The AI Data Pioneer: Building the Future of Finance with Alex Curran

Welcome to the Future Finance Show, where we talk about treasury management on Mars.

The challenge that they all have as CFOs, controllers of a division, is actually they've got to fix the architecture first. They've got to fix the data problem first before they're able to use AI technologies in an effective way.

Alex Curran, CEO of Aptitude Software, where she spent over 12 years working her way up from sector sales manager to the top seat, now leading the charge into AI-native finance ERP.

I know you work close with a lot of CFOs. I'm curious, how would you describe the shift in what's expected in the finance function today? What are CEOs expecting of CFOs?

That is Immense pressure on CFOs to be the strategic copilot of their business. Boards, audit committees, investors, shareholders want someone who can model scenarios, price decisions, support real-time forecasts with confidence.

We used to need 30 accountants just to do data entry, and that's going away. So if I'm going to be an accountant, what am I adding beyond data entry?

For me, finance teams are the teams That's it.

Future Finance is brought to you by qflow.ai, the strategic finance platform solving the toughest part of planning and analysis— B2B revenue. Align sales, marketing, and finance seamlessly, speed up decision-making, and lock in accountability with Qflow.ai. Welcome to another episode of Future Finance. Today I'm joined by my co-host. Some know him as Glenn Hopper. I refer to him as Captain AI or Captain America. How you doing, Glenn?

I'm doing good. It feels like it's been a while since we talked, but I think I'm just moving at like AI speed right now. So like, you know, minutes seem like hours, hours seem like days.

Well, I know you're so productive now that you use AI for everything. I mean, I'm surprised you haven't outsourced your podcasting to AI yet. I'm waiting for you to show up with your digital avatar. And at first I'll be fooled and then I'll ask it a difficult question and I'll be like, wait, something's not quite right. And I'll know. But our guests, we're thrilled to have you here again today. We have a guest with us, really excited to dig into AI and have some great conversations. So what I'm going to do is I'm going to turn it over to Glenn to introduce our guest today.

Really excited about our guest today. We have Alex Curran, who is the CEO of Aptitude Software. Alex has been with Aptitude for over 12 years, worked her way up from sector sales manager through VP of sales and EVP of North America before taking over as CEO in late 2023. And Aptitude has spent over 40 years building finance systems for some of the world's largest and most complex organizations. Now, under Alex's leadership, they're expanding into an area, Paul, you and I have talked about a lot, the AI-native finance ERP. They're doing this with their platform, FinApps, going head-to-head with both the legacy giants and the wave of VC-backed startups trying to reinvent the space. Alex, welcome to the show.

Thank you very much. Delighted to be here. Again, thank you for inviting me.

We're really excited to have you. And for longtime listeners of the show, and I don't know if you're familiar, but we've talked to NetSuite, we've had on the other AI ERPs. So really just, I mean, this space, it's such an interesting space right now because it seemed like the incumbents for years just didn't change. They didn't have to. They had basically monopoly power and just the same app that you had. It seems like in 1997, you know, you've just had the same thing 20 20, 30 years later, now with all the action in this space, there's a lot more going on and I can't wait to hear what you guys are doing. So I guess maybe to kick off, give us the quick version of your background and really what Aptitude Software does and where FinApps fits into the product story there.

Yeah, of course. I'd love to. So Aptitude, believe it or not, right? We have, many of you probably haven't heard of us, but we have been around for many years, as you've just said. And I would say we've spent the last 30, 40 years building accounting solutions, regulatory engines that a number of different types of organizations run their finance processes on. So, we've worked with some of the largest, most complex banks, insurers, telcos in the world. But I think essentially what we've recognized, I guess, by being in and amongst the CFO office, is that I guess the products that we had originally built for complexity at scale could also solve a much larger problem than just the largest banks, right? So, we have recognized exactly what you have just mentioned, that the choice on the market for CFOs at the moment in terms of looking at the right architecture to underpin their finance infrastructure, but also AI strategy is very limited, and what they can do is also limited. So, we have essentially built apps, which works with small ambitious organizations moving really quickly or wanting to move really quickly. But also we've built it specifically to be able to continue to support some of the largest, most complex enterprises. So again, right, same architecture, same standard of control, and no compromise either way. So FinApps is the product, the core product within Aptitude Software, and it is an AI-native finance ERP. So running the full cycle continuously, so revenue, cash costs, reporting, and essentially what, you know, we want to enable are companies that can run essentially finance in real time and not after the fact. So yeah, as I said, delighted to be here and can't wait to get stuck into the, in what I think is one of the most exciting topics that I think organizations have to solve for right now, which is how do they effectively utilize AI?

