CFO 4.0 - The Future of Finance

240. AI in Finance: From Complex Workflows to Scalable AI Solutions with Tariq Munir

• Hannah Munro

Share episode

Send us your thoughts

In this episode of CFO 4.0, host Hannah Munro talks with Tariq Munir, Managing Partner at Finspyr and author of Reimagine Finance, about how CFOs and finance leaders can lead successful transformation in the age of AI. 

In this episode, you’ll learn:

  • Why transformation should be embedded in everyday finance work, not treated as a separate role
  • How to influence change without authority and build a “transformation snowball”
  • What good governance looks like — and the pitfalls that can derail change
  • How to measure AI project success beyond ROI, including adoption and engagement
  • The role of subject matter experts in training and maintaining effective AI models
  • How to simplify workflows and manage complexity before introducing AI

Links mentioned:

Speaker 1:

no-transcript. The CFO role is changing rapidly, moving from cost controller to strategic visionary, and with every change comes opportunity. We are here to help you take advantage of this transition, to win at work, drive your career forwards and lead with confidence. Join Hannah Munro, managing Director of ITAS, a financial transformation consultancy, as she interviews key experts to give you real-world advice and guidance on how to transform your processes, people and data. Welcome to CFO 4.0, the future of finance. Finance.

Speaker 2:

So hello everybody, and welcome to this episode of CFO 4.0. As usual, I'm your host, Hanman Ro, and with me today is Tariq Munir, who is the managing partner of Finspire. So welcome, Tariq. Thank you for joining me on the show.

Speaker 3:

Thank you, Hannah, for having me on the show.

Speaker 2:

Really excited about it and we've got a lot to talk about today. But before we jump into sort of the detail, it'd be great for our audience to understand. You know your background in finance transformation. Where did you, I guess, learn your trade, as it were?

Speaker 3:

Yeah. So, like many finance people out there, I started off with a typical big four. I started off with PwC and then, you know, from there I moved on to the industry. I worked a couple of years in ExxonMobil, which is a global specialty chemicals company. It was a Fortune 500. It is a Fortune 500 as well and then I moved into PepsiCo somewhere around 12 years ago.

Speaker 3:

So over past 12 years I've worked in different areas in finance, or in fact, over past like 20 years of my career I have worked like from audit to controls, reporting, fp&a so pretty much all across finance. And whenever, when you are working across so many roles, you can very well realize that there is so much potential that is there to improve a lot of finance processes, a lot of the ways we do things. So throughout my career that is something which has always been the front and center of how I would work. So even in the roles where I was not officially, officially a transformation lead or something, I would still go in and try to transform that role. Then I did around.

Speaker 3:

Towards last five to seven years I have been moving from finance into more integrated planning, supply chain finance, and then my last role at PepsiCo was the finance transformation digital financial transformation lead for APAC, and that is where I was. I had the mandate to actually do the transformation as well, but that does not mean that that was the only time I was doing transformation. I was pretty much doing the transformation and improving our ways of working throughout my career, and I believe that is something which resonates with me a lot, and I'm sure today's finance professionals can also acknowledge the fact that we can truly do better than what we are doing today in terms of our manual processes, still relying on multitude of Excel files systems not talking to each other. So this is all frustrating, but yes, out of that frustration comes out an opportunity to transform and an opportunity to do things in a better way, and that is what effectively pushed me towards that as well.

Speaker 2:

And I think that's a really important to note, because I go into a lot of companies, obviously, when you're having conversations about change and they say, well, I've never done anything like this before, I was like, and I think it's I think the reality is, is that transformation should be a part of our day-to-day working life in finance, because that you know, there's no, there's no process I've ever seen that is perfect, because you know, six months later, changes within the business and then it needs to change. So so why do you think there's, I guess, a mindset around finance transformation as a particular role, rather than necessarily being incorporated into the day-to-day because, as you said, you lived and breathed it in your day-to-day role?

Speaker 3:

I believe one of the biggest reasons is that we are managing a packed agenda right, finance people.

Speaker 3:

Anywhere I go, anyone I talk to, including myself when I was working in corporate I would just not have enough time to actually step back and look at what I am doing. I would just be busy, you know, somehow get the month end done, somehow get this budgeting season done. I will look at my files once I come back. So stepping back and then getting that time to actually improve things, that I do believe is one of the reason. I do believe that, again, you know, finance people are smart people. Where we are like, I mean the way we are trained and the way we have been working, I mean we have our pulse in the entire business, pulse of the entire business. We know pretty much what is happening across the business. We have good commercial acumen as well. We have good understanding of the business. We have good commercial acumen as well. We have good, um, good understanding of the business also. But I believe it is just that the way some organizations are set up as well, and the way our, our whole um, organizational, uh, culture, or the way or the, the functional cultures are set up, that okay, you know, I mean. I mean this is finance, this is how I'm doing some specific work and this is how I need to do.

Speaker 3:

Transformation, as you rightly pointed out, is a mindset, right. It is not something that is going to be done by a transformation team. So, for example, in my role when I was doing transformation, I was just one person. I didn't have a team reporting into me. All I was doing I was working with different people across the organization to help them transform their ways of working. At the end of the day, it is the transformation needs to come from the inside, right? That? Okay, you know, this is what we really really need to, and I always encourage everyone who comes to me and they say, oh, I want to grow my career in transformation because it's a happening thing. Everyone wants to get into digital transformation, ai and whatnot.

