AI in Finance: FP&A, AR & Faster Close — CFO Playbook with K. M. Suresh | The BizTech Pulse Podcast

Krishna Hari (00:05)

Welcome Suresh. Welcome to The BizTech I am excited to introduce today our guest, K.M. Suresh, a global finance leader and with a proven track record across Fortune 500 and private equity-backed companies. Most recently with CoreTrust – a Blackstone company.

 

K.M. Suresh has also held senior finance roles at GDT, NCR, Microsoft, HPE, Compaq and Digital Equipment, driving strategy and execution across Asia Pacific, global services and enterprise operations. He holds a degree in commerce and law from Bangalore University and further sharpened his leadership expertise through an executive program at XLRI Jamshedpur.

 

In this episode, we will cover, how he has woven those diverse experience into his current leadership style, the challenges and the breakthroughs along the way and his vision for the finance function of the future. Stick around, you won’t want to miss this episode. Welcome to the podcast Suresh.

 

KM SURESH (01:12)

Thank you. Thank you very much, Hari. I’m really, really happy and very proud to be part of this ⁓ podcast. I’ve definitely seen a couple of them, and also I have high respect and the admiration for the work that you’ve been doing over the past several years – from being an entrepreneur to… scaling the business here. So I’m excited to be here, part of this conversation where there are so many things happening in the industry and more so in finance as well.

 

Krishna Hari (01:46)

Thank you. Thank you, Suresh. Looking forward. To start with, Gartner-tracked adoption suggests finance is moving beyond experiments. Multiple surveys show material uptake at AI across finance in 2024-25. In your line of sight, where is AI actually in production inside the CFO’s office?

 

And what makes those use cases stick?

 

KM SURESH (02:15)

That’s a great segue into probably one of the most, I would say, critical topic, I would say, and critical crossroads that we are in today. If you look at finance in general, AI can be used in so many different facets of finance. Now, if you look at the broad diversification, or what I call as compartmentalizing finance. You know, it consists of what you have FP&A which is predominantly your planning and analytics department that does your forecasts, your budgets, your planning, and then you have accounting and close – where you know – I would say it’s the heartbeat of every organization without which you really can’t do any analysis. So it’s a critical part or the critical juncture as soon as a month-end closes over, that’s where the accountants start their work. Then you have order-to-cash process pretty much right from the get-go where one goes to market and right until the end when we receive cash. Then you have procure-to-pay as part of, you know, invoicing and collections and all that.

 

And then you have treasury and cash management, which pretty much has your AR, your AP, your bank liaison. And if you have debts, how do you go and work that out with your lenders? And then you have tax and compliance after all of this, whether it’s sales tax, whether it’s excise, or whether it’s foreign tax, or whatever it might be. And then after all of this, it has to stand to the audits, risk, compliance, and all that. And last but not the least, it’s the business partnership here. So I would say these form the gamut of finance pretty much from soup to nuts. Now your specific question here says- “Look, what are things that ⁓ AI can really be firmly rooted into?” I would say there are 3 things. I would say FP&A, I would say AR automation, and close acceleration. And they stick because they tie directly to measurable KPIs or key performance indicators. For example, it could be cycle time. It could be accuracy in terms of our closure. It could be the cash impact. It could be directly driving the operational part of the business while embedding into the existing workflows – if you will, rather than just creating a parallel process here. So in my opinion, these 3 are probably very, very big ones that have a direct bearing on AI here.

 

Krishna Hari (04:56)

Absolutely, absolutely. Right on that. I mean, ⁓ especially I have seen a lot of new fintech firms which have specifically specialized in these 3 areas. I think companies like HighRadius and BlackLine and a fairly large number of people who pretty much

 

KM SURESH (05:08)

Exactly. Yeah.

 

Krishna Hari (05:17)

focus, and they are progressing in the AI front – like AI, agentic AI. It started with AI/ML and then slowly progressing towards agentic AI, where they say if the autonomous workflow can handle everything by itself more than 90–95% then they call it agentic AI. Adding to that, ⁓ I mean, ⁓ the…

 

KM SURESH (05:25)

Yes. Exactly. Yeah.

