Why AI-Native Finance Beats Bolted-On AI Tools

Speaker A: Data discipline, at the end of the day, matters more than anything, even more than AI and the tool, because I think precisely that the failures really don't announce themselves.

Speaker B: Welcome back to CFO Weekly, where we're talking with financial leaders about how to build efficiency in their teams, create time for strategy, and ultimately get results. This podcast is brought to you by personiv, the trusted leader in finance and accounting, um, outsourcing for over 30 years. See how Personiv's customized solutions can help you streamline your operations with teams that start as small as one. Visit the website@personiv.com to learn more. I'm your host, Megan Wiese.

Speaker C: Let's jump right in.

Speaker B: Welcome back to CFO Weekly.

Speaker C: Today I'm joined by Bojan Velikovsky, Vice President of Finance and Treasurer at Vltava. Voyan is a Treasury and finance transformation leader with deep experience in MA integration, liquidity strategy, risk management and enterprise finance modernization. Throughout his career he has led complex initiatives ranging from multibillion dollar restructurings and post merger integrations to ERP transformations, while increasingly focusing on how AI can transform finance and from a reporting function into a, uh, real time decision engine. In this episode, we'll explore what it really means to build an AI native finance operating model, how finance leaders can move beyond isolated AI use cases, and why the future of finance lies in combining intelligent automation with stronger human decision making. Welcome Bojan. Thank you so much for taking the time to be here with us today.

Speaker A: Thanks for having me.

Speaker C: Yeah, I'm really looking forward to this conversation. So, as you look back on your career and the journey you've had to date, which has taken you through Treasury M and A and finance transformation, how has that shaped the way that you think about AI and finance today?

Speaker A: That's a great question and I will give you a long winded answer so you can understand, uh, what I am and why I landed there. So my path into finance, finance, I would say, was not a standard one. I started with a law degree and actually was aiming for a career in European policy. I have a Master's of International law, but I then started getting involved in finance related projects like bank credit agreement negotiations and such. So what really pulled me towards finance at the end of the day was the operations management course which I took at Wharton. And again, it's not finance, but it kind of rewired how I think about problems. And from there, for me, the natural path was mba ctp Wharton's Executive program. And then I guess I was on a mission to learn across multiple industries and advance Myself. So so far to date I've spent time in automotive, public service, renewables, renewable energy that is Gaming, sports and entertainment, food and distribution, real estate, and I don't think I'm forgetting anything. So treasury turned out to be just the perfect vantage point because I think it said um, sits in the middle of liquidity risk capital and you seek another whole business. So then I developed a low tolerance for bad data and slow reporting, kind of combining my legal and business uh, degrees and that led towards AI like three and a half years ago. I think it's when ChatGPT became relevant was out there. Everyone was testing with it and it showed up and I saw something that finally was able to close the gap between technology and finance. And uh, I jumped on it, I started building it and started learning courses out there on AI, which led to publishing a book by myself called Treasury 2.0 Future Proofing Finance with AI.

Speaker C: Wow, quite a career you've had today. And yes, definitely a winding road. I think you're the first person I've spoken with that it's gone from law to finance. So that's awesome. So you often say that finance should become a decision engine rather than just a reporting function. When did that philosophy first start to take shape for you?

Speaker A: I would say that took shape where the reporting wasn't fast enough to really matter. So if you're like in a restructuring or liquidity crunch, a report that tells you what happened last month is close to useless, if not useless completely. And you need to know what happens if uh, the revolver gets pulled or a counterparty defaults or a foreign exchange moves against you. And these are obviously from the treasurer's seat. So when that's, I think when it all kind of clicked for me and uh, the reporting is at the lowest value I guess thing finance does. So reporting is looking backward. And my job really was, uh, how I kind of wired myself to look at things is let's help the business move decisions forward. And it's a choice, you make a choice. And that's what I did for myself. And I think the reason most finance functions can't do that isn't necessarily talent. I guess they're buried in manual reconciliation and closing processes. And I think it's not, I guess a philosophy that takes shape but also willingness and being punctual I guess. And AI is interesting precisely because of that, because I think it can take the backward looking culture and work of the plate and free finance to do a forward looking type of, forward looking type of projects.

Speaker C: Technology is amazing. And moving so quickly. It's hard to imagine that organizations are still at a point where they're just mostly doing reporting and not forward looking. But I do believe that most of them are still stuck there.

Speaker A: Yeah, most of them at least. When I talk to people and I just came off an AI conference, I was pretty amazed to see how many people still rely on the Yale processes.

Speaker C: Excel is still probably the number one tool used in finance and accounting. Yes, and everyone is talking about AI tools, but building an AI native finance operating model, it feels like something that's m much bigger. So how do you define the difference?