Speaker 3:

I always tell them it's not about you getting a role in a transformation or whether I mean your company has a transformation function as well or not. It is all about how you improve your current ways of working. It's a journey, it's a mindset change. It will take some time. I do see people getting there.

Speaker 3:

People are now challenging and questioning things with all these AI and all these different technologies coming up, people are questioning things, but I do believe a lot goes back to our ways of working, how we have been doing things in a certain way and then just not getting enough time to actually transform, and then the good thing is that once you do it, it creates a virtual cycle. Once you transform something, once you save up time from something, you get more time to do something better, and then, when you do that, you get even more time to do more things better because you keep on improving things. That becomes part of your DNA and when that happens, it creates a virtual cycle of productivity for you. So it's not easy, it's not simple one plus one, but this is how I this is one of the reason I would say that that that, uh, probably need a bit more, bit more mindset change yeah, I call it the transformation snowball, once you get it rolling down the hill, it starts to grow and have more impact.

Speaker 2:

You just need that successful first small win to kind of build the momentum and the buy-in from the team. Once you got that, um, I won't say it's plain sailing from there, but it's from the team. Once you've got that, um, I won't say it's plain sailing from there, but it's a lot easier once you've done it it is.

Speaker 3:

It is I mean you get some time back. You start funding the transformation internally right through your time, through your productivity, through your uh, through your uh improved ways of working and when you look back in your time in pepsico, um, as transformation leader, obviously you mentioned that you're working alongside the team.

Speaker 2:

So there's obviously a challenge there, because without you're not directly got any direct reports within those teams. You're almost influencing from the side. So you know how can we influence and support people to have a transformation mindset and how did you approach that at PepsiCo?

Speaker 3:

So I believe the biggest barrier, first of all, to transformation and again, I mean, if we step back a little bit, what is transformation? Transformation is nothing but a change. Right so effectively, and I always, always say this that effectively. Today we are all change managers. Yes, with so much happening around us, we are all change managers and we need to go back to the basics of change management. We need to make sure that we are, we are communicating a clear picture of what the future is going to look like, what it means to transform. And that is how I would I would influence as well in my influence, my broader teams to sort of sell them the transformation, to sort of show them that, okay, if they make these changes, this is how their work is going to get impacted, this much time they are going to get back. This is the value addition that they can do. This is how things are going to get back. This is the value addition that they can do. This is how things are going to improve for their team.

Speaker 3:

So, for example, in one of the projects transformation projects which, ironically, I was not transformation manager at that, I was not leading the transformation, but it was in a different role was around accelerating the book closure, right? Apparently it seemed like a no, like something which cannot be done or, you know, which will take a lot of our time. People initially thought that it means that we are now going to cram five days worth of work in two days. Our life is going to get more miserable. Cram five days worth of work in two days, our life is going to get more miserable, etc. But once you very clearly identify that, okay, this is what the future state look like, this is what the vision of the future is, you paint a very clear picture of the impact it is going to create, how we are going to improve our performance reviews, how we are going to create visibility of our reporting function across the organization, because now we are not just someone sitting at the back and doing just the number crunching for four days and everyone just coming to us and asking about what is the number? What is the number? We have it ready on day two or day three. We accelerate the things. People start to understand okay, this is the benefit, what is in it for and again, it's all basics right what is in it for them? We have been, you know, learning about this influencing 101 and our influencing 101 finance business partnership 101 class, that you know what is in it for them, right? So that needs to come out very clearly the moment we start. So that needs to come out very clearly the moment we start and especially when you are in a role now I'll come back to the point around when I was in the role as a transformation lead.

Speaker 3:

When I'm approaching a problem with a mindset or give an impression that this is something which is coming down as a top-down mandate take it or leave it right and that happens. That happens in a lot of scenarios where transformation is just a mandate and you just have to do it. That is where it doesn't work. You need to make sure that transformation there is a mandate, of course, which is a great thing to have, because mandate means you have an authority to actually drive things. But at the end, end of the day, people need to be able should be able to drive the transformation from the bottom up, because that is where the real execution is going to happen.

Speaker 3:

So it is important, it was very important for me, for example, whenever any such project that came up, which was, of course, sort of like, you know, maybe a global project or a transformation we needed to do, or things needed to move in a certain way, a process needed to change in a certain way. We need to. I needed to demonstrate to the team how and why they need to do. Again, you know the basics of why they need to do it what is the future, how the future looks like and why does it even matter for them to change? What is the cost of inaction?

Speaker 3:

And most of the times, I would just let the team drive it, let them. Instead of me dictating, I would work as a facilitator for them, to connect with them with the resources that are needed, provide them with the right frameworks, with the right mindset sort of of an external consultation or advisory kind of a thing to them, support to them, and they would own the transformation, they would own the results, they would own the credit. They will get the credit, they will own the results and they will get the credit as well. And then that will be showcased as well. That you know. Yes, this is something a team in Thailand did, or this is something team in ANZ did. That's how it created momentum, that's how it helped me create momentum.

