Trusted Data, Faster Decisions: Building the Foundation for Finance AI

Well, welcome everyone, and and thank you for joining us today. I'm Tim Minahan, the chief strategy officer here at OneStream, and I'm very grateful to host today's expert panel on critical and very long overdue discussion.

You know, for the past few years, we've all been in kinda some version of the same conversation. AI, data, speed, what finance needs to look like in five years.

But I wanna start today with something more immediate, something that our our own research surfaced and that I think kinda reframes the whole discussion a bit. Our most recent research found that more than 90% of finance executives say that data quality and trusted data are critical to AI success.

That's not a surprising number. It basically means that everyone in the room is is nodding yes.

But here's what's surprising. More than half of those same finance executives admitted that they made a material business decision based on bad data within the past year.

Same people, same year. And the Sharpen's data point, though, was that finance executives who were heavy AI users were four times more likely to make decisions on bad data than those who aren't quite so heavy AI users.

So the risk isn't that AI doesn't work. It's that AI works exactly as designed.

And if the data underneath is bad, you you get to the wrong decision faster. So that's the problem we're here to work through today.

Now, a few years ago, OneStream launched what we called the finance twenty thirty five initiative. Basically asking, how does the finance function need to evolve and what do leaders need to actually do to get there.

And we talked with thousands of executives. We held roundtables.

We went deep. And what kept coming back wasn't about technology.

It was about the role that finance is being asked to play, not just faster or, cleaner reporting. Finance is being actually pulled into the center of the business.

It's expected to guide decisions on pricing and supply chain and workforce and and growth and all while also maining the controls and rigor that make the numbers trustworthy. And that tension is what forward finance is built around.

Forward looking finance organizations are using this moment to rebuild their workforce strategies, to rethink decision making, and rethink entire workflows and functions around AI. Well, we went out and benchmarked hundreds of finance organizations that use, on their use of AI, on their experiences, on their challenges, and any early results that they're seeing.

And what we found operationally, there were five dimensions or attributes that emerged for finance AI success, data stored, the AI strategist, finance as a chief operator, a collaborator with IT, and then finally a workforce architect around how the workforce should function when using AI. And today, we're on to the first one, and that's the one that makes all the others possible.

Finance in the role of data stored isn't just about having perfect data. It's about knowing which data you can trust, being able to trace it, and governing it well enough that when AI touches it, the outputs are defensible, and that's a very different target than having perfect data or clean everything.

So what raises the question of what what this actually looks like in practice, and that's why I'm so excited about our panel. I've got, you know, three panelists here to me today that are doing the work.

They're not just writing about it. They're not just conceptualing about it.

First off, Doug Orsog leads business transformation and growth here at OneStream and has spent years as a CFO before that. So he's made the trade offs that we're gonna talk about.

He's seen what the data problem looks like from both sides of the table. Anders Lou Lindbergh is a partner at, Implementum Consulting Group.

He works across industries and has probably seen more finance transformations, both good and bad, than than anyone I know. He he's also one of the most, you know, direct and honest voices in the space about what actually works versus what makes for a nice nice slide.

And then finally, but last but not least, Nick, Comenaro. He runs strategic finance at Fortive, which is a complex multi business portfolio, and he's navigating this transition to finance AI in real time, not just in theory.

So Doug, Anders, Nick, glad to have you here. So let's let's get into it.

So Doug, we we just saw the numbers. Right? Half of finance execs, bad data, material decisions, and you live this as a CFO.

Yet the pursuit of perfect enterprise data can be, let's say, a long endeavor and one where the success line just keeps moving out as you add new data sources, systems, employees. And at what point does the pursuit of perfect data become the thing that's actually slowing the business down? So where's that line between building data you can trust versus this elusive perfect dataset?

Well, first of all, I've I've lived that moment where you realize data is inaccurate and you made a decision based on bad data and usually happens in front of the board as somebody gets you a question and you immediately trying to tick and tie and you can't, and you realize that, you know, something was wrong. And I think one of the things that people need to think about is really take a step back from trying to be perfect and realize it's more important to have full data transparency, to have that auditability, be able to go back and see where did this data come from, what does it look like, maybe make a little bit boring.

And the reason for this is finance typically has fairly transparent data. Now if you have multiple entities, perhaps you have to do something to normalize it, make it all work together.

But over time, finance has been asked more and more to handle that operational data as well. So finance data is durable.

It's same. You have to keep it over time.

