Greatest Hits: Why AI Still Feels Hard for Finance Teams

0:01The question again won't simply be what does the AI recommend, it's going to be can we trust it, can we explain it and are we confident. So overall the CFO becomes much more strategic and also operational than it's ever been before.

0:20Megan Weis: 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 Personev, the trusted leader in finance and accounting outsourcing for over 30 years. See how Personev's customized solutions can help you streamline your operations with teams that start as small as one. Visit the website at persanev.com to learn more. I'm your host, Megan Weiss. Let's jump right in.

0:55Megan Weis: Welcome back to CFO Weekly. I'm joined by Alex Curran, Chief Executive Officer at Aptitude Software, a global provider of finance technology solutions helping organizations move toward autonomous finance through AI, automation, and intelligent data management. Alex has built her career at Aptitude for over a decade, rising through leadership roles across sales, North America operations, and executive management, before stepping into the CEO role. In this episode, we'll explore the future of finance through the lens of AI readiness and data, what finance leaders need to put in place now to benefit from emerging technologies, and why clean, connected data is becoming one of the most strategic assets in the office of the CFO.

1:41Megan Weis: So welcome to the show, Alex, and thank you so much for being here this morning. Thank you, Megan. Thank you very much for having me and obviously looking forward to the discussion today. Yes, AI is such an exciting thing, and I'm very excited about this conversation. Totally agree, especially with the SaaS apocalypse that we've seen recently. I think it's super topical. So your view is that AI and finance isn't being held back by the models. it's being held back by the architecture underneath. So describe to us what you mean by that. I think, look, for me as a CEO of Altitude, it's, I think, really, really important to be out on the front line, spending time with customers and partners to properly understand, right, the pressures that they're facing.

2:29And I think what, based on all of those discussions, I would say what has become super clear that I would describe it as that finances has been in this kind of perpetual modernization cycle, but not at the core. And I would say kind of more around the edges. So looking at new reporting tools, consolidation systems, cloud migrations of GLs, but underneath it, the core finance architecture. So if you think about traditional ERPs, largely have stayed the same and there hasn't been a huge amount of kind of new entrants or competition in that area. So I think the moment that it really kind of became clear to me in terms of it wasn't just a model challenge, it was architecture, was when AI started entering the conversation.

3:20Because suddenly finance teams, they wanted real-time insight, they wanted automation, but also on top of that with some intelligence. But the underlying systems that we see CFOs and the CFO office and their wider teams use and have become to rely on are those traditional ERPs that were built around batch processing. They create kind of proliferation of fragmented data and organizations are still heavily reliant on those month end cycles. And for me, that's the issue. The AI models themselves are incredibly powerful. But what's essentially holding organizations back is the architecture that's underneath them.

4:00Because if your finance data is delayed or aggregated or fragmented across systems, AI can only go so far. And that's when it clicked for me. I don't think it's a finance problem. I don't even think it's an AI problem. I think for me, it's fundamentally an issue with the architecture that underpins every single finance team across the world, no matter what sector or geography.

4:24Megan Weis: It's always kind of surprising to me with all the technology available that there's still a huge alliance on Excel and a lot of processes are still very manual. Absolutely. As I said, Roy, you spend a lot of time talking to organisations and talking to them about their finance infrastructure and architecture. And literally all of them are reliant. You take it from the source systems all the way to the GL that in the middle is still heavily reliant on manual intervention, teams, scores of individuals, manually reconciling charts, accounts, and then pushing that data, those results through lots of different access databases, lots of different subledgers, and then posting it to the GL.

5:11And then obviously then posting the results to that presentation layer that sits off the back of the GL. But the challenge they have is that the process I've just described doesn't take 30 seconds. It typically takes weeks and tens, if not scores, or hundreds of people to support. So by the time the CFO and the CEO have those results, they're looking and working off backward looking information, out of date information as well. It's crazy. And all types of organizations are still heavily reliant on those manual processes that have been built up and around some of the more traditional systems that they've been using.

5:50Megan Weis: So can you walk us through your journey at Aptitude Software and how that progression has shaped your perspective on where finance is headed next? I've spent the last 20 years at Aptitude working with many different types of orgs, all different shapes, flavours, sizes across many different sectors. And as part of that, also worked incredibly close with the big four. And obviously, we're a software vendor, right? So a lot of that time has been spent helping some of the world's largest, most complicated organisations implement their finance and accounting systems. And that ultimately leads to us underpinning and supporting the numbers that they report to the street.

