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And now, onto the show.
From Data Reels, this is FPA Today.
Welcome to FPA Today.
I'm your host, Glenn Hopper.
Today, we're joined by Tareep Munir, APAC Finance Transformation Lead at PepsiCo, an international keynote speaker and a columnist for CFO magazine Australia and New Zealand.
With over two decades of experience at multinational corporations, including PepsiCo, Axo Nobel, and PwC, Tareep has dedicated his career to transforming finance operations and building digital -first cultures.
He's especially passionate about helping finance leaders navigate the path from traditional processes to digital transformation.
Not just through technology implementation, but through the critical cultural and mindset shifts required for success.
He brings a unique perspective on building digital literacy and finance teams.
Tareep, welcome to FPA Today.
Thank you, Glenn. I'm so glad to be on the show today.
Yeah, I mean, I say it all the time.
I love this show because I feel like a lot of times I'm preaching to the choir and get an opportunity to speak to kindred spirits and people who are as passionate about this stuff as I am.
And just from our pre -recording conversation, I feel like I've got a lot to learn from you today.
So very honored to have you on.
Same here, Glenn. Same here.
I look forward to our conversations.
I'm sure we are going to enjoy it a lot.
And there'll be a lot of takeaways for us, for both of us, and for the audience as well, of course.
So I think you and I had a similar shift where, yes, we're still finance people, but we're like the finance tech stack people, and we're focused on data and automation and a lot more than just traditional debits and credits and variance reports and all that.
So talk to me about sort of your transition.
What inspired it going from the more traditional finance roles to becoming a digital transformation leader?
So Glenn, I'm an optimist.
So an optimist in technology, right?
So I do firmly believe that technology, especially the general purpose technologies we talk about, like AI, cloud computing, has this potential to transform how we work, interact, create, and for good reasons, right?
I do firmly... I'm very confident that these technologies are going to create a future of abundance, prosperity, and will solve some of the most complex humans and business problems that we face today.
So I do have this...
So that's what inspires me that, as a leader, we need to create, we need to craft a future which is both enabled by tech and humanistic in its center and its core as well.
And I do believe that we need more business leaders focused on creating such a future as opposed to just doing the debit credits and stuff, of course.
And so we can shape up the future in a way that is both tech -enabled, of course, and that is also solving some of the most complex problems that we are facing.
And to be honest, that's what inspires me.
That is what, wherever I have seen an opportunity, I have raised my hand, and it has put me or landed me in places and roles that involve digital transformation in one way or the other.
So that's something that drives me.
That's more of my mission as well.
That's how I keep myself inspired to keep going on this journey, in other words.
And for me, a lot of it was necessity in that transition.
And I found that gaining efficiencies come from...
Well, first off, because I was in the startup space and you've got the advantage of being at massive companies that have all this wonderful data, my first CFO role was at a company that was doing under $10 million in revenue, and we just didn't have a lot of data.
So it was kind of a blank slate in trying to figure out how to report when a company is on QuickBooks.
And GL is kind of the only data you have trying to build out that architecture.
But when you're trying to raise money and trying to present as if you're a bigger company, you've got to be able to have some kind of data that you can report on.
So building out that was important, but also not a lot of resources.
So you start thinking about software and automation and, well, is there something off the shelf we can use?
Or do we have to build something?
I imagine it's different being at such a large company running digital transformation versus being at a startup, because startup being the speed boat that you can quickly turn, but these big companies, it's like turning a battleship.
It's a massive thing to transform it and change the company.
Tell me about doing digital transformation at a large company.
What are some challenges that you face there that you may not in smaller companies that are able to be maybe a little more nimble?
You are absolutely right, Glenn, and you pointed it out very well that data.
Of course, there is a lot of data, but data has both sides, right?
It is an opportunity if you look at it, but it's a challenge as well when you look at it the other way.
So again, based on my experience, what I have seen, and in many roles that I have done, a lot of insight actually resides somewhere in...
They are there, but they reside somewhere in complex actual models and disparate systems which are not talking to each other or things like reconciliation and so on and so forth.
So I have seen a lot of work or a lot of focus being done on carrying out, just reconciling and doing all these work, which is more of a manual core accounting piece as opposed to then creating value for business, which is more around business partnership and those ideas.
And there's nothing...
I'm not saying that it's right or wrong in a way, it's just that the nature of many of the finance functions that I see today, that is how finance functions are unfortunately operating.
So that is... Data is a big, big challenge, I would say, that one faces.
And then, of course, when in a bigger organization, you have matrix structures, you have a different way of navigating digital transformation as opposed to maybe in a smaller organization where you have a much nimble approach towards decision -making and moving faster.
So in a bigger organization, you need to have a much more coherent approach, a much more clear vision of where you are trying to go.
How do you actually get there having a clear roadmap?
So all those things become even more important in bigger organizations.
In a smaller organization, of course, as you rightly pointed out, it's nimble.
You can move fast. You don't have to think about a lot of different areas of the organization because it's small and it's very nature as well.
