3126: Strategic Finance for 2025: AI, Automation, and Liquidity Mastery

[00:00:04] How are CFOs transforming their role from traditional financial oversight to becoming strategic business partners in their organizations?

[00:00:15] Well today I'm joined by Bob Stark, he's the Global Head of Market Strategy at a company called Kyriba.

[00:00:22] And together we're going to unpack how AI and automation are empowering CFOs to go beyond number crunching.

[00:00:29] And together we'll explore new responsibilities they might be facing from cash forecasting to fraud prevention

[00:00:37] and ultimately how data-driven insights are reshaping the future of finance.

[00:00:43] And Bob's also going to share his thoughts on strategic implementation of AI,

[00:00:48] emphasizing its role in improving efficiency and accuracy without getting bogged down in all those unnecessary complexities.

[00:00:56] So how can technology really make financial management more agile, more forward-looking?

[00:01:02] And what are the real challenges that CFOs face as they take on their expanded roles?

[00:01:08] Well enough from me, let's get Bob on to talk about all this now.

[00:01:13] So a massive warm welcome to the show.

[00:01:17] Can you tell everyone listening a little about who you are and what you do?

[00:01:20] Sure, I'd be happy to. So my name is Bob Stark. I'm Head of Enablement and Strategy at Kyriba.

[00:01:26] Kyriba is a liquidity performance platform provider. We've been in the finance space.

[00:01:33] I guess we were a fintech before it was fintech was a word.

[00:01:37] But as a multi-billion dollar organization now, we're at a point where we like to do fun things such as help CFOs improve automation efficiency,

[00:01:46] which I know we'll get into today. From a personal standpoint, I've been in treasury payments and risk management for,

[00:01:53] let's just say a couple of decades deal. And we'll just not date myself too much when I say that.

[00:01:59] But I've been at Kyriba for 13 years since 2011 when we were a much smaller organization,

[00:02:06] really focused more on cash management and treasury at the time.

[00:02:10] And I love that line you use there, using fintech before it was even a word.

[00:02:15] And I suspect that you've seen firsthand the role of CFOs and how it's evolved significantly beyond traditional financial management.

[00:02:24] So how do you see CFOs balancing these new responsibilities in areas like strategic business partnering and cash forecasting with their core financial duties?

[00:02:35] What are you saying here?

[00:02:37] Yeah, I like that you asked the question that way because we've definitely seen a change in the past,

[00:02:43] I dare say maybe three, four years, certainly since COVID where the CFO and the treasurer even,

[00:02:50] and others within the office of CFO were put on the hot seat with simple questions such as,

[00:02:55] is our business going to survive? Do we have enough liquidity to make it through the month,

[00:02:59] depending on whatever happens? And those sorts of questions breed a different level of strategic input.

[00:03:06] So what we've seen, and it's probably a trend that was building before then, but certainly in very recent years,

[00:03:12] we've seen the CFO becoming more of a pathway to the CEO of the organization.

[00:03:18] And that's changed the makeup of how CFOs conduct themselves in the organization.

[00:03:23] As you know, to your point in the question, they're much more strategic than they previously were.

[00:03:29] They're not as much focused on accounting or FP&A or treasury.

[00:03:35] In fact, their background doesn't necessarily include all of those things.

[00:03:38] They're quite often change agents as opposed to making sure they run the finance side of the house.

[00:03:45] So they very much have that seat at the table.

[00:03:48] In many cases, they're the right hand to the CEO.

[00:03:51] And what that means is several things for the office of the CFO, which is now a term that is much more popularized.

[00:03:58] And it means individuals like the treasurer, they may be working with a CFO that doesn't have that kind of experience,

[00:04:04] at least directly before.

[00:04:06] They may be working with FP&A or with accounting where the CFO hasn't spent five or 10 or 15 or 20 years in that.

[00:04:13] So what it means for a CFO is that they have a trusted team that is delivering much more proactive insight than they previously did.

[00:04:23] And that it changes things for the team structure.

[00:04:26] It changes in terms of the focus on top line and bottom line outcomes, not just financial, but for the organization metrics.

[00:04:33] It also changes the KPIs and the strategic opportunity for those that are reporting to the CFO.

