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

AI Due Diligence Series

This article is Part 3 of a three-part series.

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

When AI Strengthens the Investment Thesis

In the previous article, we introduced a two-layer framework for AI diligence. The distinction becomes much clearer when applied to a real investment decision.

During a pre-acquisition diligence engagement, a leading global investment firm asked us to evaluate a market intelligence company serving the hardware devices industry. Frontier models were rapidly improving their ability to search, synthesize, and explain highly technical information, forcing investors to reconsider what customers were actually paying for. If AI could perform an increasing share of the work required to produce specialized research, would customers continue paying for premium market intelligence, or would the economics of the industry fundamentally change?

That question framed the engagement, but it did not determine the investment outcome.

Looking Beyond the AI Roadmap

Answering that question required understanding how the business created value long before AI entered the conversation.

Rather than starting with the product roadmap or AI strategy, we started by understanding how the company actually delivered value to customers. We mapped the end-to-end workflow, from collecting proprietary hardware data through reverse engineering, expert analysis, report generation, and customer delivery. At each stage, we asked two questions: what are customers truly paying for, and how would advances in frontier models change that work?

Looking at the business this way revealed that generating technical summaries represented only a small portion of what customers were actually buying. The company’s differentiation came from the combination of proprietary data collection, reverse engineering, laboratory infrastructure, expert interpretation, specialized workflows, and years of accumulated operational knowledge. AI could accelerate portions of that system, but it could not easily replicate the system itself.

This distinction informed the investment thesis. The objective of the diligence was not to determine whether AI posed a threat to the business, but to identify the highest-value opportunities to deploy AI across the company and the investments required to realize them.

Building the Value Creation Plan

Identifying value creation opportunities

Once we understood how the company created value, the next step was identifying where AI could materially improve the business. Rather than generating a long list of ideas, the goal was to isolate the handful of initiatives capable of meaningfully increasing enterprise value.

Several opportunities emerged quickly. Report generation contained repetitive drafting and formatting work that could be automated while preserving expert review. Market monitoring could be accelerated by continuously scanning new information rather than relying on periodic manual updates. Internal research workflows could be streamlined by automating data extraction, synthesis, and quality checks, allowing specialists to spend more time on analysis and custom advisory work.

Assessing readiness

Each opportunity was then evaluated for technical feasibility and organizational readiness. AI researchers assessed whether frontier models could reliably perform the required work today or were likely to within the investment horizon. Forward Deployed Engineers estimated the engineering effort, infrastructure changes, and integration work required to deploy each capability in production.

Prioritizing implementation

Finally, the initiatives were prioritized based on expected enterprise value, implementation effort, strategic importance, and the pace of frontier model improvement. Some were ready for immediate execution, while others depended on model capabilities that were expected to mature over the coming years. The result was a sequenced implementation roadmap that balanced financial impact with execution feasibility.

Investment Outcome

The diligence ultimately identified approximately $4 million of net value that could be realized within six months. Looking further ahead, the same initiatives represented an estimated $20 million in annual run-rate impact by 2031.

Those figures quantified the opportunity, but they were not the most important outcome of the engagement. The greater value came from separating market exposure from company capability. AI was clearly changing the economics of the market, but that did not mean every participant would be affected in the same way.

The result was not simply greater confidence in the investment. It was a clearer understanding of why the company was positioned to outperform as AI reshaped the industry and a practical roadmap for creating additional enterprise value throughout the investment period.

Read the full series:

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