The AI-Enabled CFO

Speaker A: M welcome to the Financial Executives Edge, a production of the Financial Executives Journal. Here, finance meets bold leadership. Join us for sharp insights and practical strategies to elevate your thinking, drive change, grow your impact and empower your career. This isn't just insight. It's your edge. The Financial Executive's Edge. This episode is sponsored by AI Dentified. The nation's top RIAs and wirehouses use AI Dentified to turn relationship data and money in motion events into organic growth without adding headcount. It's AI powered intelligence built for modern finance leaders. Learn more@aidentified.com welcome to the Financial Executive's Edge.

Speaker B: Today's podcast centers around the AI enabled CFO and financial executives. I am Lynn Gargano, your host and moderator with the Financial Executives Networking Group and editor of the Financial Executives Journal. In this episode we will explore how AI is truly reshaping the finance function. From operating models and growth opportunities to governance, risk and leadership required to build future ready finance teams. And that's exactly why the modern CFO becomes so important. Because becoming an AI enabled CFO isn't simply about technology projects, it's about, it's about rethinking how leadership and the finance function operates. Today we are joined by Neil Sahota, CEO of ACSI Labs and author of Own the AI Revolution, Unlock your AI strategy to disrupt your competition, as well as Julianne Averill, Managing Director at Danforth Health and Healthcare AI enabled cfo. Thank you, the both of you for joining us today. So a great way to start our conversation today is starting at the beginning, um, defining what it truly means to be an AI enabled cfo. Julianne, I'd love to get your thoughts based on your experience and expertise.

Speaker C: Yeah, well thanks Lynn. And it's great to be here and talk about what an AI enabled CFO is. And when I think about an AI enabled cfo, it really is moving the cfo, um, function into the future. And so in the past, uh, as CFOs, we're juggling a lot of different hats and we're really thinking about how do we manage operations, how do we partner with the CEO executive team and how do we partner with the board and now with AI. What it does is it really gives us a chief of staff and a full team, whether it's using your favorite LLM, um, Claude, um, ChatGPT, you name it. But it gives you an extra, um, set of hands to really help from a strategic, uh, function and really help you scale everything from the tactical all the way to the strategic.

Speaker B: Absolutely Agree. Neil, any additional thoughts on that and how AI is shaping the CFO's mandate? Like moving away from number crunching financial scorekeeper to value creator. Your thoughts based on um, what you have seen?

Speaker D: Yeah, sure. It's interesting because AI is really enabling CFOs to focus on the decision advantage. And I know for a lot of people that seems strange because they're like, they're worried that AI is really going to place financial judgment. It's not really doing that, but it is raising the price of bad judgment. You think about all the data and we have today. So when you look at like the old cfo, if you will, we're reporting on performance, we're looking at cost control, trying to explain variances and we're looking at for the most part a lot of numbers that are in the past AI enabled cfo. They're going into designing performance, they're becoming engineers of growth, they're predicting more opportunity and they're leveraging AI for the strategic foresight. So AI is helping CFOs become people that identify underutilized assets, look for the areas of like mispriced risks, capital inefficiencies in effective. The CFO is becoming the enterprise signal interpreter.

Speaker B: And with that it's not that they're doing it alone. Right. You have to have the proper infrastructure, um, in place, the enterprise infrastructure.

Speaker D: Well, absolutely right. AI doesn't come just knowing everything. And this is what I tell a lot of folks is that one, if you treat AI as just it, you're not going to get a whole lot of value out of it. In my first book on the AI Ah, Revolution, I demonstrated that leaders who treat AI strategy actually uh, tend to be very successful. And so that means just more than having the data, more than having some of these tools and APIs, you really got to effectively think of AI as an employee, like high energy intern. You need to train it just like you would do. Any kind of hire comes part, part of the employee family if you will. But that's really how you can dynamically generate and identify opportunities and create more value is to integrate AI, uh, into working with us side by side, not replacing us, but think of it as that high energy intern that can do one task extremely well.

Speaker C: Yeah, I think Neil's exactly right. When you think about AI, especially at this stage, it's really this concept of human AI teams teaming is where do you focus AI first? And it's like for example, do you take the tactical. So do you use AI to help clean up your ap Function or eventually what we're starting to see now is AI can also help from a strategy perspective. So can you use AI to help you prep for your board meetings and maybe use like synthetic characters? But uh, really AI has the potential to do a lot of things, but really it's kind of honing in on what is the best use case for you and the best use case for your company.