You can back test it. But operational data is nondurable.

It's information that is of the moment, and it may not be tracked in the same time frame that your financial data is. So trying to make that perfect can be a little bit of full Darren right there.

And so what a lot of companies try to do then is, hey. Let's put it into a big data warehouse and normalize it.

And right there, it becomes a black box, and you've lost the ability to have that full data transparency.

So Really, yeah, strategizing in a way that you have you have different approaches for different types of data.

Yeah. You do.

But if you wanna bring it all together, having that auditability, having that transparency is the key. Perfection is not.

Yeah. Yeah.

Transparency all the way through. So, Nick, you know, how how did you draw that line there at Fortive? You know, what did trusted enough to act on data actually look like?

Yeah. We we like to say, let's not let, perfect be the enemy of good.

I I don't remember a time in my career where there has been perfect data, similar to what what Doug was saying. And so, you know, waiting for that perfect state to happen before acting will just slow teams down.

And and so what we're seeing is, like, AI is is great in a lot of different ways. It can dramatically reduce the amount of time that analysts spend pulling data, combining it, and cleaning it before they can even get to an analysis.

And so it's really great with that messy, unstructured data as well as structured data and helping people go zero to one. And so it can allow finance teams to start focusing on insights, you know, capital and resource allocation, and and really doing decisions that matter.

And I think in this case, it is okay if the data is not perfect because, the AI can be used to analyze information to even help you explore areas. And and so a recent example comes to mind for me where we were working with one of our companies and doing a vendor spend analysis.

And it was kind of out of the out of the normal, normal cycle, you know, from a a from a month end process standpoint. And the AI can very quickly go through, you know, operational level data and unstructured data and bring a lot of context to it.

And and even if the number isn't perfect, it can identify exploratory areas. It can say, hey.

This is is anomaly or, you know, these are trends that should be looked into. And so I'm not necessarily making a decision on a number there, but it's exposing an area that we can go investigate, which can then, you know, lead to future insights and and drive value down the road.

And that's where I think it can be very powerful. And so you don't necessarily have to wait until you have the perfect dataset before you can act.

Yeah. Really leveraging AI.

I love that for, kind of looking under the stones that you might not have looked under before and find find patterns for further investigation where you might get some some great business results. So so, Anders, let's let's switch to you.

I mean, is perfectionism the real obstacle here or is that just a a polite version of what's actually going on in most organizations?

I mean, to to me, it's really the the mindset of people. Right? So we have this 99 mindset that is unless the data is 99% correct and trusted, we're not gonna share it.

And by all means, you know, if it's external, investor and the stockholder communication, I get it. You know, it needs to be right.

You cannot have these errors go out to the market. But if it's internal management communication, I would say as long as you are 51% sure that, you know, this is the right piece of data, then use it for something because it's much better to be involved and, and and, you know, try to steer the conversation than say, you know, it's it's probably not good enough, and then I'm just gonna leave.

Right? Because then we'll be waiting forever because perfect data, it like Nick said, you know, it doesn't really exist. So, you know, 51 is good enough.

So really, again, looking at data in different ways, reporting grade and decision grade, I really love that that framework there. Let's let's shift gears a little bit to kind of what what gets exposed for all of this, as we move into this AI era.

You know, Nick, here's something I keep hearing. You know, organizations run for years on on data that they think is fine, then they deploy AI and suddenly the cracks are apparent, you know, everywhere.

You know, the light is turned on, not just in their data, but in their processes and controls. So what does AI reveal about a finance functions data that years of maybe the quarterly close didn't?

Yeah. No.

It's a great question. And and first thing, I see AI as like any other technology.

If you apply it to a bad process, you will get suboptimal results. You know, this this is no different.

But I actually think about what it's exposing, is more of an opportunity. AI creates or or it allows you to inject a significant, more, significantly more analytical capacity into that process.

Right? The the technology can comb through and analyze data at a scale that we really haven't seen before really quickly. And most finance organizations run lean.

And as a result, they've they've built, their processes whether it's month end close or forecasting around predefined questions. Right? What happened year over year? What's what's occurring sequentially? Help me bridge margin or revenue, and what's changed in forecast? Things like that.

Right? These are the questions that we're always asking each other.

And.

so when you take the lean teams and just the constraint of time as you move through these month end processes, Analysts usually just have to focus on higher level, data, right, or heuristics to get to an answer or to tell a story. And this is where I think it's, the AI can be very powerful.