Speaker 2:

And I think that's a really good insight, because there's a really interesting stat actually came out from one of the big consultancy firms that said that if you outsource, you know a lot of your transformation efforts, you don't achieve your objectives because there is that whole buy-in piece from the finance team. That's critical to, I guess, not just delivering the change but ensuring that it stays because, again, you know, you look at all of the, the change methodologies it's, it's now become less linear and more of a lead.

Speaker 2:

Yeah, um absolutely so when you're, you know when you think about, obviously you're in a, you were in a massive company. There's a lot of moving parts and perhaps some politics. How did you, I guess, clear the way for the team to actually create the space for transformation? How did you help find balance? Because resource is always a challenge when it comes to change and transformation and finding space. So how did you support the teams to find the space for change?

Speaker 3:

Again, this comes down to if the team is really really sold behind the why they will find time and they will find the resources. And that is how it has to work, right. Again, we have certain hours in a day, right, and at the end of the day, of course, we need to do. Transformation is a job which no one external can come in and do for us, even if we have an external consultant, for example. Still, the process documentation and understanding diagnostics team, there will always be the time that team has to take out. But as long as we are making clear that why that? Why do they need to transform and why does it matter to them, they will find time and they would actually help you do that. And then, of course, you provide support through through making sure that if there are any decisions that need to be made, you're escalating them. I'm escalating them accordingly, I'm making sure that if there are any roadblocks that are happening and I guess the biggest, one of the biggest element of, or an enabler of, any transformation is its governance, which often comes as an afterthought in many projects Governance help you not just stay on track.

Speaker 3:

When I say governance, it doesn't mean that we are trying to audit something because, again, as finance people or audit people, that's the first thing that comes to our mind that, okay, governance, okay, it means we're going to stop. It's going to be a hindrance as opposed to something which is of an enabler, but I believe it is enabler just from tracking the progress, but ensuring that what are the projects that needs to be shut down? For example, what are the projects where we need to? If we are looking at a portfolio of projects, if there are something which are lagging behind, why are they lagging behind? What support can we provide? Or, as I mentioned, is there a need to actually, you know, consider shutting down those projects? Who need to take that call? Do we need to take that tough call and, you know, call it a failure and move on? All those things are enabled through governance and that, I believe, is a big role that any transformation, any transformation organization within a company, can play, and that is how you enable it.

Speaker 2:

So what does good in your opinion? What does good governance look like, you know, from a practical perspective, and what are some of the do's and don'ts around that?

Speaker 3:

For a good governance. First of all, the most important thing is to clearly identify roles and responsibilities. Be very clear on who is accountable for what. Who is responsible, who needs to be consulted, who needs to be informed. So I have found RACI metrics. A lot of change managers or project manager know about it. It's called RACI Responsibility, accountability, consultation and Information, informed. So very clearly define that. Who is going to be responsible for what? Who is going to be accountable? Make sure that, for example, there are not more than one person responsible for one task, for example, because the more we disperse responsibility, the more it becomes. It hinders the progress, because then no one takes accountability, no one takes really really the response. As I say that if it's everyone's responsibility, it's no one's responsibility, right?

Speaker 2:

So that's a great saying.

Speaker 3:

So so that's, that's it's. It's very important to make sure that we are clear in that we should be able to identify that okay. Important to make sure that we are clear in that we should be able to identify that. Okay, if the pro I know it's a hard thing to say, but if the project does not work, who is responsible for that? Yeah, right, yes it sounds a little bit uh, policing kind of thing, but it's important because otherwise we will not be able to identify who is going to be accountable and responsible.

Speaker 3:

Then, having a very clear reporting mechanism in place where we are tracking progress, where we are making sure that not just financial measures like return on investment which we are good at that, right, I mean finance people, we are good at that we can right away calculate ROI, we can make ROI right away, but it's important to track the non-financial measures, things like forecast accuracies sorry, things like employee engagement, organizational health scores, how much people are spending time on, how much time people are saving now, for example, how much? What is the net promoter score of the of the different tools and technologies that we are implementing, things like that. And then, of course, non a bit of operational as well, like your speed to market and how your, how your operational matrices are are getting impacted. So this is what I call a balanced scorecard view. So having that balanced scorecard view of your transformation journey is critical for a good governance mechanism, to have a good governance mechanism in place. So this is something that I would say are some of the most critical or most important factors of any governance and it works as an enabler factors of any governance and it works as an enabler. I will again like I will iterate this thing that governance is.

Speaker 3:

Governance might not be the only reason our transformation will succeed, but it can very well be a reason it can fail, because the lack of governance can result in massive disasters. So treat this as an, as an, as an enabler. And so in one more point, uh, this which is around the having the, the right escalations, so having the right steer course, having the right governance structure, what we call the pure governance structure around who is, who has the responsibility to? So, for example, if there is a, if in a certain scenario, we have decision making responsibilities, there should be, for example, 70 to 80% of decisions being made within a certain within the teams right within that project teams, then only 10 to 20% going up to the steer co, then only 10 to 20% going up to the steer go, then only 5% maybe going up to the Apex body, whatever you call it.

Speaker 3:

We can call it digital council or board of innovation board or whatever we call it. So we need to have that hierarchical structure, but we need to make sure that we are also empowering people to make decisions and not just every decision goes all the way up to the up to the, up to the innovation board, so it can very well act as your enabler, right? So that's that's, that's some of the.

