Building an AI-Ready Finance Team Without Losing the Human Side, with Tariq Munir

Hello and welcome to the Diary of a CFO podcast. I'm your host with Wassia Kamon. I'm a CFO with a background in accounting, FBNA, and I started this show to talk about what leading in finance really looks like and what it takes to become a CFO. Each week, we explore how today's top finance leaders build high -performing teams, partner with CEOs and boards, and lead through growth and transformation without burning out in the process.

Today, I'm super delighted to have with me Tariq Munir. He is a keynote speaker and digital transformation and AI advisor. with over 20 years of experience guiding Fortune 500 leaders through organizational change. He's also a LinkedIn top voice and the author of Reimagine Finance.

It's a book that explores how finance leaders can navigate disruption while amplifying uniquely human capabilities. Welcome to the show, Derek. Thank you, Vasya, for having me. Super excited to be here.

Oh, same here, because I really want us to be able to talk about how to build an AI finance, AI ready finance team in 12 months without losing that human side, which is really what I really liked about the angle you took in your book. So I'm curious to understand from you when you say AI ready finance team, what does that actually look like in reality day to day? So a great question was, yeah. And well, you know, theoretically, We can do a whole lot of readiness assessments.

We can do all those pre -assignment or pre -fillout checklists, do all those sort of questionnaires. However, I always say that look at just one day in practice that can actually give you a good litmus test if you are ready to have AI in your business or not. And that one litmus test is understanding or evaluating how are you making your decisions today. If data is just another chart on a PowerPoint deck, and that is it, and does not fundamentally drive how you are making decisions, I'm sorry, you are not ready.

Most of us think that once we get AI, we will become data driven because then we'll have more insights, faster insights, and so on and so forth. It is in fact the other way around. To really leverage AI, we need to have a data -driven mindset. Because at the end of the day, data -driven is not a technology issue.

It's a mindset thing. We need to be able to use the output of machines, rely on the output of the machine. And to do that, we need to be able to have that courage to take a decision based on data. Okay, while our gut or instinct tells us otherwise because a lot of times we go with our gut We go with our instinct and I don't say that, you know, don't go with your gut or instinct.

That's what make us you makes us uniquely human though but Relying on data when data is telling us an entirely different story is what makes us ready to go for any technology for that matter Oh, I would like to see how it works in real life, right? So let's say you walk into a finance department today. What are the signal that will tell you they're not ready for AI, right? And that's even if, like you said, the technology out there.

So I walk into a finance team and what tells you that they're not data driven enough already to be ready for AI? So as I mentioned, Vasya, if most of the decisions are still based on gut feel or the person, instead of the merit or what the data is telling us, they are based on the hierarchy of decision -making or hierarchy of people in the organization, we're not data -driven then. We are more people -driven. Not sorry, people -driven, I would say we are more...

guided by the instinct and gut and our experience as opposed to what data is telling us. Apart from that, I would like to point out two more things. One is, of course, being data -driven. Secondly, it's about workflows being too complex.

If I walk into a finance function and their processes are broken, their processes are not supporting automation, there are too many manual workarounds, Manual workarounds is not a bad thing per se, but doing it in the form of something like creating a manual workaround to overcome some system issue is what I'm talking about. So if those workflows are too complex, all we are going to do using AI is amplify that complexity. So I always start off with streamlining those workflows.

And lastly, a very important element I always recommend and advise my clients around is that if people are hesitant to experiment, everything needs to be safe. Everything needs to be risk -averse. Being finance, we are risk -averse. That's how we are trained, right?

But AI on the other hand requires some experimentation. It's not a kind of an implementation which will happen like an ERP or any other cloud or SAS solution where there'll be clear go, no go, no go decisions. And you will always have an output which is not very accurate. Then you will retrain your models to get more accurate output and so on and so forth.

So you need to have that experimentation mindset. Now these are the things which I'm talking about right now does not really need technology at all. as you would have noticed, right? These are mostly mindset -driven.