Right? It it can go as all the underlying data, the metadata, and it can surface things in a way that we haven't been able to to do. And I'll give you an example.

And I top line forecasting comes to mind. We're working with one of our companies on looking at just the process of top line forecast.

And and a a way that the AI has been able to add a lot of value is you can dump a lot of CRM flat files into the AI and have it analyze it. And it can analyze the pipeline data.

It can analyze rep level data. And it can expose things like salesperson a has moved the close date on certain opportunities five or six times.

Right? And so it can introduce, like, a behavioral signal and that type of data point into that financial forecasting process, which you may not have had the time or resources to go and analyze that level of detail. And so it really injects a ton of rigor, I think, into the system.

And it's allowing finance teams to take a step back and rethink or reimagine how they might approach something like forecasting now that you can use this tool to just bring in a lot more data, to maybe help you do something like produce a risk adjusted forecast. And so that's where I think it it really exposes things, but it exposes it more from an opportunistic standpoint rather than, like, a broken process or or holes that might have existed before.

Yeah. I love that.

Not not just, you know, having more accurate forecast, not just, forecasting, on a much faster velocity, but also beginning to rethink that whole forecasting process, how frequently you can do it, how you can react to changes in drivers, because of what AI affords you. So, kind of a a follow-up to that, you know, Doug, I'll go to you.

You know, AI didn't kind of create the data problem. It just made it visible faster as we were just talking about.

So what what does that moment of kind of reckoning actually look like when you're inside it? You know, a lot of our listeners are just beginning their journey and what should they anticipate and how should they handle if if they they realize that their, you know, data may not have been as up to snuff, to be AI grade ready?

Well, I mean, part of the issue there, Tim, is how are people adopting AI to begin with. You know, they have to rather than starting with policy and picking a process and rebuilding the process around AI, instead of leaving up to, let's say, Stan in Poughkeepsie.

You know, and Stan has a little hobby he's doing right now as a process he thinks really cool. It's really not a workflow per se, but it's something that's really cool.

Look what we can do with AI. They see that, and they say, this is great.

Now they don't realize that Stan just spent most of their tokens for the year on this little hobby. I call it the hobbyist trap.

And a lot of companies are adopting the AI that way, or they're adopting it on a hobby per hobby basis rather than taking a step back, top down rather than bottoms up, office of CFO saying, listen. These are the processes we want to improve.

This is what we want to automate. We're gonna set policy to attack these processes with AI and blow up those processes and rebuild them around AI, and let's keep Stan out of it.

You know, Stan's doing a great job playing off in the distance there, but the tipsy into center of the universe in this company, headquarters needs to be deciding this rather than stuff coming from the edge. Now the problem is we adopt Stan's stuff, and it doesn't work at scale.

It's not enterprise ready. And so now those datasets we need to bring in were never imagined to work in this little hobby that Stan had.

So I apologize if Stan's in the audience. I'm not picking on you.

But Stan and Poughkeepsie, hobbyist, it needs to be workflow oriented. We need to pick those workflows from the top down, not from the bottom side.

Right. So putting some some formal, governance and structure around, you know, the big the big boulders you wanna move, from a workflow basis, and I'm sure all of that will be reflected in Stan's performance review coming up.

So shifting gears a little bit, we we've talked a lot from a technical standpoint. Let's talk a bit about the context, problem.

You know, Anders, you know, we tend to talk about data quality in in very technical terms, you know, completeness, accuracy, currency. But, you know, you've made the point that the context is kind of the the variable that people sometimes underestimate.

So why isn't having the data enough? And what what breaks when you strip the financial context out of it as you're you're trying to apply AI?

So so let me start by taking you, as it's almost twenty years back, I think, Tim, back to when I started my career as a financial controller at at at Maersk here in in Denmark where where I'm deciding. And so, you know, my first job, you know, preparing the statement of accounts for the group.

And, you know, I was looking at variances like, okay. Last year's revenue was $14,000,000,000.

This year's $15,000,000,000. You know, it's just one difference, you know, not not that much.

Right? And so you're kinda blind to to to the numbers and what it meant, and, yeah, you add would ask business units for explanations. They were like, yeah, you know, some technical stuff.

I mean, you didn't you didn't really know anything. You know, the data was good.

It was high quality, well controlled. You know, we had no problem sending it into the market.

For me as a as a controller, I I had no idea what was going on. Right? So deeply unsatisfying to to me.