Speaker 2:

I think I hope I'm able to give some um idea around around what the good, what good governance look like, and maybe bad one as well I think and I think your last point was an incredibly important one about the the amount of decisions, um, and that should be made at almost the grassroots level. So I think you said 70 to 80 percent and and I and I completely agree with that. I also think it's I I liked your piece around the layers, but maybe just for because obviously in our audience we have a mix of big corporates but also smaller businesses. So if you're a team of, say, five to ten people, you've got a cfo and fc. You've not necessarily got the big, uh, multi, multi-jurisdiction, uh corporate level, how you know what are the key things that you would empower the team with and what are the, the top level decisions that you you would make sure that you have oversight of as cfo no, so, so, so.

Speaker 3:

so, hannah, that's that's. That's a great question. And when it comes to governance, or, or the oversight, I do believe it is. It is size agnostic, geography agnostic. You don't just need a CFO for the decision making. Yes, cfo is one of the persons who is going to be highly involved in this one and, in all likelihood, be leading all of this. That is where I think CFO's role really comes into play, where her thinking around governance and risk management comes into play, how a CFO can influence the rest of the C-suite to bring them on board and build these, these, these tier co structures. So, for example, in any given tier co, you must have so.

Speaker 3:

For example, if you are looking at your demand planning, revamp, transformation of your demand planning, imagine you are trying to, you know, create a bit of predictive analytics for your demand planning, it is not just CFO who is involved in this project. Demand planning would mean your supply chain directors or your supply chain executive are involved in that. Your sales directors are involved in that. Your IT team is definitely going to have a big role to play. So your governance structure will simply be, irrespective of your size, will simply be your project teams. On top of that is sitting your CFO, your CIO, your sales directors, whoever is the executive within those?

Speaker 3:

Because, again, governance is more toned from the top as opposed to just something that will happen at an execution level. It's very important for that impression to come from the top management that, yes, we care about governance and we want to make sure that we succeed in this project. So we are here to help you clear all the roadblocks and support you in these projects, and we are not just here to just do progress tracking or budget tracking or something like that. That can happen on an email as well. That's not something for which we need a committee to, or we need a steer to do that. So, irrespective of the size of the organization, we always need to have a multi-functional, cross-functional team of executives who are sponsoring the projects, who are making sure that there will be sponsors of the project, and then there will be the people who are the key stakeholders in the outcome of that project or the outcome of that transformation. So, irrespective of your size, I do believe we can always create this governance structure.

Speaker 2:

And we've talked a lot about what good governance looks like. When you think back through your your work history, where have you seen examples of bad governance and how did you deal with the challenges that that posed?

Speaker 3:

so I guess the biggest reason any governance would go bad is again comes down to that point around the accountability part. So, for example, it and it happens that we have a project right or we have a transformation agenda that we are trying to run and then there is just not any clarity around who is going to do what, who is responsible for what output and who is going to be eventually accountable for the project, who is the project sponsor, when these all these different things do not play well together and not clearly defined, that is a sign of a bad governance and that really, really impacts the process, the output of your transformation agenda. Whenever it happens that you are trying to get an output, trying to get a progress tracking from the team, people would not know whose responsibility is it to report, whose responsibility is it to, so everyone becomes a little bit of Not a little bit, actually a lot hands off. That okay, you know, this is not my responsibility, so I'm not going to talk about this. So that is one element. Then the other element is not having a reporting mechanism in place. Now, again, this sounds very, I would say, unglamorous, I know, but unfortunately in transformation the biggest impact happens by doing most of the unglamorous work. It does look like you know transformation, you know I might be, you know working with AI and you know God knows what. But under the hood, there is a lot of unglamorous process understanding and you know workflow understanding, documentation. That goes into actually all that, and governance is one of that piece as well. It's unglamorous. It doesn't look like you know that something is happening. People see it as a hindrance, but having a reporting mechanism in place is very important Because without that, we will not be able to know where we are.

Speaker 3:

Where are we actually putting our resources or, in fact, are we putting right resources for the right projects? So it's important to have a proper balanced scorecard view and then, plus, you know, a portfolio. I always, uh, advocate the portfolio review, that we should have a brief, one-page portfolio review, that this is the project, simple, green, red lights, where we are, and then we drill down into different projects, key areas that, okay, these are the kpis which are missing. These are the objectives we defined at the beginning with. These are, these are we are not able to do. This is something which, which, which is lacking. Again, I do see a lot of companies, a lot of work that I do the first thing. We do that that is always.

Speaker 3:

There is a steer cove, right? So there is always a steer cove present, right? I mean, no matter what project, there will always be someone, because somehow people just assume that if there's a steer cove it is getting governed. Yes, that is an important piece, but the input and output of steerereoCo is even more important.

Speaker 3:

A lot of times StereoCo in my experience, I have seen StereoCo becoming more of a good conversation, where you come in, you listen to a few people, a decision gets bounced off around the room, the hypo effect, which is like highest paid officers effect, comes into play. A lot of people who have a bigger voice. They then, you know, drive the agenda of the entire meeting. People then start getting into that group thinking and all those issues around group thinking comes into play. This is something we really want to avoid. Issues around group thinking comes into play. This is something we really want to avoid. And for that to happen, we have to be very objective with our inputs, our outputs of the end. What is the expectation, what is the terms of reference of a steer as well? So, yeah, that's something I would say has been my experience as well.