These are mostly behavior -driven things, which would actually drive and get you ready to leverage AI. And I also don't say that you do all of these things and then go for AI. Of course, that's also not practically possible. But starting to realize and starting to acknowledge that we need to change is the first step.

And then working on this while you are, of course, working on your AI and digital roadmaps as well is what sets us up for success. Okay. So if I understand correctly, we start with the idea of we need to be more data -driven. So let's say I'm running a forecast and for the past three years, I see that sales have been increasing at 5%.

If it's data -driven, when I'm looking at my next year, I should, you know, also feel like it's going up. for example, but let's say the marketing director comes in and say, oh no, let's lower it, right? So now I'm almost not as data driven, but now it gets into the politics. Is that correct?

Yeah, yeah, yeah. You are absolutely, you are absolutely nailing it. And that's where the biggest issue comes in as well, right? I mean, if market is sending us different signals, But because of our sales, our marketing directors based on their, or even CFOs for that matter, based on their experience, they believe that this is something which is temporary or, you know, it's fine.

We'll be able to get over with that. Or we have done that in the past and we know how it's going to work. So if we ignore what data is telling us, then of course we are not data driven. Having said that, there is always that balance between the two.

Yes, as a human, we must use our experience. This is what makes us at the end of the uniquely human. What I'm saying is that if data is telling us an entirely different story and our gut instinct is telling us an entirely different story, we need to really critically evaluate that situation as opposed to just, you know, saying data is fine, data is not right. Or, you know, most of the time you might have heard in a boardroom or in those those planning meetings, oh, I don't trust the number, I don't know if this number is correct or not, and so on and so forth.

So again, there are a lot of different elements associated with building that trust as well. But inherently not trusting data or inherently having that outset attitude towards not relying on data for decision making is how we drop the ball on being data driven. Okay, and then I understand the part about the processes where you have to do patchwork So if you used to do it that I can imagine how it will be hard to automate those kind of things with technology, right? You have to fix how you're doing things first.

But then when you talk about that mindset of experimenting, like you said, in finance, it's hard to just experiment because it's like compliance is reporting to the board. If there is an hallucination or something like that, like, how do you overcome that? How do you prepare that mindset to be willing to experiment and start moving toward AI readiness in finance? Great question, Vasia.

And whenever I say experimentation, we need to step back a little bit. OK. Experimentation is one thing and making mistakes is another thing. Right.

If we are. We are trying to do something say for example I get a chat dpt or some enterprise version and I'm just using it to it to as an experiment in my mind I am just using it to create a board report and then I just send it through to To board of directors. That's not an experiment. That's a mistake Right.

So so so we need to very clearly understand that difference between experimentation and mistakes number one number two Experimentation is not something we just do like, I mean, we're not sitting in a lab and we are just trying to, even scientists don't do experiments by that, right? By that, that they just start combining two things and think about, okay, something comes up. Combine tomatoes and oranges. Yeah, right, something comes up.

Experimentation, what I always say that you need to earn the right to experiment. And how you do that is by clearly understanding first of all, what are your clear business needs? Number one, what are the problems that you are trying to solve and then finding a different solutions against those problems and then based on the impact value complexity, identifying those top two, three candidates or one candidate where you can actually go. in and start to build those experimentation, build those experiments and do experiments around that.

It does not mean that you just go in blindly and start using AI to create output and then in the name of experimentation, you are submitting a wrong reports for the compliance or doing doing other or exposing organization to the risks. It should not be exposed. So that's that's not experimentation again, you know, that's that's not not not how we do it. We need to be thoughtful about our experiments.

And then there are certain core elements. So you need to do your text submissions, for example. You need to do your SEC submissions. There is no room for us to experiment on the output.

So output needs to be clear. But how we generate the input, we can always experiment. We can always try to find different ways or better ways of doing things. But that does not give me any right.

to not validate or verify the output and just send it through to the board or to the regulator. So experimentation needs to be thoughtful, needs to be very clearly tied into your specific business need. It's not about that just because we want to use a native AI somehow. So let's do an experiment around that.