And, you know, I wanted to, get closer to the business to understand what is this what is the numbers really, really mean? Right? But you can look at a a p and l statement and you can look at a balance sheet statement, but I have no idea about the business activity behind it. So I think that's step number one, really understanding that business context of, you know, how are the numbers actually coming about.

Because if you don't understand that business context, you can just provide that, you know, surface level explanations, which, and I'm sure Nick can attest to this, it doesn't really help anyone make better decisions. So I think that was really, part number one that, you know, get closer to the business, get that context in which the numbers come about.

But that that's that's really not enough either because, you know, there's so many things going on in the business and not everything is important. Right? So you can ask every single finance person out there, do you know the three most important priorities of your business overall or, you know, the different part of the business that you support? And many of them will may maybe they will give you a high level answer, they will not really know.

And until you know those three key priorities or those four must in battles, whatever they are, you cannot use the data for anything. Because you can just report a p and l income statement.

You can explain maybe a little bit better the variances, but it's not related to what's important to the business. So business leaders, they don't care to have you in a room because you're not providing any value.

So until you target your data and your reporting and your analysis to watch those critical business priorities, you might as well just sit from anywhere in the world and do some high level controlling events reporting, actually, that's finding any value. So that's why companies really matters.

Yeah. That that that idea that finance is at that intersection of finance, you know, business operations and strategy is key, and it it does talk to the, keeping the human in the loop, if you will, even even Stan from Poughkeepsie.

So building on the context topic here, Nick, you know, over over the past thirty years, you know, enterprises have really embedded those processes, those controls, those hierarchies, that that institutional knowledge into systems of record. So why is that context just as important as the data itself when it comes to scaling AI in finance?

Yeah. And I think Anders hit the nail on the head.

You know, the data is a fact or it's it's history or it's a moment in time. Right? And what comes to mind for me is forecasting.

Right? Context is very critical part of the forecasting process. Right? The data can say, here's the history or the period repeat period over period trends, but a strong forecast is more than just an extrapolation of that data.

And finance teams work very closely with sales and revenue operations, customer service, pricing, whatever it might be. They take all these different data points and context from relationship throughout the business to try to put together a strong forecast.

But if you don't understand what's happening in a certain territory or with a specific customer, you know, doing a trend analysis on data is only gonna tell you part of the story. And so I think that now, more than ever, it's very critical that, these finance teams are working at the intersection of strategy, business operations, and data because more and more data and more and more insights are just being brought into these processes, but it's gonna take those teams to kinda stitch it all together.

Right? What is the right story? And then, ultimately, how does this flow into a risk adjusted forecast or something that we can stand behind as a forecast? And so context, again, it's it's it's very critical part of the process. I I see the human loop continuing to play a more important role moving forward as more data insight just become part of these processes as you apply AI and then introduce these types of tools into those workflows.

Yeah. No.

Absolutely. We hear more and more about the importance of having, that human in the loop or what, you know, a Malcolm Gladwell might call those ten thousand hours, to become an expert, really knowing where to probe on the data, knowing where to, you know, dig in a bit deeper, knowing when an answer just doesn't feel right.

So so, Doug, you know, we talk about the, that human in the loop here and and, as a, finance organizations look to scale AI, you know, what's the biggest, you know, misconception, if you will, that senior finance leaders bring into an AI implementation? You know? The the data is out there. The the, you know, AI tool will just figure it out.

Yeah. I I mean, Tim, I remember the good old days, I guess, two months ago when humans weren't gonna be needed anymore.

Because that that proved to be wrong. What we're finding both internally and externally is this is actually creating more work for humans, but it's good work.

It's not the it's not the repetitive. It's not the building.

It's the explain me. And so what it's allowed folks to do is the idea was we're gonna be more efficient.

AI is gonna automate processes. And, you know, like I said back in the good old days, it was gonna eliminate people.

What what he found though is it made stuff more efficient, but now you can attack stuff you could have never attacked before. It's allowed enterprise to scale.

You're making decisions on problems you didn't even know you had, but that still requires a human being. Now we had one group we were working with.

They created 28 new FTEs. Well, those FTEs never took a smoke break, never took the weekend off, you know, never took a vacation, but they didn't cut anybody.

They actually added FTE, but those FTEs, those extra ones, that's what the AI was doing for them. You know, we were promised, you know, for at least most of us on this screen here, some younger folks may not remember this.