Speaker 2:

Yeah, and I think those communication channels are so important. People like you say it's not the glamorous part sending an email out just to inform everybody about what's happening. Wave up to you know what's going on in the project, but it's so important to make sure that the wider business is aware of what's going on. And you mentioned key. You know if you're working across a portfolio of projects. This is what we should look at. So say you're, you know, you're I don't know project manager, or you know internal project manager, for you know a finance transformation project internally. What sort of things would you report on on a regular basis to keep the wider business and your project team on board? What does good reporting for an individual project look like?

Speaker 3:

So it depends on from a project to project. But of course, if it is we are talking about, say, for example, we are, if we are talking about doing an AI implementation, for example, right, so a good scorecard would look would have, of course, your, as I mentioned, you know, I mean we are good at financial piece, so I will not touch that a lot. I mean your rois and everything. But yes, there is and there is a point on roi and I will come back to that in a bit uh, those, those financial measures are going to be there. But then, uh, for, for a typical ai project, we need to make sure that we are also looking at the model accuracy, both in the development and in the production. So, because we want to make sure that when we are developing the model and it is showing a high level of accuracy, but when in production it is not showing a high level of accuracy, this might mean there is something issue with the over training or under training or something like that, right. So we want to get to that root cause. Also, of course, we will do the financial tracking, as I mentioned, around what is the budget that we have already spent and how we are going to do that. Another important piece is around making sure that we are also also reporting on once the implementation is happening, once the project is, once the AI is being used. How are people using it? What is the adoption rates? What are the, as I mentioned, the net promoter scores? What is the overall engagement of the team? Because, through use of those different tools and technologies that we are building, because, again, if we just look at one thing, which is project is implemented, ai is implemented, we are good done with that. But if a person is imagine, an AI model is giving us, say, 90% accuracy, right, great, everyone would love that. But if my team is still sitting in the office staying back because they need to now do five extra steps to get the AI output to be more accurate than what it was, I mean my team will be still frustrated. So would I call it a win or a failure, right? So it's very important to understand the pulse of the business, pulse of your team as well. That how, what is the organizational engagement, what is the team engagement that is happening and we miss that part and I, I must say I mean this is it's not easy, but we, we, though it's not easy as well, but still we not a lot of people take an effort to actually go down that road and and build that scorecard.

Speaker 3:

Now, from our typical AI project point of view since I gave that example it is also the financial becomes even more important. A little bit more important than your conventional projects is because AI has a cost. Ai is not a kind of a project where you just implement and you forget. You implement a project, the first cut would likely to be 60% accurate, hypothetically speaking. You will retrain the model and then it becomes 65% accurate. You retrain it again. 70% People start using it.

Speaker 3:

All those retraining, all those usage has a cost associated with it. So throughout the life cycle of an AI project, you will have a cost. Your cost will keep on increasing, so your return on investment will keep on changing. So how do you capture that? That is a very important piece, that is if in the financial measures, it's very important that we are tackling that.

Speaker 3:

And then, apart from that, I mean depending upon, of course, your project. If it's say, for example, demand forecasting project, you will look at your forecast accuracies, your preferred supplier scores, for example, because the better you predict your demand, the better you are able to supply your product as well. Again, it's important to have all these measures identified at the beginning of the project, at the beginning of your transformation, when we are building those roadmaps, when we are building those and I always recommend that, doing it in a certain systematic way. But again, there is no one size fits all, but some of the some of the things that I would say will always be there are are the ones that I have mentioned. They're, in one form or the other, they will be there yeah, and I think that's a really.

Speaker 2:

You made a point that I always I always think is really key is to focus on the outcome that you're going to be delivering, and I call it shiny toy syndrome. There can be a tendency, especially with technology projects, to focus on we've got the technology in, it's being used and actually what I love about what you were saying there is that the output. It doesn't matter if the AI is being used, if, like you say, the finance team are having to do more work to get the actual results out in the first place. So I think that's a really important point for people to note that it's about the outcome on the business, not just the outcome of the implementation of the project itself, of the implementation of the project itself.

Speaker 5:

Now for the £1 million question what is the best finance software for your business? Is it A Sage 50?, is it B Sage 200 Standard C, sage 200 Professional or D Sage 200, standard C, sage 200 Professional or D Sage Intact? An impossible question to answer without a lifeline, but we have the perfect lifeline for you. Our free quiz which Sage Product is Right for you will tell you which product is the best fit for your business in just 5 minutes. All you need to do is head to wwwitassolutionscouk and answer a few simple questions.

Speaker 3:

Positioning around my thinking, positioning around my transformation work is that any initiative that we are taking, any work that we are taking, what we are doing, must be tied to a specific business need. Yeah, if it is not, it is should not be part of your roadmap. It should not be part of even you should not be even considering it unless it is. It is solving a business problem, it is creating an avenue of growth and creating a better ways of working. Yes, go for it. If not, you're just doing it for the sake of doing it, because you know competitors are doing it, because everyone is doing it. There is something wrong. We need to really think, step back and think about it. We really need to do it.

Speaker 2:

And you mentioned the ROI. You know you need to think about it slightly different with AI versus traditional technology projects. Are there any other aspects of traditional project or program management that you see as needing a little something extra when it comes to AI projects?