And I guess you said data has both sides.
And one thing is...
But at the big organization, the one advantage you do have is more data.
And data... We talked a lot before the show about AI.
And everybody's talking about AI with generative AI, but you and I were talking more classical AI.
And you said you called yourself a traditionalist, which I would completely agree with that.
And I think... We'll talk about it more, but I think that something that people have a hard time with is classical AI machine learning is a deterministic science versus generative AI.
You could get a different answer each time.
And there's obviously guardrails in ways that you can use it for finance.
But let's back up and talk to me about what you mean when you say you're a traditionalist when it comes to AI and finance.
I guess explain why you believe finance departments need to focus on traditional machine learning before they jump into the generative AI side.
Yeah, great question, Glenn.
And first of all, let me clarify one thing.
I have no doubt on the potential of generative AI.
Being all in for technology and how I talked about how technology can revolutionize what we do.
So no doubt about that.
But having said that, as I already mentioned that many organizations today finance...
In fact, some of the studies also suggest that around 40 to 50 % of the time, finance is still spending on transactions, right?
Mostly transaction processing or the other problem around inefficient processes.
Again, I mean, that's one of the challenges many finance functions face over the period of time M &A, acquisitions and just by organic growth itself things have become so complex that we waste a lot of time in those areas.
Now what generative AI has done, unfortunately, I say, is that it has diverted the leaders, business leaders and finance leaders' attention from these core problems, right?
What it has done that people are starting to see this sexier interactive AI experience, they have started to have that experience where you can actually talk with AI, it responds, you see the result right away instead of a traditional AI where you need to do all those boring stuff and you don't really
see an output in a way that you actually see in a generative AI which is very, very interactive and very on the spot output, on the spot results.
Now, it's a dangerous trajectory, I say.
Reason being a technology with a lot of potential, no doubt about that.
When we put disproportionate resource and attention on generative AI which has less potential at this stage for the traditional processes like transaction processing, process improvement, those kind of reconciliations and stuff.
When we put disproportionate amount, we see the organizational technical deaths keep on increasing and then we see that, you know, I mean, most of the projects getting into what we call a pilot purgatory.
So that's a dangerous trajectory to be on.
What, unfortunately, generative AI has done is move that shift, move that attention.
I hope it makes sense to put some of the context around why I feel that generative AI is probably doing a little bit more harm than some of the benefits that we are actually able to reap today.
Yeah. And actually, maybe now it's a good point.
So, for finance professionals who might be new to the space, I mean, again, we are finance and accounting people, not machine learning engineers.
So explain the differences and their respective applications of classical AI, machine learning, versus new generative AI.
No, absolutely. So, as you mentioned, as well, that traditional AI or machine learning, what we say, is more deterministic in nature.
It learns based on historical data and then it predicts output based on a very specific outcome.
It can predict results, it can classify information, or it can detect anomalies, for example.
It is, of course, powerful at pattern recognition because it is an AI, it is a machine at the end of the day, and it can carry out complex analytics as well.
So, for instance, if, in a traditional view, in a machine learning, if you want to predict your future cash flows or identify a fraudulent transaction or segment your customer based on purchasing patterns, you need to write down that algorithm, and that is the output it will give you.
For a specific cash flow forecast, it can tell you, okay, this is how your next three months look like from a cash flow point of view, for instance.
Generative AI, on the other hand, as the name suggests, it can generate new content.
So, for instance, predictive analytics can give us a cash flow for the next quarter, but then, generative AI can then go a step further and can provide recommendation based on, of course, all those large data that it has been trained on and the large language model it's based out of.
And then it can also provide summaries or variance analysis and anything that can generate a new content.
Now, having said that, today, most of the technologies or the underlying architecture that sits under traditional AI is also part of the generative AI, right?
So, generative AI effectively can do predictive analytics in a way, and then the layer which actually works around this generative part can sit on top of it with whom a CFO or a team can interact to actually interact with those numbers.
So, it pretty much adds a layer.
So, again, if we go back, if we step back a little bit, the core still remains that traditional AI.
The core of finance transformation or business transformation still sits with that customer segmentation, cash flow forecasting, process mining, for example, process optimization, and so on and so forth.
Generative AI on top of it at this stage is more of how do you actually interact with those results and then come up with new ideas, maybe.
Yeah, and I think right now it feels very, you know, you can do a lot of parlor tricks with generative AI.
You know, it's like a magic trick, and you can impress people and you can do some pretty amazing things, but it's not going to be fully mainstream.
And don't get me wrong, I'm all in on give your teams access to a generative AI tool, make sure it's in an enclosed environment where it's not, you know, the data is not going to be leaked out and used to train models and all that.
But generative AI in finance and accounting isn't going to be wholesale widely adopted until it's integrated into the software that we already use.
So Data Rails, you know, has AI built in where you can ask it questions, and I think you're going to see ERPs, CRMs, you know, Salesforce, obviously huge on generative AI as is Oracle with NetSuite and the list goes on.