Speaker A: So I think this is the distinction I'd really like and care about because buying AI tools is really bolting or implementing something on top process that already exists. So take your messy let's say month close forecast and you point really a co piloted it demos well and it usually dies in production because the tool kind of inherits the mess. I think an AI native operating model is the opposite. So it means that your data, process, governance, everything you kind of built before are designed assuming AI does the work. It is not retrofitted around the tool you bought. So native means building from the ground up and bolted obviously added after the fact. So I uh, will even say this is a test I'd give any cfo. If you remove the AI tool today, would your process still make sense? So if the answer is no, then you redesigned around it. Then you're kind of the native environment and if the answer is yes, you just go back to spreadsheets and like you said people are still in Excel. Then you just bought a tool and that's where you fail. So I mentioned my book earlier. So it's built around the tested and proven framework, which I think solves a pain point that everyone recognizes. It is building on a strong governance and data transparency first because a lot of AI and a lot of these things we see today are just a hike. So who should read your book? Hopefully everyone. But it's kind of interesting because when I was thinking about how to really title it and I went with Treasury 2.0 because I was working on the next thing in treasury and then I said future proofing finance with AI. It is really a framework for finance but if you think about it and take a step uh, back, you can really apply the framework in anything. The whole book is based on a framework called Dash, Digitize, Automate, Streamline and harness AI. So if you kind of follow that path, you can get to the place where you want to be. And I Implemented it across many companies and industries.

Speaker C: And for organizations that are just beginning their AI journey. What needs to change first? The technology, the operating model, the data or the mindset?

Speaker A: I kind of alluded, uh, to it. I think the mindset first, then the data. Because if you build that framework, if you build that mindset and culture, I would honestly put technology like dead last. It is probably the opposite what most people do do, or I guess that's what I've seen. And I'm saying mindset first because if your leadership thinks AI is a purchasing decision, then everything, uh, downstream fails. The mindset shift is understanding that this is an operating model change and it's not a software outlook. And once that lands with people, I think then the work is the data. Data hygiene, data governance, single source of truth. And I mentioned nobody really wants to spend time on this. People say they do, but if you look at the solutions out there, I don't think no one wants to really fund it. And it's the complete game changer. And I would say one other thing. The temptation is always to buy like the agent or the thing that apparently solves something because it's just the shiny part on the top and there are a lot of beautiful demos out there, but you're kind of building on the top of something that is not going to hold. And it's very short to midterm. So technology is the easiest piece to acquire, I think. But you first need to go through that mindset and data building.

Speaker C: And you've led the Treasury Department through major restructurings, integrations and volatile environments. Where have you found that AI can genuinely improve forecasting and scenario planning? And where is human judgment still critical in making the biggest difference?

Speaker A: So I would say right now AI genuinely helps in modeling in you kind of said in the question, scenario planning. And number one actually is probably speed. It can get you the data. Now we have the luxury to choose between models so you can log into your LLM of choice and you have models to choose from because one does the job better than the other and you ask it to think more and to resonate better. However instructions you give it. Volatile environment. The value, I would say, isn't a single more accurate forecast. I think it's the ability to run 50 models, let's say, instead of three, to run them in a few minutes. And when you're managing liquidity through restructuring. And I think that the range of outcomes is worth more than a single point estimate. And AI is very good at expanding the option space first. But here's Where I would say on um, the brakes. Cash management forecasting is what everybody rushes. If you look at the a lot of responses out there, surveys show that 70, 80% maybe of finance teams name those cash management forecasting as the first couple things that they would fix. And there are thousands of tools chasing the demand. And I think not a lot of them gives you kind of hands free, reliable forecast. And I have not to this date seen something that is allowing you to be hands free and then go from push a button to your executive presentation just with that. So the benefits are real, but they're low hanging fruits and you have to be honest with what they are. It gets you the speed and range, but what it does not get you is the judgment. So every time what I actually say to my team as well, and to anyone I talk to as part of consulting work I do as well is remove the judgment layer from AI, let it do whatever, just don't be the judgment layer. That's every time you need to make a judgment, don't let AI do it. The way I think about it is the human sends the guardrails and you can always do that. And the AI works inside them. So you define the assumptions, the constraints and boundaries and whatever needed. So the moment you treat it as the final answer, you're kind of one continent wrong number away from a bad decision. And that can be very expensive, right?

Speaker C: Definitely. Plus yeah, you're just kind of taking something out of a black box and taking it for fact when many times it might not be. But as we've been discussing, many finance teams are experimenting with AI, but few have truly embedded it into how they operate. So in your experience, what separates organizations that successfully operationalize AI from those that simply run pilots or bolt on products?