Yeah, I think everybody's trying to solve for that, how to effectively use AI. It's a little Wild West, I think, at the moment for, for people. I'm sure you see it, Glenn.

So absolutely. And, you know, what I'm seeing is the, unless you have your own, you know, AI engineers, machine learning engineers, and a huge dev team that you're incumbent on people like Aptitude to bring AI to you, otherwise to sort of take out that Wild West nature of it, where you've got everybody vibe coding their own apps and, and, you know, creating their own data dictionaries and all that too.

So Alex, I know you work close with a lot of CFOs, you know, some of the largest organizations in the world. I'm curious, how would you describe the shift in what's expected in the finance function today? Like, what are CFOs expecting? What are CEOs expecting of CFOs, people? Like, kind of how has it changed to say 5 years ago? I'd love to get your thoughts just because you talk to so many. We see, you know, what people say on social media, but there's social media and there's reality. And usually the two don't meet.

Yeah, totally agree. And like, look, I have to have some sympathy for CFOs that are out there, right? I think for many years, I think they've had limited choice in terms of the solutions that they're actually able to select from to be able to enable them to modernize their finance function. But I guess, you know, obviously going back to your question in terms of what's expected of the finance function and how's that changed, I would say That there is immense pressure on CFOs to be the strategic copilot of their business. I think ultimately the CFO's job has changed, and what we're seeing is, you know, boards, audit committees, investors, shareholders, and also, you know, the wider functions that the CFOs support want someone who can I guess, kind of model scenarios, price decisions, support real-time forecasts with confidence and in the moment. But I think what we typically see, and obviously to your point, right, we speak to lots of different CFOs, their teams, also the IT function that obviously underpins and supports those finance team members. And what we see, no matter what type, shape, size of organization or region, we see most finance functions are still struggling to close the books over a 5 to 15 day period. So, what we see is that they're still manually reconciling, still producing, I guess, insights over a period of weeks after a decision should have been made. I think we've talked about AI a lot, right, but I think the pressure to deliver AI has made that gap really impossible for CFOs to now ignore because AI will fundamentally and should be an enabler and really critical to their finance operations. But I think that gap is the data problem, which is then also becoming really visible at the board level. And just to give you, I guess, an example of a discussion recently. So I sat with a leader of a bank within a capital markets division who literally spent the best part of an hour talking to us about how painful, you know, their GL upgrade process had become. And his question was a really good one, right? So again, right, why— also going back to what we've already talked about, but why are there so many limited choices when it comes to the products underpinning their finance ERP requirements? And they're under so much pressure from their boards and audit committees to make sure that they're utilizing AI, utilizing the right technologies. from AI, and then also proving and demonstrating the benefits. But the challenge that they all have as CFOs, controllers of a division, is actually they've got to fix the architecture first. They've got to fix the data problem first before they're able to use AI technologies in an effective way.

Funny enough, we were having this conversation today. I just got out of a course I'm taking around AI, and somebody was saying, well, what do I do if my data's messy? And, you know, we were all like, well, you either Have to be able to give tools to AI to clean it and the processes or clean it ahead of time. Ultimately, you want to fix it at the source, but if you give AI garbage, you've just automated garbage faster. Yeah.

And also kind of the level of granularity and detail of the data. If you only have aggregated information, the AI scenarios that, you know, you can support and generate are not going to be very sophisticated, right? So you're still going to have to do that manual reconciliation to then provide and prove the results of the results of AI. So yeah, I think there's, you know, people are challenged at the moment.

And I think you guys are in a unique situation to really understand the data foundation, whereas, well, maybe not unique. I mean, any, if you are the SaaS provider of the tool that people are using, you know the data structure and sort of how it's supposed to be, and you have an innate understanding of The data architecture and where everything lives. For a lot of CFOs right now that don't have that data foundation, it is what you were just saying, Paul. It's like, it's garbage in, garbage out. And if they don't, if you haven't built that foundation yet, you're going to have a harder time with AI. So are you, as you're thinking of an AI-native ERP and as you're continuing to develop FinApps, I guess you're, you want to give, in any sort of talk to your data methodology, you want to give users the ability to be able to query directly and not have to go build their custom reports and all that. But how is that, how are you thinking about that data foundation within the application itself and how AI interacts with it and how that might be better than just building an MCP server and letting people connect to the data on their own?

