Kaizen X Capital founder, MIT Sloan MBA alum, and ex-Mubadala investor Ilia Lotov on why AI now needs to be part of how a buyer evaluates a business
Two forces are about to collide in the American small-business market, and most people watching only see one of them. One is demographic: by 2035, roughly six million companies will need new owners as baby boomers retire, according to a February 2026 McKinsey report. The other is technological. A January 2026 study by business.com found that investment in AI among small and mid-sized firms jumped 58% over two years, with 62% already deploying the technology in at least one core function. Yet 77% of those businesses have no formal AI strategy, no written policy, and no way to measure whether the tools they adopted are actually working.
Each trend alone would reshape the market. Together, they create a problem that almost nobody is talking about: the person stepping in to run these companies needs to understand not just how to operate a business, but how AI is about to change what that business is worth. Ilia Lotov, founder of Kaizen X Capital, previously worked as an analyst in Goldman Sachs’ Investment Banking Division and later in investing at Mubadala Investment Company. He sees AI as one important lens in evaluating how a target company’s economics may change over time.
Learning to tell real AI from a logo on a pitch deck
Before launching his search, Ilia Lotov spent years evaluating businesses on behalf of other investors. At Goldman Sachs, he worked in the Investment Banking Division, building financial models for M&A transactions. His job taught him one specific thing that turned out to matter more than anything else: how to spot the gap between what a company’s numbers say and what its economics actually are.
That skill got a stress test at Mubadala Investment Company, Abu Dhabi’s sovereign wealth fund managing over $300 billion. Lotov joined as an intern and left as a senior associate three years later, having screened more than two hundred potential targets and closed six direct deals in technology-driven sectors, including cybersecurity, ed-tech, and marketplace platforms. A pattern kept surfacing. Companies that loudly announced AI adoption were typically using it at the margins: a chatbot here, an automated report there. Firms that had genuinely rebuilt operations around the technology rarely mentioned it in investor presentations.
“After evaluating a large number of companies, you start to see the difference between real AI adoption and cosmetic AI adoption. A business that has actually changed its workflows or economics looks very different from one that has simply added a tool and built a narrative around it. In my experience, the second case is much more common.”
Scoring sectors before scoring companies
At MIT Sloan, where he earned his MBA, Lotov co-developed an investment scoring framework at the Joint Finance Lab in partnership with BlackRock. The framework’s central idea is simple but goes against how most investors think about AI. Instead of starting with a company and asking “did they adopt the technology?”, it starts with the sector. Which industries are mature enough for AI deployment at scale? Where will regulatory or structural barriers slow adoption? In which industries can the technology actually shift margins, and where is it just cosmetic?
“A logistics company using AI for route planning and a law firm using it for contract review are both ‘adopting AI,’ but the economics are very different. If you evaluate them on the same basis, the analysis becomes noisy. You have to start with the sector.”
Only after mapping the industry landscape does the model drop to the company level. And there the question changes: not “is AI relevant here?” but “is this specific firm capturing value, or just performing adoption?” Lotov says that the most dangerous metric in an AI discussion is one that sounds impressive but has no economic consequence. A 40 percent improvement only matters if it changes revenue, cost, service quality, or some other outcome that actually affects the business.
What this means for the businesses changing hands right now
Lotov did not build the framework to publish a paper. He built it because he needed it. Through Kaizen X Capital, his search fund, he is looking to acquire a single B2B services company in the United States, run it as CEO, and grow it over the long term. Fourteen investors, including s16, Gestalt Capital, Legate Partners, and Riviera Capital, committed more than $520,000 to back the search.
One question he now applies consistently in diligence is how AI may affect the target’s sector economics, cost structure, and competitive position over time. Does technology make the business more valuable or less? And if the answer is “more valuable,” does the current owner even know that?
“Small and mid-sized businesses are going to face many of the same AI questions that larger companies already face, but with far fewer resources. Often there is no CTO or dedicated data team, so the decision sits with a very small group and sometimes with one person. That makes disciplined evaluation especially important.”
Why AI now belongs in acquisition diligence
Most entrepreneurs entering the search fund world come from MBA programs with strong analytical training but limited deal experience. Lotov brings something different: six years of institutional dealmaking across Goldman and Mubadala, the academic rigor of MIT, and a specific methodology for evaluating how AI changes business value. Rather than treating AI as a buzzword, he uses a structured framework to assess where it is likely to matter and where it is mostly noise.
Millions of boomer-founded companies are about to change hands. As more founder-led businesses change hands, buyers will need to think carefully about both succession and sector change. AI will not determine every outcome, but in some industries, it is now material enough to deserve explicit analysis during diligence. In that sense, technology is becoming one more important part of serious acquisition work.
Written in partnership with Tom White