Speaker B: When we think about the overall infrastructure and I think about the operating model, I think that's a challenging area in most places for AI disruption. Building and developing a strong operating model from your experience. Julianne, what does AI powered finance operating model look like?

Speaker C: It's a great question, Lynn. It's usually three components. So step one is really, and this actually goes back to a little bit of what Neil was saying in the first um, question is make sure the data is in a place where it's comprehensive. So especially with um, early to mid stage companies and even later stage companies, this data may be in silos. So really get your data, whether it's in finance or operations or even in your revenue generating sides, get it all in a place where it can be basically AI ready. So have it in some kind of data lake, some kind of processing area, but have that infrastructure where it can be translated and processed. And then the second UM layer is basically that processing layer. So really decide what your tools are. Are you going to build your tools in house or are you going to buy your tools? And then that third layer is what is your basically human in the loop? So where your, where are your team members going to manage your analysis and where are they going to choose to say, okay, where is AI going to kind of run and do that, that um, processing piece?

Speaker B: And as part of that integration then, Julianne, how, how does the CFO from a leadership position help facilitate those conversations? And Bridge FP&A and the AP, the accounts receivable teams, all the extended functions. So everyone is on board. Right. Instead of jumping straight to technology, you don't miss that human in the loop and that integration.

Speaker C: Exactly. Yeah. Because I know the tendency is to go, okay, let's buy this new tool and implement it. It's thinking back to what we do best. And typically this is like back in the days when we were doing those ERP transformations is thinking about the people first and the processes first. So really what are those workflows that can be transformed or vice versa, what shouldn't be transformed from an AI perspective? And the CFO role is one of those roles that's really well Suited for that because as, uh, a finance leader, we typically see across the organization, so we see what's driving revenue, we see what's moving the costs. And we're really that business partner at the top levels all the way down to the bottom levels.

Speaker B: No, absolutely. So Neil, you had mentioned thinking about AI as an employee, but as a strategy. Right. And so using AI to move from cost control to growth acceleration, whether it means identifying revenue opportunities, improving capital allocations. Can you maybe share some of the three top, um, AI use cases that every CFO should be evaluating based on your experience and what you've seen?

Speaker D: Yeah, no, absolutely right. The, uh, key thing here is to start thinking of AI as a capital partner that never sleeps. And so we think about where are the top three use cases CFOs are undervaluing today. First is capital allocation intelligence. So AI has the ability to stress test investments before the money actually moves. We look at things like climate change, the start of the COVID pandemic. Well, that's AI superpower. Right. It's actually something it's very good at is complex scenario planning. So we actually can use tools, we actually have solutions even like ACSI Labs, where you can actually do complex scenario simulation. You leveraging AI to look at cross macro markets opportunities and literally run tens if not hundreds of millions of different simulations. The AI, uh, looking at things that sometimes we think are less likely or black swan events, but actually have a much higher probability as a result. AI can effectively see around those corners to help us understand where to better allocate our capital. Second is hidden margin detection. So there in some cases we have price leakage and we can identify some of those unprofitable segments fairly early if AI is partnering with us to look at the information. And I've seen my own work with some of the big banks as well as in certain industries, particularly insurance, where there have been some silent cost centers that didn't know. But we've been doing the same process, like underwriting for the same way for 60, 70 years because we didn't really think it was broken or there would be a better way of doing it. Didn't realize that, uh, we had built in a lot of inefficient costs as a result. So AI really helps us find the profit we didn't know we lost. And the third is around strategic pricing risk. So think about the ability to do real time risk scoring to look at all these subtle factors for monitoring. Experian, I, uh, think it was about eight, nine years ago, revealed that in Their own work, the best indicator of someone repaying, uh, any debt, or one of the biggest factors in a person's credit score, like behaviors and similar thinking. And there's also kind of the judgment factor. So looking for some of these counterparty stability signals, looking at psychographics, looking at neuro linguistics, which is the science of language, all these things can help us then understand not just the customers, the markets better, but what actually the prices and elasticity or inelasticity will actually bear out. So risk isn't really managed annually anymore, which is what we typically do. We can actually price it continuously.