 

Krishna Hari (05:40)

…just some IDC projections, which we did some research. AI spend is projected to be $235 billion in 2024 and doubling to $630 billion in 2028, with the Gen AI share accelerating. Which finance workflow deserve the next dollar and which ones are still overhyped – in your opinion?

 

KM SURESH (06:02)

Yeah, I would say, you know, to answer this very, very specifically, the best ROI – the return on investment – today is in accounts receivable, forecasting, planning, budgeting and analytics and both revenue and expense analytics or analysis. These 3 have the highest ROI. And the reason why I’m saying this is these 3 directly tie to cash, cost structure and speed, which are the 3 most critical things that are required of finance. So anybody looking at finance is going to tell me how can I save and where can I save? Where can I go for speed in the sense I need the data today, I need the analysis today, not 10 days later. And how can I improve my bottom line?

 

Krishna Hari (06:33)

Absolutely.

 

KM SURESH (06:53)

So overhyped, what are overhyped? Things like, for example, a complex judgment areas like in M&A, for example, where you have to do due diligence or modeling and tax strategy. I mean, these, you know, I would I wouldn’t say that we are mature today to get AI backing. So that still would require what I call it as the human nuance here. So I would say to answer your question specifically, accounts receivable, forecasting planning, and I would say revenue and expense analytics.

 

Krishna Hari (07:27)

Very nice. Very nice. What about treasury? Even treasury operations – automation of especially the banks – if you are dealing with multiple banks, reconciliations and stuff like that?

 

KM SURESH (07:33)

Yeah. It does, it does. So for example, I would say that treasury related activities – as it requires a lot of back and forth with the banks and the negotiations and all of maybe that process still has a human nuance to it. However, when I say that these 3 have got tangible ones – like for example, AR to improve your current company’s bottom line – yes it does. Now for example, when you have issues with the banking reconciliations in the process – yes, the order-to-cash cycle process definitely has a big bearing.

 

Krishna Hari (08:11)

Very well, yeah, I totally agree with that. The next question is, AR collection automation is one of the few places with hard KPIs, as you mentioned, the DSO days, past dues, write-offs. What’s credible “before and after” you have seen, and what guardrail kept you from being a one-off win?

 

KM SURESH (08:30)

Yeah, I have seen DSO – which stands for Day Sales Outstanding – cut by 5 to 10 days from the receivables or from due-down, which basically is double-digits after your AR automation. And I would say that every company looks at two things here. Number one, what has been our historical run rate in terms of – from the time you drop the invoice until the time you know the cash is actually at the bank. And so many times you know there are contracts in place that say the customer has to pay within you know 30 days, or it could be 45 days, or it could be a negotiated settlement of, you know, it could be 60 days or whatever. And you are then looking at the customer or the client taking their own time and paying it could very well go into a, I would say a deep socket where you wouldn’t even know how much of it is going to really come back within even 90 days. So I’ve seen worse cases of going even beyond the 180 days. And I’ve seen super best cases even within 15 to 30 days here. But I would say in a normal… I would say, run business, you should be able to improve 5 to 10 business days here. Now the guardrails that you were specifically talking about include clear, clear exception policies, and finance ownership on dispute resolution, ensuring automations don’t just ship problems downstream. Now what I mean by this is if your order-to-cash process has all the kinks ironed out – in terms of how I’m going to be invoicing the customer, whether it has all of the stuff that the client requires to be paid so that you don’t go back and forth in terms of removing the errors – then you are in a much better position. 

 

Meaning, you need to handle those upfront, in trying to make sure that your accounting department does a very good job in automating the flow, of ensuring everything is in the invoice so that when I get the invoice, I know exactly what I’m paying for and that I don’t have to call you and say, “Hey, look, you guys have billed me 10k higher because these milestones or the statement of work does not match with what I’ve got.” Then it’s going to unnecessarily go back and forth on the reconciliation; that’s going to create more time here. So those are the guardrails I would say, you better put things in place well before you get to the invoicing stage.

