Episode 4: The Future of Private Equity: Leveraging AI, Data, and ML for Smarter Decision-Making with CRMIT

Vishal: Welcome back, guys, to session four of Winning Moves. Today, as we spoke about in the last session, we delved into, we looked at marketing and service and how they go together in the PE world and what is expected of a provider like us, like CRMIT, and the difference it can make to enhance the overall marketing and service of a platform company. 

Today, we’re going to be diving deep into the future and the economics of the PE world and a fund level that is we’re talking about the whole idea of decision making um deal making as well deal-making and decision making slowing down over the last one to two years and companies you know what I mean is volume of deal-making and I also wanted to understand at the PE fund level given your understanding as a service provider partner of platform companies and PE funds how do you see see them enhancing their operations given the kind of deal-making that they want to achieve like the level of deal-making they want to achieve versus what they’re able to do at the moment despite having huge amounts of funds.

Paddy: That’s a very hard question to be honest Vishal, so let’s try to unpack it. The current trends in the PE fund, I think it’s well documented, all the the big advisory firms like Bain, and McKinsey have been reporting on it, the data is all there out there, very transparent. It’s not just the interest rate trends in the developed markets, there’s also a more secular trend, as it were, as to why the deals making has reduced gradually right it’s that the opportunities for PE right the kind of opportunities PE traditionally has gone after right those kind of opportunities are thin right today they’re not available in as plentiful as they used to be so why is that it goes back to more than a decade ago the one can characterize it as a low interest rate causative right but yeah literally the operational what what has been the the effect of it right that was a causative what has been actually affected is that businesses are not there are no there are there are relatively fewer businesses for a PE from a PE perspective uh with strong fundamentals see for a business what matters is the fundamentals you know right start with your solid product which is satisfying a given product market right and then you’re able to the next fundamental is that you have the ability some competencies some strengths uh barriers to entry for your competition which you can execute upon one you may have barriers but you may not be good at execution right that’s how the fundamental you’re also good at execution right so these are standard time-tested you know ionic epochal fundamentals of any business right so the low interest rate the gene had caused some of these fundamentals to be a bit weaker in businesses so at a very abstract level we don’t have any abstractions here right at a global level therefore if you just took a sample of businesses worldwide right and you try to do some strength level comparison scoring right versus what they are in the last 10 years how they evolved versus maybe 40 years ago yeah you would find that these moves you have more numerous businesses smaller businesses in that sense a bit more weaker from a fundamentals perspective right and these always first try to identify what the fundamentals are and then they identify gaps and opportunities to build and realize more right so so that’s the one of the other more you know foundational thing there the interest rate regime cost these things, right? The interest rate is just another number. The actual value of interest doesn’t really matter from a money and financial capital leverage. That’s the…it’s important to understand why we are where we are, right?

Why the industry is where it is, right? So, at a high level then, the solution to that, therefore, if that is what is described is true to whatever extent, the solution to that will not lie in something like AI, to some extent, at least to a large extent. We’ll come to it, we can debate about it. But just quickly addressing one side point on that which you brought up, which is that in terms of just the operations and processes of a PE fund, right? 

The PE fund, where it is trying to find the right opportunities to invest in, do the due diligence rapidly and so on, right? So there, these things have been traditionally manual processes and through their AI, ML technologies can bring some efficiencies in some of those aspects, some of those parts, you know, finding the right opportunities, right? And there are firms which are writing products and selling services using AI and help to help the funds identify the right acquisitions, right, to look after opportunities and even do the due diligence more rapidly right. Due diligence is always a risky affair and nobody in PE will ever claim to do a complete will ever claim to have done a complete due diligence so some of those things can be improved by the application of machine learning and artificial intelligence from that and that there are products already doing that and that that thread can continue will continue on its own to constantly evolve and improve things. And that will improve the shop side of the house side of the PE fund.

But the fundamental problem of not there not being enough opportunities to buy for PE funds, to invest their capital in for the right return or what you put in a different way in that

whatever opportunities do exist, they are forced to quote them value them at very high levels because you need the right returns right so you can only get those returns if you quote if you buy them at very high levels and there are not enough businesses which can be bought at that high levels right so that’s the kind of frustrated let’s call it right situation of the industry right that that will take time to resolve itself, right?