Speaker B: So, you know, we talk about all these great things that, you know, AI tools can provide for in gleaning insights, right? Predictive analytics, identifying risk. But uh, you know, there also has to be a way to roll out AI adoption in a responsible way so you can never overlook the risk and governance. So Neil, from that perspective, um, what are the new risks that are emerging when a finance team or an institution relies on AI and predictive models?

Speaker D: There are a few big ones. Everyone talks about bias, for example, particularly unconscious or implicit bias. We live in an age now where we're using so much like gen AI that we have to consider model hallucination risk. You talk a lot about LLMs, large language models, which is great if you want to try and access a broad set of information. But in the age of magentic AI, what's more successful is SLMs, small language models. We don't want a whole universe for those things. An agent should be able to do one task extremely well. So we want to be able to constrain that. So that body of knowledge, or what we call a corpus, the algorithms, the models, are all new risks CFOs have to deal with in conjunction. There's data drift risk. That data today gets stale very quickly. In fact, we generate so much data today, most of it is for machine consumption, not human consumption. And then there are already regulatory audibility gaps that exist. Look, we know that regulators, lawmakers, it doesn't move as fast as business or technology and you know, as we use some of these tools and there's some of the unintended consequences start popping up. They're a little behind the curve, right? We're always kind of counting on these folks to help us create the guardrails, but they're uh, struggling just to keep up with what's just recently happened, let alone what might be happening in the near future. So as a result, the CFO is really starting to become the chief accountability officer when it comes to providing safety and guidelines. So remember AI, ah, it scales decisions, but it also scales mistakes.

Speaker B: So Julianne, just in keeping with what Neil is saying, how have you as a CFO enabled cfo, um, defined kind of proper discovery or AI readiness, um, within your organization and in investing in AI solutions.

Speaker C: Lynn, how we've defined it within our organizations and I work across multiple organizations, is really encouraging the teams to put guardrails in place around how and when we use AI. And what I mean by that is, for example, if the risk around um, the data management is pretty high, we tend to use less AI versus if it's something where we can have flexibility around it. We just make sure that we are really analyzing what the outputs are. And so a good example is we'll use um, one of the more common tools, perplexity, for technical accounting research. But the team member who's using that is a team member who is very skilled and typically has a CPA or has a strong background in technical accounting research. So that's how we put those guardrails in place right now, where we pair the AI with an individual who has that expertise so they can understand, okay, if it spits out something around for example revenue recognition and it's making something up or hallucinating, we can say, okay, we double check it. And then as another example, we'll um, use AI oftentimes in our analysis around financial um, projects such as if we're doing something around, um, debt analysis or term sheet reviews. And when it pops up, something like if it prepares an Excel spreadsheet, we'll make sure one of our FP and a team members reviews the spreadsheet and double checks everything because we want to make sure that whatever we deliver to a client is accurate. So there still is a very um, human aspect to whatever we're using AI for.

Speaker D: The honest truth is the machine will never be perfect. Right. Just like humans are never perfect. The future of work is what we call hybrid intelligence. It's people using machines, not people versus machines. So there are things that we know. AI is better than us. It can crunch lots of numbers very quickly for example, but people are actually much better at first of a kind. Problems at creative thinking. Some things will require a level of intuition or instinct. So what we want to really do is take our strengths and augment them with the machine capabilities. The goal is the person using the machine is more accurate than just the person themselves.

Speaker B: Absolutely. And that ties into leadership and talent. Right. Because we talked about being an AI enabled cfo. A strong operating model, responsible adoption. But you have just spoken to the human in the loop as part of that integration and buy in. So what are the skills that define a future ready finance team from your perspective?

Speaker C: I'd say the number one skill is adaptability. Because right now everything is changing so fast. It's almost like I was in a training yesterday where they're talking about, um, these AI models are training almost hourly at this point. And so from a finance perspective, the individuals who really understand that our jobs today may not be what our jobs look like in the next year or so, those are the individuals that are going to be successful. Um, the other piece is individuals, um, who are really good at training others will do well in this new environment. Because one of the things, when you think about what an LLM M is, back to our earlier comments commentary about if you treat it kind of like a person or think about that, if you're very good at teaching others, then you'll be good at teaching machines and also teaching yourself and teaching your team members how to use this new technology.

