AI is shaping up to become investors’ darling, but how can private equity investment teams differentiate between signal and noise when assessing AI in tech-enabled businesses?
Hg Capital’s Serena Doshi joined proSapient Co-Founder Margo Polishchuk to discuss how investors can go past the initial investment deck and uncover tangible proof points.
Some of the key takeaways include:
Agentic AI and co-pilot enablement are no longer nice-to-haves, but have become baseline expectations for many investors
Investors are interested in tangible cost saving measures
and time efficiency signals from AI implementations
Starting small and in areas where AI will have the most impact internally might move the bottom line more than a sudden, wider-scale shift
Implementing change is often underestimated and igniting a cultural shift to empower everyone to utilise AI can have a positive impact
Watch the full clip more details, or read the transcript below:
Margo: AI features in almost every investment conversation today, but not all AI stories stand up to scrutiny. I'm here today with Serena Doshi from HG Capital to talk about how private equity managers distinguish between signal and noise when assessing AI in tech and tech-enabled businesses. Serena, when you look at an opportunity, how do you separate actual AI capability in a company versus something that just looks great on an investment deck?
Serena: It's a good question, Margo, and very topical, as we're seeing the word Agentic everywhere, co-pilot as well. And while at a certain level it is table stakes to have some sort of co-pilot enabled, I think for us, we're looking for some tangible proof points.
You're not going to get anyone who's fully embedded a workflow in an AI-native way end-to-end, but at least to have some proof points and understanding where people are using AI the most, what the penetration is within a certain team, and any metrics on efficiency savings. I think that's very helpful for us.
Margo: Are there specific signals you look out for that tell you that AI is deeply embedded in the business and is actually making a measurable impact?
Serena: Yes, and I think that that's the main point around measurability. If people are tracking their hours, for example, and doing certain tasks, and then you see a tangible uplift in efficiency by using AI or time-to-completion of those tasks, that's quite handy. Obviously, as investors, we all want to see where AI is helping top line or cost savings, but it's so difficult at this point in time while there's so much trial and error.
So, I think having these proof points, whether it's in customer support and time-to-completion of tickets or resolution, is very helpful.
Margo: So basically, AI impact is measured in time saved or efficiency of the business?
Serena: I think that's definitely one way of measuring it. If you're able to measure in any other way in terms of number of support tickets addressed or anything quantitative, it just helps put a stamp on it. Otherwise, we go back to those buzzwords of using agentic, efficiency, automation, but it's very hard to measure otherwise. And again, we all want to understand what the bottom-line performance is, but at the outset, at least, having some metrics is helpful.
Margo: I think all of us business owners hope that it has a direct impact on EBITDA.
Serena: But that's the thing. It's so challenging to measure. But I would be bold enough as to say that there are such cost savings to be had across so many areas. And I think that's where starting small and in the areas that have the most impacts internally, and that might be on cost rather than thinking about the next AI product that you can see sell, it's probably a little bit easier and a bit easier to communicate to the market as well.
Margo: Looking ahead, what do founders consistently underestimate when building AI capabilities into their businesses?
Serena: I think the number one thing we've noticed is how difficult it is to implement that change. They might have bold ideas, which is always encouraged, but getting people to change the way they do business or the way they work. And it goes back to how we even do our work. We all have little nuances that we're used to. It's very difficult. So, it does take an AI, forward-thinking CEO as well as teams to have that top down and bottom-up approach.
So, it's not just a case of having a few change champions but rather igniting a culture shift so that everyone is using it and sees the value and knowing that it's not any sort of replacement of jobs or replacement of tasks but freeing you up to do more exciting things.