But I think you're going to see those software engineers are going to put it into their systems you're already using.
So if you're used to seeing a dashboard that is, you know, giving you two dimensional view where you can, you know, dashboards, you can click in and see additional data, what you're going to start seeing though is the ability to get even more detail in natural language.
So you're not having to write a SQL query, you're not having to know Python to be able to slice and dice the data.
It's going to be integrated there.
And if companies have their own, you know, machine learning, data science, BI teams, they're going to build applications internally.
If they don't, though, they're going to be, they're going to have to wait for these software providers for it to come out.
Absolutely, yeah. So true Glenn, that's the thing, right?
I mean, what I do firmly believe as well is the convergence of the technologies, right?
And that's where the real benefit comes into play, right?
When we actually start seeing different kind of ERPs and traditional AI and generative AI all converging into one evolving.
So it's about all the human, just like human evolution, AI is like an inorganic green and it is evolving.
However, I do call it that it's from a, currently from a traditional AI.
If traditional AI is a single cell amoeba, then the generative AI is nothing but a multicellular organism.
Still far away from becoming a human, as we tend to believe.
Or we actually think like, you know, it's so advanced that it can solve, pretty much solve all of our problems.
It's more about taking a reality check that yes, it is cool, but not so cool to solve everything that we have today.
Yeah, and that's why I always push for, you know, if you're going to be using these tools, especially if you're going to be kind of out on the leading edge using these tools, you have to understand how they work.
You don't not, not saying you have to be an engineer, but if you're going to be using these tools to analyze data and all that, you have to understand roughly how it's working.
Otherwise, you can't tell the auditor, oh, how did I get this number?
I put it in the magic black box, and this is what came back.
I mean, that's not going to fly, but it is.
I also, because I talk about this a lot, I do get opposition from, mostly it's the more senior seasoned finance leaders who say, I spent my career learning to be the best, you know, finance and accounting person I can.
I'm not a computer developer for, if I wanted to do that, I would have gotten my, a master's in computer science, not in, in finance or whatever.
So I, you know, and I know with your digital transformation work, you're very passionate about upskilling finance leaders.
And I'm wondering if you see that opposition and if so, how you overcome it, what do you, what are key challenges to build that sort of, I don't call it digital literacy and, and get that mindset among finance people to speak this language and use this technology?
Like, how do you help finance professionals kind of evolve themselves and transform themselves to be able to understand and work with all this?
So Glen, a lot of this apprehension towards digital literacy or this, this protected stuff kind of a thing that, you know, yes, this is my tough, my tough is accounting.
I need to focus on that one.
This is technology.
I don't need to know about it.
I have IT, I have stuff.
A lot of that is, is coming out because of number one and a fear of unknown, right?
People don't know. We don't, and it's an, it's a, it's a lack of understanding.
I would say that how much, and you rightly pointed out as well, that how much digital literacy is needed.
Actually, do I need to know how to code?
Because if, for example, if you are asking a CFO who has been in the role for 20 years or, or who has 30 years experience, I mean, you will talk about digital literacy and you, the first thing that will pop up will be probably to her will be that, you know, I need to start doing the coding, right?
She might not be interested in that.
Okay. So, so I believe first of all, it's important.
And that's what I, I mean, I, I did some of the, I led a couple of initiatives around, around building a digital literacy and a digital mindset within the organization, within the finance team, specifically a big APAC finance team.
And I believe the first and foremost theme thing was to educate people on, in understanding that what is actually a digital literacy all about for a finance person, purview, what it is all about.
And as you also also mentioned, it is not about coding.
It is simply understanding as a rule of thumb, it is simply understanding that how the output of an algorithm or machine can help you solve your specific business problems.
That is it. It is all about just understanding that it is about understanding how machines are calculating and arriving at those insights, how data is generated, categorized, stored, and then consumed within the organization.
That is all we need to know as a finance leaders.
Once you build that clarity, that this is what we talk about when we talk about digital literacy, it opens people up.
It makes them warm towards, yes, this is something I can learn, right?
This is something I would like to know.
Now, the next hurdle comes.
Once you do that, the next hurdle comes, you have an over...
We are fortunate and both unfortunate to be living in a world which has an overload of information.
It is very hard today to go out and if you want to understand something, if a finance person wants to know what to do with, for example, what to learn about AI, there will be thousands of training courses available out there.
I do believe that where we need to focus and that is what I personally have done as well, that instead of...
We have a lot of content already on the internet and everywhere.
Creation of content is not an issue and quite a good quality content available everywhere.
It's all about curating the right content for the right people.
That is where I do believe we need to put our focus more and that is where I have put our focus, and that has worked.
Actually, people appreciate that things bringing out in front of them which have very clear pathways, which are clear roadmap towards their upscaling journey.
Yes, you need to put a lot of work and up planning behind that, but once you do that, you can build that culture.