[00:04:41] So there's a lot of, I guess, shake up.

[00:04:43] Maybe it could be the right word, Neil, but we'll say a shake up, a transformation that has really changed the game for what CFOs are now bringing to the table.

[00:04:54] And I think if we go back to when you first started your career, I think it's fair to say that finance was very similar to the legal industry and often accused of being slow to adapt to the pace of technological change.

[00:05:07] But all that has completely turned on its head now and AI and automation have advanced rapidly across the finance industry.

[00:05:14] And yes, we've got that new word fintech as well.

[00:05:17] But how do you see CFOs leveraging these new technologies or emerging technologies to improve things like efficiency and accuracy, especially in cash forecasting and liquidity management?

[00:05:31] It feels like there's so much or so many opportunities around this.

[00:05:35] There are a lot of opportunities, and I very much agree with all of that.

[00:05:39] I almost want to fast forward right into the here's the ways that CFOs can improve their efficiency, their accuracy and be more data driven in their decisions.

[00:05:48] But in fact, it's not the technology.

[00:05:51] It's actually what is the business problem that they're trying to solve and then finding technology to support that.

[00:05:58] So I like to reverse the order.

[00:06:00] I know coming from technology that would seem surprising.

[00:06:03] But in fact, we what we don't want for AI is let's just say, make comparisons with blockchain as an example, we don't want a solution that may have tremendous potential, that really isn't solving a problem that's a problem.

[00:06:19] And we don't want it to be in search of the problem, we want the problem to be defined first.

[00:06:24] And, you know, to what we were talking about earlier, there is a significant issue in terms of getting work done, there is a significant issue in terms of making finance teams leaner.

[00:06:36] And there's a significant issue in terms of being more effective and rapid and accelerated in decision making.

[00:06:43] And these are all significant challenges that drive, let's say, pain and suffering, if you will, but compelling events that need to be solved for the CFO.

[00:06:52] And I'll give a simple example, you mentioned cash forecasting in the question.

[00:06:55] So cash forecasting is one of those eternal challenges that for the decades I've been in this space is always one that even when you solve it, there's still more, there's still more predictability, more accuracy.

[00:07:07] And really, we've seen a shift from visibility into cash or liquidity and moving into action ability.

[00:07:15] And it's the not so much how much cash am I going to have in three months or six months or nine or 12 or whatever the duration is, but what are you going to do with it?

[00:07:24] And that's the question that CFOs ask treasurers.

[00:07:27] And therefore, when CFOs, they're looking for an answer, technology, especially in the form of AI, because we're seeing a lot of interesting, interesting opportunities to improve predictability and accuracy, etc.

[00:07:40] That's where we see AI becoming a solution to that problem once that problem is defined.

[00:07:47] So I know we can go into more detail, I'm sure we will in this conversation.

[00:07:51] But let me do the final part of the answer to this question.

[00:07:56] AI is all about either automation or introducing data into decision making.

[00:08:02] And it satisfies both incredibly well, especially in areas like forecasting and liquidity planning.

[00:08:10] And as this is a tech podcast, we have both mentioned the AI word several times.

[00:08:15] It does have considerable hype.

[00:08:17] There's a lot of question marks about things like ROI and what business value and what can we do differently, etc.

[00:08:24] And with this growing reliance on AI in finance, what would you say are some of the most critical factors for CFOs that they should be considering, especially when strategically implementing AI solutions to ensure that cost effectiveness and ROI?

[00:08:41] Because that ROI is a word I seem to be hearing more and more recently.

[00:08:45] It seems to be something changing around that, too.

[00:08:47] Well, yeah.

[00:08:48] And actually, even to kind of go in reverse order, you're absolutely right that now that finance teams, you know, give or take whatever stat you look at, whether it's 90%, whether it's two thirds, some majority number of finance organizations are in exploratory phase of AI.

[00:09:05] And as they get through the height of how can I use chat GPT or how can I use this or how can I use machine learning?

[00:09:12] Once they get past the how, then they have to answer the question, why would I do this and why is it better than my current options?

[00:09:21] And that's where we really get into the business case and the ROI of it.