Speaker 3:

Oh, absolutely Absolutely. Ai needs to be treated in a very different way. In our typical scenario, when we are implementing an ERP even, or implementing an ERP software tool, what happens is that we have a certain we pretty much there is like a business requirement phase where, as a finance person, I'll get involved. I'll tell the IT team or the vendor that these are my business requirements. They will go away. They will, you know, develop the product, come back, implement it, do a UAT, user acceptance testing or different forms of testing. We'll say yes, go, no, go, okay, let's go ahead. We are fine with the product, with the software In AI. It's an entirely different. If we want to get AI right, my argument is that and this is what I have found working is involving smes, and that is where, in fact, finance becomes so, so critical and so so important, involving smes from the start of the ai development life cycle and just to clarify their sm for those perhaps not used to consultant terminology.

Speaker 2:

Subject matter experts right 100 percent.

Speaker 3:

Yes, yes. Subject matter expert, finance domain experts needs to be involved in the AI development lifecycle, throughout the AI development lifecycle. So I will not go into the details of the entire development lifecycle, but it involves things like starting off with your business requirement, understanding, of course, problem definition, data pre-processing, model training. Every single step requires finance teams or the subject matter expert teams, domain experts, to be involved, because only then you are able to create and build an ai model which is according to what we want the output to be. Then, even once you do that so in fact, I will I will just to clarify a little bit more. I'll give an example of the training part of the of the development lifecycle. On the face of it, it might seem that, okay, model training is a very technical piece. What does a finance person can do? Why would we need to get involved? We need to get involved to make sure that the training data that the vendors are using, the training data that the development team is using, is representative of the population that we are trying to build a model for, because if that not happening, you will train a model which will perform great on training data, but as soon as it will be exposed to the unseen data, the new data, its performance will go down, and we do not want that because number one, there is time involved. There's a lot of cost involved in the in training of an ai model and we want to make sure that we are tagging with the technical teams to make sure that this is happening. And that's where, of course, you know building digital literacy, ai literacy all that comes into play as well.

Speaker 3:

And then once, even when you have done your AI implementation per se or deployment, that's not the end of it. That is where we call the AI flywheel, or the virtual cycle of AI, comes into play. You implement a first version of AI. It will create people, will start using it, it will generate data. That data will then it will then use that data to get trained. So your model will theoretically improve its performance because now, as more and more people use it, more and more data gets generated and more and more training happens.

Speaker 3:

So if, theoretically, it will keep on improving its output as and when people are using the data more and people are using that model more and more, but of course you need to keep track of that. Also, you need to make sure that, as I mentioned earlier that there's a cost involved with retraining, we need to make sure that the objectives that we set at the beginning, that these are the forecast accuracies, for example, model accuracies or different OKRs or different objectives that we set at the beginning are we meeting those or not? So it's by far. It's not a linear process at all. It is very agile. We need to embrace an agile mindset. We need to embrace the minimum viable product failing fast mindset if we want to really work with AI, because AI has an ambiguity associated with it and we should be able to embrace that ambiguity.

Speaker 2:

So what and again, you know what I heard from that conversation I think there's a couple of critical points where you need to make sure, especially for finance specific AIs, that you have that involvement.

Speaker 2:

So it sounds like in the definition making sure you're really clear at that beginning stage about what you're defining so that we've got and we've got measures to track that all the way through as we go into that. Making sure that the subject matter experts are involved in the training phase, particularly around validating the data before you start training to minimize the cost of that training aspect. And then Iating the data before you start training to minimize the cost of that training aspect. And then I think the third thing you said is almost like a there's like a, there's a, there's a recheck phase post. Then you know the actual implementation or deployment piece, which is where you need to then go back to those measures that you've defined beautifully well in your initial phase to make sure that you're still delivering against that, because there's a danger with the retraining I guess the more data coming through that it might go slightly off course potentially 100 and and even even hannah.

Speaker 3:

It happens that you're again. I mean no one predicted covid, for example. No one predicted supply chain disruptions, for example, in in 2022, 23, so I mean a lot of this happens suddenly and your model needs an entire retraining. You cannot stick, get stuck with that old model. So you have to come back and you, as a subject matter expert, need to bring out and say that okay, you know, gas prices have increased by 100 percent, or electricity prices have now increased, or commodities are going in this direction. Network costs have increased in this way. How does that impact your model assumptions? A lot will change, so it's an iterative process.

Speaker 2:

And there's an interesting piece there, isn't it? So we live and work in an incredibly volatile environment, right, um, and we're designing ai, yeah. So how do we build in, I guess, the capacity to identify what is a one-off volatility versus something that is needs to be assumed or taken into account? So, you know, are those the sorts of things that we need to think about with the data that we're giving ai to work with? To be clear on what is, I guess, normal or, at the moment, perceived normal I don't think any of us thought it was normal, as we're in there but how do we, I guess, the ability to cope with volatility into this, into these learning models, cope with?