One thing I would, however, like to mention here as well, and that's to your initial point also around that leadership piece as well, is about building a continuous learning culture within the organization.
That's the key. Now, one misconception, and I'm also a culprit of that.
I wouldn't say that that's as others.
I'm also included. A misconception around being continuous learning is that the more hours we clock or the more number of certificates we earn in a month or in a year, that means we are becoming a continuous learner.
Unfortunately, that's not true.
Continuous learning is all about how much we are willing to update our knowledge and our assumptions in the light of new knowledge.
When we are exposed to something new, are we ready to stick to what we know or are we ready to open up ourselves and update our assumptions?
That is where the leadership role comes into play.
Leaders need to lead from the front and demonstrate that continuous learning behavior that yes, I don't know everything and I'm okay to go back and learn more.
When you say that, yes, I'm open to learning, your team will say I'm open to learning as well, and that's how you create a digital culture, that's how you create a continuous learning culture or a digital mindset at the organization.
That's very Bayesian of you, the idea of updating your thoughts based on the new knowledge as it's presented, and that is, it's almost cliche now.
People say it all the time of, oh, business is moving faster than ever.
I feel like we've been saying that for 30 years.
We've been saying digital transformation for 30 years.
What I found a few years ago is when I would say digital transformation to people, they would say, I feel like we already digitally transformed.
I feel like we did this in the late 90s, and I think it's because if you say transformation, people think of it as one and done, already transformed.
It goes hand in hand with the continuous learning.
CPAs obviously have their CPE credits.
They have to keep learning.
If you're just a finance person, as I am, there's no professional mandate that you keep learning.
But if you don't keep learning in finance, whether it's the latest software that's available, the latest methodologies that are available, you're going to get left behind.
So it's interesting how digital transformation and continuous learning go hand in hand.
Oh, absolutely. Absolutely it does.
They are one and they have to go hand in hand because otherwise, because at the end of the day, what happened in the 90s, and I can totally resonate with that, that was the first wave of digital transformation, I would say, which was mostly centered around ERPs and mostly around accounting systems,
reporting systems, consolidation, and so on and so forth, which was very digital in nature.
There was a transformation element, but it was very digital in nature.
It was a one set deterministic piece of technology you put in, it goes live and it performs in a certain way.
AI or cloud computing, these are entirely different ballgames.
You have to continuously evolve yourself.
So I always say that digital transformation is more about transformation than actually digital.
It's just that we have to put a word to it, right?
So to make it so that it makes sense.
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Before the show, we were talking about that McKenzie case study about using generative AI and the M &A process.
I think as someone who's done a lot of transactions over my career and thinking about the long days and late nights and putting together data rooms, back in the days when a data room was actually a physical room, tell me about that case study and kind of walk us through how AI is transforming the M
&A landscape from everything from target identification to post merger integration.
Yeah. That's a very interesting case study actually I came across.
As you pointed out that in M &A, your first step is probably to find the potential targets, to know what you are looking for, which industries to target, which specific stage an organization is, whether it's a startup or it's in a growth phase or a more mature organization.
McKenzie published this study a couple of some time ago.
They have a proprietary tool, cower through and sift through a vast amount of data of different repositories out there.
It scours through thousands and thousands of potential targets.
Then based on the specific acquisition criteria, it then creates a list or more of a visualization of, if you actually search out the M &A, McKenzie, it has a very good visualization of how it actually tags all those different targets based on their different characteristics or based on different criteria.
Then once you do that, you can actually then shortlist those based on certain prompts on what are those out of those, say, 1 ,000 or 2 ,000 potential targets, you bring it down to 30, 40, 20, whatever is your specific criteria.
That's one part of it.
On the other hand, it is actually creating a lot of benefit and productivity is around the due diligence process.
Summarizing your key documents.
You can imagine in M &A how many documents you have to sift through.
What are the potential risks you don't have to have a highlighter to highlight it.
Every contract, you can draft initial memorandums.
You can research the relevant legal and regulatory information which is sitting probably outside of your internal databases as well.
Generatively, I can do that.
Another way they are actually helping their organization is around analyzing a lot of past M &A performance.
Based on that, they are able to generate personalized training programs for the new people, for the onboarding of the employees.
It also helps them with the integration piece.
Because at the end of the day, once everything happens, you have to integrate the new organization into it.
It can actually, based on the different M &A playbooks that the organization has, the different policies, procedures, and frameworks have, it can actually bring out that these are the nuances.
This is how different organizational structure.
The new organization, this is the structure of the organization.
This is the structure of your organization, how we can bring it online.
It can recommend you that some of those integration.
It can give you an advice as more of an assistant for you on that one.
I do believe it can revolutionize the M &A space.
It can create a lot of productivity in that space.
You don't have to spend.
I'm sure. Again, it makes our lives more humanistic.
I know how much time M &A people spend in actually going through a transaction.
When a transaction is happening, that's their life.
But I do believe that, again, A .I.
has that potential to make it more humanistic for people as well.