That's not how we do experimentation. So that needs to be that distinction needs to be very, very clear. And I agree. I mean, sometimes we take that as our experimentation as a wrong in a wrong connotation and think about it more like, in fact, what we are doing is not experiments.

What we are doing are making mistakes. So we need to make sure that we bifurcate and we separate the two. I hope that explains a bit of that question. Oh yeah, absolutely.

And I'm curious to hear from your work, what have been some of those mistakes that you have seen? Because clearly one of them is just not double checking the work. Like we do it in accounting, internal controls, right? View the work or you sign a journal entry.

But what are other mistakes that you've seen people make as they're trying to be ready for AI or actually implementing AI within their organizations? I think the biggest mistake and sometimes I call it bit of a pandemic kind of a situation is becoming too much tool or AI focused, to be honest, and not actually thinking about the business problem. The very essence of experimentation, what I was talking about, if we are ignoring that, that's the biggest mistake in itself. So what happens in various scenarios?

I have seen that. We start the conversation, so I approach or someone approaches me around AI transformation or this kind of work that I do. Many times the conversation unfortunately starts with, how can I automate this task? How can I use AI to do this thing better?

That's a wrong question to ask. that I would sometimes say as close as doing a mistake because what we do is then when we start finding point solutions which might give us some productivity benefits or might you know automate some of the task but at that very specific boundary within which that AI would be operating. AI's real value comes in when it integrates across the organization, when it fits into your broader vision, your broader organizational operating model for that matter.

That is, I believe, the biggest mistake that we make when we are. I have seen AI projects ranging from cash flow forecasting to revenue analytics to process optimizations not performing well or not doing as planned because there was nothing wrong with the approach. There was nothing wrong with the tool or the technology itself or its its appropriateness for that problem, but it didn't fit into the broader organizational operating model or the broader organizational structure.

People did not rely on the output. At the end of the day, people did not adopt the technology. I mean, having a tool and then using that tool are two different things, right? I mean, adopting and deployment.

So a lot of times, these are the kind of, I would say, mistakes or or not the right way of doing experimentation that I have witnessed quite a few times. Quite a few times. Sometimes I sound more like a broken record as well when I keep telling people that, you know, please let's be a little bit more thoughtful around our experimentation. Let's not talk about tool first.

Let's not talk about the task even. Let's look at the holistic system. What are we doing, right? I mean, how our work is impacting.

At the end of the day, remember, finance is up. is a function of every other function in the organization. We cannot say that even the basic accounting work that we are doing can happen in isolation without the input from commercials, from operations, from cost of goods sold, for sales from the sales team. It cannot happen.

How that linkage, how that end -to -end workflow, the end -to -end processes work in order for us to get the maximum utilization of AI or the maximum leverage out of AI. So that is what I see a lot in the work that I do. OK. And so I'm curious too, in your work and your book, you talk a lot about human centric enabled finance.

So what needs to change first in how finance people think or behave for AI to work in our favor? Like, what do we need to change? Because I know you said the first question sometimes is, how can I use it to automate this? Well, I'm trying to make my life easy, so I'll likely ask the same question.

So what is a better question to ask? What actually needs to change for finance to be more ready for AI? I believe the fundamental change which is needed in any finance function or for that matter, I mean broader organization because I do work with broader organization as well. So is how do we build a digital mindset within the team?

Now, again, as I said, I sometimes sound like a broken record when I say that digital mindset is a very simple thing. It is how well the finance team or business team is able to utilize the output of machine to solve a real world business problem. That is it. As long as you are able to connect the dots between the two, you have a digital mindset and what that enables us to become what we are supposed to do.

We become better business partner and we actually navigate the ship as opposed to just being those stewards of the data. When we build that digital mindset, two things happen. One, by default, we get process efficiencies, faster insights, of course, because now you are able to connect that what are the different business problems I'm facing. And how do I use technology holistically to solve those business problems?