Yeah. So I think, look, for me, it's really important that when an organization is evaluating any product going forwards, whether or not it's CRM, finance-related, that you are making sure that the product that you're selecting is AI native. And what I mean by that is that it's a product that can work with any form of AI technology, any form of AI model to make sure That your organization doesn't get stuck based on the AI technology that some organizations, some vendors are kind of pre-embedding into their technology product, whatever that is, whether or not it's a general ledger, a subledger accounting engine, or an FP&A tool. So I think that's a key core fundamental principle with any evaluation that any kind of IT team or finance team are going through. I think, you know, we've just seen with Fable, with Mythos, you know, being tied to one model or one type of technology can obviously pose quite a big risk. So making sure that you are selecting a solution where you can swap technologies in and out is, I think, key core fundamental for Any risk management, cost management, as we also continue to see the commercial models changing, you know, right now on a, on an hourly basis.

Yeah, you may remember this, Glenn. I'm, you would probably completely agree. It's one of the first guests we had on. It was like guest 5 or 6. They shared a story about when AI first came out, they built a tool, but they made it completely dependent on that version of AI, that model. And when the model changed, the tool pretty much died. You know, it was that very early days, first learning. And I think now, you know, people are realizing, okay, you got to make sure it's independent, model agnostic. Doesn't mean you don't, you're not using a certain model, but you got to build it in such a way that you can hook and unhook them. And so I think you make a great point there, Alex. And it made it immediately made me think back to that, uh, episode we had where the person's like, yeah, pretty much killed my product. I'd love to get your thoughts. The, the ERP space, I think, has never been more crowded. Never had more innovation that I can think of, at least in the last 30 years. And, you know, you think of, there's the traditional players, they're going the bolt-on route. You know, maybe some of them are building new things. There's been a lot of rumors that NetSuite's going to come out with the new modern version, but at the moment you see mostly that bolt-on route. You also have, you know, these VC-backed-ups kind of building from scratch. I think you guys are somewhere in the middle and you see some different approaches. I've seen some say, Don't switch your ERP. Just, you know, let's be that agent layer. And so you don't have to deal with the pain of a new ERP. You see some say, hey, we're going to be the full ERP, including the resource planning, the asset. A lot of them are more what I call kind of GL light. And so I think it's really interesting, all these different things. And I'd love to get your thoughts with all this, right? You kind of have the— ERP Lite, the older tools, this layer approach. You have some that I'll call GL Plus. Some new tools are saying we do FP&A and a lot of other things, kind of a platform, but not the ERP side, a finance platform. I've seen a couple trying to do that with ERP. How do CFOs decide? I mean, what, what would you tell them in this space with, you know, so much noise going on?