 

Krishna Hari (11:05)

I agree. I mean, especially making sure you have those policies very well defined before. There are cross checks and balances are there so that…

 

KM SURESH (11:14)

Exactly, Yep.

 

Krishna Hari (11:15)

The next question is many FP&A leaders say AI helps in forecasting, scenario planning and strategic planning, but finance hates black boxes. What proof do you require before letting AI influence targets or resources’ allocation?

 

KM SURESH (11:32)

Yeah, so there are quite a few, right? I mean, you know, the forecast predictability is something that, you know, I have faced pretty much in every single job that I’ve had so far in my career. And every time you get to this, this is probably the fundamental aspect of the business where you can actually make or break the company.

 

You know, I had a mentor once who I was reporting to who always told me this and said, look, if you beat your forecast every single time by 5% and 10% and 20%, or if you miss your forecast by 5% or 10% or 20%, both mean that you don’t understand your business well. So you getting to that accuracy of the forecast is such a balanced act.

 

And in my opinion, forecast is both an art and a science. It’s never black and white. There are lot of gray areas. You have to model the futuristic view of what your sales is going to be closing, how you’re going to be recognizing revenue. Not all of the business that we have are all, you know, 100 percent in the bank.

 

There are many parts of the business like services, for example, where it is dependent on your monthly sales. So therefore, I would say the forecast credibility from an AI perspective, even AI to that extent, it only goes with a lot of the historical information that you feed into it, and then it gives you a good modeling here. So there are different things by category. For example, they call it as the MAPE, which is otherwise called – Mean Absolute

 

Percentage Error – that you define and you could define an error rate and say “Hey look if I’m within this tolerance range of ±5% I’m good here. This is one of the reasons why if you look at all the public companies in your investor relations today they only give you a range from an EPA standpoint. They don’t give you an absolute dollar number so that you have some wiggle room to play with in terms of your forecast. They say you know will still be in the plus or minus, you know, 10 cents per share or 15 cents or so. So you have, you know, some room to kind of move about because you’re always going to see things happen, both positive and negative, and you’ve got to be prepared for it in terms of how you go and measure them. 

 

So I would say the MAPE thresholds by category on a regular basis – and only when models prove that these replicate and are beating the human element of the forecast every single time – you can then take that into consideration and say, “Yes, now I can confidently use AI.” And to be honest with you, in my job at CoreTrust too, we noticed that we used analysis relating to AI and we found that not every single time we were accurate, especially when you are looking…

 

Some industry might be more predictable and some are absolutely not. So you’re going to be – overall, you might still be okay, but when you look at it by industry, things can be plus or minus.

 

Krishna Hari (14:45)

Wow. Interesting. all these predictions and all this forecasting, you depend on your past data, and also your current data and the futuristic forecasting real-time information.

 

KM SURESH (14:59)

Yep.

 

Krishna Hari (14:59)

That also factors in. Coming back to the data quality issue, across Big Four and enterprise surveys, CFOs cite data quality integration and AI literacy as a drag on value capture. If you had to fix one thing first – data, contracts, lineage, ownership or training, what would it be and why?

 

KM SURESH (15:22)

I would start with clear ownership absolutely without any going back here. So without accountability none of these things that you just mentioned here – like lineage or contracts – hold water here. So ownership defines clear accountability and a finance data steward always can model only when someone is responsible for the right quality, which is the foundation of the AI trust here. So, in case an example, I talked about in the different compartmentalizing finance – accounting close. Now, you can have the world’s best predictor, you can have the world’s best AI tool to predict your business, but if you have a bad accountant who has closed the books making wrong entries and who at the foundation level has not reconciled the basics, then everything falls apart, because you are dealing with the foundational mistakes from an accounting standpoint. So I would say you fix that and then you have a very strong foundation that you can lay your AI tool to be able to have a very strong front end tool.

 

Krishna Hari (16:36)

Very nice, very nice. The next question for me is, IDC’s “Human-First AI” theme puts governance and decision velocity at the center. How are you structuring controls, approvals, thresholds, audit trails, model monitoring, so automation speed decisions without eroding financial discipline?