6:36And a large proportion of them are obviously large public organisations. And I think what that experience has given us is a very, very broad view of how finance technology has kind of evolved over the last two decades. And I think what is really, I would say, quite interesting is actually how consistent the challenges are across lots of different industries and geographies. Same, same, but slightly different. And organisations, right, as everyone's aware, they've been investing billions into finance transformation. But what we've recognised and what we've been seeing, even after making that investment, a large number of them are still struggling with fragmented processes, not having the right level of information at the right time.

7:26And again, to my point earlier in your question around the reliance on Excel, still hugely reliant on manual effort underneath all of that. And I think what we've seen then on top of that and over time, that has created quite a lot of paralysis and inertia and quite a lot of fatigue around traditional ERP transformation. Because they are associated with them being extremely expensive, taking obviously a long time to implement. And at the same time, in parallel to that, we've also seen, which I'm sure you've seen and I've listened to many people on your podcast talk about, but at the same time, the expectations on finance are also changing, right?

8:08So they are in need and under pressure to make decisions faster, have that real-time visibility and actually undertake a lot more of a strategic role as a CFO. But again, going back to the architecture point, the underlying architecture hasn't moved at the same pace. And that's really what has shaped my perspective on where finance is headed. And I think finance is moving towards a far more real-time intelligent, I think intelligent is an important word here, that requires a very, very different type of finance architecture than the industry has historically relied on.

8:47Megan Weis: And tell us a little bit about Aptitude Software, what it is they do and how it's differentiated. So Appitude Software, we've been around for a long time, but the last five to 10 years, we have been in the finance, accounting, regulatory space. And that has enabled us to gain access to obviously our high value client base and enabled us to gain access to the CFO office where we have been tracking and monitoring trends. and through that again have identified that the CFO is essentially under pressure to be the strategic co-pilot for their business. But the challenge that they've had as I've mentioned is the systems that they've relied on aren't able to support those pressures.

9:33So to drive revenue faster, to manage risk, to support compliance and also be AI enabled and all simultaneously. And therefore, we've seen a gap in the market for a modern AI native finance ERP that has the heritage of all the intellectual property and experience that we have developed over many years, but also provides organisations with that modern AI native tech stack. and also in addition to that enables organizations to go live in a handful of weeks or a handful of months versus a multi-year rollout and actually enables organizations to support a real-time P &L and we're doing that not just for small organizations that are growing rapidly but we're also doing that for very large global multi-entity multi-currency blue chip organizations where in In one instance, we're processing 400 million journal lines over a two-hour period and providing them with a live P &L.

10:37So, yeah, that's what we do and that's who we are. And we feel that we're well positioned to obviously support CFOs and the pressures that they're under in this current market.

10:49Megan Weis: Most finance leaders and or vendors, they think they're closer to being AI ready than what they actually are. So where's the gap between what they believe and what's true? I think the conversation has changed dramatically over the last few years. I think historically, finance transformation was mostly about automation. So making, I would say, existing processes kind of faster, cheaper or more efficient. but today the conversation is more about my favorite word at the moment which is intelligence right so cfos want that real-time insight the predictive decision making and ai embedded kind of directly into their finance operations but for us and what we see when we're out talking to our prospects with our partners is that's where the gap starts to appear because a lot of organizations think that they are ai ready because they've maybe simply kind have implemented a dashboard or a co-pilot or an AI pilot, or they have a general ledger that has the concept of an AI agent.

11:53But for us, and what we talk to our customers about is that AI readiness isn't about the tools necessarily. It's about the condition of the underlying data and architecture. And what I talk to my teams about is that essentially it comes down to, I would say, quite a simple question. So is your finance data real-time structured and fully traceable and auditable or is it still fragmented batch-based and reconstructed at a month end and i would say for most organizations it's still the latter and the problem i think becomes obvious the moment you then try to operationalize ai and finance because in finance ai doesn't just need to produce an answer really importantly it needs to produce an answer but that answer needs to be provable and auditable so you need trust you need lineage your governance and you need to be able to explain every output.

12:45And that's why we still see such a big gap between AI ambition and measurable enterprise impact. And the models, as I said, are moving incredibly fast, but the real constraint is the finance architecture and that data foundation underneath them.

13:02Megan Weis: And many companies are obviously very excited about AI and rightfully so, but as we've discussed, their data foundations are still fragmented. So how big is the gap between AI ambition and data reality? And how can companies begin to close that gap? I think the gap is significant. I think positively, though, there is a willingness across many organizations now. And I think boards, audit committees, placing a lot of pressure on organizations to become AI ready. I think, my guide to the audience is to take a step back and rethink and look at the architecture that you have supporting your AI strategy.