They focus on the areas which are more important, which are more human, which are more value -added as opposed to just sifting through the documentation and everything.
M &A is one that during the time of due diligence and going through an entire transaction, you still have to do your normal finance and accounting tasks.
You still have to close every period and you have to do your reporting and your forecast, let alone what's going to happen post -merger.
I think about the expectation that we do all that.
Then, in recent years, ESG has been thrown on top of us.
Across the board talking to CFOs, you think, okay, this is in the office of the CFO.
I guess because it's tied to compliance, which goes into the CFO.
But now you have this whole new level of work that has to be done.
I know another area where generative A .I., we could potentially see some efficiency gains would be in the ESG reporting.
How do you see generative A .I.
helping finance leaders address ESG reporting and mitigate the greenwashing risks and everything around ESG?
You are absolutely right, Glenn.
This is a new role coming up.
From an organizational placement point of view, CFOs are the ones who are actually...
In most of the organizations, CFOs are the ones who are eventually responsible for the reporting piece, the compliance piece, to ensure that there is no greenwashing happening.
We have seen, lately, even in Australia, we saw some of the fines through the regulator on some of the organizations going through that greenwashing now.
Intuitively, when we think about generative A .I., similar to how we talked about MNA, that is how it works, right?
Intuitively, you can train it on the data, and it can then help you with all those compliance -related stuff and audit, but the way I see it will work out from a more technical perspective as well.
For example, there is this technique called retrieval -augmentic generation, right?
Or RAG, you would, of course, know about it.
So, where a large language model is trained on a company's specific static data, your policies, your procedures, but we know that in ESG space, a lot is happening outside of the company domain, right?
There is so much of regulatory updates, and then there will be publishing from other similar organizations in a public -listed space, their reports, their data.
You are doing scope -3 emissions, which you are not really able to control.
So, how do you bring that up -to -date information into your large language model, which is sitting, residing within your organization?
And that's where this RAG technique comes into play, and without going into a lot of technicalities or details, it's pretty much plugging into, for the audience, it's more about plugging into an external data source, which always stay up -to -date, and then you can bring that external data source within your
large language model, which is being used for your internal reporting and stuff.
So, it's about, you know, you can, through your internal model, you can bring all those reporting.
You can do all the reporting and analyzing and summarizing of the stuff, but then through the external data, you can bring in sources like corporate reports, regulatory filings, industry standards, which are changing, or any regulations which are being passed on.
So, that can create a holistic view of how do you take your ESG, not just from a reporting perspective, but from a compliance and a strategy perspective as well.
And that is where I believe the future is going to move towards.
And I know there are solutions out there which are actually working towards that space of building RAG capability within the local large language models and take it to that level where you can actually build that visibility.
And what it does is also that, again, you know, for not just for finance, for everyone, it is important.
Accuracy is important.
We have this concern around hallucination and around incorrect outputs.
And large language models are designed to give you an output, and one way or the other, they will just give you an output, whether it's correct or not.
RAGs can actually help bring that piece of sanity within that large language model.
And that's very important from an ESG point of view, from a greenwashing point of view, because you are now more and more organizations are...
It is now becoming part of your public statements that you are issuing.
So it's your part of your earnings call, for example, now.
So it's as important as any investor making a decision based on your quarterly earnings.
Now investors are making decisions based on how you are reporting or what are you doing on your ESG front.
So that's why it becomes very critical and very important to be 100 % accurate and not just rely on probably a lemon and maybe look at different ways of doing it.
And RAG is, I suppose, is one of those ways it can actually transform.
Yeah, and that's really where it's going to start making a lot more sense, is when you're using RAG and you're using AI in your workflow and where you have live data coming into it.
So, you know, updates to accounting regulations, updates to policies and all that.
Where it's in the workflow where you don't have to kind of stop and step out and go use this bot over here that's querying our vector database of these RAG files and everything where it's just all seamless.
And I think it's coming sooner than later.
Yeah, and I think one very important aspect is also that whenever a regulation change or whenever something major happens outside of your data, you don't have to retrain your entire language model.
That's a cost in itself as well.
It's not a small and easy thing to do.
So, you don't have to do that and you're always plugged into that.
And it's a very fascinating technology.
It's a very fascinating technique to bring that in.
And you're right. I mean, it will help us with that workflow and so on.
You know, going from that to really thinking about integrating.
So, I know you have a background in supply chain finance and FP &A and reporting.
And I think about, and maybe you've got some examples, but if you can think about some of the kind of the biggest data and process challenges that you encountered in knowing what you know about generative AI and kind of seeing the path there, how could you see emerging AI technologies addressing those
issues that, you know, years ago would have been, that gave you heartburn years ago, but now maybe there's a shining star at the end of this future where we see potential solutions that technology will bring for us.
Absolutely, Glenn. And I guess data, we thought data was one of the problems.
And of course, I mean, we can see today, we can find today that AI is actually helping a lot with harmonizing the data and, you know, creating those workflows of data.