[00:09:24] And the ROI, it's not really about AI.

[00:09:28] It's actually much more about solving that business problem that we were speaking about earlier.

[00:09:34] And so if that business problem is not being able to rely on your cash forecast, that is where the ROI is going to come from is not just, oh, we saved this much about this number of hours.

[00:09:45] That's not incredibly relevant.

[00:09:47] It's what did you do with that time?

[00:09:49] Or if you were able to improve the forecast, what were you able to do?

[00:09:53] Were you able to invest more?

[00:09:55] Were you able to protect the income statement and balance sheet from say FX or interest rate volatility, or even commodities, depending on your business?

[00:10:02] These are the sorts of things that you can build into your measurement.

[00:10:07] We spend a lot of time at Kariba focusing on what we call value engineering, which is exactly what we're talking about here.

[00:10:14] It's how do I find the ROI in really like real quantification, not soft benefits such as productivity, but significant top line or bottom line impact for organizational KPIs.

[00:10:29] Because that's what, you know, that's what drives the business case is it's not about, oh, we made ourselves more efficient.

[00:10:36] Like it's a great word, but it doesn't mean anything when you stack ranking against other projects for the office of the CFO.

[00:10:42] When you're competing, you know, we use an American reference like a shark tank episode.

[00:10:48] Then it doesn't matter.

[00:10:50] Like every business case is great, but you have to be the greatest in order to get funded and get that approval and go ahead and organizational support to drive a project such as AI for cash forecasting or fraud detection or improved liquidity planning or better FX hedging.

[00:11:07] These are the sorts of things that you really have to do well in order to make sure that AI or whatever technology is going to be something that you can invest in.

[00:11:19] And although I do talk a lot about technology on this podcast, I'm also drawn to the problems experienced by some of these technological changes.

[00:11:27] And I would imagine many CFOs listening to this right now are feeling overwhelmed by the scope of their new responsibilities if they keep increasing too.

[00:11:36] So how can automation and AI maybe help alleviate this burden and allow them to focus on those higher priority tasks, an area that AI is repeatedly or we're repeatedly told can excel at?

[00:11:50] I couldn't agree more that that is the challenge that CFOs run into is that they're too busy and overwhelmed is a really good word.

[00:12:00] First things first, the trust that CFOs have in their teams or at least should have in their teams is paramount because no CFO, no matter how amazing they are, whether it's Harry Potter like wizardry or whether it's just practical real world experience, they need to be able to rely on their teams to drive transformation in their respective areas and to be able to nominate not just business problems, but also solutions.

[00:12:27] So assuming that they have a controller ahead of FPNA, head of even payables and receivables or procurement or treasury, being able to nominate and say, here's the challenges we're facing.

[00:12:40] Here's the opportunity we see like what this solution might be looking like and the value and ROI of that.

[00:12:47] Then we get into a point where we can start solving some of those problems where a CFO can be a recipient of improved data insights as opposed to a, oh my goodness, how am I going to figure out how to get this number to the CEO or the board?

[00:13:02] So it's a very, it's a shifted dynamic, but it all starts with building a team and they can trust having that team recognize the scope of their business issues and then being able to nominate solutions with obviously impactful ROI that goes to top and bottom line KPIs.

[00:13:20] And ultimately then, and only then is AI going to be something of, oh, we can use AI to solve this.

[00:13:28] And those examples, there's going to be a lot of different ones.

[00:13:31] I mean, we can dissect it in a lot of different ways.

[00:13:32] The ones that I see the most are going to be either to support data-driven decisions like cash forecasting, like liquidity planning, like how do I ensure that I'm in alliance with my hedge policy?

[00:13:43] How do I make sure that the payments I'm sending out are the right payments?

[00:13:47] And so I have a payment strategy that not only increases efficiency, but doesn't increase fraud.

[00:13:52] These are great examples of business cases that AI, whether it's machine learning, whether it's more gen AI, that they can really bring some value to the table.

[00:14:03] But it has to be linked to ultimately something that impacts the top and bottom line.

[00:14:09] Otherwise, the CFO shouldn't really care about those in comparison to other priorities.