Speaker A: Great question. I actually talked to someone about a week ago and what I told them is successful companies environments are those that um, stop treating it like a science project. And really a pilot is comfortable and it's a little sandbox off the side with some clean data, no real stakes. And of course it's going to work. There's nothing really riding on it. There is some data and usually when you work on something like a pilot, you also biased towards getting this to look nice in front of whoever needs to approve that. And a lot of teams get addicted to that stage I think because it feels like progress without any of the risk. But then you can run pilots forever and never actually do anything and change anything. And now people are starting to notice. Pilots were the thing to do. I think now that the team that breaks out, they do two things successfully. They first fix their data before they scale. So I kind of go back to a couple questions ago. So the thing that doesn't fall apart the second they touches real production data. And then I would say actually change how people work around that. So the process, who owns it, that's very important how the team operates day to day. And the second part is where most of the pilots quietly die. The tech works fine but I guess nobody change how they did their job and just never got, gets absorbed. It sits there. And one final thing, I would say that the way it actually and I'll say sticks. It's never a mandate. You can really force this. The teams that get embedded to do it through people. So sometimes someone needs to champion it, show a real win. I guess I'm saying is once you do that you see who's the champion. You show a real win. People would like to join and mandates get you only compliance. People nodding and going back to their spreadsheet. And what you're after is really someone wanting to use something, someone that can get immersed into it and I guess make their week easier. So I think that's the uh, whole difference between a pilot and something that the business genuinely can run on longer term.

Speaker C: And treasury often sits at the center of liquidity, risk and capital allocation. How do you see AI changing the role that treasury plays in enterprise decision making over the next few years?

Speaker A: This is going to be biased but I think the treasury has always had the best position and the worst and this is not biased. The worst voice and really the worst voice because the people who are in the treasury seat, regardless if it's a treasurer or treasury analyst or a Treasury inter, we actually see things that determine what a company can and cannot do. But if you look at like from today you look backwards. Treasury got pulled in late, gets buried with strange reporting cycles that just take time. I think at one point used to be a position that had uh, visibility towards the board but for some reason that changed and I think AI changes the whole scene because it kind of collapses the gap between it which has the ability to build and Treasury's knowledge that comes and matters. And I'm seeing specifically it here because whenever there was a project in treasury that needed automation now AI it was there to kind of approve or not approve. That's changed so anyone can really work within guardrails and develop something. So now I think suddenly treasury can turn into a um, real time view of cash, of risk, of forward looking input really at the speed that the business moves in. And I think that means a lot because a lot of people in treasury want to stop being the group you call to execute something and become the go to group where the decisions uh, gets made. And I would just say one thing that, that really still surprises me. The seed is not guaranteed now with AI, that that's even more relevant. And I see a lot, a lot of treasurers who haven't done great based AI. They'll wake up hopefully sooner and they will find that their relevance has narrowed and someone else is holding their pen. And I've seen this, there are executives who don't care who delivers the result, they just want the result. There is even within the team, the treasury team, someone that can show up and do something and same function, completely different level of influence. And the difference at the end of the day is was able to adopt something.

Speaker C: And having led ERP transformations including SAP, S4HANA, Oracle Fusion and Kyriba implementations, what lessons have carried over into AI adoption? And are uh, you seeing organizations making similar mistakes as they made in implementing those big erps?

Speaker A: Honestly yes. I think almost everyone, if not everyone makes the same exact mistakes. I think the number one lesson from every ERP implementation I've been here, the technology is never the hard part. These providers that you mentioned, they're top notch. I think it's kind of the data migration, the change management, how the team approaches this. And you can stand up a beautiful system, but if you have garbage data and you pour into it now you have an expensive garbage can and every failed implementation that I've seen has been exactly, I guess affected by the data readiness because of the people not being prepared. And I don't think it's ever really a software issue. And AI is now bringing AI to the picture. I think it's repeating this or uh, allowing people to follow the same pattern at a high speed and organizations are buying the AI equivalent of a shiny new erp. There's a lot of, as I said, hype out there and people think that they can just create something that's going to replace that software. But I think it's always the same thing. You never clean anything up, nothing's covered, nothing's reconciled, you just pretty much put a new logo and a name on top of something. And the one difference that I think should scare people a little, it's the failure mode. So a bad European implementation is very visible. We all know that something breaks, let's say invoices don't go out and you can immediately know, but a bad AI implementation is quiet. You can run on something, be so comfortable, you can think that you have the best answer and it's going to be unnoticed until a decision's already been made. And I think I kind of said once, uh, that's a very expensive decision at that point. So the data discipline at the end of the day matters more than anything, even more than AI and the tool, because I think precisely of that the failures really don't announce themselves.

