Organisations are making bold bets on AI for the long-term, with 89% planning to increase or maintain their AI investments in 2025*. Moving beyond experimentation, organisations are now zeroing in on the bottom line - and it’s CFOs who are responsible for making sure these investments deliver real business impact.

In this episode, Monica Proothi, IBM Consulting’s Global Finance Transformation Leader joins David McClelland to discuss finance transformation and the role of AI in enhancing financial operations. They explore the changing landscape of finance, the importance of integrating technology, and the challenges organisations face in adopting AI.

With over 20 years of experience in finance transformation, partnering with CFOs at some of the world’s largest companies to drive growth and productivity, Monica shares insights on the need for clear strategy and collaboration across departments, the importance of bridging the skills gap and understanding barriers to change.

A passionate advocate for diversity and inclusion, Monica is dedicated to fostering an inclusive culture, serving as a champion for women in senior finance roles. The episode also explores the cultural and technical barriers faced by different industries and the significance of continuous improvement in finance practices.

*IBM ROI of AI Report December 2024

Find all episodes of The CFO Playbook here.

Hello and welcome to the CFO Playbook podcast.

My name's David McClelland, I'm a journalist and broadcaster.

I cover business, technology and leadership.

And here on the CFO Playbook, I get under the skin of how world class finance leaders from across various industries leverage technology, set goals, manage teams, make plans and much, much more.

Coming up in today's show, we're talking about finance trade transformation and the role that technologies like AI can play.

A colleague of mine, we were, we were working on something for a European accounting conference and decided to put in like, give me the top five companies that focus on AI and audit in a specific country.

And it, it gave five and then it said, you know, do a competitive analysis of them.

They gave a whole competitive analysis and then it's like, you know, which ones do you recommend?

Right?

And gives you which ones you recommend.

And then the last question was, please cite your source.

And it came back and said, only two of these are real companies.

I made the other ones up.

Oh.

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To find out more or to book a demo, visit soldo.com Monica Pruthy, VP and Senior Partner, IBM Consulting and Global Finance Transformation Lead.

Thank you for joining us on the CFO Playbook.

Thank you, David, whereabouts are you joining us from today?

I am joining us from New York City today.

Oh, fantastic.

I love New York.

I just intro'd you there with an incredibly long job title.

Forgive me for saying, but there's a lot in there.

Let's try and just break that down a little bit.

So what does your role, what's the Global Finance Transformation lead?

What does that mean for you?

Yeah, I know it's a bit of a mouthful of a title there.

So I lead Global Finance Transformation for IBM Consulting.

And if we think about a little bit like what does finance transformation actually mean?

And this is globally end to end to end for clients in Every geography and also every industry.

But as we think about finance transformation, this is really how we redesign financial operations and processes to improve efficiency, effectiveness, accuracy, strategic value, experience for both the customers and the employees.

And this is really done through kind of four levels across people, process, data and tech.

And how we can leverage new technologies like generative AI, we can implement better controls or reporting systems, we're streamlining workflows end to end.

So beyond finance, breaking down those barriers across the business and really integrating financial planning across that business.

So integrated business planning.

A lot of my role is working with CFOs and advising CFOs because if, if we think about the office of the cfo, it's really the decision making arm.

So it's setting that goal to enhance the speed and quality of decision making with real time insights, with reusable and scalable assets and methods.

And this way we can help to reduce costs, mitigate risk, and of course accelerate growth.

So it's really kind of looking at all of that.

There's a few things there that I want to tap into over the course of our conversation.

One of those things, I guess, is the reasons for transformation.

And very often when we look at transformation and goodness knows, across the broader technology industry, we've been talking about digital transformation for a long time.

And I think there's at least a couple of reasons why an organization might go on a transformation journey.

Either because the context in which they operate is ripe for a refresh, or because something new becomes available to them.

A new technology, a new opportunity means that, well, we need to refresh in order to make the most of that opportunity.

With regards to finance specifically right now, what is the, what is the, the carrot or the stick?

What's the impetus, if you like, for transformation right now in this industry?

Yeah.

So I think it's good to almost take a step back on how the role of finance is changing.

So where we used to report on historical numbers and report on the news, we're now creating the news, working with the business.

And because of that, that's changed the focus.

So while traditionally it is still is, cost optimization is driving this.

There's also growth and margin expansion.

So being that catalyst for growth, supporting and enabling the business to optimize their portfolios or investments, their pricing decisions, cash flow and liquidity, capital allocation and management, regulatory compliance.