I believe, and that's where I generally mostly talk about, is that at the end of the day, transformation is all about a mindset shift.
Yes, it is a lot about technology.
And one key mindset shift that we need to make is around process excellence.
And that is where finance, or any business function, to be honest, for that matter, is struggling, right?
Our processes are so complex, there are, over the period of time, they have become so integrated or disintegrated with each other, that it becomes very difficult.
Using a technology might not help.
As Bill Gates said, we would actually probably augment or amplify its inefficiency as opposed to eliminating that.
And that is where I always talk about this very exciting technology.
Not a generative one, but, of course, generative can always become...
See, remember, generative can always be a layer on top of it, right?
So, at the core, what I believe that technology is like, tools like process mining and task mining, they are some real game changers that can actually help address some of those biggest challenges.
So, I had this experience working on a process mining, for example.
That's just fascinating to see that how within a system, how many touch points a certain document has?
Where does it go from where and what's the workflow?
It's like a revelation that comes to you.
And then, based on that, you can then simplify your processes.
And once you are able to simplify your processes, putting technology on top of it becomes relatively much easier and you are able to actually extract more benefit out of that.
So, that's my one learning, I would say, is one key area that I do believe that technology can help us shift if I had that technology.
Maybe a few years ago, I might have done a few things in a different way.
I love that you can separate the technology from the transformation because, to me, so many people think that software is just going to be a magic wand or AI is going to be a magic wand and you just buy this one piece of software and it's going to fix all your problems.
But really, you have to back up and identify the processes and find where the bottlenecks are and where the roadblocks are there because otherwise, on what cause are you picking your software?
You have to know what problem you're trying to solve, then buy the software to solve it or build the solution.
And I think, to that end, you have a whole framework for how CFOs should approach leading digital transformation initiatives.
And it's more than just the technology.
So can you expand on that a little more, too, on that framework?
I would love to. I would love to, Glenn.
And you are absolutely right, and I am a huge proponent of this point of view that digital transformation technology can only take you so far.
If you are not solving the right business problems, if you are not identifying the right business problems and you do not have the right mindset and behaviors within the organization, no technology can do anything for you, whether it's the most cutting -edge generative AI models or it is the traditional
AI we're talking about or some simple workflow automation.
The way I look at it, the way I talk to CFOs around how to approach their digital transformation journey is a much more holistic view.
And I'll give you a very brief of that framework, of course.
First of all is around having a very clear vision.
And right now, today, I was reading this report, which was published, I think, a couple of weeks ago or a couple of months ago.
80 % of AI projects are still failing, which is twice the rate of normal IT projects.
Some of the reasons being, in fact, 84 % of the vast majority of the reasons, of the top reasons were people not leadership, not understanding the capabilities of what AI can and cannot do for them, trying to solve problems, which are not actually AI problems or even, at some point, overestimating AI
capabilities. So the first step I always recommend to CFOs or business leaders is around having a crafting, a very clear digital vision.
Understanding why, starting off with your why, why you need to transform, what is it that bigger picture, what does technology got to do with all of that?
And in other words, we need to paint a picture where, if anyone in the organization looks at that vision, they care about that vision, that why they should be bothered about transforming.
It should be moving, it should be very specific.
And then comes the building the roadmap part or building the action plan part.
That's where the key action, as the name suggests, that's where the key action happens.
And unfortunately, that is where we generally start off in a reverse manner, where we start off with the technology and end up trying to solve a problem.
While we need to start off with the other way around, identify all your business problems, create a register of your business needs, move on, go on to the next step on mapping your AI capabilities with whatever or whatever your technological capabilities are at the moment, what you need to build, and then
identify some quick wins.
Again, quick wins, I always say that, and I repeat that pretty much in every talk, I say that quick wins are not the smallest projects in the smallest possible time, they are the smallest projects in the shortest possible time with the biggest impact.
So they need to have an impact, they must be solving a business problem.
Otherwise, they're not a quick win, they are just, they can be a momentum builder or something, but they're not really a quick win.
So go for those quick wins and then iterate, do it in three to six months, three to six months iterations.
The third element of the framework that I refer to is around engaging the organization.
It's all about communication.
Some of the tips I always give is around having some key digital ambassadors or key digital evangelists within your organization who are tech savvy, of course, and they are not necessarily part of the leadership.
They should not actually be part of the leadership because they can actually help spread your word around transformation, building that culture amongst their peers so they work really well in that space.
And even you can go a step further, I have seen successful organizations using crowd sourcing, internal crowd sourcing to craft their digital vision or craft their digital transformation journey.
This helps you help organizations with owning or finance functions with owning that digital vision or owning that digital transformation.
The fourth element I always refer to, and that is probably one of the most important one, is governance.
Now governance is not just about covering the risk or privacy or security.
Yes, that's one element of it.
Governance is also about how are we tracking against all the projects.
Who is responsible?
Who is that one person or that group of people who are responsible for leading the digital transformation.