 

KM SURESH (16:59)

Yeah, I would say, you know, control mirrors traditional financial discipline, right? I mean, it’s a finance discipline, I would say. Approval thresholds for material entries are a key aspect here – meaning, you know, you’ve got to have the governance model that has various levels of risk thresholds and who does what. So within the finance space, you got to be able to define who does what and what is the level of entry. So for example, I can tell you in Hewlett Packard, when we used to go through close, we used to have a 5-day close after the month end. So right after 5 days, the books are closed for I would call it as 85% of the people. However, the balance 15% of the people who are in the senior finance community can make entries provided you get their manager’s approval. And the last 5% – unless the CFO of the company signs off saying that yes, these entries can be made – you cannot open the books; in fact, and after that, once the CFO signs off then the books are closed as far as the company is concerned, then it’s only the auditors who can help you with the fact that if you have made a wrong entry or if something requires correction, then they can help you open the books here. So I would say recommendations for continuous monitoring of these models in terms of levels of , are absolutely critical here. So the balance keeps the velocity high without sacrificing accountability here. So end of the day, you have to have that right balance when you look at the controls and governance model here.

 

Krishna Hari (18:45)

Actually, I have a question with this, I mean, today with all real-time data coming in, real-time ERP, does it take 8 days to close a book on a monthly basis?

 

KM SURESH (18:57)

In fact, Oracle has the world’s best accounting systems to close. They close in 3 days. I can tell you that I take about 6 to 7 days to close in CoreTrust. In GDT, it used to take almost close to about 8 days. HP, we should be able to get it in about 5 days or so. NCR, same thing. Now it all depends upon your ERP that you hold as well. Not all companies have the latest and the greatest and not only that, if your company has had a number of acquisitions then you are literally at the mercy of multiple systems that has to run well. So I mean I can tell you when Hewlett Packard acquired Compaq, Compaq had an ERP system, and HP had an ERP system, and Compaq had just then acquired Digital. So Digital had its own ERP system and then Compaq had acquired Tandem as well. So you’re looking – I mean right there, there is 4 major companies’ complexities as you close. Ideally, I would say that if anybody is taking about 8 days to close, they are obviously working off

 

Krishna Hari (19:59)

So many.

 

KM SURESH (20:10)

you know, either a dated ERP system that probably requires a good amount of overhauling of your ERP system and you should be able to improve at least a day or so every 2 months and you should be able to get to a plan to close the books by about 5 days.

 

Krishna Hari (20:28)

Absolutely, absolutely. Amazing. The next question, which is more on the leadership and career path: What technical fluency should a modern CFO and an FP&A head actually demonstrate in 2025, in your opinion?

 

KM SURESH (20:43)

See, I would say, you know, a modern, so this is again an excellent question here, Hari, because I would say, you know, this is the topic that I would say most companies are grappling with, both public and private. I have spoken to a number of them – not only the companies or private equity or so, but also a number of the search firms as well. And they’re also dealing with the same because if you look at the job specs of what they’re looking for in a CFO – know, 3 years ago, you would have said, “I need a classic CFO with, you know, very good accounting background, somebody who can strategically tell your CEO what’s going on in the business.” – the usual. But a modern CFO today,

 

I mean, things have changed so dramatically over the last year or 2, I would say, of course, the CFO doesn’t require to be coding particularly – I won’t say that – but must have a good grasp of what I call as Gen AI concepts, the basic concepts in terms of what is it that an AI can do, whether you have your own in-house AI model that you want to adopt, or and/or you want to adopt a I would say off-the-shelf AI model that can be used for your business or for example, I used Adaptive – which is a Workday model – which does have the AI built into it while doing your forecast. So I would say a CFO now

 

I would say almost that one needs to have the basic knowledge of the general AI concepts, cloud economics as to how that works, and the basics of what the machine learning concepts are. So enough fluency to be dangerous when you are sitting down in the table to review the numbers with your technical experts, whether it is the data scientists, or your IT here. Now if you come in with absolutely no knowledge, you are quickly going to be taken over by these guys, who are going to give you things that you may not need, or they’re going to give you things that you don’t need here. So I would say you got to come to the table with some of these basic knowledge.