So there's a lot of different factors that we think of in terms of control and growth and cost and accuracy as what's driving finance transformation.

That's changed a little bit over the over the past, you know, significantly over the past five to 10 years and how we do that and how we leverage people, process, data and tech to really drive transformation and improve that decision making.

I want to come back.

You've mentioned it a couple of times now.

People, process and technology is a triangle that we're well used to.

But you stuck a D in there as well.

I did the data in there.

And I want to come back on that as we start to talk about AI and the fuel for, I'm sure some of the transformation that I know we'll talk about.

But first of all, we will concentrate on organizations transformation journeys in a moment.

But I want to talk about your journey briefly, finance technology.

What were your first steps in this space?

Because, well, I see IBM and Accenture and some others have been kind of bookends of your career so far with one or two other stops in the middle.

Yes.

So personally for my career, I've looked at both of us from a consulting side and an industry side.

So I've spent about 10 years on the industry side in financial services, so working at UBS and Morgan Stanley, but really in a finance transformation capacity there.

So again, looking at how to improve the finance organization through things like ERP modernization, business process outsourcing, just intelligent operations, AI, all, all kind of aspects here.

And operating model being kind of the key around there.

Because if you think about an operating model, it's not just about people.

Everyone just goes right to labor arbitrage.

But it's labor arbitrage, it's process arbitrage, it's technology arbitrage.

It's all of them together.

So my personal career, I started in consulting, I moved into the industry side and then I came back to Consulting almost 15 years ago.

Right, okay, okay.

And in your position now, like you say, vp, senior partner and that global transformation leaders, it's that global viewpoint that you have.

And that's something else I want to tap into in our conversation today as well about maybe any differences or any insights that you have with that global hat on.

But let's come back to transformation Monica.

And you've already mentioned AI in this chat and its potential role as an enabler for finance transformation.

Now, we've spoken about AI here on CFO Playbook, and goodness knows those two letters have been thrown around an awful lot over the last three or four years in particular.

So I'd love if we can, to get a bit more specific on exactly what it is we're talking about with AI and exactly how you see that it is helping can help finance organizations transform their financial operations.

Because AI is this umbrella terms, it covers a lot of different applications of the technology.

So what can you tell us about specific ways that you're seeing specific types of AI have a real impact for organizations?

So I think there's a couple of things here.

It's using the term AI as everyone has been using it recently as this is something new and AI has been around for many years.

So it's used analytics AI even in our own organization we've been applying AI for over a decade and it's now the gen AI side of things that makes it kind of a little bit more interesting on driving those trends and helping to get to the better insights.

So there's the short term value that you can get from AI and there's the long term value.

Lots of quick wins in the finance space around reconciliations, collections.

So as we think of kind of the different processes between R2R, P2P Odyssey, FP and a tax and treasury is kind of the big functions within finance.

And there's I think the biggest thing is see everyone just needs to start and there's a lot of kind of people are scared because we want the big value which is what you're going to see in more of the FP and A space.

But where do I start?

You can start small, you can start with the journal space and recons and then you can look at things like contracting, how to reduce risk.

So there's a lot of different kind of use cases where you can get some quick value you and then reinvest that back in to those longer term things because and I know we're going to unpack data in a little bit but FPA is probably the biggest value that you see.

But that also only works if you have your data in a good place.

Right.

So if you're looking at enterprise performance management data on the glass that that's a lot of what'll drive it.

And we have been on our own internal journey at IBM.

So the role that I play while I sit in IBM consulting, I actually partner directly with our internal organization.

We've been on this journey for over 30 years and don't let 30 years scare anyone because it's just a continuous journey.

You know, starting with setting up a chart of a single chart of accounts, making sure the data was in place, then looking at let's set up kind of verticals to get all of the processes working correctly before we break down the walls and look at this end to end as a Workflow so then setting up shared services, coes and then we kind of pivoted, you know, to this.

So we've been spending a lot of time.

So as I said in my role it's not just working with our clients, it's actually working with Jim Cavanaugh and his internal organization.

So I've got this outside, in inside out perspective.

And then even people from my own team, I've pulled from his organization, they sit on our team and in the same respect that we now go as consultants and transform our own organization.

So it's really this like magical partnership that we have here where we're learning from each other.

So value I see, like I said, lots of short term areas, kind of what we've used as ourselves as a sandbox and then the long term where we see forecasting as kind of the biggest value there.