So as an output, you get process efficiencies, you get productivity, you get faster insight. And what that does, secondly, and most important, I would say, this enables us to do what we are designed to do as humans, what we are meant to do. critical thinking business partnering and not that manual tedious manual work that we are we are we we spend so much time on so as as humans I think we can do much better than what we are doing today as finance leaders we can do much better than how we are we are operate making our business plans or how we are doing our back office accounting for that matter or how we are paying invoices to our suppliers and so on and so forth.

We can do a much, much better job. I'm not saying that we're not doing a great job. I mean, finance is one of the most intelligent teams in the organization, and they are one of the most hardworking as well. But how do we make them a little bit more smart workers using technology?

And this is what helps us then build a human -centric tech -enabled finance function. And so building that digital mindset, when you think about building that digital mindset, what a practical step to get there, right? Like, because I know it's a bit out there. I see the bigger picture.

But for somebody listening, I want to build a digital mindset within my team. As a leader, what do I need to do? And what does my team need to do? What do I need to expose them to in order to really elevate that mindset?

Great question. And I actually talk in quite a detail in my book as well, specifically on this building a digital mindset. So my argument is that we need to build two things. First of all, is your digital literacy, of course, right?

Now, that does not mean that you need to become a data scientist or you need to become an engineer unless you want to, of course. But you need to understand that how different technologies that exist today, what can we Use to solve some of the business problems today. What are the trends? Read a lot of books.

I always recommend that there is no substitute to reading good books. Understand there are a lot of free courses available out there where you can just get an understanding of what AI is all about, how it can help us solve some of those pressing issues that we face today. What are the different? different methods within data analytics that we can apply and use them, and depending upon, of course, where you are in your hierarchy in the organization, number one.

And number two, by building a digital mindset requires us to have a growth mindset. So it is almost like a combination of the two, digital literacy and growth mindset gives you a digital mindset. Growth mindset means that as a human, and that applies of course to both divided teams, but sometimes I say that it applies more to the leaders in the organization because sometimes that's where people look up to and follow the lead and follow their footsteps. If as leaders we have a growth mindset, we believe that as humans we can learn any skill, there is no limit.

to human mind on what can be taught or what can be learned, we build that growth mindset and that requires us to do continuous learning, understand different aspects. And first of all, be very open and acknowledging of the fact that, yes, I mean, I don't have answers to everything, but I'm willing to go out and I'm willing to answer, to find those answers. When you create that kind of a culture as a leader, within your team, your team follows through your footsteps and then they also work towards finding those answers.

And when you are trying to find those answers, you will, again, I mean, there is no simple one, one tool or one course available out there or a magic training available out there that I can just tell you to do it. And it will get, it will, it will, you know, help you become a digital mindset. It's a continuous learning journey and the continuous learning as I always say is is not about how many trainings you are doing, how many hours you are training, hours are you clocking or how many certificates you are getting.

It is about acknowledging that it is about the willingness to challenge your very own assumptions in the light of new knowledge. That's what growth mindset is all about. That's what continuous learning is all about. So it's all behavioral thing.

It has to be ingrained within our DNA. We need to ingrain this within our DNA in order for us to be able to build that digital mindset eventually. And it won't happen overnight, but we have to take the first step towards it. Yeah, thank you so much for defining and I'm curious, you know, I understand and I've seen how inspiring it is for a team to see their leader.

learn something new and share it with them during meetings. And they're like, OK, so they're coming up with this. I probably need to catch up. And I can see how it will contaminate, quote, unquote, the rest of the team to kind of elevate their game.

So as we look further and we look further out, if you had to list the key skills that an AI ready finance professional needs to develop over the next few years, what will be on that short list? Of course, the first one will be that digital mindset, right? As you always say, but I think as as as future finance leaders or as future business leader for that matter, there is one thing which is common, which is going to be the common through line, which is actually the common through line at this stage as well.

And that is we are all change managers of the future. Change is the is the is the I mean, it's a cliche that change is the only constant. Yeah. So we need to become excellent at managing change.

which of course requires us to systematically understand what are the business problems we are facing, what is in it for people, how we keep them engaged, how we get them on board with the change. So first of all, we need to build our skills around becoming a better change managers. With AI coming in, there is going to be a lot of productivity. that comes out of course there is a lot that happens as a as as part of the output of technology how that technology works and what it does is still kind of a black box and that is where the skill set around responsible and ethical AI becomes the one of the most important ones in future as finance leaders we are yes we and we are in in a bit of um advantageous position, I would say, because we have a bit of a head start with our training and our predisposition to risk and governance and control.

