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

AI Due Diligence Series

This article is Part 2 of a three-part series.

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

An AI Due Diligence Framework for Private Equity

In the previous article, we argued that AI diligence is about more than evaluating technology adoption or mitigating AI risk. When executed correctly, AI due diligence is key to unlocking outsized returns, allowing investors to identify exactly where targeted investments can help companies evolve into the next-generation versions of themselves.

At Mercor, we anchor our AI diligence around two closely-related questions:

1. How will AI change the underlying market? Will AI shift profit pools toward specific players, compress or expand margins, unlock new customer segments, or create an existential threat where frontier models out-compete legacy capabilities?
2. Is the target positioned to win in that market? Does the target have the assets, technology, talent, and operating model required to capture this upside? If not, what specific investments are required for them to become a leading player?

Layer One: Understanding Market Exposure

The first layer examines whether AI will substitute the market’s underlying value proposition or materially compress the workflows used to deliver it.

For example, a compliance software company built around deep regulatory expertise, is unlikely to disappear simply because frontier models improve. Its economics may still change considerably if AI makes research, document review and policy interpretation faster and less expensive. Companies that adapt can use those advances to improve margins, launch new products, and serve customers more effectively. Legacy players that fail to respond may instead face margin compression, new competitors, and declining market share.

This analysis helps investors understand how the market is likely to evolve. It may reveal that the cost of delivering the core product will fall, that new entrants can compete with smaller teams, or that value will shift toward companies with proprietary data, trusted distribution, or the ability to integrate AI into existing customer workflows.

Market exposure alone does not determine whether an investment is attractive. It establishes the conditions under which the target will need to compete.

Layer Two: Evaluating the Target

While underlying market conditions set the baseline trajectory, the second layer evaluates the Target's specific right to win.

Rather than asking whether the company has adopted AI, this assessment evaluates where AI can create meaningful business value, whether those opportunities are technically feasible, and whether the organization has the capabilities to execute them.

The core objective of this assessment is to pinpoint the specific investments required for the Target to deploy AI effectively and adapt to an AI-evolving market. Market tailwinds mean little if a company lacks the operational foundation to capitalize on them. Identifying these practical value creation opportunities, such as structurally reducing the cost basis, accelerating commercial velocity, or capturing net-new revenue through AI-enabled features, can become the primary lever for driving outsized returns.

To define this investment roadmap, our target assessment centers on three specific analyses: identifying value creation opportunities, assessing organizational awareness, and prioritizing implementation.

Identifying Value Creation Opportunities

The aim is to identify the small number of AI initiatives that could materially improve the investment outcome.

Some opportunities reduce the cost of existing work. Others support revenue growth by improving the product, increasing customer adoption, entering a new market, strengthening pricing power, or creating an offering that was not previously possible. Cost savings are often easier to quantify, but the largest sources of value may come from revenue opportunities that change how the company competes.

Finding those opportunities requires a detailed understanding of how the business operates. Customer interviews, workflow analysis, and input from industry practitioners help break the company’s core activities into their component parts and show where expertise creates value, where work is constrained, and where AI can meaningfully change the outcome.

That analysis must then be paired with an accurate view of model capabilities and production requirements. AI researchers can assess whether models are capable of performing the relevant work today and how quickly those capabilities are improving. Forward deployed engineers can determine what it would take to turn a promising use case into a reliable production system.

Assessing Organizational Readiness

A feasible initiative may still be a poor investment if the company is not equipped to deliver it.

The readiness assessment examines the company’s existing technology architecture, data infrastructure, engineering capacity, internal skills, and available headcount. It also considers the capabilities required to build and operate agentic systems, including agent orchestration, LLM applications, evaluation and benchmarking systems, enterprise integrations, workflow automation, production machine learning, context engineering, and prompt engineering.

Many companies lack meaningful experience in these areas. They may be able to build an initial prototype but struggle to integrate it into existing systems, evaluate its performance, or operate it reliably at scale. The diligence process should therefore determine which initiatives the company can execute internally, where important capability gaps exist, and whether an external partner could materially reduce execution risk or compress the timeline.

Prioritizing Implementation

Implementation planning is not a rote ranking exercise. The most valuable initiatives are not always the easiest to build, and the initiatives with the highest modeled ROI are not always the ones that should be pursued first.

Each opportunity should be assessed based on the business value it creates, the engineering time and direct investment required, its relationship to company strategy, and the urgency created by market conditions. The analysis should also identify dependencies between initiatives, including foundational work that must be completed before higher-value opportunities become feasible.

Estimating implementation effort requires practical engineering judgment. Teams need to understand the likely person-weeks of work, the cash investment required, the complexity of enterprise integrations, and whether the initiative depends on model capabilities that are not yet reliable enough for production. In some cases, the right decision is to move immediately. In others, it may be better to prepare the underlying infrastructure while waiting for model performance to improve.

The result is a sequenced implementation plan grounded in both financial returns and strategic judgment. It identifies which initiatives matter most, when they should be pursued, and whether the company should build them internally or use an external partner to accelerate execution.

Why AI Diligence Requires a Dedicated Team

Conducting rigorous AI diligence requires a forward-looking view of model capabilities, practical deployment experience, and detailed knowledge of the target's workflows.

Mercor brings those perspectives together through AI researchers who understand frontier model capabilities, Forward Deployed Engineers with production deployment experience, and a network of industry experts who understand the target’s workflows and competitive dynamics. That combination enables investors to identify the opportunities that matter, understand what it will take to execute them, and prioritize them based on both technical feasibility and business impact.

Read the full series

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