 

Krishna Hari (23:00)

Absolutely, absolutely. It’s a fast changing world, especially for the CFOs, even for the CIOs, as well as the CTOs. think the challenges to bring what technology and the pace of technology is amazing. I think the next question is also about multiple CFO surveys show strong intent to invest in AI, paired with concern about role changes.

 

KM SURESH (23:04)

It is. It definitely is.

 

Krishna Hari (23:26)

How do you re-scope finance jobs so teams are up-skilling rather than being obsolescent?

 

KM SURESH (23:33)

Yeah, see – the shifting analysts, your accountants, your analysts, from being what I call a spreadsheet jockeys to model designers and prompt engineers. You know, these two, I would say those two are probably the big ones. So you’ve almost got to go back and tell your FP&A analyst who’s just soon out of the college

 

Krishna Hari (23:50)

Absolutely.

 

KM SURESH (23:59)

– almost like saying, look, the way you’ve got to think is not how I would have thought probably 20 years ago when I started my career or 30 years ago – but like opening an Excel and saying, “Let me do this.” But instead think of it as, “I have this issue and I have this big data set in front of me. And my boss is now asking me to come up with assimilating this huge database and coming up with my insights,

 

Krishna Hari (24:05)

Absolutely.

 

KM SURESH (24:23)

so that he can make the decision tonight, not 10 days later.” So, moving from that spreadsheet jockeying to what I call as, know, model them; you know, you have a number of things that you can learn from financial modeling and also prompt engineering. I mean, today it’s not about AI, it’s about what prompts. you give to your AI to be able to get the right answer. 

 

See if you don’t ask the right question you’re never going to get the right answer. So I think today we are we are using a lot of the ChatGPT and AI in a very very basic and formulaic way but I think you know the right prompts can give you probably the most powerful answers in my opinion here. So the message is AI takes the grunt and the brunt work but while your value your analyst’s value comes from shaping those questions and validating the output from those questions and then telling your boss, “Look I’ve got, you have this problem and these are the 3 solutions that you have and here is how we address them.” Now, you know, his body language comes to an extent saying that “Wow, you know what, now I know at least which direction I need to take it.”

 

Krishna Hari (25:38)

No, no, absolutely. ⁓ This has been, even in our own operation, it has been a huge change – be it even to prepare for this podcast, be it to prepare, I mean, any even normal operation, I think. I mean, just like people used to use Google, we use extensively all the… especially ChatGPT, Perplexity, and Grok. I think all 3, pretty much, I think, it has become a day-to-day usage. And presentation – Canva, think ⁓ yesterday, today I had a presentation. Yesterday evening, I wanted some 5 slides. In the morning, I had my 5 slides. This is never possible. It will take at least 3, four days to prepare a fresh topic slides. Nowadays, think within one hour or half an hour, you have your slides.

 

KM SURESH (26:17)

Yeah. Yeah, fantastic. I mean, it’s amazing. don’t know. Sometimes it’s both a boon and a bane and it all depends upon how well we use it. And like I said, you let AI do all the most difficult work. You just feed it with the easy and the most important question that’s in your brain and let it do all the difficult work, so that you can take the human element out of it.

 

Krishna Hari (26:52)

Absolutely, absolutely. Going to the next stage, I think the agentic workflow and sector playbook. From copilot to self-driving workflow, industry outlooks point to agentic patterns emerging across finance. Where would you pilot hands-off agents first – working capital, reconciliation, marketing-mix to revenue bridge? And what would be your red lines?

 

KM SURESH (27:17)

I would say start with reconciliations or working capital tasks. Very critical because they are high volume, they are rules driven, there are guardrails, and they are auditable. So they are extremely important. So red lines – anything tied to external reporting or investor guidance that you give until models prove I would say explainability and the control robustness, you have to be extremely careful about it. So I would say that the red lines – those that are requiring external consumption, investor guidance, where you give like, for example, “I’m going to give an EPS guidance for my company – you know, at the end of the quarter as to what the future because that has a direct bearing on your stock prices.” then I would be extremely careful about how I would do that, unless you have a proven track record of where, and what, and how this has been – I would say historically – been proving out here. But things like the reconciliations or the routine work, or things that are working capital stuff – yes, of course, you can always get those high volume ones just behind your AI horse here.