[00:14:15] And on the darker side of AI, there are a lot of bad actors out there using AI for fraud.

[00:14:23] Essentially, we've seen everything from deepfake audio to deepfake videos and pitching attempts, etc.

[00:14:28] So fraud detection is obviously going to be a growing concern, too, in financial operations.

[00:14:34] So how do you see it as a force for good AI and gen AI tools enhancing fraud detection for CFOs?

[00:14:41] And are there any steps organizations should be taking to maybe implement these technologies more effectively to protect their users?

[00:14:50] Yeah, there definitely is.

[00:14:51] And I like the way you asked the question because cyber criminals and fraudsters using deepfake is utterly horrifying.

[00:14:58] And it should be for any CFO that thinks, ah, it's probably not going to affect us.

[00:15:03] That's as big a problem as fraud is itself.

[00:15:07] We see stats after stats where organizations are targeted continuously.

[00:15:13] And I always see, I actually playfully say it this way.

[00:15:17] When you see a survey where 80% of CFOs say they've been attacked or targeted by cyber criminals and fraudsters,

[00:15:25] say for payment fraud, as an example.

[00:15:27] And I always say, well, that means that 80% were telling the truth and 20% were not wanting to admit because everyone is targeted.

[00:15:36] Now, it doesn't mean that everyone's necessarily impacted.

[00:15:38] There's great safeguards.

[00:15:39] But the problem is, and I think you've probably seen, heard these examples too, Neil, is that if you rewind back five or 10 years, you know, before deepfake was really a thing,

[00:15:49] then we would have things such as there was phishing emails, there was impersonation or spoofing of phone calls, you know,

[00:15:57] trying to hack the CFO's calendars so that you send the email or the urgent phone call impersonating them just as they got on the airplane.

[00:16:04] So they really can't respond in real time.

[00:16:07] Now that's heightened to the point where, you know, you try and do a Zoom call and you're not absolutely sure that who is on the other end,

[00:16:15] even the video is actually who they're supposed to be.

[00:16:18] Like, that is horrifying.

[00:16:19] So fast forward to how do we actually deal with that if we're a CFO?

[00:16:26] First things first, it needs to be data-driven.

[00:16:29] And when I say data-driven, it means leveraging tools such as AI and APIs to ensure that you have a digitized version of your payment policy.

[00:16:39] And that digitized version of your payment policy is able to run at machine speed.

[00:16:44] So I'll give a certain example, especially as we get into the payments world where we're getting things like instant payments.

[00:16:51] Everything needs to run immediately.

[00:16:53] There's no way that the sheer amount of data to support a payment policy can be looked at by a human and execute same day, never mind within seconds.

[00:17:03] And so that means things like AI, even just simple machine learning to load up and be trained on the history of all the payments that you've made, as in good payments,

[00:17:14] so that machine learning can identify the anomalies, the not good, forgive the poor grammar, but I meant it that way intentionally,

[00:17:21] the not good payments so that you have one checkpoint, one checkpoint that this is something that is different than the rest.

[00:17:29] Add into that a whole sequence in your payment journey around things like validation that this is a proper bank account, validation of the owner of that bank account.

[00:17:38] If you think you're paying ABC company, but the account is actually open, you know, owned by an outfit, and I'll just pick on a country here, North Korea,

[00:17:46] and that's not who you thought you were paying.

[00:17:48] These are additional data points alongside the anomalies that can be detected that in whole should be part of your payment journey to understand this is something that violates our payment policy.

[00:18:02] When it comes to fraud detection in finance, payments is the biggest threat.

[00:18:07] It's the one because that's where the money is.

[00:18:09] That's the one that fraudsters and cyber criminals are targeting.

[00:18:13] And unfortunately, too often when there is not a machine-driven, data-driven approach to that payment journey in terms of digitizing that entire experience and leveraging data at each point in that journey,

[00:18:29] then that organization is at risk.

[00:18:32] And that's when fraud happens.

[00:18:34] It's when some part of that payment policy breaks down.

[00:18:37] And it generally breaks down because there was too much data or there was data that wasn't looked at.

[00:18:43] This is where AI really, really helps in the equation.

