Mike Lynch on How AI is Transforming Finance Teams at Auditoria AI

ai Leadership Insight series. I'm your host, Mike Bazar today with Mike Lynch, who's the head of the AI transformation Services for Auditoria. And we're talking about, well, how AI is impacting the financial services sector.

Mike, welcome to show. Thank you Mike, for having me. I think everybody out there is grasping for some way to understand, well, just how are we using ai?

What impact is it having, and where is that elusive ROI? But you guys do a lot of work in the financial services sector, and they're usually at the forefront of these things. So what are you seeing?

Yeah, no, it's a great question, Mike, and uh, again, thanks for having me. So it really, when we talk about AI and where we've been over the past, really six to seven years, as the office of the CFO has really tried to transform forward, it's, it's not a question of if we're gonna in integrate AI into our processes anymore. It's really a matter of how fast and in what areas.

And so at Auditoria, what we've seen most of the offices of the CFO start with is areas of the office of the controllership. So mostly in their accounts payable in accounts receivable space. And we actually run an annual report where we look at the state of AI transformation in the office of the CFO.

And, and it's really interesting to watch those trends over the past six years as we've done the report to, to think about where things were before. And then of course, when you have the advent of chat GBT just a few years ago, what has kind of, uh, like a hockey stick accelerant of, of what's happening there. And so what we're primarily seeing is that a lot of customers and a lot of companies are trying to find value in automating those repetitive tasks they're doing on a regular basis in a, with, with AI that is auditable, transparent, and understandable, so that they don't necessarily have to go, well, we sent it into a black box and some other stuff came out on the other end, and they, and then they don't know what happened and where it went.

So, uh, we're seeing that that actually really transformed. It's, we're talking about about an 85% reduction in manual things like reading email boxes and responding to supplier inquiries or responding to customer inquiries where we have to go in and, and grab a copy of a bill or a copy of an invoice and send it back to somebody. So that's a significant ROI where, where companies can choose to say, okay, we wanna realize actualized cost savings, or in many cases what we're seeing is they're saying, we wanna take those people from a, a menial non-value add task to strategic level tasks that such as supplier relationship management, such as chasing discounts on, on payments.

The other area that we're seeing a significant amount of value is, is companies being able to reduce their day sales outstanding, their DSO, which really is a, it, it's a, it's a key way for them to be able to put their money back in their pockets instead of having their money in their customer's pockets. And so it's a, it's a great opportunity. So when you talk about, you know, the, the ch biggest challenge with ai, especially in the office of the CFO, has been how do I actually see ROI, these are some areas that our customers are seeing some, some very significant ROI in those spaces.

Mm-hmm. I think one of the issues that I keep hearing a lot about is the process matters and what you're using AI for, especially with Gen ai, and are people starting to understand that, you know, the more deterministic that process is, the more challenging it becomes to apply AI to it. It's not impossible, but there's a lot more work because well, deterministic means, you know, it's gonna be done the same way every time, and AI never does the same thing the same way twice.

Yeah, no, it's a great question. And I think that's where, when you, when you think about your processes, and, and this is actually why I think controllership becomes the, the forefront of opportunities is because in many cases there, there are processes that are driven by some pretty standardized processes at, at organizations. So they have rules about how they process invoices, they have rules about how they send out bills, how they do dunning processes.

And so the, because there's some clear rules and regulations within those companies, they provide really fertile soil for automation. And so when you talk about how to apply AI in that space, that's where, especially when you see some of the companies, when they try and kind of go it alone and figure it out, they get into a little bit of trouble because you can't just claw or chat GPT up some automation in your, in your finance space, because while those are great models, they do a lot of things and everybody uses 'em probably daily at this point. They're not gonna be specific to the office of the CFO.

And so what, what we do is we'll bring in our specialized reasoning model, which is a, a specially trained small language model to sit on top of the large language models, which help it do from doing what you're talking about, right? So it's not just a, I come up with a new rule, I come up with a new thing every time because it's kind of generating on the fly. This is where it really helps them dial in those processes and repeat them in, in that fashion.