We can actually lead the charge in this responsible and ethical AI. As CFOs, we can ask the question that why we should be doing something. What are the consequences of doing that? How data is currently flowing through within the organization?

Having those right. making those right investment calls and right ethical decisions. Again, at the end of the day, responsible and ethical AI is a proportionate ownership. It's not something that only CFO can do or CSO can do or CTO can do.

But as a CFOs, as a finance leaders, due to our position within the organization and how people look at to us for the ethical land and governance decisions, we can influence that a lot. So that is one skill set, which I always recommend a lot of finance, which I always recommend to finance leaders to definitely get ahead on. get understanding of various aspects around what are the different regulations coming in, what are the different policies, procedures, and recommendations that come from different organizations around global organizations like UNESCO and OECD around responsible and ethical AI.

Number two. And then I think another important skill that we need is When AI is determining the most of the output and now we are talking about agentic AI, which is actually have that agency and doing stuff for us. A lot of times we get there is this curtain of technology or curtain of AI that sits between us and the humans on the other side. So understanding the emotions of humans within the loop, within that orchestration of agentic AI and all the technological transformation is the key aspect and what we can also call emotional intelligence.

That is going to be one of the most, and in fact, that is one of the most important skill set to have and to build with us in order for us to be able to, because again, when we talk about technology, our first reaction is, oh, it is going to take away jobs. Yes. We forget that jobs or roles is just a description on a piece of paper. There are real humans who are working around those tasks and roles.

What are their emotions? Being aware of what are their training needs? How does our decisions are impacting the people? They are not just the numbers or they are not just a cog in a machine.

They are real beings, conscious beings who have. emotions who have social structures who have social needs so how do we how do we remain aware of their needs when the technology comes in and take away and automate roles or automate tasks how do we separate humans from tasks and how do we understand their needs and how do we actually help them grow as well while while of course making some tough calls as well and they will be tough right i don't hide away from the fact that roles are not gonna get impacted they will but how we manage those Again, goes back to change management as well as being emotionally aware is a very critical skill set that we need.

Wow. Thank you so much. That was great because you started with, you know, when we talk about the skills that people will need to develop, digital mindset, definitely change management, having emotional intelligence and also the idea of responsible and ethical AI, which that's a part. I don't think we talk.

a lot about. So when we think about responsible AI, what are some examples of cases maybe where people used AI in no ethical way, but it made business sense or it was a good shortcut or productivity that happened? And what should people really be aware when it comes to using AI from a governance and regulation standpoint? You are absolutely right.

First of all, that we don't talk about it a lot. right and the reason being the technology takes precedence business use business case takes precedence and then governance and these ethical issues or responsibly it comes as a as a second thought we have seen a lot of examples which are like pretty available published as well examples out there where it made a business sense but it was not ethically ethically right to do so For example, first of all, before I give an example of or a case study around that, if we have data from our customers and we are just using it, we are just getting that data while we are, of course, selling them or are transacting with them.

Just because we have that data does not mean we own it or we can use it to train AI models. We need explicit consent from them. That is where these ethical things, ethical dilemmas comes into play. We need to be very 100 % sure that, yes, our customers, our consumers are willing to give us that data.

They have already given us that data, but they are also willing for us to use that data to train our algorithms. So for example, I give this example of EverApp. There was this this app, which was something like Google Drive, where people would upload their photos and save their photos. So what they were doing, they were using those photos or people's pictures to train some of the algorithms on facial recognition, and then were marketing and selling those algorithms to the other companies.