I think everyone in the industry knows that, right? The fundamentals have to improve, right? So that’s one part of it, right? But there, so that’s on the fund side, right? But now if you just take that last point which I just mentioned, which is that, that’ll take time to resolve itself, right? So that’s where the AI, ML as a technology piece in the economy has a great promise, not just for the PE-focused businesses, for any business, right? And therefore, more business can therefore become better candidates for PE acquisition to, you know, but this availability of AI and ML makes it possible for many businesses to become sharper better and more efficient and then and in and in a sense more better candidates for acquisition by PE businesses too right so that there that angle that it is very scarcely and very rarely explored in the in the current business literature and maybe we should run another episode on that but there is how the value of how AML can actually improve businesses, can help businesses become better candidates for acquisition by PE, right? The ones which are not in the current radar, you know, diagram as we call it, right? But which can come into the radar ability of a PE, right?

AI, ML can actually help some businesses fall into the radar ability of PE businesses right that’s just an unexplored territory and we can probably talk about it later sometime but back to the the current enthusiasm and excitement around AI and ML for general businesses definitely there is a great thing to be said for it on the pe businesses themselves it goes back to the same old concept e businesses are highly focused on their opportunities.

They are always hunting for the best solutions that will help them address what they’re going after. AI, ML have very promising capabilities on improving efficiencies on certain elements and aspects of operations in a given business.

So PE businesses are always going to be looking out for right AI and ML related products and services by suppliers, which can actually very effectively solve very important key processes and bring efficiency to businesses.

So in that sense, they are a huge demand base for AI and ML products, and probably many of the serious ones will already be doing in-house investments to build their own capabilities so that’s another somewhat relatively a unique aspect of AI and ML technology as it

were in the industry it’s more valuable when you own it yourself compared to the last 30-40 years where you had these more commoditized packages and software outsource you know capability, for IT, right? But ML, AI and ML is partly IT, it’s also partly intelligence, it’s business, it’s algorithms, it’s intelligence. So it is a huge debate and it’s probably gonna continue whether it’s totally, really IT or it’s really business. And of course, if it is business,

then it doesn’t make sense to outsource, it should be part of your own in-house. But the extent it is IT, yeah, there are serious capabilities and skills and technologies that are involved in it and you rather outsource them because there’s a whole industry of people who specialize in them so it’s a it’s a mixed area but to the extent is feasible serious key businesses are building in-house capabilities or partnering with firms and making them you know very deep partners long-term partners and so on as you know in our company to be we are constantly in our R&D mode, building solutions for our customers and even prospects, and thinking of solutions that can be generally made available to a category of customers and so on.

I think all IT firms that are invested in AI and ML are doing the same thing, so it’s not unique, I would say. But there’s a lot of opportunities there, serious opportunities with AI and ML for improving operations in PE firms. They’re a huge demand base and suppliers would do well to build solutions for them. 

Vishal: Sure, yeah, I think it’s really interesting to see how the whole idea of AI and ML along with being a value add like any like for any other business also for private equity-backed businesses but also the fund level how they’re using it in some other marketing activities right to spot potentially the right organizations companies that they can potentially acquire doing highly highly specific targeting and to obviously increase the chances of finding the right firm to acquire so moving on again when we talk about AI and then we talk about machine learning, we also talk about data science.

And as a company, we CRMIT which focuses on understanding the customer problem, using the appropriate tools and also having the skills to analyze the data that’s available and it’s also about making the best use of data that’s available and not necessarily always

asking for more because there’s always that whole idea of security and other limitations when it comes to dealing with excess amounts of data. So I just wanted to understand what is the benefit that a company that’s offering a service like Decision Science as a part of its activities can enhance the overall value of post-merger integration or platform company. 

Paddy: Agree, I mean, the starting point there, I think we’re all clear about it, which is that a business is simply a series of decisions, right? So IT is a decision tech. All IT, just to clarify, right, all IT is decision tech, right?