And so that's where they're able to see it. And I think that's, that's where, you know, when, when we do these with, and when I work with customers to kind of align their, their processes, that's a big part of it. They need to make sure that they have a good sense of what data looks like for their organization.

They need to make sure they have good processes. Where does the data reside? What are the flows?

Where do we go? And what do I really want my people to be doing, uh, going forward? And so that, that really helps avoid the, the AI churn in the office of the CFO.

And I think that's, that's where a lot of, a lot of finance teams are starting to see some value. Mm-hmm. Um, early on, everybody was talking about how AI would, you know, replace people and everything would be highly automated.

I think maybe as we've gotten into it and understand some of the limitations is, is that vibe a a little less out there these days and people are more realistic about what, what the benefits are? I think, so we we're strong believers in the, in the concept of human in the loop ai. And, and that's really that AI is not just gonna turn on and it's not a fire and forget type of type of activity.

It's really an opportunity for them to turn it on. And, and they get to be the strategic storytellers with it. They get to be essentially the supervisors of these new digital coworkers, these agents that are now working alongside them.

And, and so the, the humans, you know, are, are upskilling to not necessarily be the ones to process an invoice, for example. They're letting the AI process the invoice, work on the coding, get everything teed up to move into their ERP systems, and then the humans are reviewing that work. Essentially.

They've now become the supervisors of these digital coworkers, kind of a junior accountant, if you will, that's now on their team. And so they get to then say, yes, that's great. Go ahead and write that into our system.

And so it's not eliminating the humans, it's reducing a lot of manual time. And so I think there's a luxury that, that folks in finance now have to, to really think about it, it, and then they've never had this luxury before. It's, what would I do if I had some extra time?

And, and, and it's, they've always been buried underneath paperwork, buried underneath emails. And so now because they have this opportunity to let the digital coworkers kind of go and do a lot of processing at, at high speed, they now get to think about what's next and, and what other value can we provide for our organization. Many of these of these groups are, are running a shared services function, they're responsible to the organization.

And so now they get to talk about insights and data storytelling and analytics, whereas before they were just kind of treading water to keep up with the, the pace of, of the transactions that they were trying to make. So moving, you know, it, it sounds kind of cliche, but from transactional to transformational is really kind of where we wanna get them. What is their reaction from people that you've talked to about this?

'cause on the one hand, man, I'm not a finance person, and I look over there and sometimes to me I see a lot of what I would call, you know, mind numbing, soul crushing work, but then there are other people who love that stuff. So, you know, they're kind of heavily into it. Um, when they look at ai, what do you hear from those folks?

Yeah, no, and you know, there was a saying, um, actually a colleague of ours uses all the time. He goes, you know, finance people aren't boring people. We just like boring stuff.

And, and so because they kind of like the boring stuff, uh, it's interesting because the finance folks have a really analytical mind, and so they really want to dig in and figure out how AI works. And, and so I think for them, in some instances it's kind of a new challenge, right? It's kind of a new, the new crossword puzzle or a new thing for them to kind of figure out how this thing is working for them.

So there was definitely some initial trepidation. And I think especially to, to your earlier point, they were worried about things not being exact. Now in many cases, you have senior level finance folks that say, look, get it within 5%.

I don't need everything to be exact. I need everything to be directionally correct, mostly complete, mostly accurate. But most of those senior leaders weren't demanding 100% every single time.

And so, but the people wanted to produce it at a hundred percent. And so I think when you talked about the early stages of AI and how they were adopting it, that was a big concern for them is, is it's not 100% accurate or they couldn't, they couldn't prove that it was 100% accurate. Now, over time, they've seen the results of it and they realize that, oh yeah, this is, this is processing what I'm asking to do.

It's, it's moving forward. It is not a silver bullet though. That that's, it is one of the big misconceptions that I think folks had initially of AI is that it was gonna come in and do everything for me.