Yeah, so I think, look, really positively. I think finally, you know, organizations are starting to see different options of ERP vendors become available. I think historically there's been, you know, the, the, the big players, so the top 3. And then if you look at small to medium organizations, you've got the Microsofts, you've got the, the NetSuites, you've got Sages, you know, you've, you've got the, the Epicals as, as examples. And I think, you know, Back in the day, I think all of those vendors were phenomenal, right? Fantastic, extraordinary in their support of organizations. And it was relatively easy for a CFO to make a decision, right? They could make, if they're a tier 1, that you've got 3 choices, you've got 3 choices, you pick one, and then you can run everything operational and finance through whatever solution that you've bought. But the market's moved on. Right? You need real-time, you need real-time streaming, as we talked about, right? You need a solution that is going to be AI native. And in line with that also, you know, we've talked about how the CFO role has also changed, right? So the CFO is expected to do more things than just close the books, which is historically what those systems have been designed to support. So I would say, obviously going back to your question, right, how should a CFO kind of evaluate this market with these different choices? I think every camp in this market currently has a bit of a limitation, and I think a CFO needs to be honest about which one they're walking into. So, I would say you've obviously got the legacy ERPs, again, right? Trusted, scale, decades of it, but they've really been built by batch logic. And we've seen those vendors bolt on the AI, and people don't want bolted-on AI because they're worried about what they're going to get left with. Because AI technology is changing all the time. So there's a fear that they're going to become Jurassic if they implement one of these solutions that has acquired a company and embedded that AI technology. They want that optionality. So what we've seen here are organizations basically waiting for an alternative on the market to support their complex multi-entity, multi-currency environments. And then on the other side, quite right, right? You've seen the VCs, you've seen a lot of cash from Sequoia or others. Go into some of these AI-native entrants, and again, right, they look cool, they look fresh, genuinely modern architecture, but you push them on, you know, being able to support multi-currency, multi-entity consolidation, FX translation, intercompany eliminations, you know, an organization that has the scars of going through finance transformation, I think they'll get there, but they're just not quite there yet. They haven't lived through the regulatory scrutiny that comes with, I think, operating at that level. So, the market, we have recognized, has been left with a gap. So, what a CFO actually needs is a vendor who genuinely has AI-native architecture combined with 30, 40 years of working with accounting, finance transformation programs at scale. And for us, you know, that's where we believe FinApps, our product, sits AI-native architecture already in production at multi-entity, multi-currency scale, working with, you know, some of the largest organizations in the world processing, you know, up to 400 million journal lines over a 2-hour period and actually enabling a real-time P&L. So I think it'll be interesting for the AI-native entrants, but also obviously Aptitude Software, and will be interesting to see how the traditional ERP vendors respond, which I don't think will be, you know, in, in, I'm not quite sure that they will consider retrofitting and rebuilding their entire infrastructure.

Maybe they will, but I'm with you. Most of them, that's a very hard build for something that old and all those customers and so forth. So it'll be interesting watch. And yeah, the newer tools, there's a learning curve when you go with the new tool. I mean, they're growing fast, they're doing a lot of great things, but That anytime you select a new company, there's going to be some risk and learning curve with it. Doesn't mean they may not be the right choice for you. You just have to go in eyes wide open with any finance tool if you decide to do that. So that's a good, all good points. Glenn, I know you're chomping to say something. I knew it. Ever feel like your go-to-market teams and finance speak different languages? This misalignment is a breeding ground for failure. Impairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet Qflow.ai, the strategic finance platform purpose-built to solve the toughest part of planning and analysis: feed B revenue. Qflow quickly integrates key data from your go-to-market stack and accounting platform, then handles all the data prep and normalization under the hood. It automatically assembles your go-to-market stacks, makes segmented scenario planning a breeze, and closes the planning loop. Create airtight alignment, improve decision latency, And ensure accountability across the team.

Well, no, I mean, I actually, I am chomping at the bit to get at my next question, but I guess one thing I would say, and this is the moat that is coming down, especially as fast as we can develop now, but it's not just the learning curve on new products. It is, Alex mentioned sort of the battle scars of going through and And seeing how people are using it, everything. And even like every time we talk to any of the new AI native ERPs, I think what a bold and audacious thing to do is like, we're not going to just build a sliver tool. We're going to build the whole ERP system. And there's just so much in that. And I know, you know, I'm not here to evaluate each of the ERPs, but I know some of them aren't great if you have, you know, manufacturing and inventory and all that. It's like, there's, they haven't had an opportunity to build out and address every different vertical and every different industry that's out there and the different types of accountings and all that FX and everything that Alex was talking about as well. But maybe with AI, and Alex, this is what I really want to get to your thoughts on. And I have a client right now, they're a legacy accounting system, not great reporting. I'm building a dashboard for them. I've tied in a connection and they, The dashboard's great. I can get a live feed, but what I can't get across to management right now is this isn't BlackLine. This is not real-time close. And they want, they're like, well, we want to see the current month. And I said, well, you know, your different bills come in at different, it's not a good snapshot. And they say, well, just let's do it for a couple months and we can build an estimate. I said, but the estimate's going to be different on the 3rd versus the 13th versus the 23rd. And it's, I'm having a really hard time with that right now. So this question's really top of mind. And when you said near-time or real-time data, we've talked about for at least 10 years, I'm thinking maybe even longer than that now. Probably longer.

The holy grail of accountants is the real-time continuous close, if you want to call it that.