So FTC, Federal Trade Commission, they imposed on them a penalty called algorithmic disgorgement. So one thing is ethical, but the other thing is the regulatory impact as well and getting in the public eye as well. What that meant for them was they had to destroy all their algorithms and had to pay fines and so on and so forth. So again, we should not be ethical because Just we should not just be only be ethical because if we have a if we have a If we if someone if we have a regulator sitting on top of us, of course, we should always be ethical But there are of course those consequences as well secondly one of the very important aspect that comes out of AI and and sometimes we we see that a lot and a output comes from AI and we say that oh This is the output from the machine.

This is the output from the AI. So as a human, I'm not responsible for it. However, this is the basic premise of responsible and ethical AI that any output by machine is the responsibility of AI or is the responsibility of a human at the end of the day. We are responsible for the output of the AI machines are not so taking that ownership is critical and that is where where that proportionate ownership comes into play or CFOs role also can can play up CFOs can play a bigger role in enabling that to happen that yes we are saying so I always say that use up what I what I advise is that whenever you have these agentic workflows of today or a eyes of today follow a no offense policy every AI must have an honor and that honor should be responsible for the output of the AI.

If something is going wrong, that owner, they should be raising the, they should be raising an alarm. They should be raising their hand and saying, this is not working or this is how it is. So, I mean, again, it's a complex topic. It is, it is an, and I would say it's an evolving field because we, what happened, unfortunately, I would say, or whatever, for whatever reasons, commercial reasons or, or, or, or corporate reasons, we build technology and we did not think about responsibility and ethics.

till a later stage. So even now we are building technology at a much faster pace than responsibility or ethical practices can actually catch up. Wow. Or sometimes I say that we are, a lot of corporations today are building technologies around AI and responsible, sorry, building technologies on around AI and are expecting someone else to do those regulations and ethics.

So they, their role becomes more about innovation and they expect governments or other regulators to come in and build responsible and ethical AI frameworks. That is not how it will work or that is not how it is sustainable. Right from the beginning, right when we are writing the first code for any algorithm, we need to have responsible and ethical frameworks in place, which unfortunately is not. that prevalent at this stage.

But we are moving in that direction. There are people and organizations who are actually trying to get ahead of this innovation spree, if we call it. Wow. But there is so much pressure, though, and also so much money to be made, right?

So that's usually what gets us in trouble. Yeah, that's the reason, right? I mean, that's what I was talking about, that the commercial reasons have made it much more faster. deploy and then just you start using AI without understanding what are the responsible ethical social implications of those right I mean we don't know today that the kids that are using we didn't know about social media how it's gonna impact us we don't have a clue what AI is going to do for us so going to do with our brains going to do with our with the fit how in a society we are operating as as a human as a human society, the way we are operating right now, what AI is going to do with that.

We don't know because we haven't really thought about it. It is coming as an afterthought once everything, everyone has started to actually use AI, which is scary as well. It is. It is.

It's very scary. And I'm curious to hear from your standpoint as you think about the current organization, the current structure or org chart of a finance department, what do you see as roles that will disappear, how some of the current roles will evolve, like who will be part, besides AI agent, because now I'm getting used to the idea that it will be AI agent on, you know, on finance teams, like, beyond that, like, how are those roles that we're used to going to evolve? It's, it's the, again, first of all, the, the, the, the org chart is, is one thing is for sure that org chart is going to change.

Right? It is and it is already already we see that happening in many organizations today. Now, many of the roles might not even exist today. So, for example, when AI agents as we talked about when they are carrying out the actual execution solving problems, we will need humans in the loop.

Right? And these could be your FPNA AI controllers, your AI agent coordinators, your AI controllers. Again, it's very hard to say what they will look like, what those role will exactly look like. And I acknowledge that we don't know 100%.

We can think of having some of those humans in the loop within that structure, of course, definitely. But again, we don't know clearly at this stage that how that exact organization are going to look like. Having said that, of course, there will be roles that will disappear. those roles that are that have some problems even even the rules which require judgment and some problem solving agents can actually do that quite quite diligently and in a quite efficient way, but we will have a lot of new roles as I mentioned coming up which will help us to ensure that a agents are performing their tasks correctly or Identifying their objectives or defining their objectives or what problems they are going to solve Wow Now irrespective of whatever happens in that scenario how that future org structure looks like it's evolving at this stage So it's hard to say that whatever it will exactly look like one role or one skill is going to Stay and that is I think is is going to become the most relevant for anyone in the org structure And that is the critical thinking piece With AI, we have too many answers.