Speaker B: Is that an important element of what some organizations are missing, allowing for that room for employees to grow and adapt?

Speaker C: Ah, yes, I am. So I'm seeing, because I'm seeing both sides of it, when you encourage, um, adaptability and you encourage training, people get excited about it. So for example, a few weeks ago, Claude, um, rolled out a new finance plugin and some team members were super excited to see what that did. But then if you're at organizations where for whatever reason they've decided that AI should not be adopted, or they're saying, okay, they're going to do layoffs with AI, that tends to discourage that mindset of discovery. So when you're thinking about how to roll this out to your teams and to your organization promoting that mindset of discovery and um, training and exploration, that tends to go very well.

Speaker B: And Neal, to that point, um, Julianne touched upon mindset. So in your experience in the organizations that you have presented to and some of your clients, how would you define the mindset and cultural shift that's required for AI adoption to succeed?

Speaker D: It is, um, I'm going to walk back and add something to Julianne's points because she made some really good points and I can tie this into your question, so bear with me. But you know, talking about the discovery because it's very important for employees to try understand these tools. But my experience is that a lot of AI, uh, projects really fail in procurement, not deployment. A lot of organizations, they tend to Buy the tools first and ask the strategy questions later. And that's actually a huge issue in that, again, we're not really buying software, we're hiring an intern. And so if we don't really understand what we want to do with them, what we wind up doing is automating a lot of bad processes and scaling the flaws that exist there. So that's why one of the key things I talk about in my AI activation code playbook is you got to think about AI readiness and the value you want to generate before we start talking about automation. When we talk about then what are some of the cultural changes that are occurring and how do organizations get ready. We have to move from thinking about certainty to actually probability. We have to live in a world of risk, more exposed risk. But risk is not a bad word. I know that's where we tend to gravitate. Risk just means uncertainty. It could be negative risks, which are threats we often think about, but they can also be positive risks, which are opportunities. That's one area that we always tend to underscore. You also have to think about from control mechanisms to orchestration. So rather than trying to manage, uh, every piece of the process and force the constraints in. I'm not saying those are bad things. You have to think about now how we're really guiding all the work and unlocking those opportunities, particularly those three use cases we talked about earlier. And we have to think about moving from reporting to modeling as, uh, CFOs. So we're not just looking at all the past information anymore. We want to try and model portfolios, model opportunities. So the ironic thing here, in my experience is most people think AI is like, uh, a technical challenge. The real issue here is that AI adoption is really a cultural problem because it's a whole different tool set, a lot of new capabilities. And then you get into the fear factor. Right? A lot of people worry about is it going to take my job, is it going to replace me, all these things. So I always talk about people process technology. Change in one changes the trigger, and the other two, we just don't go into our tech stack and add AI. Uh, think about then how do we redefine, re optimize our processes and how do we get the people to understand the wifm? What's in it for me? For them, how do they get the chance to explore some of these tools? They can actually find the opportunities to unlock the value from them.

Speaker C: Yeah, it's a great point, Neil, especially around the people side of the house. Because what we're finding is you have the fear side and then as they move into the adoption side and you're starting to see this, you're seeing a little bit on finance, but you're really seeing it uh, with software engineers as Vibe coding has come up is they're so productive. We're now hitting the point of there's a whole thing around AI burnout. So part of it is back to your point about people, is how do we manage this transformation process? It really goes back to actually what CFOs do best, which is how do we manage the people and the overall organizational impact of this change and not just really say, okay, let's implement a tool and just run with it.

Speaker B: I think we talk a lot about kind of internal readiness, but what about external readiness? Your third party vendors that also introduce risk, what are your thoughts on AI readiness when it comes to third party vendors and assessment and risk?

Speaker C: So with third party vendors and assessment and risk, it's really deciding the make. It's that uh, build versus buy again. But it's also really important to decide when and how you implement a new tool. But also too looking at your existing tech stack and seeing what they're doing with AI, because what we're finding is especially with um, legacy finance tools, they're actually rolling out AI, sometimes monthly or even quarterly and just putting it into the system. So the thing to keep in mind is really partnering with your IT organization, if you have an organization or if you're a smaller organization, really ensuring that you're talking with your management team around what are that, what is that governance framework for evaluating AI and technology within the organization. And so how are you going to go through that adoption process and that vendor evaluation process to say how and when are you going to bring um, these tools in house? Or what are you going to look at and say, how are we going to roll these out? And a good example is, um, some of our more common tools that we use within um, the finance industry industries, such as like for example bill.com, ramp, netsuite, all of them over the past year have implemented some kind of AI component. So it's really understanding what that AI component is and does it work for your finance organization? Yeah.