We were promised that AI was gonna be Arnold Schwarzenegger coming back in time, you know, to take people out, the Terminator. The reality is it's a clerk with a pencil pocket.

That's the cyborg. The cyborg is human beings using AI, and that AI really enhancing what they're doing, allowing them to be more efficient at first, but also not allowing them to scale.

And that's really gonna affect maximizing the profitability. You can touch stuff you couldn't touch before.

Yeah. That AI augmented, finance professional.

Indeed. So speaking of the the the human and and, the need to have human in the loop, that also needs to, build relationships in order to make AI successful, particularly the relationship between, you know, finance and IT.

And so, Nick, our our our data shows that organizations with, strong finance and IT alignment are, you know, more than five times more likely to fully trust their data. They have confidence in it.

But here's the problem, you know, 85% of CIOs thinks IT owns, data governance and a near equal amount of CFOs thinks finance does. So neither side is really wrong.

They just don't agree. So who actually should own it and and why does this this tension keep breaking down?

Yeah. So I like to think of data governance along two dimensions.

You've got the technical dimension, which is owned by IT. Right? That's the ingesting of the data, the cleaning, the organizing of the data in the data warehouse so that when that data ultimately makes its way into a dashboard, it has high fidelity and and integrity around it.

And the other dimension is what I would call, like, the business meeting or the source of truth. Right? And that's finance.

People go to finance, ask them, hey. What was the number? What should I have for this customer? What's the, what's the share count? Right? And so I think within the organization, people look at finance as really the steward of that data.

But, realistically, both of them, it's a shared responsibility with it's a shared responsibility and shared ownership between both a, IT and and finance. Just, you know, two different dimensions in the way that they put governance over that data for two different reasons.

Yeah. That's an excellent way to think of it.

Right? The the the quality of the data, coming from IT and then the, the context that we talked about, coming from finance to really deliver the data that's that's needed to scale AI. So a follow-up on that, Anders, you know, what what what kinda breaks first when finance and IT aren't aligned on this? You know, kinda what's what's the most expensive, failure mode?

I mean, I think, first of all, there's gonna be a lot of headaches around the company. Right? I mean, business leaders rely on, you know, these functions to to figure it out.

And then when they don't, you know, you could easily have, several sets of numbers, right, instead of one set of numbers or one, trusted trusted data source. So, yeah, it it it's really, really hard, Wig, when these two, two teams don't go work well well together.

So I think what breaks down is really, you know, the business being able to make data driven decisions. Right? Because you cannot trust the data anymore when, you know, two functions are fighting on who should own this because you don't know what to trust and what not to trust.

And I think that's, that that's really dangerous for for a company. So please finance an out IT out there.

Please find a way to to get along.

Excellent. Really the dynamic duo and and trust really becoming the the new currency for for AI.

So then how do we make this happen? You know, Doug, what what's the conversation that the CFO and the CIO or finance and IT need to have that most of them are are still avoiding?

Well, I first of all, I think it's it's a there's a positive development because, you know, I'm not a I wasn't a math major in college, but that was a 163% ownership. So I think it's great that IT and and finance believe in a 163% ownership combined, which is in all joking aside, it's kind of a sea change.

You know, I think if you look back with the advent of SaaS, software as a service, the democratization of IT, so to speak, Finance could go out and get whatever applications they wanted, and they did. And so IT was oftentimes chasing after and saying, listen.

We still have to support this stuff. We don't even know what you bought.

And so communication was not key. I do think that AI is a forcing mechanism to force AI to force the IT departments and the finance folks to start actually talking.

Because there's so much, you know, as both Nick and Anders have said, there's so much where each side is a little piece that they have to govern, and that has to come together because AI forces that. So I think what you're seeing more and more is that collaboration.

It's required. You're not gonna be successful without it.

But I think you start with rather than trying to play catch up like IT has in the past or IT having a very restrictive policy where no one can get IT unless we AI, unless we say unless IT says it. I think what you need to do is take a step back and what's our policy? You know? Let's have those let's craft that policy together and work through that policy.

So we start with an AI with an AI policy that's authored by finance and IT. What are specific things we wanna attack with the tool specific to finance? Because let's face it, IT is not just looking at finance with AI.

They're looking at everybody else. But I think if we start with policy and we focus on one of those processes we're going to attack, it's a lot easier to collaborate, and we're on the same page now.

