentic AI, et cetera? And I think another interesting one is storytelling.

Finance professionals really become storytellers of the environment of the organization. So it's how do you tell the story of what has happened, what is going to happen, and why?

And so I think it's just a whole new way of thinking about the role of finance versus the more traditional skill sets you would think of.

Fantastic. And Dhruv, I think this one's directed towards you. But you mentioned earlier about how there's various use cases, and we talk about the 95% non-win rate for AI-driven projects right now. So the question is, what are some quick and easy type projects that we can leverage to start our AI initiative?

Sure. So the quickest one is, you know, can you deploy a chat bot or a conversational UI on finance domain knowledge, right? So all finance practitioners have access to this. So that's a quick one. The other one, which I've seen some quick value being delivered, is embedding AI around vendor management, customer management, handling their queries, using AI for processing invoices, right? And again, some of those capabilities are embedded in platforms. Can we use AI for...

The other one, which is actually not that quick is, can we use AI for investor relations, preparing for investor relation calls? Can we use AI for internal audit?

And it's interesting, the internal audit and the investor relation call wouldn't be intuitively easy ones to implement. But the reason they've become easier to implement is they don't rely on structured data or data being prepared and ready within the enterprise, right? A lot of it all depends on unstructured data. And surprisingly, it's easier to use the unstructured data because it is unstructured and that is the intent, rather than using structured data, which comes... So these are some of the early ones where we've seen a lot of adoption, a lot of value. And yeah.

Great. Thank you very much. So I'm just going to leave the questions there. As they keep coming, we will do our best to get back to you guys, whoever has asked those questions, as soon as possible between the three of us to make sure that they all are left answered at some point in time. So with that in mind, thank you guys, Jose and Drew, for joining us on this and providing us with your expertise.

I know it's been insightful for me and I'm sure it's been insightful for the audience. For you, the audience, what I encourage you to do is we've got a QR code up here in terms and it provides a one stream ebook that talks about how CFOs are navigating the journey for AI adoption for the future. I think it's a great ask and a great read. Definitely click the QR code and capture those details.

For more questions and really to learn more about OneStream, follow us on onestream.com or contact us at salesononestream.com. We're also available on various social media channels from LinkedIn to Facebook to Instagram to X. Please follow us on any of these channels and you'll get the latest and greatest, not just on what's happening with us, but what our customers are doing on their AI journey as well. So with that, thank you very much. And we look forward to talking to you all soon. Thank you, Praz. Have a good one. Yeah, thanks, everyone.