True. You ask something, you just ask a simple anything, you know, do this or do that. It'll give you a page full of recommendations and stuff. The most important skill is the ability to ask the right questions.

As humans or as finance leaders, we will need to be able to identify or define those objectives. or the right, we cannot just say to AI agents or just do this, process this invoice. We need to be able to ask the right questions. We need to be able to define the right objectives for us to be able to leverage the maximum out of these machines.

Why an output of machine is what it is. We should be able to ask those questions. We cannot just rely on the output of machine, become victim of automation bias, because if it's created by machine, it must be correct. No, we cannot do that.

We have to have that critical mindset, that critical thinking, that why should we even rely on the output of a machine? This ability to challenge the status quo, I think, is the single most important skill or the role that humans are going to play in that new org chart. So everyone is going to become that change manager, that critical thinker, for them to be able to orchestrate orchestrate these different machines or these different agents together to solve the business problems that we are trying to solve.

Yeah, and I can see how you have to have that critical thinking to ask the right question, but also validate, like you said, whatever the output that you needed on both sides from end to end, which is quite. Quite fascinating, but I'm curious to also hear, what are some AI cases that you think CFOs should try? Or maybe a use case where it sounded great and it didn't go so well. I'm curious if you have any success stories or any oops, it didn't work out as we planned stories.

Yeah, so again, I mean, to answer your first part of the question, the art. What are some of the use cases that a let's see if we should try now I always say that it all depends on first of all the business needs Right. What is our strategy? How we are what is our broader vision that looks like?

So so I mean rather than going a use case approach or point solutions look at the bigger picture But as a as a principle as a principle I would recommend go for the well -established and mature solutions in the beginning You don't need to, yes, AI agents or generative AI looks very fancy on the paper, but technology is still evolving. It is untested in many business scenarios. We have seen it not scaling in MIT's report, that famous report that came up a couple of months ago that 95 % of AI pilots cannot scale because I mean, the technology, it's nothing wrong, nothing inherently wrong with the technology, but it's just in its maturity phase.

It's going through that hype cycle right now where where it will eventually get mature and it will eventually have more use cases. So go for those well -established use cases like process mining to understand your business processes. And secondly, I mean, I always say that if you are in 2026 and you are still using Excel or you're still doing manual three -way matches. You are you are not in 2026.

You should be using air and you should be using AI for your account receivable account payable They are much more there are a lot of established solutions out there well tested in the market you can you can actually get a lot of uh sort of endorsements from from other other industry partners as well to understand that okay what how did it work or how it did not work so there are a lot of feedback available for those uh for those tools as well so you can actually actually find a good mature solution because these problems have been quite they're common across across all the verticals mostly across every organization has a problem Every organization had bank recon problem.

So there are quite a good mature solutions available out there, which you can then try on and use. So go with those in the beginning and then more on the on the planning side, go for demand forecasting. Again, quite a mature predictive analytics capability is out there. People are now actually being able to predict their revenues.

And I bought on something as well a couple of a couple of months ago. revenue analytics or demand forecasting is another very well established and a good use case for AI to for finance to start their journey in the beginning. And of course, it depends on again, as I said. a caveat that it depends on what's your current business need.

It might not be demand forecasting, but having said that, thinking in those directions, the principle remains the same that go with the traditional AI and think about those traditionalistic, more deterministic artificial intelligence. Okay, one use case to coming to your second question around that use case which didn't work Well, I think is is more around and I gave this example quite often in my workshops as well is around the cash flow forecasting Okay, somehow couple of years ago.

We were sold when you know before generating AI and all that high we were sold this this concept that AI is pretty when it takes is going to just Use your cat you just forecast your cash flow and all your training needs right away and you know, you're you're just have your cash flow for That I believe is is a bit of and I have seen that not working because That is bit of an over simplification of the problem at hand cash flow at the end of the day is not just a number It's a function of your receivables payables your funding your financing activities operating activities so on so forth It is much more complicated than just a simple number.