Exactly. But in real, I mean, yeah, we can hear that in a pitch deck or whatever, but how many people have actually operationalized it? And I know the company I'm working with is not. They're not even, they're doing bank recs once a month. You know, it's like we're not even close to being—

Yeah.

The continuous or day zero seems like a wishlist still for 99.999999% of companies.

So, I mean, I guess from your perspective, and I know, I mean, if people really were reconciling bank accounts every day and if you had accruals in the middle of the month, I don't know. I don't even want to get into how you'd solve for it, but I've, I'd love to hear how you guys, I mean, I guess that it, maybe it's 2 parts. How are you guys seeing the real-time close? And then how do finance teams have to change how they operate to even be able to exist in an environment like that?

Yeah, look, a good question. I think for us, when we look at a close cycle of whatever it is, could be 5 days, could be 1 day, could be 15, could be more, I would say It typically isn't obviously like an internal kind of process failure. I think for us, it's definitely kind of the systems that underpin it, an architecture challenge. And I think that's really, I would say, quite an important distinction to make, because I think we do see a lot of organizations trying to fix it as a process improvement versus actually kind of looking at the foundation that they have within their organization, And starting kind of from bottoms up. But I think historically, again, right, as I said, I've got sympathy for these finance teams and IT teams, the systems that they've historically had at their fingertips to use, because most of everything that they've utilized to date, right, has batch logic baked into the system. And again, right, you can improve discipline, you can add more reviewers, tighten the checklist, but the underlying system has to run its cycles. before it then even produces a number, right? So therefore, you're kind of optimizing around a constraint and not really removing it. So what we talk to our customers about is that what actually has to change is that you've got to try and get data captured at the event level. So as it happens, then you've got to get controls applied in the flow rather than obviously checked afterwards. So we see, you know, some reconciliation tools and consolidation tools being used to the backend, but they're kind of a plaster over a problem. And what you want to see is reconciliations running continuously in the background. And I think when that's the case, and close stops being obviously a bit of a sprint and actually becomes a formality, and you're not reconciling the past anymore, and you're building the future, you're essentially kind of confirming that the work, you know, has already been done. And I would say then the working day changes with it, right? I think, in terms of how we are seeing organizations operationalize these real-time P&Ls and having a real-time close. And we're doing it for some organizations that are $300 million in revenue. We're processing 120 million finance records an hour and literally double-checking payments, reconciling payments with payment providers, merchants, enabling organizations to be able to drive new price pricing strategies and launch new products in minutes, in seconds, and also be able to obviously keep a, you know, a check on margins. Doing it for small companies, and we're doing it for a $100 billion organization where again, where we're processing 400 million journal lines over a 2-hour period to enable that. But in terms of how they are changing their function, what this enables is that— give you one example— one of our customers, I think, had 100+ accountants, and they have been able to I guess, to change the profile of those accountants, obviously reduce the team number and size, but actually inject, I would say, more data scientists, more data analysts into their team, because they've got that real-time information that they need to analyze and then work out how they support the wider functions. And we're also seeing marketing actually being integrated into the finance team so that both teams can work really effectively together on that real-time information to be able to look at how they can better launch promotional schemes, launch products, and what changes that they need to make to those pricing structures, methodologies for a specific demographic, as an example. So yeah, an interesting time for the organizations that have been able to achieve that. But it is, I think, which is a real positive. I know people have been talking about it for a long time, but it is now possible. And I think that's a key takeaway for the audience.

Yeah, and I love to hear you say that because just yesterday, I get asked this all the time and I'm a terrible futurist, it turns out. I think I can directionally figure out where we're going, but trying to put timelines on anything, I've given that up.

You're Captain AI.

Yeah. I'll ask Claude what—

Yeah, exactly.

Predictions for me.

Yeah.