13:51So you need a system that underpins it that is your system of records. So it holds your financial information, even if you have vast quantities of financial information, at a very granular level in a really cost-effective way. And that information obviously It needs to be books and records quality. But then you also need to have an AI native technology that underpins that system of record to then be able to connect with market leading AI technologies of their choice. And for that organization not to be wedded to one particular AI tech, because I think as we've seen, right, the AI apocalypse has happened.

14:33And we can see how quickly organizations are needing to move and the speed of change of tech. So you want a technology that's open, that can integrate with many different models, different agent types, provides the ability for them to bring their own agents to that software solution. And I think that will change the way in which people go out and evaluate software. I think there are a lot of software products out there at the moment that, as I said, are kind of batch, so you can't really support real-time processing and are essentially kind of layering in AI. So the challenge with that AI technology within that software product is fixed.

15:16So when that organization goes live with that technology, then they will be wedded to that particular AI tech. And that's just not something organizations that are forward thinking in terms of AI ambitions are comfortable with in this modern kind of finance era.

15:32Megan Weis: And if you're a CFO sitting on Oracle or SAP today and you want to be ready, AI ready in 18 months without having to endure a multi-year transformation program, what should you do? Where do you start? I think the first thing CFOs need to understand is that becoming AI ready doesn't necessarily mean replacing the kind of the operational component of an ERP. I think some of the traditional vendors, Oracle and SAP, I think still play an important role in large enterprises, particularly around operational workflows like procurement, HR and supply chain. But I would say what's becoming increasingly clear is that they have not been designed to be AI native finance platforms.

16:20And that's why we're seeing the emergence of a new category, which is called finance ERP. So it's not operational ERP, it's finance ERP. So these are platforms designed specifically for real time finance, continuous accounting, as we've talked about, and to support obviously being AI first. So I think the market, in my opinion, is starting to split. So operational ERP continues to exist, but finance, I would say, is increasingly being modernized separately through a next generation of finance platforms built for the AI era. And importantly, that changes then, I think, the implementation model as well.

17:00So if someone is telling you it takes 18 months or two years just to modernize finance, I think that raises a bigger question about whether the architecture that they're promoting and underpins their software product is truly modern. because one of the defining characteristics of the new generation of finance ERP platform should be speed, right? Faster deployment, faster configuration and faster time to value. And the organisations moving fastest today, I think, are proving value in weeks or months and then expanding incrementally over time rather than committing up front to another massive transformation programme.

17:39And we're already seeing that with organisations that we work with. HCSC is an example where they significantly reduced their M &A finance integration timelines by modernizing that finance layer alongside their existing systems. Again, we implemented that in a handful of months. And then we've also gone live with a company called Pay by Phone, where we implemented full-blown accounting engine and sub-ledger, where we're processing 150 million journal lines across the day to support their AI strategy and provide them with a real-time P &L. And we did that project in six weeks. And I think that is where the market is heading.

18:19Megan Weis: And I'm just curious, do you feel like maybe smaller mid-sized companies have an advantage right now when it comes to AI because they're more nimble and have less investment in legacy systems? A good question. I'd position it as I think it doesn't matter on the size of organization what matters is the ambition and the openness to truly adopt an ai native finance platform but critically right with a vendor that has a proven heritage of having done this before and i think organizations that take that leap and move whether or not you're big or small i think can very quickly get a get ahead of the competition But I think I was reading an article basically stated that they'd canvassed hundreds of thousands of organizations and only a small percentage of them have enabled a real time infrastructure and architecture.

19:22So those that get ahead of that and can actually enable that and therefore also utilize AI effectively on top of that AI native infrastructure, I think can, as I said, get ahead of the market very, very quickly.

19:35Megan Weis: And where are you seeing the most immediate and valuable use cases for AI inside the finance function? Is it AP, FP &A, reporting? I would say what we're seeing first, I would say our AI use cases that move finance from being kind of reactive to proactive. I would say one of the biggest areas is around continuous close and reconciliation. So instead of obviously finance teams spending days at month end identifying kind of mismatches and exceptions, what we're seeing our customers look at AI to support is AI monitoring those transactions on a continuous basis, kind of detecting anomalies in real time and actually looking at automatically resolving those routine wrecks before finance teams ever see them.