Speaker 3:

volatility into these learning models. So, again, from an outlier point of view, that is where, again, the subject matter expertise will come into play. So your treasury team will know that, okay, these are the things which, these are the cash payments or these are the payments that happened because of an M&A transaction, and these does not need to become part of my normal cash flow, overall cash flow or treasury forecasts. And that is again where I am a big, big advocate of this that subject matter experts will be the ones who will be able to identify. Number one, that what are these true outliers? And then again, it's, it's, it's like anybody's guess that if that is an outlier, it is going to become, uh, sometime. I mean people. I mean, if you have like two data points, you can draw a line, so that can be a trend right so so.

Speaker 3:

So again, I mean it's it's anyone's judgment that it is a one-off. There will, of course, always be one-off, but again, as finance teams or as any team for that matter who is working on AI, there is always this level of ambiguity and uncertainty that we need to be comfortable with, and uncertainty that we need to be comfortable with. Any AI model in the world will not give us a 95, 9900% accurate results. I mean, there will be models, of course, depending upon what the complexities are, but in a complex environment, if we are operating, we are not going to get a 9900% accurate model, except for what a vendor demo shows us right. But in reality, there will always be things which would be a new data, which would be a nuance. So we will need to either call it out as a noise and again it's a very important point you have raised, anna and when we are training a model technically, when we train a model and there is a lot of noise in our data around these outliers and we over-train our model, that is where we again create a bit of an overfitting issue with the model. That model is now actually reading the exact data pattern as opposed to. It's actually getting trained on data as opposed to recognizing the patterns. So it is actually looking at what is the exact pattern of the data, as opposed to just looking at the general patterns and then performing as it as as it gets exposed to new data. So it's important to.

Speaker 3:

Again, that's where subject matter expertise, that's where involvement of your, your, even your. Again, that's where subject matter expertise, that's where involvement of your, even your senior executive team would come into play, because a lot of information in the market they might have, your sales director might have a lot of information which you might need that insight to then treat it as a whether this is your baseline or this is something which we need to overlay on top of our baseline model. So again, no, I wouldn't say there is a one exact answer for that. That you know you can find, you know you can do these three things and then you can identify outliers. It will always be a lot of judgment call in this one, but of course you need to get involved with it, with your developers, early. You need to stay on top of the model early, involved with it, with your developers, early.

Speaker 2:

you need to stay on top of the model early and very aware that we're we're running rapidly out of time, just as we get into my favorite topic, which is ai, which is always the way, so, so, so I guess, if I was, maybe you know, because I I think there's been some great learnings, not just about AI, but also about general projects. So my first question, my first of two questions to you. So the first thing is, when you're looking at AI projects, you know, maybe you're a CFO, you know, or a financial transformation person that's been asked to get involved in an AI piece what are some of the things that you should look for in an you, you know, either an existing project plan or an existing brief that will tell you that that project, you know, is show will, will be successful or has a high likelihood, um, of being successful, and I guess, the reverse of that, which is, what should you be looking for before a project starts to see whether it's got a good chance of succeeding, particularly when it comes to AI?

Speaker 3:

I would always say look at your, and no one would know better than yourself. Look at your process first. Understand, when you are looking at any AI solution to resolve to, to automate something, the process that you are trying to automate. How complex is that? Yeah, is it something that involves a lot of decision complexity where you know I mean you are doing, you are raising a purchase requisition, it goes through 20 different steps and then you know, it goes back, someone changes it, then it goes to someone else who changes it. Is there a lot of manual intervention that is happening? Is there a lot of judgment calls that are happening? Again, ai is smart, but it is dumb as well, right? Yeah, so I mean, it depends on how you use it or which process you are using it to automate. If you will expect that, if someone will expect that, you know I will just put an AI on my purchase to pay process, for example, and it will somehow magically transform it. That's not going to happen. Your complexity is going to be amplified, so I can pretty much assure you that the project is not going to work. I can pretty much assure you that the project is not going to work, right, I mean, if your underlying process is very complex, if it has a lot of handovers.

Speaker 3:

And I also do not say that first simplify everything and then start looking at AI. Yes, of course that is also not practical right. We need to move fast. On one hand, we are saying we need to agile and, on the other, we need to move fast. On one hand, we are saying we need to agile and on the other, we need to say simplify everything but go in a bit of a modular way. Look out your project and look out your processes end to end, and identify areas which are really complex, which needs a bit of streamlining, identify areas where you can actually just simply use basic automation to make them even more smarter or more streamlined or optimized, and then look at those areas which you do believe have some kind of judgment involved, which have a certain pattern, which have a lot of data, requires a lot of data analysis and something that ai can help you do.

Speaker 3:

Yes, on chat, gpt, it seems like it can do everything, but trust me, it cannot. It will, it will break. So it will not work if our processes are too complex. So I would always always, always start with with what is the workflow, again, at the end of the day, what? No matter what we do, what is the workflow? Again, at the end of the day, what? No matter what we do, an organization or a function is a sum of different workflows. And when I say workflow, sending an email is a workflow as well. Yeah right, sending a message on on zoom, for example, that is a kind of a workflow as well, as long as, of course, it's not just a joke and it's related to your work.

Speaker 3:

But this is how complex your workflows are will determine whether your AI project is going to succeed or not, and you can tell that right away If your workflows are too complex, too dated. You have multiple systems not talking to each other. No AI in the world is going to come and solve it for you. You have to do again, I'm really sorry to say, but the real output comes in that unglamorous work. Hate to break it to you, but I mean that's where the real work needs to happen. We have to roll up our sleeves and get into the grand of it and then simplify our processes. Use basic automation to simplify the workflows and then use AI. That's where the value will be amplified. Otherwise, ai is already so complex that it is hard to explain its output.