 

Krishna Hari (28:31)

Very nice, very nice. Well said. The next one is a high profile CFO – especially the HP’s Marie Myers – publicly described using agentic automation to reshape finance operating rhythm. What lessons translates to a mid-market P&L with lean teams and tighter payback windows?

 

KM SURESH (28:50)

Yeah, she’s done an awesome job, Gartner just announced that the HPE’s finance team actually were the winners of adaptability and embracing the AI world here. And she herself is the finalist of championing AI here from a finance standpoint, which is fantastic.

 

Knowing Marie Myers for a number of years, having worked alongside with her, she comes from a very classic accounting, I would say, background to managing business in the Americas, and then going into transformation and now being a CFO of, I would say, large companies like HP and HPE. The lesson is to start narrow here, I would say.

 

I would say use cases for immediate cash and efficiency payoffs is going to give a tremendous amount of confidence. Mid-market finance wins is basically by avoiding what I call as platform sprawl. I would say that unless you prove value inside a very, very tight payback cycles, before you kind of scale them into really high heads.

 

One, it’s a big market here. You have to have some provability use cases. So I would back-end that and look at what the historical cases. I think that the common theme that you are hearing here is that “I’m going to go after AI, but I’m going to kind of be – I’m going to put all those things in AI, which are high volume, easy ones, but I’m going to slightly scale back when I require an M&A, validation or if there is external reporting guidance issues – I am going to be quite careful about it.” So that is how I would approach it.

 

Krishna Hari (30:35)

Very well said. Very well said. I think it’s amazing. Especially compared to other areas, HR or Sales and Marketing. think AI has – as you said, the impact in the finance is very high and it’s very visible as well, unlike the others, right?

 

KM SURESH (30:53)

That’s it. Yeah.

 

Krishna Hari (30:54)

It is a very visible and that’s – again, companies have benefited who have used this, especially bringing their DSO days down and automatic reconciliations and things like that. There are a lot of fintech companies doing it. We also have partnered with HighRadius, which also has a very good track record of doing this for all big guys – Walmart, Adidas, and big retailers and consumer product companies. Coming back, our rapid fire round Suresh, this is just to get you… your personal side, compared to your professional side – which one book or podcast you always recommend to colleagues or clients?

 

KM SURESH (31:37)

I would say ‘The Monk Who Sold His Ferrari’ by Robin Sharma.

 

Krishna Hari (31:41)

…On the podcast?

 

KM SURESH (31:42)

On the podcast, I personally enjoy, 45-minute capsules by, you know, life’s lessons that you get in terms of how to navigate the complexity of life, both from a personal standpoint and

 

you know can be used at the professional standpoint by Swami Sarvapriyananda.

 

Krishna Hari (32:02)

Wow, wow, okay. With the podcast is named after his name or?

 

KM SURESH (32:06)

There are plenty of podcasts.

 

If you Google Swami Sarvapriyananda and podcasts, there are many lectures that he has specifically given to IIT Kanpur and he used to be a professor in Harvard as well. He’s actually the president of the Ramakrishna Order at New York.

 

He is somebody who I listen to a lot and there are both podcasts as well as several YouTube videos that you

 

Krishna Hari (32:35)

video…Yeah, thank you. Do you prefer early morning or late nights for your best work?

 

KM SURESH (32:40)

I’m both, but I prefer early mornings. With a cup of coffee, would say that my gray matter starts to work much better.

 

Krishna Hari (32:47)

That’s nice. If not technology and AI, what field would you have pursued?

 

KM SURESH (32:52)

I would have probably ended up definitely wanting to be at least an actor – if not in the main roles, but somewhere as an extra.

 

Krishna Hari (33:01)

Of course, of course, Bollywood or Hollywood? That’s nice. What’s one daily routine or habit that helps you to be effective?