When I build a database, I’m just building the database, not even applications, just the old database of 1970s, right? And the relational database was invented, right? I’m building that just to collect and organize my business data for what for? So that I can, you know, there’s somebody whose job it is to review that data, analyze that data for what job? To make better decisions, right? Sure. So all of IT as it stands today is only for decision support, right?

It’s just that in the last 60 – 70 years, these technologies, IT, has been largely for more and more of senior management, as you may call it, in a business, right?

Enterprise level, corporate level, senior management because the key decisions that in addition you don’t know no business wants to get that wrong so all IT has been designed, architected, build, you know develop, produce, consume, largely with senior management as the as a target consumer of these things, right? What has changed today, right? And that has had a great run and has done a great job, right?

It’s hard to imagine what our economies worldwide would look like if there was no IT in the last 70 years, right, so that’s a great thing, right? What we need to distinguish from that history and the current and the future is that AI and ML brings that whole world of IT into everybody’s hands. Everybody is a decision maker in a business, right? A call center agent receiving a call is a decision maker.

What to say, how to respond, what not to say what decisions to make on a given whatever is transaction issue that they are dealing with right everything is a business your decisions we only decide what to do and then we just act on it right so the promise of AI and ML is to bring that decision ability to bring information much larger volumes of corporate information to every person in the business that is the promise of AI and ML right when we talk of decision science you know as an offering we are saying guess what has hitherto been available for the senior managers in their decision jobs right AI and ML is bringing that capability to everybody in the company, everybody in the business. That’s the capability. What is an AI and ML? When we talk of all these models, OpenAI and all of them, Anthropix and all of them, GoHere, all of that, and today I think somebody is giving a contest on some 800 models that exist there, and they’re exposed as production models. People are consuming their production, right? And it’s going to keep exploding, right? These bring in, they gather, they’ve been trained on a humongous amount of global data, right?

And they’re packed into one simple machine, right? Which can be deployed with every node and every nook and corner, right? That global data is what our traditional IT used to do, you know, when I built my massive enterprise-wide database, right, to bring my finance data, my HR data, my sales data, my marketing, my service, that is what I was doing within my company, right? Now these machines have done similar stuff. They have compressed that by training.

They have brought that whole data into one machine, and that machine is relatively cheaply available and deployable to every desk as it were goes back to what Bill Gates said you know the software industry to have a computer on every desk was the mantra right so the goal of AI is to have an AI in every desk right that’s what business science is so so yeah that’s what the capability is there is no limit to this. It’ll certainly improve decisions.

And yeah, friends in the tea industry are certainly going to be exploiting them. So will every business. 

Vishal: And yeah, I know we spoke about AI, and we spoke about ML, and what CRMIT offers as a decision science, what is unique to CRMIT as a decision science, what is unique to CRMIT as a decision science, in terms of decision science service offering. But I also wanted to change the gears a little bit that we at CRMIT have for our customers, which is called as ASVP or Application Success Value Plan. When we launched this, we really felt this would give customers flexibility.

It’s not just about in the PE world, but other customers otherwise in other industries. Give them the flexibility and confidence to gain the trust in CRMIT by having hire them for a short term basis for a particular requirement and then move on to more long term relationships and work being delivered by CRMIT. So in the whole and again you guys can check out more about this on our website about ASVP on the website but I want to understand how does or how will ASVP really truly benefit a dynamic world like that for platform companies. 

Paddy: That’s a great question actually and honestly the answer is going to be hard I guess I’m sure ASVP is a great offering. It suits all businesses who need flexible services and providers. But to your question on how it might be relevant to be businesses, right?

There is one straightforward solution to that, fitment to it, fitness to it, which is that where platform companies are engaged in a pretty high frequency of mergers and acquisitions,

an offering such as ASVP is very relevant. You can consume the hours or short-term contracts, so to say, without too many obligations, because that’s the nature of the game in an M&A process in a PE business. So to that segment and to that process and to those requirements in a PE business, it’s a great fit, right? But that’s really the low-hanging fruit, if you really want to call it that.