Speaker C: That's great insight. And how would an organization know that they're ready when their data is clean enough, when they've cleaned things up enough to then go on to the technology?

Speaker A: I think when you follow, however, obviously company want to do this, but when you follow specific guidelines, steps, frameworks, however you define that, you go through your checks and balances. I think the company will know when they're ready because there's different people, different departments within the team. So it's not just one person or one department that needs to run this. And obviously I'm stating the obvious, as you bring more people in, there's going to be more and more opinions, but I think everyone deserves a chance to speak up. And as you go through the implementation, you figure out what is good data source, what is a, uh, good data, even what is a good file, what is acceptable in this model and how you can run through it. I guess same goes with the data and with the system. I guess you measure compatibility as well.

Speaker C: And as AI becomes embedded in finance workflows, how should finance leaders think about governance, trust and accountability so that the automation is strengthening their decision making and not just creating new risks?

Speaker A: Great question. So I think governance comes first. So if you bolt governance on after the deployment, you're kind of auditing a system you can't fully explain and it is already kind of failing there. I think governance is the first, the most important principle. I think accountability, being human, making the judgment AI can recommend, can flag, can draft, and I think the person owning the decision that makes the judgment, that that's pretty important, that the moment you say the model did it, and that becomes an acceptable answer first to you and then to those that kind uh, of approved this to go forward. I think that point you kind of lost control of your own function and it feeds into that good data, bad data that I mentioned. Because everyone who sits in a certain function knows what a good data is for them and for their team. So I think this is pretty important. And what I would also say is being able to explain the output is Significantly as important as the other system. If you cannot trace why the system produced a number, you cannot really put that in front of anyone. Audit, uh, board, whoever that is. So I think governance is what gets you the win internally. And when you leave that, then present the ROI hype, you kind of build trust internally and executives then I want to say will trust your judgment and risk taking capabilities. Now as AI evolves, they would understand that you understand what you're doing and you know when to hit the brakes, but when to also hit the gas.

Speaker C: And I'm just curious, like when you look at AI, are you more excited about the future and being like a finance professional or are you scared that someday AI is going to replace the need for human judgment?

Speaker A: I don't want to m think that AI will replace the human judgment. That's going to be scary for all of us. Not finance. If I'm being honest, from today's standpoint, I am excited about AI. I use AI 100% of the time when I can. And I'm not just using it for the easy wins. Like, hey, summarize this email. I actually, and I don't think this is too common, but in my seat as VP of finance and treasurer, I also code. I also build agents of my own and I really deploy them. Like I developed foreign exchange agents for myself that I ask questions and I don't need to chase people. I am excited about it. It saves me so much time. It made me way, way more productive than I could have been.

Speaker C: And looking ahead in maybe the next three to five years, what new skill sets or mindsets do you think are going to be critical as AI takes on more of the transactions? What new skill sets are going to be irreplaceable for people?

Speaker A: I think data fluency is definitely number one. And I would say data fluency or financial fluency because financial skills are kind of table stakes right now. The differentiating factor is understanding the data architecture, governance, how AI actually works. I think how actually works and you would understand this well enough to lead instead of being sold to a CFO or a, uh, treasurer or whoever. If they cannot tell a real AI capability from a vendor demo, I think they're going to make expensive mistakes. Then after that, what I would say is systematical thinking. Want to say throne at finance, maybe not too long ago with the development of new software and processes. So people that see it as an operating model, say connected to every other part of the business and they'll design it that way, those people will be successful because that's kind of the framework. I also organized this train of thought for myself and I kind of talked about the framework that I've implemented, developed and implemented. I think it's exactly this shift from managing tasks to designing systems. I kind of mentioned stepping into consulting as well, which is my, uh, job as well. I always lead with framework because I think it guarantees long term success. And finally, I think one other skill, influence and culture. When AI handles really mechanics behind solutions, processes, policies, our edge becomes the human stuff. So building the coalitions, running the trust right now, it's very important because if you want to build an AI native culture, you also need to understand how the organization changes and you change with it. You cannot just go and mandate this. So I think those three I would single out.

Speaker C: Thank you so much for taking the time to be with us here today and sharing your insight, your experience and knowledge.

Speaker A: Yeah, thank you so much for inviting me and I hope you and, uh, everyone who's uh, going to listen is going to find this valuable. Yep.

Speaker C: And to all of our listeners, please tune in next week. And until then, take care.

Speaker D: You've been listening to CFO Weekly presented by Personiv. Please subscribe wherever you get your podcasts to hear all of our episodes. Want to learn more? Check out personiv.com thanks for listening.