Many times I have seen those failures happening because there is no one person who is responsible for delivering or accountable for delivering the projects.
There is no clear race, for example, responsibility, accountability, and those roles are not defined.
So that's very important.
Understanding what your KPIs are that you are trying to achieve, what are the measures that you are trying to improve, for example, whether it's just your financial measures or it's your non -financial measures which includes your AUG health scores or your employee engagement and so on and so forth.
And lastly, the last element of the framework that I always refer to, and I do call it as the base of everything, is about building a right to succeed.
And when we say building a right to succeed, it means building a digital mindset.
What we all talked about in the previous discussion as well around building a digital mindset, having a continuous learning culture in the organization, and very importantly, having a psychological safety for people to come up with the ideas, to bring out new things, giving them permission to fail.
When we talk about failure and when we talk about finance, people suddenly start jumping on their seats that, oh, it's finance.
We don't have permission to fail.
But it's not about mindlessly doing or failing in your core work or your regulatory compliance and stuff.
It is about giving yourself permission to experiment and acknowledging that, yes, if you are doing experiments, some experiments will be success, some experiments will be failures.
So having that psychological safety that people are able to take that risk, that calculated risk is also very important.
That was great. And I think there is one piece of that that is easy when you're in project planning to underestimate how significant this is.
But I know we talked about you doing Power BI implementations and when you were an advanced analytics lead, and we talked about change management and building user adoption.
And it's so... I've had to deal with saboteurs who are so upset about something in their job changing that they're going to try to derail the whole project.
Is there... Are there any takeaways in your implementations where you learned how to best manage change management around this and to help people get...
You mentioned the quick wins too, and I think that that's always a way in sort of having the ambassadors out there and not trying to do it as a one -person, like, you know, everybody get on board my train, here we go, whereas you're making converts as you go, but is there anything you can say about kind
of change management and building user adoption on these new technologies?
I think, Glenn, it all comes to the basics, what I have learned over my past years of experience.
What we generally tend to do whenever we are looking at a project or whenever we're trying to solve something, we tend to, as humans, we tend to superimpose our perception or project our perception or project what we are thinking, right?
We make an observation, we draw a conclusion, and then draw a conclusion that that business is facing a certain problem.
And then that's what we project across everywhere.
Now, that might not be...
And that's the problem.
That might not be the problem which your stakeholder is actually facing.
So the first step, and that's where I always, always, I cannot stress that enough, is anchoring everything into specific business problems.
Now, change management, of course, is a big subject.
There are so many techniques out there.
But again, the basics remains the same, understanding what your stakeholders want.
Creating a dashboard which is giving an information or even if it has an element of that generative AI of interactivity, if it is not solving the business problem of your stakeholder, you will have that pushback from them.
You can never bring people on board if you are not trying to solve their problem.
And it's basic human nature or basic human psyche, right?
If someone is trying to solve my problem, I would always try to side with that person that I would think highly of that person that, yes, you are trying to solve my problem.
This is what I have.
So spending time with your stakeholder is very important.
As a finance team, we love to spend time at the back end and try to come up with different solutions and look at technologies as well as different things.
That's great. But till the time we go out and talk to our stakeholders and understand specifically what do they need.
And all that they need might not be doable or might not be true.
You sometimes realize that the problem that they have is probably something which is more of a behavioral issue of certain people or some process issue and not really has to do with any dashboard, for example, or any technology for that matter.
And many a time that has happened.
I have done workshops.
I have done full day working with workshops with business leaders and finance leaders.
Many of the problems come out to be either a misconception or two people did not just end up talking to each other for a very long period of time and realize that, no, if they would have just talked to each other, the problem would have already been solved.
So a lot of that is driven by that.
So to manage the change, I think the most important thing is to manage people's expectations and understand their problem, where they are coming from.
What is that? What is in it for them?
What do they, what problem are we trying to solve for them to make their life easier?
And that's when, that's how it sticks.
And it has worked for me.
I mean, it's my personal experience, but that is, that has worked for me.
That's great advice.
And it is always, it always surprises me because I always just think, all right, we're all going to get on board and be excited, but there's, it seems inevitably there's going to be at least one person or a pocket of people who are digging in their heels and do not want to change anything because this is the way
I've always done it and this is the way that I want to keep doing it.
And I don't like change.
So, all right, we're getting, we're getting really long here, but I do want to squeeze one more question in before we get to kind of our, our personal fun question, just because I know you are out there keeping up with the latest in technology.
So just, I'd love your take on looking ahead three to five years from now.
How do you see the role of finance professionals evolving as AI and automation in this technology is just changing so rapidly?
Like what do you see, what do you see happening to the role and intro to kind of the traditional finance functions and what we're outsourcing to the robots versus what we're doing?
Absolutely. So, then organizations of today or finance functions of today are no longer just typical input, you process something and you create an output or just for that matter, people and processes or technology.
It's a complex interplay of humans, algorithms, digital networks on top of all those people process systems that we have been talking about.