If we take a step back in a PE business, the ASVP, an offering such an ASVP, when you combine it with something like AI and ML, if you bring AI and ML capabilities into that service offering, there’s a lot that can be relevant and meaningful for a PE business. I don’t know if you have a specific offering in an ASVP that brings the AI, ML capabilities into the service offering and where it does, that will be relevant for a PE business.

Also, as I said, one of the problems with AI and ML today, not the problem, the situation, it is what it is, right? It’s a radically different technology, right?

Radically different technology compared to the past. It’s radically different as compared to traditional technology, right? So one of the core things, one of the main things that you currently, that you will find happening in the industry on the AI and ML is that literacy cannot be taken for granted, in a sense that not everybody understands the technology, even the senior decision-making level, right? It is not understood uniformly across the world. There is a process of global education that is going on, the industry is doing, and it will take its time to accomplish. It happens with any new technology. 

Vishal: As the technology matures, right? 

Paddy: Exactly, it matures, absolutely. And this one just happens to be a slightly more complicated technology, and therefore it’s probably going to take longer for people to really assess and evaluate and understand the use cases and how best to exploit it and so on, right? So that will probably take a bit longer so it’s in progress and it’s going to take its time right but the implication of that is also that the other point to understand sorry the other point to understand the AI industry itself is also growing rapidly at one point the usage literacy is is not falling behind but is going a bit slower but the production literacy is not falling behind, but it’s going a bit slower, but the production side is moving rapidly. So the gap between the two is actually widening, you know, at any given instant time, right? So, which means what? From a demand-supply perspective, from a central service provider perspective, there is a greater need for partners, if businesses need partners who can actually, not handhold-hold but who can run along with them or help them run along with the AI industry faster, so that they don’t lose the opportunities, they don’t lose the avenues available to exploit the technology, to apply the technology, not even exploit, to apply the technology towards their own PC and their object of the business. But there is a need, and yeah, which again goes back to the old point talked about earlier in a past episode, that you need a partner who understands your business and also is skilled in the technology side. In this case, it would mean who understands the AI ML ecosystem, what’s going on in the industry, how much of it is in the production-grade, can be made into production, can we walk through be made into production, can we walk through from pilots to production and that will all mean a lot more intense education and understanding of your businesses.

As usual it goes back to having the right, good, sound, solid partners. That never takes away from that. Sure. 

Vishal:  Which is why I thought ASVP is particularly, connects well with the whole needs of PE. And I wanted to understand how have customers typically seen us walking up to them, asking them to sign a long-term engagement with us for a particular activity or an ongoing activity versus walking up to them and saying, hey, I’ll deliver this particular work for this week, two weeks and then we’ll come back to the next yeah next project or next activity or problem to be solved in the next stint or session right so how have they seen.

Paddy: I think what what I have seen with our customers some of our customers everybody is aware, because there’s a lot of awareness of AI and ML as an available technology.

So one of the first things we always face with our customers is when we do want to know if the ASAP offerings that we have can include ex-pilots or exploratory researchers, maybe largely pilots, we’ll say, of AI and ML into some process or corners of their business so certainly it is something that i’ve asked for whether you know it is a separate aim and can be included in the ASVP offering service offering or if it’s a separate thing obviously i think in our company we offer it as part of our regular ASVP it is not a separate thing as it were 

Vishal: It’s actually called ASVP innovate.

Paddy: That’s good to know not sure if you went yeah so so it’s included as part of the package right uh and i believe it must be one of our most popular packages among the other packages.

yeah definitely sounds unique to customers so they’re inquisitive about what we have to give them in that ASVP innovate 

Paddy: Got it so that that that that aligns with that uh like said, education is a big gap there, so service provider we have to offer it and there is a demand for it, customers have been asking for it, there are two ways about it.

But in the longer run, these ASVP contracts have to become bigger as well, meaning when these pilots have to move into production, AI, and ML are big things.

So the starting positions are correct, but eventually, we will see them progress. They’ll become big services and then that need to be applied and large teams have to be brought in case and so on, yeah.

Vishal: Sure. All right, some interesting conversations. We’ll try to keep this short, although conversations can go on. But thank you for listening. I hope it was helpful. And a lot to be learned from our conversation is something that we expect. Thank you, Paddy.

Paddy: Thank you.