And I'll say forecasting is really Jim's superpower, as I like to say.

Yeah, and it's always good to eat your own dog food, as you say, and.

Or drink your own champagne.

Or drink your own champagne.

There we go.

Taste your own medicine, as it were as well.

But I guess the thinking there is that hey look, if an organization of IBM's size can implement this, get value out of it, not only are you testing your own capabilities, technically your own capabilities as a kind of internal facing consultancy organization, but being able to offer the wealth of that experience to your customers and clients as well is incredibly valuable.

You mentioned about how some organizations are I guess, trying to make their first steps there testing the water with new technologies.

And often that begins with not projects the size and scale of I'm sure what you've done at IBM but with pilots and proofs of concept.

But there's a lot of data, anecdotal and actual data that suggests that a high proportion of AI projects and proofs of concepts, they fail.

So maybe internally, but maybe out outward facing as well.

What's your experience in picking the right projects and delivering them in the right way, that from a practical point of view, if you are an organization that is investing in their first steps on a journey to involve AI in their processing, what are the best steps and the best counsel that you can give to help them pick the Right.

Projects and demonstrate value with those and not have them fail.

That's a great question.

So I think there's a couple of steps that you can take.

One is making sure whatever project you're breaking, you're, you're looking at is aligned with the strategic objectives of the organization.

So that, that's one, because the value you bring, yes, you want it to bring it to your finance organization, but you also want it to bring the organization as a whole.

Like I said, you can start small.

I think it's thinking about though the holistic picture.

What is my North Star that I'm trying to get to?

As you're thinking about this, where, where do I start?

So no matter what, what you pick is, is something that aligns to that North Star vision.

And then in terms of, in terms of value, where you have buy in from the business is going to be really, really important to.

So if you've got buy in and you've got consensus from your top down, you know, CFO down and then you also are bringing everybody along for the journey with you.

So where do you have people, champions who sit within the organization, within the finance organization who are going to be able to help you drive this?

And then of course, obviously looking at the business case, you've got to pick an area that you're going to have some immediate value, some return quickly and as I said you can, you can focus on that kind of low hanging fruit.

Typically as I said we would see this more in like a record to report space then looking at the bigger value.

But if you can go after that FP and A, that's where I would go.

I mean, so personally I always tell my clients that's kind of where we go.

If you think about the CFO study that we run every other year.

So we had a recent one that just came out at, in Q4 of last year.

The biggest priority for our CFOs is around experience.

But the biggest challenge is accuracy.

And that accuracy comes back to how do I get better accuracy?

You can, I mean like I said, you can do low hanging fruit.

You can find quick, quick wins.

But is it going to look to what you're actually trying to achieve in that bigger value?

Yeah, and you mentioned it earlier on as well about the short term view versus the long term view.

And sometimes, you know, when there is, when there has been some investments, it's easy to go brilliant.

We want to turn something around so that we can show that back to the board, so that we can show that back to senior stakeholders quickly, but you almost end up shortening your goals, shortening the effectiveness of what it is that you're putting in place at the expense of the longer term business values.

That, that's something you've come across perhaps as well?

Yes, absolutely.

It's what I've come across with all of my clients as well.

You know, where you're looking at the short term versus the long term value that you can gain.

But everybody want, as I said, you just have to start.

I think if you start then you can start to see some value and that'll help reinvest that value back into an innovation fund or whatever you can do to get the, the bigger, the longer term.

But you've got to have that holistic kind of transformation office set up that's looking at this, a design authority board who's accountable.

So understanding all of those different pieces as part of this and in terms.

Of gathering requirements, you make a very strong point there about of course ensuring that what it is you are putting on the ground aligns with business objectives.

One would love to say that it's always easy to get that alignment and understand exactly what it is the business wants.

But of course there are multiple stakeholders often involved in that in terms of gathering requirements and understanding where there might be some different low hanging fruits.

Do you encourage things like steering groups, you know, people who are interested from different parts of the business to come together and go, brilliant.

We have this opportunity to tell me what requirements you have, tell me where you think there's opportunities here and we'll see what we can do to make it happen.

Yeah, absolutely.

Steering committees, design authority boards, all of that we.

And that needs to have not just finance on there, but it the business.

You need everybody partnering together in this journey.

Yes, there are lots of different stakeholders here.

You could say HR is also a part of the stakeholder group here as well.