So if we are just using historical cash flows to predict the future outflow, we are just using a statistical model. We are not really using predictive analytics in its true sense. What we need to do in order for us to make the right treasury decisions or cash flow decisions, we need to, in fact, be predicting our accounts receivable correctly. We must be predicting our accounts payable correctly, other cash inflows and outflows correctly in order for us to then eventually get that cash flow forecasting right.

I see a lot of times, in fact, in one particular case is see, I saw that happen where the organization just used a simple historical cash flows to predict the future cash flow, which is again, nothing better than you know, what I would do as a last last 12 months moving average and then just say that, you know, this is what it is. There is no underlying business driver that you're going to be using to predict that cash flow. So these are some of the things we need to really think about in a little bit more deeper way, unpack what that is all about.

And again, it comes down to that building, again, that digital mindset, the ability to critically think and evaluate, ask the right questions, and understand what is it that we need to really solve and then go from there. Wow, thank you so much for sharing. And I think sometimes we oversimplify things because it's sales versus the actual substance. Because I was in the midst of implementing a new system and they were like, oh, with this new system, it has AI will allow you to do X, Y, Z.

Yeah. And, and, and the, the, the epic one is that where people just look at, just get that chat GPT enterprise version or, or copilot enterprise version. And they expect that to somehow transform their data, somehow transform their operations. Yeah.

That's not what chat GPT is meant to do. It's a very different, it's just a sort of a chat pod, which can work in a very different way. For your data to work, you need to have something else. You need to build data pipelines.

You need to build data architect, right? Data architectures, right? Mindset to be able to use that. So again, oversimplifying this or seeing the generative AI or agentic AI as a magic, as something like a silver bullet, that's pretty common.

You are absolutely right. Yeah. So I'm now curious, how would you finish this sentence? The finance teams that will thrive with AI are the ones that...

Okay, that's a good one. The finance teams, do you mind saying that again? The finance teams that will thrive. So how would you finish this sentence?

The finance teams that will thrive with AI are the ones that... Are the ones that will... re -imagine the fundamental of business operating model. Again, as I always say, now to unpack this a little bit, we need to, that's what even my book re -imagine finance is all about, right?

We need to re -imagine what AI, how AI can help us transform our business operations. How do we fundamentally redesign our operating models? Because the current operating model is not working, is not going to work in the new digital or DNA or the... with AI not just helping us do work, actually doing the work.

So you need a redesigned or reimagined business operating model. That was a good one. Thank you. Thank you so much for sharing.

I have a last question coming up on time here. So away from your work in AI, I'm always curious to hear what's your favorite thing to do outside of work? Oh, I love I love music. You might just notice that there is this.

So I like Pakistani classical music. So I do a bit of that helps me with refreshing my thoughts. And it's quite refreshing, actually. Then I'm also I also do a bit of oil portraits.

I'm a bit of I have this thing around around art. So I do portraits as well. And I'm a bit of a history buff. In fact, that's why a lot of references in my book as well are based on human history.

So I love that as a subject to understand how as humans we evolved, how where we are today, how did we get here? That's something which fascinates me a lot. And I think a lot of that has implication towards where we are going. and AI in the picture, how that is going to look like.

So yeah, this is what I like doing when I'm not talking about AI. That is so good. And I love history as well, because it reminds you that you think you're different, but you're really not. You're really not.

Yeah, we are overestimating our intelligence. See, I mean, we have already built things which can actually totally destroy humanity, right? So I don't think so. We are very smart.

We are probably the only, as Yuval Noah Harari says, one of my favorite historians, he says that, he is my favorite historian, that we are the only species who has the ability to destroy ourselves fully. But it's so true. Well, thank you. Thank you so much, Derek, for being on the show.

I really, really enjoyed our conversation. Same here. Thank you so much, Wasiyah, for having me on the show, and I loved our conversation as well. Thank you.

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