20:29for us is what we're seeing is starting to enable concepts like a zero-day close. So I'd say that's the one biggest area. I'd say the second biggest area is definitely around FP &A, so real-time profitability and operational decision-making. So in banking, as an example, AI should be able to analyse profitability, liquidity and risk at a customer or transaction level as the events happen, which then obviously in turn obviously right helps organizations make much faster decisions around pricing risk and the things and then in telcos which is obviously another key sector tech as well as well as insurance for aptitude but we're also seeing similar things around billing and usage patterns and maybe churn and margin prediction and optimization and all of that happening while the accounting period is still open rather than after the fact and then i think the third area which i think people are still exploring i would say is around ai agents kind of being embedded directly into finance operations and for example like using agents for variance analysis maybe journal recommendations policy checks but i think the really important point is that the value comes when AI is obviously embedded directly into the architecture of finance and not simply kind of layered on top of some of the legacy systems.

21:59They are typically the most common use cases we've been working with our clients on.

22:04Megan Weis: And this next one is always a hot topic, but as finance becomes more autonomous, how do leaders balance efficiency gains with governance, control and trust in the numbers? That is a very, very good question. I think that's definitely something that we're seeing organisations needing to spend a lot more time on. I think everyone's kind of seen in the press that a lot more governance is going to obviously come into play and I think that is definitely something that people have to be fully aware of. I think AI is very effective in enabling organizations to kind of move the business ahead and move the business forwards but you still need that trust, that lineage, that governance and the explainability behind a free output and therefore making sure that you are using AI safely and within a robust system of record that has the kind of sector specific frameworks that enables the AI agents to work within is incredibly important because I think as I said earlier right AI is fantastic for all different types of teams to generate an answer.

23:31But for finance specifically, you have to be able to prove what made up that answer. It needs to be fully auditable for the finance team to be able to trust it. So I think you do have to balance that ambition with obviously utilizing AI in a safe way.

23:51Megan Weis: And how do you see the skill set of finance teams evolving as AI and intelligent data platforms become more embedded in day-to-day work? So I would say, working out whether or not it's five or 10 years, but I'll say both. So I'd say over the next five to 10 years, I think the role of the CFOs quite significantly. I think, as we're all aware, right, that historically the role has definitely centered around stewardship and kind of being in control and explaining the performance, but explaining the performance kind of after the fact. But as we will start to see, finance will become more real time.

24:32And as AI becomes, I think, more embedded in those operations, the CFO, the wider team around them, becomes much more involved in actually actively shaping the direction of the business. And I think the biggest shift is really the mindset, right? The role becomes less retrospective and more forward-looking and less focused on what happened last quarter and much more focused on what we should do next. And I think that changes the skills required as well, because I don't think CFOs, I don't think they necessarily have to be technologists, but they do need a much stronger understanding of how data, AI and a modern finance architect actually drive business decisions.

25:15because increasingly, we talked about earlier, it doesn't matter if you're big or small, it's those that use AI native architecture and fully embrace AI in a safe way. That's where the competitive advantage will come from. And I think you'll see a lot of organizations, big or small, who do that, thrive and become the market leaders based on the power of having that real-time information at their fingertips. And therefore, I also think CFOs will need to become much more comfortable operating in much shorter decision cycles, right? So as I said, historically, finance, monthly, quarterly, I think in the future, that insight becomes continuous.

25:57And alongside that then comes, I think, a much bigger responsibility as we've been talking about in terms of governance and trust. But as AI becomes more embedded into that finance team, the sort of kind of finance operations, the CFO then has to become the executive owners of that explainability, the governance and the confidence in AI driven decisions. So the question, again, won't simply be what does the AI recommend. It's going to be can we trust it? Can we explain it? And are we confident when acting on it? So overall, I think the CFO becomes much more strategic and also operational than it's ever been before.

26:37Megan Weis: Such an exciting time to be in finance as it just evolves into so much more of a strategic function and role within the organization. Absolutely. I think, yeah, right now, I think being in the finance role enables you to be at the forefront of your business and be able to be responsible for making sure that you have the data that can service the wider functions and to be the strategic steward. So, yeah, no, super exciting and very much looking forward to seeing how the market evolves. Alex, thank you so much for being on the show today and sharing your insights with us. Thank you very much, Megan.

27:17Megan Weis: Great to meet you. And thank you for having me. And to all of our listeners, please tune in next week. And until then, take care.

27:27Megan Weis: You've been listening to CFO Weekly presented by Persona. Please subscribe wherever you get your podcasts to hear all of our episodes. Want to learn more? Check out persona.com. Thanks for listening. You