And with any new technology there are naturally concerns and such as the disruptive, transformative and frankly still unquantified power of AI.

These questions and challenges are absolutely justified.

So what are the kind of pushbacks that you've seen when your clients do embark on AI programs within their organizations?

What are the things that they are having to address, justify, overcome in order to start making steps?

So I think there's, there's kind of the barriers around finance transformation as a whole and then the ones around the AI program.

So if we think about AI, a lot of that is going to be around trust, transparency, governance and getting people in there resistant.

There's a lot of resistance to change and getting people on board with being able to use it.

I'd like to call it AI stuff.

Stickiness of really getting it to stick with, you know, humans and the workforce to, to augment their day to day life.

And I think there's an, there's an overall kind of barrier that we're seeing on finance transformation as a whole around things like again, there's, there's really kind of five barriers that I like to think about resistance to change.

It's not just the technical side but the, you know, the people side that we're seeing as the biggest barrier.

The fear of job loss or increased workload.

I mean, people shouldn't be scared of losing their jobs to AIs, but you should be scared of losing your job if you are not using AI every day as part of your job.

And that kind of resistance can really hinder the implementation of new technologies methodologies.

The second biggest barrier is that lack of a clear strategy.

And this goes across all finance transformation programs, including AI.

Without that well defined strategic vision, transformation initiatives can lack direction, they can lack cohesion, it can lead to even more inefficiencies or failure to kind of achieve any of your desired outcomes.

The third and the other big one with the people side is going to be the data.

Having disparate and siloed data and data systems that can really hinder the effectiveness of this for the finance function.

You know, success depends on how quickly you can turn data into insights and then in those insights into driving, making, making decisions and partnering with the business.

So this is where a technology like AI, AI and Genai really comes into play in how do you get those better insights and how do you enable the finance team to really tap into the power of data to navigate these complex risks.

And then the fourth one is around skills gap.

So there's a shortage of employees with the necessary skills.

You're not going to find, you may, you might find one or two, but there's not really unicorns out there.

Someone who has the perfect skill set of, you know, finance and accounting foundation plus being a data scientist, it's more about understanding how do you derive value out of the technologies.

So in our own organization we did put data scientists onto the finance team, so they're working together.

So I kind of like to think of it as, you know, a whole bunch of workhorses that make up your unicorn.

So it's really diversifying that skill set.

And as I said in my own organization, I have folks who came directly from our own IBM finance organization who lived through the transformation and who are now helping clients understand it.

Because yes, I happen to have worked on the industry side and the consulting side, but much of our team comes just from consulting or just from industry.

So it's kind of partnering together and saying we understand your challenges because we've done this day in and day out as you have, but we can also help you transform.

And you're not always going to have someone who can get out of the details of understanding the foundation and then figuring out what's the strategic vision to kind of go forward.

And then the, the fifth I kind of barrier that I'd like to say is around cost.

So a lot of these transformations, like an ERP modernization, they can take years and you're waiting to see value.

And everyone wants quick wins and immediate roi.

You know, tactically it's easy to just say, okay, I'm just going to focus on like a band aid fix because I want some immediate value.

But we really need to be looking at this as a holistic approach, end to end, how you can truly transform the workflow.

So if you think of an area like order to cash, which is the finance side of it, you need to be thinking about lead to cash.

So lead to contract, contract to cash.

Starting with the customer experience first, like the customer side of it, and then going into the finance side and underpinned by data.

So it's really looking at all aspects of a workflow and not just in the finance space.

So going kind of beyond finance, I realized that was the lot.

But no key barriers for finance transformation.

And then specifically around AI is going to be the people side and data and data governance.

There is a lot there and I've certainly got things that I want to pick up on on each of those.

But I wonder if you mentioned an IBM study there, for example.

I just wonder with your global hat on, are there any, are there any trends or patterns around barriers and blockers that might be more true in some markets than others or maybe some industries than others?

Have you seen anything around, I don't know, cultural barriers as well as technical barriers, for example, in different places.

So I've seen at least from an industry perspective obviously bigger barriers in the financial services industry around regulations.

So that's kind of big.

There is some kind of culturally organizations with hierarchical structures and a risk averse culture might find it harder to embrace the innovation and change.

So you've seen a little bit of that.

On the other hand, you know, more agile organizations might find it easier to adopt Kind of new ways of working.

So I've seen a little bit more of that probably in Asia PAC and then for, for regional trends.