Speaker B: Now I know there are many different facets across the whole financial, uh, landscape which AI can touch and integrate and it could be a bit overwhelming. So I'd be remiss in not, um, asking this question to close out uh, this episode. What advice, uh, Julianne, first would you give CFOs who feel like they're falling behind.

Speaker C: Yeah, it's a great question, Lynn. I'd say number one, you're not behind. I actually saw a matrix that showed like, here's the overall world and here's the world of who's seen AI and then here's the world of actually adopted AI and it's something like less than 1%. But if you feel like you're behind, what I would say to do, and I know CFOs are always so busy, I would say choose, uh, a tool and download it and play with it and play with it like on some kind of personal project and just get comfortable with it. And either it's um, you can choose uh, one of them chatgpt, cloud or something like that, but you can spend time because they have the voice component or they have the chat component and just use it in your day to day and that's how you get familiar with the tool and then you know how to ask the questions to your team of what should the AI be doing?

Speaker B: Yeah, Neil, final thoughts on that and if you will, um, maybe you can give one bold move that every CFO should consider for this year.

Speaker D: All right, sure. So I totally echo what Julianne is saying, right? If you feel like you're late, not so right. Think of it as you're really early to structured advantage. However, the worst AI strategy is waiting. So if you're waiting for big tech companies to build the tools you need, the truth is that's never going to happen. That's not their business model. So three practical moves or two pieces of advice. Think about where you want to use AI. Look, uh, at capital decisions. Second, very much agree with Julianne. You got to audit your AI, uh, exposure. Understand where all some of these things are. Start writing some of these parallel forecasting models. Start small, start strategic. Think about some low hanging fruit to create value. If you're thinking about a bold move this year. Well, the honest truth is at this point, if your finance team isn't running simulations, it's actually running blind. You're not using a lot of data. Your competitors are actually leveraging. So think about building like a central hub. Leverage that hub for forecast intelligence, risk telemetry, performance modeling, allocation simulations. There's a lot of things you can actually do. There's a lot of tried and tested tools out there already that you can build upon. Let's start somewhere. That's the key thing. So don't just copy what other people are doing. Don't copy what your competitors are doing. Because all you're doing is playing catch up the other day. You need to find that leapfrog opportunity. So this becomes more now about not so much trying to solve a problem, but just find a better way of doing the work. And that moves us from automation to innovation. And I know that can be a challenge around the board for a variety of reasons. If you're looking for that bold move as a cfo, be the innovator.

Speaker B: So and with that, that brings us to the end of UM the AI Enabled CFO and Financial Executive Podcast. A huge thank you Julianne to you and to Neil for providing your insights today. It's clear from our discussion that AI will continue to reshape the business landscape and that the financial leaders that will stand out are the ones that will be able to translate AI intelligence into financial intelligence and then financial intelligence to, as you call Neil, the strategic advantage. Um, so if you enjoyed today's conversation, be on the lookout for the AI Enabled CFO and Financial Executives Workshop series where we will continue the discussion covering the full landscape with practical use cases and candid conversations around the CFO's expanding role in AI adoption from operating models to AI tools and integration, FP and a Leadership Governance, Data Management, Cybersecurity and Risk Oversight. Thank you for joining us today and until the next time, stay informed.

Speaker A: This episode is sponsored by Aidentified. The nation's top RIAs and wirehouses use AI Dentified to turn relationship data and money in motion events into organic growth without adding headcount. It's AI powered intelligence built for modern finance leaders. Learn more@aidentified.com thanks for listening to the Financial Executives Edge. If today's episode sparked new ideas or helped sharpen your perspective, be sure to follow and review us on your favorite podcast platform. You can also visit financialexecutivesjournal.com for more insights, articles and upcoming episodes. Until now. Next time, stay sharp, stay strategic and maintain your edge. The Financial Executive's edge.