With Braden Holstege, Han Pei, Linshu Li, Isaac Chung, Mia Watanabe, Johnny Kim, Brandon Lei

AI Due Diligence Series

This article is Part 1 of a three-part series.

• Part 1: Why AI Due Diligence Matters (current article)
• Part 2: The Mercor AI Due Diligence Framework
• Part 3: When AI Strengthens the Investment Thesis

The Missing Question in Private Equity Due Diligence

As AI has become increasingly relevant, many firms now include AI in their traditional diligence process. However, unlike standard commercial or tech diligences, the industry lacks consensus on the critical AI questions to evaluate targets against.

The key opportunity is identifying which targets have untapped AI-driven value creation opportunities and using that insight to make better investment decisions.

Sophisticated AI diligence is not simply about identifying risk. It helps investors determine which companies are best positioned to benefit as industries evolve, where AI can create additional enterprise value after an acquisition, and ultimately which investments are likely to generate stronger returns.

Doing that requires answering two connected questions.

The first is how AI is changing the underlying market.

In most B2B markets, the deep domain expertise, customer relationships, and institutional knowledge required to operate make wholesale disruption unlikely. Instead, these markets are evolving. The primary risk is the rapid shift of profit pools and which player(s) are best-positioned to capture it. The first layer of diligence assesses this underlying market exposure: how the overall market dynamics will be impacted by access to frontier model capabilities.

Frontier models are reshaping dynamics across industries by reducing the cost of some workflows, lowering barriers to entry, and changing where value accrues. In some markets, proprietary data, specialized expertise, or customer relationships become even more valuable. In others, previously durable competitive advantages begin to erode. Understanding those changes helps investors evaluate how the market itself is evolving, but it does not determine which company is most attractive.

The second question is how well the target company is positioned to capture that change.

Two companies operating in the same market can have very different futures. One may have proprietary data that becomes more valuable as AI improves, a product strategy that expands into new customer segments, or the technical capability to bring AI-powered offerings to market ahead of competitors. Another may have a strong business today but lack the engineering capabilities, organizational agility, or operating model required to adapt as the market evolves. Sophisticated AI diligence helps distinguish between those outcomes before an investment is made.

That distinction leads to better investment decisions. Rather than narrowly viewing AI as a source of disruption risk or productivity gain, investors can identify overlooked assets that are well positioned to benefit from shifting market dynamics and generate outsized returns. For example, AI may unlock entirely new customer segments that were previously uneconomical to serve. Moving quickly to capture those customers can create a durable first-mover advantage. AI can also enable new pricing models that better capture increased consumption and emerging usage patterns, such as agentic workflows, creating meaningful upside over the life of the investment. The objective is not simply to understand how AI affects a business today. It is to identify which businesses are positioned to generate outsized returns over the life of the investment.

Answering those questions requires capabilities that extend beyond the traditional scope of commercial and technical diligence. This on-the-ground experience is essential for understanding real-world feasibility, practical ROI, and the specific organizational adaptations required to implement AI effectively. Ultimately, this provides a clear lens on exactly what offerings and processes are genuinely being disrupted, empowering investors to make accurate "build vs. buy vs. wait" decisions. That requires AI researchers, engineers who have deployed production AI systems, and domain experts who understand how work actually happens inside an industry. Bringing those perspectives together makes it possible to evaluate not only what AI can do in theory, but how it is likely to change the economics of a specific business.

The firms that consistently outperform over the next decade are unlikely to be those that simply identify AI risk. They will be the firms that recognize where AI creates new sources of enterprise value, identify the companies best positioned to capture that value, and develop a strategy to realize it after the investment is made.

The obvious next question is how to evaluate those opportunities systematically. In the next article, we’ll introduce the Mercor AI Investment Framework, a two-layer framework that evaluates both market exposure and target-specific capabilities to help investors make better AI investment decisions.

Read the full series

• Part 1: Why AI Due Diligence Matters (current article)
• Part 2: The Mercor AI Due Diligence Framework
• Part 3: When AI Strengthens the Investment Thesis