Speaker 2:

Yes.

Speaker 3:

Right Today, if you are doing, imagine you are inputting a complex process into something which is already very complex and then you are making decisions based on an output of which you have no idea how that gets generated. Yeah, because it will set overly complex. The complexity will be exponential. So it is not just just a breakage or a breakdown of a process. It will have real impact on your business, your decision making.

Speaker 2:

And I think that's a great point to finish on is that the principles of you know implementing AI are very similar in some ways to the principles of you know implementing ai are very similar in some ways to the principles of automation. Right, if you've got different systems that aren't talking to each other, if you've got data sets that aren't clean and validated, if you've got processes that are overly manual and not been thought through with a lot of complexity, like you're going to spend a lot of you know, potentially a lot of money and a lot of time with maybe not the guaranteed success piece on the other side 100%, 100%, Hannah, and you have summarized it really well.

Speaker 3:

A lot of my time actually goes in helping educating people in this space. Right and this is a great platform we are having this discussion on as well is that AI is not a rocket science. It's not something that is going to come and just somehow solve all your problems. We need to go back to the basics. We need to stick to the basics in order for us to move forward. So, yeah, greatly, very well summarized.

Speaker 2:

And obviously we're not just here, like I've been picking your brains for a good hour now, longer than a lot of our episodes but I think that shows the validity of the content. But we've also got a book being released and potentially will have been released by the time this podcast comes out. So you know, if people want to learn more about you know how to do transformation well and you know, and and you to learn from your experience, tell us a little bit about that book and where they can find it thank you so much, anna.

Speaker 3:

Yeah, absolutely, that book is something I have read and overlasted. It has taken a good, good time. Over last two years I have been, I have been working on that and that's my, I would say, a purpose and a passion project. That was it is getting released on September 9th or if by that time the podcast is already out. So it is available, of course, on Amazon, it is available on Violet, it is available on a lot of different Barnes and Nobles. So I mean, if someone will search Reimagine Finance by Tariir, um, they will, they will find the book.

Speaker 3:

Now, the premise of the book, the entire book, is around and and like the title of the book is reimagine finance and the subtitle is the cfo's leadership playbook to lead in the age of ai, data and digital. So, effectively, the premise or the through line of the entire book is that CFOs or finance leaders, being at having a unique vantage point in the organization, are uniquely placed, are in a position, in an advantageous position where they can actually lead the entire transformation, digital transformation for the organization, whether it's demand planning, it's supply planning or it's, you know, because any process that goes cuts across the, goes through the organization's finance has a role to play in it. So finance can lead that, and this book actually helps them with busting a lot of these myths that we were talking about that you know what are different technologies, what are the real world applications of those technologies, how AI, cloud computing and different process mining digital twins are actually helping organizations transform their operations. How finance can do that. How finance can do that. But where it becomes really powerful is that how do we re-architect the finance operating model around data, digital hubs and talent to enable us to lead the transformation, to enable us to wire the transformation. And then it goes into.

Speaker 3:

There's a playbook, a five-step playbook, which I have built based on my experience and based on a lot of research that went in, and, of course, it is built on a lot of work that has already been done in the space as well. So full credit to those thought leaders as well on how they can actually lead the transformation across their organization, how CFOs can actually influence that. So I do believe that this will help a lot of finance professionals, a lot of finance leaders and CFOs can actually influence that. So I do believe that this will help a lot of finance professionals, a lot of finance leaders and CFOs to really take their digital transformation to the next level.

Speaker 2:

Amazing, and so, for those that are interested, we will, of course, put the link to the book in the show notes for you to click on and go have a look. Thank you so much, tariq, for joining me today. It's been an absolute pleasure to talk to you.

Speaker 3:

Same here. Hannah, thank you so much for having me, and it was a pleasure having this conversation.

Speaker 2:

Fantastic, and thank you to our listeners as well for joining us. If you have enjoyed this episode, please do not forget to share it, to leave us a review on your favorite podcast platform, as we help spread the word of the CFO for Project Podcast. Thanks for joining me and we'll see you next time. So, for me, one of the most important things about any transformation project is the partners that you work with, and whilst I'd love to list off a whole host of reasons why ITAS is the perfect partner for your transformation project, why don't I let our customers do the talking for us?

Speaker 4:

one really good thing working with itas is it's dramatically reduced my blood pressure. Um, obviously an account system is critical to anyone's business, so innovation data without that, like every company, we couldn't function as a company. So you know it's one of the most critical pieces of software and any sort of vulnerability we have with that sort of keeps you awake at night. And now, working with itas, I don't have any concerns about our account functionality and our account system and the usability and all of that. Working with previous partners, I've got some grey hairs and there's no slicks from that, as I say, because it's so critical. So it's been an absolute pleasure and long may the relationship continue.

People on this episode

Head Shot

Hannah Munro

Host

Podcasts we love

Check out these other fine podcasts recommended by us, not an algorithm.

CFO Bookshelf Artwork

CFO Bookshelf

Mark Gandy
FP&A Tomorrow Artwork

FP&A Tomorrow

Paul Barnhurst