So I was asked yesterday, you know, how worried should I be about my job? This is by a a controller. And I don't want to be overly optimistic and I don't want to err on the other side of doomsday-ish, but I do think that, you know, what, 120 years ago, you know, 80% of the jobs in the US were in agriculture and Industrial Revolution happens and we move on and then the web happens and, you know, nobody knew what a web content developer was in 1981. I mean, so, There's going to be new opportunities out there. But when you were talking about this specific example, and yes, there is a push to get more efficient and I get it every day from clients is I either, I want to reduce the size of my team or I don't want to hire any more people, but we want to grow the business. And I think that what this just reiterates, everything you just said just reiterates to me, you talked about bringing in data scientists and all that. And I understand we as accountants and or finance professionals and That we've picked our lane, we've picked our domain that we're going to be experts in. So it's a big ask for us now to become, you know, pseudo data scientists or engineers or whatever, but it's a lot easier with the technology to be able to do it. We don't need to be able to write Python anymore or whatever the barriers used to be. But I think that there's going to be opportunities in the future. It's just that Just being an accountant or just being a finance person, it's not going to age well. So it's really important that we all, like, if you want to stay relevant and promotable and hireable in this AI era, you have to understand that we used to need 30 accountants just to do data entry, and that's going away. So if I'm going to be an accountant, what am I adding beyond data entry or, or whatever those sort of basic level tasks are. So, you know, I don't know how I got up on that soapbox, Paul, but I think it's, it's important as we look at the, how these tools can do more and more that used to require people, like the number of transactions you were talking about processing. I mean, that's a few years ago would have been unfathomable because you had to have a human in the loop at way more steps than Probably are required now by your software.

Totally agree. And obviously, you know, understand kind of some of the nervousness out there across finance teams. But, you know, for me, finance teams are the teams that are intimately, right, familiar with the information, intimately familiar with the data. And I think for me, it's all about then thinking through how that data is used effectively. to make sure, right, that an organization stays compliant, that risk is mitigated and monitored, and also that profitability and revenue growth obviously stay top of mind, and that everyone's doing everything they can across the organization to make sure that the company stays and remains safe and successful. So, I think in reality, right, I think accountants and finance teams I think that is still part of their job. I think the challenge that they have at the moment is that a large part of their job is kind of in that manual reconciliation, manual effort to get to the right result. So, they still have to make sure that results are correct, but it's then what do they do with those results to service the wider business? And I think that's actually a really exciting place to be. So, I don't see them being made redundant. I think it's just an evolution of Yeah, definitely.

We're seeing an evolution and how it all plays out, we'll see. But as we, you know, I think, you know, a lot of people thought when the computer came out, we'd get rid of a lot of accountants. We ended up needing more. And so it's just, but it's different, right? If you ask an accountant, the work they were doing before the computer 50 years ago was a lot on a green ledger and a pen and key and still reconciling. Now we're eliminating more of the reconciling.

So it'll be interesting to watch how it all changes, but Yeah, and I think everyone said the same about AI across not just finance, but across every, you know, industry sector that there is, right? That, you know, AI is going to, you know, have an impact and a reduction on the workforce. But again, I think if you look at AI, if you're looking at AI and utilization of AI in marketing, no offense marketers, but if you get the wrong result, I don't think it's disastrous, too dangerous, right? Like it's an adjustment of a promotional activity, product launch that needs to be tweaked. Again, right, I think the role of the accountant becomes even more important with the advent of AI and the use of AI in finance, because if you're using AI on information, you're generating a result that then is going to be published to a market which impacts a share price, as an example, you have to absolutely make sure that you get that result correctly. And I think for a long time, probably an elongated period of time, you know, that human interaction, those checks, balances are gonna be key core fundamental across most organizations in the utilization of AI within a finance team because that result has to be provably right with the auditability. the governance, the compliance, and the controls around it. I think it's gonna take an awful long time for people to get comfortable before they trust AI implicitly.

Yeah, no one has to attest marketing copy, you know, no CFOs or no CRO or whatever is signing off on the marketing.

So I have a question that has not on our schedule here, but why is it every finance person uses marketing as their example when they're like, You know, marketing doesn't have the same— I do it. Glenn does it. You just, I just think it's funny. We all immediately go to marketing. Is there something with finance and marketing that I'm missing?

I mean, couldn't you think? I mean, there's, it seems like the thought process is like, it's completely left brain, right brain, right? That's probably completely different.

We picked the one that's most opposite from us.

Yeah. No offense, marketing teams out there.

Well, I got myself in trouble in grad school and now obviously I run my own business, so I have to do marketing. But all us finance people would always say, you guys are the soft ones that can't do math. That was always the, you know, the joke to the marketers in grad school. Now I, I have much more respect for marketers. I only say that tongue in cheek. There's a lot of great marketers.