 

KM SURESH (33:04)

I would say Bollywood, yes.

 

I would say that two things help me a lot navigating through – I would say any challenge that is thrown at you – meditation is one, and the breathing techniques are the other. So those two I don’t skip a day, and those two are probably the most that I ⁓ enjoy, however busy you are.

 

Krishna Hari (33:33)

Nice nice. So you do you meditate for half an hour? 15 minutes? What is your routine?

 

KM SURESH (33:37)

I meditate both in the morning and in the evening for about 25 minutes, a piece. So I meditate for about – call it 45 to 50 minutes daily. So somebody asked – you know, just a sidebar here that – somebody asked Gandhi that… Gandhi used to meditate a lot and there was his – Gandhi asked his secretary

 

Krishna Hari (33:50)

Very nice, very nice.

 

KM SURESH (34:04)

you know, how does my day look today? And his secretary said that you have absolutely no time for anything. It’s one of the most busiest days that you have pretty much starting from now until late night. And then Gandhiji usually meditates both in the morning and the evenings. And he told the secretary that, as it’s a very, very busy day today, I’m going to meditate about 30 minutes longer.

 

Krishna Hari (34:29)

Hahaha

 

KM SURESH (34:30)

Just to make sure that he goes through the day much faster.

 

Krishna Hari (34:34)

That’s wow, interesting. So what’s your go-to activity to relax outside of work?

 

KM SURESH (34:40)

I would say travel is one. It’s been a part of me for a very long time now, ⁓ both domestic and out. I would say that’s one. And second, I would say I catch up on movies for sure. And I like to dissect them as much as I can, not just watch it for watching its sake. So those two are probably my hobbies, I would say.

 

Krishna Hari (34:46)

Nice.

 

Absolutely, absolutely. Again, that was fantastic. Thanks for playing along, Suresh. It’s always great to see you, see the personal side behind the professional expertise. By the way, You have any closing thoughts, Suresh, for the audience?

 

KM SURESH (35:18)

No, I mean, thank you. Thank you, Hari. It was wonderful. really, really enjoyed talking about various aspects that has AI as a common, I would call it as a fabric. I think the questions that you have kind of put together in the podcast today are very relevant.

 

You know, definitely the role of the CFO is changing and it has. It’s no longer about, you know, reporting the numbers or closing the books and telling your CEO that I’ve closed my books and here are the results. It’s no longer that anymore, but it’s about how quick and how fast can you tell your management, your stakeholders, your sponsors, about what’s going on from a rear view mirror. But that’s only for 10 minutes. The other 30 minutes is about looking forward. You know, what’s going to be happening? How can you project roadblocks? How can you remove them? What are the obstacles that’s going to be in the way? How do you take that out? 

 

So it’s all about forward looking and it’s less and less and less about your rear view mirror which used to be the role of a CFO once upon a time – and that has completely shifted. Not that the rear view mirror is unimportant, but I would say it’s less important as your management wants to know 12 months from now, “Am I going to hit the plan? Am I going to have a problem in my supply chain? Am I going to have a Forex hedge issue and things like that?” So you’ve got to be prepared as the finance head to be able to navigate through those obstacles here.

 

Krishna Hari (36:59)

Well said Suresh. I think again, I think the role of CFO from being an account keeper to more a strategic player for an organization is already in and with so much uncertainty with the tariff and the global wars going on and ⁓ so many uncertainties, I think CFOs definitely play a very strategic role in every aspect of the organization. ⁓

 

KM SURESH (37:22)

Yeah, absolutely. I would say that.

 

Krishna Hari (37:25)

Thank you very much Suresh. By the way, think to our listeners, if you enjoyed today’s episode, don’t forget to subscribe, rate and comment – The BizTech Pulse Podcast in your favorite bit of feedback to help us bring you more conversations like this. You can also follow us on LinkedIn, Spotify and Apple platform for updates. And if you have a conversation, and if you like the conversation with Suresh – sparked new ideas and reflection – we would like to hear from you. Thank you again for tuning in. Stay curious and stay innovative. And we will see you next time in the BizTech Podcast. Thank you. Thank you.