You know, in developed economies the barriers often kind of revolve around technical aspects of implementation and that resistance to change.

So in emerging countries, in emerging economies will sometimes see the regulatory hurdles and a lack of necessary infrastructure.

That's kind of the other big thing that can pose a challenge.

Yeah.

You mentioned the IBM study there, for example.

Well, Soldo, which powers CFO Playbook here, it recently had a study of its own.

Productivity at work was the study and it found that almost 3/4, 71% of of finance teams reported that what's stopping them from supporting new initiatives are outdated processes.

It's the processes that are stopping them from doing some of the new stuff here, which also speaks to me.

I don't know about you, but as that barrier to transformation if you need to, or a reason to transform, perhaps there.

Let's talk about data.

Can we.

And you've mentioned this people process technology plus data piece and it's the first time honestly that I've seen the D included in that.

Just talk to me about the importance of data here.

We say garbage in, garbage out.

And we need to make sure that the quality of the data that is being used to create the insights that ultimately we're making business decisions on.

You mentioned the hierarchy there, from data through to insight and action there.

The output is directly related to the quality of the input, whether that's the accuracy or inaccuracy of the data or some more nuanced issues such as bias that might also feed into the data and therefore into the actions as well?

So what kind of interventions have you seen teams put in place to try and make sure that the data layer is as reliable, is as trustworthy as it needs to be in order to make those business actions, those business decisions?

Yeah.

So obviously your AI is only as good as your data.

And having that strong data governance in place is going to help ensure that the data driving the technologies and driving the insights is accurate.

It's timely, it's secure.

What we're seeing is you have got to put a data governance layer in place.

You have to treat data governance as a strategic priority.

You need to embed it in every stage of AI development.

This way, you know, AI systems aren't just effective, they're also ethical.

They're transparent.

That there is certainly bias.

And I've seen it because I play around too just to, just to see, you know, like what something's Going to come, come back with.

I can give you actually a funny little anecdote.

I have a, a friend of mine, a colleague of mine, we were, we were working on something for a European accounting conference and decided to put in like give me the top five companies that focus on AI and audit in a specific country.

And it gave five and then it said, you know, do a competitive analysis of them.

It gave a whole competitive analysis and then it's like, you know, which ones do you recommend?

Right.

And gives you which ones you recommend.

And then the last question was, please cite your source.

And it came back and said, only two of these are real companies.

I made the other ones up.

Oh, now imagine if you taken that and like showed it in front of a conference or like without having.

So it's also like what prompts should I be asking?

Does everyone notice I cite your sources?

Right.

And you, it just kind of went off on its own and said I made three of them up.

But that's a really important point on this trust, on accuracy as well.

And you mentioned earlier about how AI is certainly nothing new.

IBM's had a big part of the AI story.

You look back to Watson 10, 15 years ago, which was a big thing.

IBM was very early to the races with Watson.

I know Watson X is a thing now, but gen AI has been this kind of flag bearer for AI, broader AI over the last two or three years.

But it literally makes stuff up.

That's what the generative AI does, albeit very confidently as you found with your piece of research there.

So in the world of finance where accuracy and trust are so highly valued, what role does generative AI have to play?

Do you think you could still, you can still leverage generative AI for the amazing technology that it is?

It's just got to be on what, what is the, what's the LLM you're using?

What is it?

Are you, do you need a large language model?

Do you need a small language model?

I mean, do you really need a, a large language model that's going to have a whole bunch of stuff about like Taylor Swift in there if you're asking it a finance question.

So it's focusing on these very specific kind of enterprise wide models, how they're built and the, the data governance layer on there.

So IBM has Watsonax.gov and that's our data governance layer.

We will indemnify the information that's on there because we, we're the ones looking at it, we understand what is there.

So that's kind of looking at it.

Too is where do you, where do you have companies who are applying a data governance layer, know exactly what's in there, know how the models are trained, and that's going to be a big part of it.

So that this way you can still leverage the power of the technology, which is absolutely incredible and truly changing the way work gets done.

And it changed the way I would do my job every day.

I, I leverage our own IBM consulting Advantage assistance every single day on how I perform my job and then what I can take to my clients.

And part of my job is to create reusable assets and methods for clients for their own finance organizations.

Developing a Persona for someone going through a 16 week change, those things you can get.

But, but then I'm going to go in there and validate it as an expert.

I can go in and I can understand.