[00:18:47] And it doesn't have to be complicated.

[00:18:48] It doesn't have to be, you know, complex jet AI that's, you know, a chatbot running your entire payment procedure.

[00:18:54] In fact, it can be as simple as machine learning pattern-based recognition on top of rules-based automation driven by APIs.

[00:19:02] And, yeah, actually just that, APIs to support AI to ultimately support a digitized payment experience.

[00:19:10] That is, for many organizations, the next step that's not too hard to realize and ultimately gets us to a point where we have a much more,

[00:19:21] I guess we'll say reliable, defense against fraud and cyber crime.

[00:19:26] And I suspect for most people listening, they've seen and heard these stories, especially in our news feeds.

[00:19:32] But I'm curious, as someone that's right in the heart of this fintech space,

[00:19:36] what are the biggest challenges that you're seeing CFOs facing in adapting to AI-driven automation?

[00:19:43] And how can companies like yours support, I mean, overcoming these obstacles?

[00:19:49] I suspect it's something you hear a lot and get asked a lot.

[00:19:53] But what are you seeing here?

[00:19:54] Yeah, so there's two things I would say to this, Neil.

[00:19:57] Number one is trust.

[00:19:59] Making sure that you can provide a trusted AI.

[00:20:04] And I know that seems like a very obvious statement.

[00:20:07] But in fact, I speak with CFOs and finance teams, especially on AI, on a weekly and many times, multiple times per weekly basis.

[00:20:18] There's a common thread that comes through every conversation, whether it's en masse at a conference or it's more an individual conversation.

[00:20:25] The second part is they want to understand enough, not too much, but enough so they can trust the AI.

[00:20:32] They want to make sure that they understand how it's working for to solve their business problem.

[00:20:38] What data is being used?

[00:20:40] As an example, to train the AI is a big concern.

[00:20:43] Data privacy is very important when we're talking about finance, treasury payments, risk management.

[00:20:47] It's huge.

[00:20:49] Second part, besides trust, is going to be actually quite simple.

[00:20:54] It's, is this something we can do ourselves without having to make this a very, very big project?

[00:21:00] Do we need significant amount of IT resources?

[00:21:03] How do we get the data?

[00:21:04] Like just simple questions where CFOs, one of the first things they ask is, is this embedded within a solution or is this something we have to do ourselves?

[00:21:14] And that's a really good question that CFOs are asking, Neil, because it's great to get all wound up like, wow, Gen AI is going to help us this and we'll get chat GPT.

[00:21:24] And, you know, which is a great tool, except for one thing is that most organizations aren't allowed to use it yet unless it's the enterprise version because of data privacy.

[00:21:31] So it's these sorts of things.

[00:21:33] Will you handle it for us?

[00:21:36] Is it embedded in a software that we can have on a turnkey and prepackaged basis?

[00:21:41] And can we trust it?

[00:21:43] Those are the critical questions that are being asked reasonably by CFOs today.

[00:21:49] And yeah, we do have an answer for that.

[00:21:52] And of course, Kyriba has been at the forefront of liquidity performance for over two decades now.

[00:21:58] So as we look to the future, we're already a few weeks away from 2025.

[00:22:02] How do you see the future of liquidity management evolving with the integration of AI and cloud-based platforms?

[00:22:09] There's probably so much going on here.

[00:22:11] It's almost impossible to predict what's going to happen next.

[00:22:14] But how do you see all this evolving?

[00:22:16] Well, I'll tell you a funny thing and then I'll absolutely answer your question directly.

[00:22:19] But the funny thing is that I used to do presentations with the future of treasury technology or future risk management or the future of payments technology.

[00:22:28] But the future of tech, we're in some part of payment or some part of finance.

[00:22:32] I mean, and it would always be, you know, it's 10 years out.

[00:22:36] And then it was five years out.

[00:22:38] And then it was really, what's next year going to look like?

[00:22:42] And now we can say, what's next month look like, especially when it comes to AI as part of those conversations.

[00:22:50] So, you know, what does the future look like?

[00:22:52] I think what is very obvious and I think there'd be very little argument is that the future of the CFO and the future of liquidity management,

[00:23:01] the future of risk management, the future of payments, it's data driven.