Now, what technology is doing is what Kareem Lakhani and Marco Yanciti refers in their book, Competing in the Age of AI, it pushes labor towards the edge of the processes.
And that's a great book.
Actually, I always have that in my.
I'm looking for it on my shelf right now behind me.
I've got a signed copy.
Yeah, and I always have it as a reference.
It's a great book, amazing book.
This point, it's so amazing.
And when I apply that to finance, I think of it.
Yes. If we automate our core, which is the core is what?
What is our core? Our core is our accounting transaction.
That is what we tend to believe what finance is all about.
And in a way it's a reality as well, right?
The traditional finance.
So, if we can automate that core and we build a digital operating model, which is purely in, which is, which is again digital to its core, finance professionals will move towards the edge of the finance, towards the edge of the process.
And beyond that edge lies different functions like sales, marketing, category, operation, and so on, so forth.
So effectively we will move into the right place where we should be, which is the people facing position, in other words.
So we will be moving from our core work, manual work into more business facing roles, which lies towards the edge of those processes.
And that is where we want to be.
And I do believe firmly that, believe it or not, AI will increase our human interaction.
As we will see more and more finance people moving into these meaningful roles, into these meaningful places towards the edge of the processes, it will make us more human because we will be able to interact more with our business partners as opposed to sitting in the back end and just trying to do transactions
and stuff or creating reports or building dashboards.
We have this obsession for dashboards, building dashboards, right?
We need to get again, maybe a discussion for another, another day.
And I do believe there are, and I would like to like to point out a couple of things that will be needed for the future of these finance leaders to survive and thrive in this, in that, in that world where we are in those business facing roles, we need to be emotionally intelligent.
We talk about technology and we talk about, okay, technology, nothing to do with emotions, but it's very important.
We need to have emotional intelligence, being aware of what our actions are doing to other people.
We need to build digital mindset.
We talked about, we need to have a continuous learning mindset and very importantly, we should be able to think critically.
Technology will give us output.
We should be able to evaluate what that output means in context of, of the business problem we are trying to solve or in context of the business partnership that we are trying to do.
So this is how I believe that the role of finance is, is, is evolving.
That's great. Great insights and great book.
We need to put a link to that book in the show notes, the link to the McKinsey study and all that as well.
So, uh, so getting to, uh, getting to our boiler plate questions, I always love to see what kind of responses we get on these.
So the first one is what is something that most people don't know about you?
Something we couldn't learn just by searching you on Google.
Okay. It's very hard to say that you can't find anything on Google about, about someone today in one way or the other, you will be on Google, but okay.
I will tend to believe that it's, it's, it's, that's not the case.
And that, and Google does not know about everything about me.
So one thing I love is I love doing oil portraiture.
Uh, so actually in fact, all these paintings that you see at the back, they, they are all, all mine.
And this is something I just enjoy as it creates a lot of mental space for me when I'm not talking about technology, when I am not reading into the, into the tech subjects around AI, machine learning and retrieval, augmented generations and stuff.
Oh, that's great though.
That's a perfect balance of right and left brain too.
It's just that way you get to fully remove from it.
Those are beautiful from what I can see.
So if there's, uh, you know, and our final question is always where people can learn more about you.
Do you have any of your artwork posted online that people could, could check out or?
On my Instagram, there is some, there is some.
Well, we'll get to, uh, we'll get to contact methods in a, in a, in a moment.
But I guess the, uh, the last question that we ask everyone, and we haven't talked really about Excel at all.
We've been all in technology, but, um, what is your favorite Excel function and why?
Oh, all, always we look up.
Yeah. So I love this.
So I just had this conversation with someone the other day.
I'm a V look up guy too.
And someone told me that's how you show how old you are because everybody else is going to do index match match or power query or whatever.
Yeah. But I'm going back to the old day.
I know there is no an ex lookup, ex lookup, which is much more efficient.
But again, if over my last 20 years of career, we look up has saved me thousands and thousands of times.
So I think I still regard that as my go to for now.
I love it. I haven't heard V look up in a while.
And I'm so excited because that when I was a guest on the show, that's what I answered with too.
And I really, when someone told me that the other day, it never occurred to me, but that is how you show you've been doing this a long time.
So to read great, great episode, I guess our last question, just how can, um, how can our listeners listeners connect with you and, and learn more about you?
Of course. So, so the best way is of course, uh, on, on LinkedIn.
I'm very active on LinkedIn.
So you can always reach out DME there or otherwise, uh, my email address is pretty simple.
Hello at Tarik Munir dot M E.
Or you can also go on my website, which is a www dot Tarik Munir dot M E.
But I guess, uh, for a quicker response, generally it's easier on LinkedIn or, or just the Emmy.
And I'm, I'm, I always respond to people I'm, I'm generally good at engaging with the like many people out there.
Well, thank you so much for coming on.
Great episode. Loved your insights and experience and I love your oil paintings too.
That's how learn something new every day.
Same here, Glenn.