So while it can save me weeks of my job and putting this together, I still need to have my own expertise in there as a human to go and validate.

But that process is a lot faster than it would have been.

So, so there is a lot of power in there.

You just have to be really smart about what were, what models you're using, what's the governance layer around there?

Where is the, you know, how do you know if there's bias in there or not?

As they said, you don't need to just use any, any big, you know, model to run something specific for a workflow flow or an enterprise.

The right tool for the right job and make sure you're using it in the right way with the right guardrails around it.

Yeah, okay.

Okay, so that's great.

I'm going to take that.

You are welcome to it.

Monica, Skills gap, that was the fourth of your barriers here.

And I just want to lean into that one for a moment because it strikes me there's something of a, of a paradox here in as much as, yes, you need people with this new suite of skills in order to implement this technology within organizations and been able to, in order to get the most out of it.

But at the same time, the story you just told is how you were issuing some pretty advanced queries using natural language into a kind of chatbot interface, I presume.

And with the skills gap piece there, in a way, particularly the generative AI models, anyone is now able to query those.

Anyone who can talk to a thing, whether that's into a microphone or into your phone, or can type into a chatbot interface, now has the ability, using natural language processing, to issue the most advanced queries.

Potentially that going back only three, four, five years ago, you would have need a data scientist or someone with a sexy job title in order to execute within your organization.

So in some senses, you could argue that that skills gap has shrunk quite a lot because there is much more accessibility to this data through interfaces like that.

That does come with some concerns about, again, the kind of guardrails and governance that you put around it.

But in terms of the skills where you feel we are lacking and ensuring you've got the right skills profiles throughout the right places of the organization, what is your counsel to ensure that you do have?

I guess it's not chief data officers or chief data scientists throughout the organization.

Or maybe it is, but what's your counsel to make sure that an organization embarking on a financial transformation, leveraging AI does have the right feet under the table to make it work?

Yeah.

So you do need, obviously you need the finance and accounting understanding, but I do think you need.

Not everyone has to be a data scientist.

And I think there was a couple years ago kind of a frenzy of, oh my goodness, everyone has to be a data scientist.

But you need to integrate data scientists and that skill within your team.

So it's not that everybody has to be there.

It's important to have some people who understand.

However, every finance professional needs to know how to get the value out of the technology.

And if that's understanding how to prompt, I mean, that's a big part of it is learning how to prompt.

Or if you, if you want to be a real value creator, you can be a prompt engineer.

So you take it a step further.

Not just, oh, I know what prompts to ask, it's actually creating the prompts for your organization.

You know, what questions should I be asking?

And how do I know that these prompts are going to give me the insights that I need and the trends that I need to make the decisions.

So that's the big thing, is the skill isn't just I have to be a data scientist, but you certainly have to understand how the technology works and how can you maximize the value out of that so that you're getting the roi?

Because as, as anyone sitting in a finance organization, they're the, they're the stewards of investment capital.

They're responsible for the AI.

And that's for not, not just the bottom line anymore.

It's the bottom line, the top line, the green line.

Think about the sustainability line of this.

And so how do you maximize that value?

And that's the, that's the skill is understanding the tech, understanding how to prompt, understanding how to use the tech to get the value.

But it's also embedding.

And to do that you, I mean, it's important to embed that data science in that, in with the finance team.

Now what's really incredible is if you can take, and I've seen this within, within IBM is we had people who were finance professionals who wanted to learn tech.

And so we do have a number of folks on our own team who said, you know what, I want to, I want to be more skilled in technology.

And so they actually started as finance professionals, learned a lot more deep technical expertise and kind of, and now they have that finance plus tech plus industry skill.

I mean, that's the, that's the unicorn you get.

The finance plus the tech plus the industry side of things.

And that's great.

You're not, you're just not going to find that everywhere.

But there are people who want to upskill and who want to learn.

And that's amazing.

And that's when it really, really works.

Finding those, those champions, those unicorns, as you call them, nurturing those as well, sounds like a very, very strong leadership strategy, whichever level you sit at.

And it does seem as though we are looking ahead to what will be a very different experience of working, working alongside not only intelligent people, which we've done for a while, but also intelligent systems, or to your point, intelligent people who are using intelligent systems.

So you've spoken about what skills it's helpful in an organization, in the finance part of an organization, but also more broadly as well, to have to be best make use of this technology.