Like CFOs will be able to kind of kick back in the nice leather chair and go, uh, tell me about this or do something about that, or what happens if this, and, and while it does great at pulling data together, it it's getting better at, at being able to pul data together, put those analytics together. And I think really the next, you know, over the horizon a little bit, but not too far out of the way, uh, not too far out there is what we're gonna talk about in terms of causal ai, which is actually AI that's gonna help understand the root cause analysis of something. So really, when they're getting down to the root cause of why did my numbers go up by 10%, that causal AI is gonna be able to kind of do some of that recursive searching back through the data, understand what are some of the, maybe some, some micro things that happen at my company as well as macroeconomic things that may have happened writ large, and then be able to deliver those insights.

And, and I don't think we're too far away from that. We're not there yet. But again, it's not a, it's not a bullet magic bullet.

So it's, it's kind of an opportunity to, to understand what can AI do. And so the finance teams are kind of now, now gelling around that fact and saying, okay, it's really great at information extraction out of documents. It's really great at automating some of these rote tasks.

But again, it's, it's not magic. And, and so, you know, there now at the rate of change of technology and the rate of change of how people are, are developing this, uh, there's probably some stuff we're not even envisioning today that a year from now might look like magic to us today that's gonna be, uh, really impactful for the office of the C ffo. So we're getting a little more agile than we used to be in the finance space.

'cause we were pretty, you know, you could take excel when you pry outta my cold dead hands type of thing. Right. You know, Excel just turned 40 this year and I think many of the folks that work in finance were there from the beginning.

And so it's, it's really, uh, you know, it's a unique opportunity to see this, this, this back office function that, um, while, while it's rife with data and, and has all of the other information that's great for automating, uh, we're kind of, they weren't the ones that were getting all the shine, but now they're really able to to accelerate forward. Well explain that a little bit if you don't mind, because there is generative and there's causal and there's predictive ai and all these things are a spectrum of tools that we'll be using, right? That's right.

And, and I think, you know, most of the finance teams, I, I by and large are, are generally staying away from generative AI because again, they, you know, you worry about model hallucination or something else where, uh, or to your point to your question earlier, it's coming up with a different answer every single time. And that's not what I want. I want something that's you kind of, you know, taking a consistent approach.

And so some of that, that predictive AI that that's looking at information helping to make insights and drive and driving that forward, um, that's I think where people are gonna really wanna be. I think, you know, Gartner's, you know, Gartner did some research and they had said that causal AI is probably only impacting maybe two to 3% of companies today. And that's really at a very nascent, almost an infancy stage at this point.

And so I think that'll, that'll start to grow over time. They, they envision it's gonna be a huge impact to, to teams, but it's just, we're just not there yet. What I think is, what I think is, is going to be the, the, the next wave that's really gonna impact the finance teams is the ability to pull data together better than we are today.

So instead of having to go build a bunch of reports in your ERP or in a data lake or however you set it up within your organizations, they're gonna be able to use AI to basically go grab information out of their data stores and then provide those analytics using natural language processing. So being able to start having those conversations of, you know, create a report for me that's telling me about my sales over the last three quarters, lay it up against this type of information and, and AI is gonna start to be able to do that for you. So there's some players in that space that are starting to get there.

We're one of them, uh, with our smart research tool that, that I think is gonna be an opportunity for, for, you know, finance teams to really start moving forward in that space, which starts to pick up other parts of finance, right? We talked about, you know, the office of the controllership, but this is areas where, uh, your FP and a teams, your GL teams, your financial reporting teams are, are gonna start to see oppor more opportunities for them to take advantage of AI in their space without having to kind of build everything from the ground up. Because in many cases, for, for those teams to see a lot of value, they're having to create large data repositories and then stack analytics on top of it today.

But I think there, there are tools that are gonna start coming out to help them do that in a much more rapid pa rapid pace than they can today. Um, they say that, you know, the thing about AI is that the AI we have today is the worst we're ever gonna have. So as you look into 2026, what are you looking forward to?

What do you think's gonna amp? Yeah, I mean if, if, if the worst that we have today is something that could automate 75 to 80% of my manual tasks, um, I would say I'll take it. But at the same time, I think, I think so.