So I do all joking aside, it's been nice to see there's a little bit of a sea change here where when we're talking to folks, we used to talk either or. Now we're talking to both.

Great. You know, governance is that it's a shared responsibility between finance and IT, and and policy is that glue, that that really makes it happen.

Well, hate to say we're getting close to the the end of our time here. So we'll do one more lightning round.

I'm gonna go around the horn. You know, what's the what's the one belief, about data that finance needed leaders just need to let go of if they wanna scale AI? So so really, what's what's that one belief around data that they need to to to let go of? Let's start with you, Anders.

You know, for the past, twenty years, basically, since I started working, you know, the the topic of data and especially master data and everything else has has been there, like, some latent thing that we knew we needed to do something about, but we didn't really do something about it. But maybe it was in that financial information strategy, but but we didn't really do something about it.

I think now with with AI, and we talked about it, you know, during this, this talk here that, you know, if if if the foundation is shaky, then, you know, AI can do a lot of damage rather than a lot of good. So so really, you know, to the finance leaders out there, the burning platform of fixing your data has never been a more fine than it is right now.

And unless you want it to explode, please stop fixing it. Don't fix for perfect.

Like Nick said, it's not there, but but, you know, you need to fix it.

So so, Doug, you know, in your mind, you know, what's the one bit of advice you would give finance leaders about, about data, that are looking to scale their AI operations?

I think it's easy to to overcomplicate what we need with data. And I think once, you know, people grab grab a really fancy data warehouse and normalize data in there, they think they're doing something helpful.

They're really not. Let's take it make it real simple.

Can you trace back where that data came from? Can you understand what the origins of it and why it is specific? We talked about context and what context was it created. Transparency.

That's really just the key thing. And that's what transparency is.

That auditability both backwards and also from a planning perspective forwards. And if we're gonna use both that durable information and that non durable information, like, that there's some make sure there's similar datasets, and they can trace back and have explainability as to where they came from.

Well, Doug, you're nothing if not consistent, you know, really sticking on the the importance of traceability and, and transparency with your data. So, Nick, not to be left out, we're gonna end with you.

You know, what if you had to give, look in your crystal ball and give one bit of advice to your fellow finance peers about about data, that they need to let go of, if they're looking to scale AI, what would it be?

You know, Tim, I would go back to where we started the conversation. The data does not need to be perfect to get started today.

And I would I would encourage CFOs and finance teams to start ex exploring the technology, understanding how it works, seeing where it can be applied and how it can drive value. And you may not need to, you know, swap out an enterprise grade, you know, production ready dashboard that the entire business uses or do things like SCC reporting today.

But there's a ton of different individual workflows that you can analyze and ad hoc, you know, finance apps that come across our plate where you can apply this technology. And I think people just need to get started.

You know, the tech will continue to evolve, but, becoming fluent with how it works from a process enhancement standpoint or a financial rigor standpoint, I think is really critical because the teams, and and goes, you know, top to bottom, they can start to identify, hey. Here's an area where I can take an AI first approach or I can automate it so I can begin to focus on other, you know, high value activities.

And so I would just encourage people to start using the technology and, you know, start bite size, but just, you know, jump in. And you can eventually work your way up to something that is, scaled across the organization.

But, I would wait until the data is perfect or you have a perfect data warehouse, before you start to to incorporate the tools into your daily workflows.

Yeah. Ending where we began.

Don't let perfect be, the enemy of good. So, Nick, Anders, Doug, thank you.

Right? This this is the conversation. Here's here's what I'd like to leave everyone with.

The organizations that that win with AI won't be the ones that that waited for a perfect foundation, as as Nick just said. They'll be the ones that that figure out, you know, which data they could trust and moved on it while everyone else was, you know, still in that assessment phase.

So don't let AI, you know, become, a faster route to the wrong action. That's or the wrong answer.

That's, you know, that's the risk. The opportunity is is the opposite.

AI is a reason to finally build that data foundation that makes every decision better. And so, you know, here today, our our conversation was organized around the forward finance blueprint, and it really walks through the practical steps of all five dimensions, you know, starting with the data stored as our conversation was today.

And so, you know, the link is on your screen. For those of you that haven't taken a look at the blueprint, it's definitely worth, downloading.

Session two in our series is coming up. We'll be moving from, data to AI strategy.

So how do you actually take the AI from pilot to enterprise scale? So again, thank you to our panelists, and we hope to see you all, on the next session.

Thank you.

Thank you.