But as finance leaders, as CFOs, listening to this podcast right now, what skills do you think they would be best served to cultivate right now in order to make sure they are able to work as effectively as possible, to lead their organizations as effectively as possible in this new working future.

So, you know, and I'm going to harp a little bit back to where I was, I think this, this skill is understanding how to get that value from the tech.

That for a cfo, that's the biggest thing is what.

And that's partnering with the cio, partnering with the business.

So that goes back to understanding the strategic value, the strategic vision of the organization.

What direction are we trying to go in?

And that's working with the CEO, then working with tech on how do we do that, how do we enable that from a technology standpoint and how do I make sure I'm getting the ROI out of there.

So for a cfo, that's, that's kind of the skill that I would, I would think is most needed is on that understanding of the value.

It's about the, you know, the value chain and value orchestration.

All of that is kind of in there because that the role is to help make the decisions and the decisions rely on the data.

The data comes from tech where you can use things like insights, where you can get insights faster and that's going to lead to decision.

It's less about running reports now, which, you know, it used to take like a month to put together a report for a CFO briefing and by the time they get there, it's outdated and everyone's like scrounging through, you know, a PowerPoint deck trying to find one number.

Now if you have data on the glass, that's the other piece of this is the technology is the skill is making sure that you have real time data readily available.

And I mean, we don't show up with PowerPoints anymore to these internal finance meetings that we have in our own organization.

Everything is done on the glass, everything's through enterprise performance management.

So decisions can be made quickly.

And it's incredible to sit there and not have anyone ever say, I think the data is wrong.

And it's something we see all the time, oh, where are your numbers from?

Or where are your numbers from?

I think your numbers are wrong to have one place where that sits.

So the, you know, the skill comes with understanding the value, but that value also comes with where did the data come from.

And if you don't have to question the data, you can get things done a lot faster.

That, that point you make about partnering is, is really, really important.

And obviously the role of, of people, the CFO as the finance function, as the co pilot, as the partner to the CEO and so on.

That's all great.

The one phrase that sticks in my mind here is the art of the possible.

As a cfo, you need to be aligned with whatever the objectives are.

But sometimes you don't necessarily know what is going to be possible.

You know, there's a challenge.

You know, you've got some data that you think is good, but has it occurred to you that there is this tool, there is this capability, there is an ability to do something that would help your organization.

I guess that's where partnerships, that's where partnering with your cio, cto, all of those other roles who maybe are very, very plumbed into what AI, for example, is doing, knowing what the likes of providers, partners like IBM, are already doing with other customers, I'm sure you would agree, is probably a happy place as well.

Yeah.

And it's also your ecosystem partnerships, too.

You don't have to be best at everything.

You can leverage your ecosystem, and that's what we do.

And that's what I do with my clients, too.

I'm not, you know, I don't have to come in with.

I do everything.

It's.

I have this partnership across our, our business, our it, you know, all these different parts of the organization, but also across the ecosystem as a whole, which is really important.

I know what I know.

There are some things I don't know.

And sometimes hearing from one of my colleagues who's doing something, I go, that's really cool.

Why did I never think of that?

And that's when I start doing it as well.

Listen, Monica, we are almost out of time for today, but I can't let you go quite yet.

Each of our conversations here on CFO Playbook, we like to ask a final question to our guests, and we call it our Shop Showstopper question.

And this season on the show, we're asking all of our guests to give us their reflection on the phrase progressive finance.

So, Monica, what does progressive finance mean to you?

That's a great question.

It needs to be continuous.

This is a continuous journey that we're all on.

This is not.

I'm going to do one initiative and then break.

So progressive finance is what, what does the next like, what does finance look like?

Where, where were we?

It was enterprise, you know, it was enterprise finance.

Now it's kind of like an intuitive business that we're in.

So I think it's really driving this continuous journey, leveraging the tools that we have to be more effective to have a better, better experience.

And I'm going to really wrap this around experience because I think that's kind of the key.

And the most important thing is how do we make sure that it's a better experience for our customers and for our employees and we're able to make decisions faster.

So if I think about progressive finance, it's how are we continuing to progress in the, in the finance function?

And right now, that's being that intuitive business arm.

It used to be historical data, and now it's driving the business.

We'll leave it there for now.

Monica Pruthi, thank you very much indeed for joining us on the CFO Playbook.

Thank you for having me, David.

And don't forget to join us every month here on the CFO Playbook for more insights from finance leaders.

But for now, from me, David McClelland.

Bye.