So the causal ai, I'm excited to see that. Are we gonna see it in 26? I, I hope so.

Um, I think that would be great to, to have those opportunities. I think other opportunities to continue to refine language models to be more trained for finance is gonna be key for people to see that value. So not trying to go with some, you know, giant LLM to, to use against that data, but really starting to find those, those specialized models, which of course means you have to find specialized data to go train them on.

And, and so I think, I think that's where we're gonna see a lot of value is, is those smaller models, those reasoning models, helping them move forward in that space because it just understands finance, uh, better than just, you know, pulling up my chat GPT on my phone and saying, Hey, tell me about my, you know, DSO and it's like, I have no idea what you're talking about. You know, it can define it for me, but it has no idea how to go grab my data. So I think that's gonna be the big piece, is being able to take those tools and apply them more directly into the finance space.

So it almost seems to me, and I think we're kind of trying to figure this out, is, are people just gonna consume this using some sort of AI agent that's built into the software? Are they actually gonna go to the trouble of building their own AI agents that are unique for their business? Yeah, And that's a great question.

And there's a lot of companies that are kind of going through that build by, uh, conversation today. There, there are a lot of great commercially available tools that are out there that can help them accelerate and see time to value today. And so I think when you have customers and, and, and companies who are, are really struggling really underneath it today, that, or, or there're under a mandate, you know, the, the CEO, the CFO, somebody says, the CTO says we need to do AI now.

And so in some instances they may not have the luxury of time to truly figure out how to line everything up internally to go kind of build it. Um, I get to build a second, but I think, you know, there, it's, it's actually never been easier to build your own agents, uh, within your ecosystem. So I think, but I think a lot of those companies, because they're under a time crunch, they're under a huge amount of pressure to actually just do their, do their day-to-day job.

I think many of them are, are pursuing kind of commercial solutions that are out there to move forward. As far as the building goes, you know, all of the ERP systems are starting to create essentially the opportunities to build agents within their ecosystems. A number of organizations, and we're one of them.

We'll use Claude internally to build smaller agents to kind of help us manage work to, to help with project workflow. Um, you know, you'll get Microsoft copilot and organizations, I know a couple of our customers that have, that have figured out ways to some kind of nifty ways to use Microsoft copilot to build some agents internally. And so I think what we were thinking about maybe three years ago in terms of a citizen data scientist, I think that's almost been overcome by this concept of kind of almost like a citizen agent builder, if you will.

Um, I, you know, I'm sure somebody has a better name for it than that, but it, it, it's essentially, you know, these people are kind of going, alright, what if I could do X? And they're basically just kind of going and figuring it out. They, they're not experts in the data, but they're using the tools that exist within their ecosystems to kind of go build it.

And so I do think you're gonna see a proliferation of agents both commercially available as well as self-built, um, really, you know, really over the next kind of year, year and a half. I, I think, and, and I talked to this about our customers because when they talk about where we're at in this space and really this transformative, you know, changes over the past number of years, I, I tell them, I said, we are the last generation that will hire exclusively humans into our organizations. At this point.

You're hiring humans and digital coworkers and, and that's not going to change. We're gonna continue to move forward in that space. And so, uh, I think the future is gonna be a hybrid workforce and it's gonna look that way.

Uh, we, you know, five years ago during the pandemic, we talked about a hybrid workforce as, uh, some people working from home, some people in the office. I think in this case we're now talking about who's actually doing the work. And in this case it'll be both hybrid, uh, human and and digital agent.

All right folks, you heard it here. The finance teams are gonna be at the forefront of this transition. There's no doubt about it.

And we're all gonna watch and see what happens. 'cause hopefully we're gonna learn from their adventures. Hey Mike, thanks for being on the show.

I appreciate it, Mike. Thanks for having me. And thank you all for watching the latest episode of the Techstrong AI Leadership Inside series.

You find this episode and others on our website, we invite you to check all those out. Until then, we'll see you next time.