Yeah, exactly. And the partnership that finance and marketing teams can, you know, can establish with, with real-time information and AI, I think it's a fantastic Yeah, a fantastic partnership to, to, to, yeah, to be sweated as an asset.

So, Glenn, we need a marketing agency to sponsor this section. Love your marketing team, we'll call it.

And I'll even, I'll even sprinkle some more praise on marketing. If you think about the data that marketing had and the analytics they were doing, they went way before finance did on analytics.

Oh yeah.

And machine learning and Of course, when you're having, when you have purchase behavior and A/B testing and all that, they had a lot more data than just the general ledger that we had. But I was always surprised because all the things that we just said, it's like, how is marketing so far ahead in machine learning than finance is? We're the OG business analysts. How do we not?

Yeah, it's funny. All right. So I think we're going to move into our kind of AI section here. So what we do, Alex, so you know how this works, is we had AI create 25 questions based on what it could find about you on the web, the questions we gave it, your bio. And we asked it to kind of create some of the unique, fun questions. And Glenn and I ask one question, but we do it a different way. So I give you one of two options. You could do human in the loop, if we want to call it that, and pick a number between 1 and 25.

Yeah.

Or I could let the random number generator pick a number between 1 and 25, and we can just keep going with technology.

Keep going with technology. That's what I say.

All right. It picked question 23. I have no idea what question 23 is, but we're about to find out. All right. You've worked with Big 4 partners for years. Without naming names. Oh, interesting. Where is this going to go?

Oh gosh.

What's the Funniest or most absurd thing you've seen in a big consulting engagement?

So I would say I'd bring it back to maybe, you know, a process that we were running, I guess, a few years ago. And I think what highlighted why organizations needed an alternative to some of the maybe bigger vendors was that we were seeing estimates from some of these consulting teams around, you know, 100,000 to 150,000 days of implementation to get, you know, a finance system live. And I think in this day and age, if someone's being told that that is how long it's going to take you to implement something and get live, then I would move very quickly on to an alternative partner and software provider. So yeah, hopefully that doesn't get me into too much trouble.

I think you'll be pretty safe with that one. We can't, we can't share that one out there and wait for the phone to ring. All right, Glenn, explain your process, Captain AI.

Yeah, so normally, and it's been a minute since we created these, so I'm not sure which AI did, but I just fed them into Claude. And basically my thinking is I'm just trusting the bots. I think, well, if the bots came up with the questions. I ask it, what's the best question to ask here? And let me run it now and see what it says.

Well, and he's doing that every so often. We end up with the same question. It's happened like twice. It's kind of funny, but we'll see if it, see what he gets.

Oh, this is a good question. If it, if the facts are correct, I don't remember on our research, but it says you joined Aptitude as a graduate hire and became CEO of the same company. Was there a specific moment where you thought, I could actually run this place someday?

So, I'm pretty ambitious, I would say. So, probably from day one, I probably set my sights on leading a team, I think, first of all. And then as soon as I kind of came into the organization and saw what I think were quite, you know, 2 quite inspirational CEOs, definitely very quickly thought that that would be something that I would, would like to be in. Yeah, thankfully, right, had some fantastic opportunities, I guess created some opportunities, but also supported by, you know, some fantastic talent at Aptitude Software. And as a team, right, we have done incredibly well as an org and grown the North America business and obviously now focus on the global business. So, yeah, I would say pretty early on, probably within a matter of months of joining. So, yeah. Wow.

Well, Paul and I need to take any sort of leadership mentoring coaching you can do because it's frequently been said that neither of us could lead a 3-car parade. I think, isn't that accurate, Paul?

I might be able to do a 2-car one, maybe if I'm towing the other car.

No, that's great. And I was a little bit worried. I sometimes, you know, we haven't been bitten by a hallucination yet, I don't think, in our portions.

Or the guest has been too nice to Tell us.

So, well, Alex, we really appreciate you coming on. I know we've been planning this forever, um, and I'm glad we were able to get our calendars aligned. This has been an episode and we really appreciate your time.

Yeah, very much so. It's been very enjoyable. You guys do a great job and, uh, yeah, big fan. So thank you very much for having me on the show today.

Well, thank you for joining us. Thanks for listening to the Future Finance Show, and thanks to our sponsor, qflow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice. And may your robot overlords be with you.