[00:23:05] And any organization that doesn't recognize that they need a data strategy before they can even think about an AI strategy is absolutely going to miss the point of what AI can do for them.

[00:23:17] So becoming data driven, understanding what your data strategy is going to be and understanding what those building blocks are needed to drive that data strategy are absolutely critical to be ready for the future.

[00:23:32] So that future, I mean, you think of just simple workflows or simple use cases, your forecast is going to be data driven.

[00:23:39] It's going to be very actionable and it's going to be very automated so that there's a human on the end of that decision making process.

[00:23:46] That human is going to evaluate everything, all the insights given to them.

[00:23:51] Here's how much cash you should have.

[00:23:53] Here's your borrowing and investing options.

[00:23:54] Here's what your balance sheet will look like before and after.

[00:23:58] And here's the policy that we're working against.

[00:24:00] Do you agree with this decision?

[00:24:02] Yes or no.

[00:24:03] So it's very, very automated, Neil.

[00:24:06] It's very, very insightful.

[00:24:08] And it's very, very focused on efficiency, but ultimately not resting on efficiency.

[00:24:14] It's effectiveness making more impactful and more, I'm going to say accurate, but I really will rely on the word valuable here.

[00:24:25] Data driven decisions that impact the top and bottom line of the organization in a more profitable and valuable way.

[00:24:32] That's really what that future is.

[00:24:34] And humans have a big role in it.

[00:24:36] You know, we're in finance.

[00:24:36] We're not in other parts of the organization where maybe we get automated away.

[00:24:40] In finance, there's so much judgment.

[00:24:43] There's so much experience.

[00:24:44] A human is on the end of that decision making chain.

[00:24:47] It's just a decision making chain that can be much more AI driven than it is today.

[00:24:54] And I think one of the biggest problems around people getting excited about AI is they often get distracted and start looking at the world with a tech first mindset and then look for a problem to solve rather than the other way around.

[00:25:07] And as CFOs continue to adopt AI and automation, any advice on how they can ensure that these technologies ultimately align with their broader business strategies,

[00:25:17] posturing things like long-term growth and sustainability rather than making that mistake, as I said a moment ago, looking at the tech first.

[00:25:25] Yeah, it's, I playfully say this, mainly because my children are a little bit younger, but it comes from the same place.

[00:25:34] Always ask why, you know, like a toddler would.

[00:25:37] Um, and you know, if your dog could talk, probably what it would say to is why, why, why, why are you doing this?

[00:25:44] And I think it's a, it's a really appropriate litmus test to ensure like every CFO knows they shouldn't put tech first.

[00:25:52] Every CFO knows they're solving business problems, but especially with AI, there's so much hype.

[00:25:58] You know, there's boards and we see this, uh, all the time is there's a mandate that's given down.

[00:26:05] You are going to use AI.

[00:26:07] We're going to become AI driven.

[00:26:08] We need to become AI.

[00:26:10] Our marketing team needs to use AI everywhere we can because it increases our valuation for the organization.

[00:26:16] Not getting caught in that and remembering why do we need to solve this problem?

[00:26:22] And is AI the best suited to do that?

[00:26:25] Probably the answer is yes, but let's make sure it's the business problem.

[00:26:29] And always at some point, you know, in that stage gate process where you're checking at each point in a project, should I be doing this?

[00:26:37] Is this still on track?

[00:26:39] Are we still solving the business problem that we start out with?

[00:26:43] Or did we actually get off track a little bit?

[00:26:46] Why is the right question to ask every single time?

[00:26:51] Wow.

[00:26:52] Such a great answer and a perfect moment to end on.

[00:26:55] I can't thank you enough for sharing your invaluable insights with me today and everything that you're seeing.

[00:27:00] But before I let you go, I'm going to ask you to leave one final gift for everyone listening.

[00:27:04] I always ask my guests to leave either a book that we can add to our Amazon wishlist that you'd recommend and why, or a song for our Spotify playlist.

[00:27:13] It could be anything.

[00:27:14] Guilty pleasures are allowed.

[00:27:15] But what is that final thing you'd like to leave everyone listening?

