Five Minutes with Bain's Richard Lichtenstein

INTERVIEW
AI Adoption in Private Equity

Richard Lichtenstein, Expert Partner and Chief Private Equity Data Officer at Bain & Company, shares his insights on AI adoption in PE and financial services. In this interview, he discusses the current state of AI implementation, challenges, and future prospects in the industry.

This interview has been edited for clarity and length.

What’s the state of AI adoption in PE?

There’s a lot of excitement. PE funds are eager to adopt AI to improve efficiency, enhance customer engagement, and increase profitability for their portfolio companies. This is the type of technology that people are always excited about.

Today, what I hear from PE is: "We're really excited about this. We think this has a lot of promise." Some have seen success in one or two portfolio companies, which have implemented promising AI initiatives. There's another chunk of companies doing stuff that seems cool, but they don't know yet whether it's going to work, or they hope it'll work, but they're not fully sold. And then there's another group of companies that haven't really found religion yet, and we're hoping they will. That's where probably many funds are thinking right now.

What are the main challenges in AI adoption for PE firms?

One of the biggest challenges is the limited in-house expertise. Most investors do not have armies of engineers working at the fund level. Even PE funds that we would consider to be large might only have one to three data scientists working at the whole fund. They're just not in a position to start engineering complicated solutions to problems because they don't want to maintain them. That's a blocker.

Another challenge is the lack of trustworthy vendor solutions. There are a lot of use cases that I can talk about, but there really aren't many companies who've actually built plug-and-play solutions that deliver those use cases in a consistent way yet.

How does AI adoption in PE compare to other areas of financial services?

If you think about other types of investors besides PE, groups like sovereign wealth funds are all over this. They're even more enthusiastic than many PE investors because they have investments across a wide variety of companies and asset classes. They see a lot more data than many PE investors do because they just have more investments. They're sitting on a lot of data, and they believe that if they can act on that data, it'll be exciting. Also, they have fairly deep pockets, so they can invest money in things that will make them better.

Hedge funds are a bit different. There's probably appetite for hedge funds to use this to detect market signals. For hedge funds trying to find things quickly, using AI to detect market signals is sort of a no-brainer, and the most sophisticated ones are probably all doing that. If you're a value investor, your behavior is more similar to a PE fund, so your adoption is probably similar as well. You might be using some productivity stuff, but you're also starting to invest with a thesis of which companies could be interesting for AI or which companies could be the winning picks and shovels for AI.

How is AI helping PE?

Right now, it’s helpful for getting a quick read on a company. You can do that faster. If you're an associate at a PE fund or a value investing hedge fund, you're looking through company after company to try to find the one you want to move forward on and do proper diligence. You might look at 20 deals before you find one company that you get excited enough about to really pursue. That's a lot of time. You might spend a day or two on each of those 20 companies, so it could be a month or more of work before you find the thing you want to go forward with. That's not an amazing use of people's time, spending all this time looking through companies you're not going to invest in.

AI can really help with that. You can build a bot that can sift through at least all the publicly available information, and then maybe all the information that's private that you subscribe to. It can look at it through the lens of your specific investment strategy and evaluate it against those criteria.

Today, that might be a day of work. If you fully AI-enabled it, maybe that becomes two hours. With today's technology, that might be a couple of hours, and in another six months, that might be five minutes. Think about how much time you've saved - instead of taking 20 days to do what you've now done in one day, you've dramatically improved your time spent on stuff you actually should care about.

How early are we in the AI adoption process?

With the models at the state where they are right now, there is a ton of opportunity. If the models never got better than GPT-4, if that was literally the best model that ever existed, there is still an enormous amount of gains in terms of productivity and new features and things that are possible as it slowly kind of worms its way through the economy and through different sectors.

Now there are lots of obstacles to it being rolled out. I think the biggest one is the dearth of trustworthy vended solutions.

What the world needs for this to really be a rocket ship - and I think "rocket ship" feels a little overblown to me at this moment - We have the rocket, but it’s still on the launch pad. For AI to take off, we need more vendors providing ready-to-use solutions.

In the next year, I expect there to be more and more vendors that come out that offer in-a-box AI solutions that can really do exciting things. Things like a call center chatbot that's really good, that someone can chat with out of the box, that can take actions on your behalf. You'll start to see more and more AI-powered chatbots pop up that work really well, that people will be happy to talk to.

What are the most common questions you get from PE firms about AI?

The most common question I get from PE is actually about knowledge management. They've figured out that knowledge management is the first use case they need to figure out, so they ask, "Who should we use for knowledge management? How do we get going there?"

I also get a lot of questions around, "We are a fund, we have or are planning to implement ChatGPT for enterprise. How do we actually get value from that?" The answer to that question is basically building a bunch of good custom GPTs and figuring out a mechanism for how to implement them across the organization. Some funds have done hackathons, which I think is a fun way to do it. But it's about building custom GPTs that can do useful things, and then training your senior people on how to use them.

For example, you can build a ChatGPT bot that can slice and dice a CIM (Confidential Information Memorandum) according to the standard way that your fund dissects them. But you need to explain to the senior MD how to do that, how to use that, why it's helpful. And that's not so easy, because they're busy. They're super, super busy people, and they have a system they want to use. So getting them to use that is hard. It's easier to get them to use a custom GPT and just say, "Go here and push one button and it'll work," versus having them do prompt engineering.

On the portfolio side, the question is always, "What are the key use cases you're seeing? What are other portfolio companies doing? How do I get my portfolio companies to do more of that?"

What's a concrete example of how AI can help a company make money right now?

If you're a B2B company, mid-sized company, and you're thinking, "How do I use AI to make more money?" - assuming you've already got a coding assistant helping your engineers - I think the use cases that we see print money are sales enablement use cases.

Especially if you've already got a CRM that has AI stuff on top of it, as Salesforce does, but lots of others do too. It's simple things like helping identify leads more effectively. Can AI scrape the web, find a bunch of leads for you, and then write emails for your salespeople to send to those potential leads that are really customized to their needs? That's a really good use case because you're contacting people you wouldn't have otherwise.

Or your salespeople get an email, a set of emails to send every Monday, like "Here's five people you haven't followed up with recently." The idea is you're getting your salespeople talking to people they aren't talking to now. So even if your response rate is low, it's fully incremental. And if the emails are decent, people will respond to them, or respond to some of them, and it's likely to generate leads, and that leads to money.

So that pretty simple sales enablement type stuff of just creating emails for people to send that are tailored is where I would start. And then if you do that and it's a win, then you can start to think about the more complex stuff.

Thanks for reading!

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