[00:27:19] Well, it's a tough choice because I can think of excellent answers for both.

[00:27:25] They will just be very different, Neil.

[00:27:27] So if I was to say a song, that's not going to be my answer.

[00:27:30] If I was to say a song, it would be totally off the reservation.

[00:27:34] And I would say it is one of my favorites, Chris Cornell, specifically with an audio slave.

[00:27:40] And Like a Stone, perfect song.

[00:27:43] That has nothing to do with what we talked about.

[00:27:45] So let me tell you the book that I just read based on the talk that I just saw from the author.

[00:27:50] And that's Malcolm Gladwell.

[00:27:52] And the book, his most recent, Beyond the Tipping Point, I find it fascinating because it's so data driven.

[00:27:59] If you know Malcolm Gladwell, I'm sure you do.

[00:28:02] Everything he does is around data, letting data find the problem and not being over prescriptive as to what conclusion you should be finding.

[00:28:13] And that's the way, you know, when it comes to AI, I think it's a perfect, perfect comparison.

[00:28:18] Because with AI, don't let AI find the problem.

[00:28:22] Let AI provide the data that can lead you to a conclusion.

[00:28:26] And that's the way Gladwell approaches his most recent book.

[00:28:29] And when I saw him at a conference just two weeks ago, I was fascinated at the way he was able to use example after example, some of which are actually finance related, some of which have nothing to do with it.

[00:28:42] That are all explained by data with data being the driver as opposed to just the validator.

[00:28:48] So there you go.

[00:28:50] Malcolm Gladwell, Beyond the Tipping.

[00:28:52] Wow.

[00:28:53] I've read the first book, but not the second.

[00:28:56] So I'm going to add that to the wish list.

[00:28:58] I'll be picking that up myself.

[00:29:00] And you know what?

[00:29:00] I'm in a good mood today.

[00:29:01] We're going to add Like a Stone to the Spotify playlist too.

[00:29:04] We'll add both of them there.

[00:29:06] But anybody listening that just wants to find out more information about yourself, anything we talked about, where would you like to point everyone listening today?

[00:29:15] So absolutely.

[00:29:16] For those that want to have a further conversation, catch me on LinkedIn.

[00:29:19] It's easy to find.

[00:29:21] That said, I definitely engage on all our channels and assets on Kriba.com.

[00:29:26] So either way you want to engage, please do.

[00:29:29] There's a great set of conversations around AI to be had.

[00:29:33] Well, we've covered so much ground today.

[00:29:35] And I can't thank you enough for coming on here and just talking about the role of AI in automation and forecasting for CFOs and the importance of strategic implementation and the cost effectiveness and how it can easily be overlooked as well as the challenges in adoption.

[00:29:51] I'd love to hear from CFOs listening.

[00:29:54] They could share their thoughts too.

[00:29:56] We can keep this conversation going.

[00:29:57] But just thank you for shining a light on this today, Bob.

[00:30:01] Thank you, Neil, for the opportunity.

[00:30:02] It's always fun to have this conversation.

[00:30:06] I think as we wrap up today, Bob has given us plenty to think about, especially when it comes to the evolving role of CFOs and the potential of AI to support in this transformation.

[00:30:18] And with so many opportunities for automation and data-driven decision making, the path forward is both exciting as it is complex.

[00:30:27] But what are your thoughts?

[00:30:28] Could AI be key to helping all CFOs meet today's challenges more effectively?

[00:30:34] Or do we need that more cautious approach to balance technology with human oversight?

[00:30:39] It's a big question, that one.

[00:30:41] And I'd love for you to let me know by emailing me, techblogwriteroutlook.com, X, Instagram, LinkedIn, just at me or C. Hughes.

[00:30:49] Let me know.

[00:30:51] But we covered a lot today around the world of CFOs, fintech and the world of finance.

[00:30:56] Tomorrow is going to be a completely different area that we'll explore.

[00:31:00] But it all comes down to the same thing.

[00:31:02] How is technology impacting our lives, our work and our world?

[00:31:06] Why not join me again tomorrow and we'll do it all again on a different topic.

[00:31:10] Speak with you all then.