How to Assess AI in Technical Diligence
In today’s M&A landscape, artificial intelligence (AI) has become one of the most critical - and misunderstood - dimensions of technical due diligence. It’s no longer enough to ask whether a target company “uses AI.” Nearly every software organization does, in some form. The real questions are: how, where, and to what effect?
Savvy investors and diligence teams increasingly view AI not as a single category but as a spectrum of adoption, with each layer carrying distinct implications for valuation, risk, and moat.
Three Layers of AI Adoption
AI in the pipeline.
This is where AI enhances the software development process itself, through tools that accelerate code generation, testing, and deployment. It can improve product velocity and reduce costs, but it also introduces new risks: quality assurance challenges, dependency on third-party models, and hidden technical debt and security holes generated by automated coding.AI in operations.
Many companies are using AI internally to augment or replace human labor, e.g., automating support functions or optimizing logistics. These efficiencies can strengthen margins, but diligence teams need to assess sustainability. Are the cost savings real and repeatable, or dependent on fragile integrations and bespoke data pipelines?AI in the product.
The most transformative,and risky, layer is when AI is embedded directly in the product. AI-driven features that understand, predict, or generate insights from unstructured data can create entirely new markets. But defensibility depends on data access, model differentiation, and the company’s ability to maintain compliance and trust.
Why This Matters for Private Equity
From a private equity perspective, AI changes both sides of the diligence equation: risk and upside.
On the risk side: AI introduces new vectors that traditional diligence frameworks often miss: data governance, security/trust, model explainability, and exposure to regulatory change.
On the upside: it can dramatically compound enterprise value by unlocking automation, accelerating time-to-market, or creating recurring revenue streams from intelligent products.
Yet in many deals, AI is still assessed as a “feature” rather than a fundamental driver of business performance. The result is that investors may overvalue AI-led differentiation that’s not defensible, or undervalue operational efficiency gains that can meaningfully improve EBITDA.
Key Questions for Diligence Teams
When assessing a company’s AI posture, diligence teams and executives should move beyond technical evaluation to focus on business alignment.
Durability: Is the AI capability sustainable if data sources or model access change?
Differentiation: Does it create a competitive advantage, or could competitors replicate it quickly?
Governance: Who owns model validation, data ethics, and security?
Compounding value: Does AI accelerate the investment thesis, or distract from it?
Answering these questions requires collaboration between engineering experts and commercial diligence teams to connect what’s technically possible with what’s economically meaningful.
The Future of Technical Diligence
The new generation of technical diligence treats AI as both an asset and a liability class. It’s an asset when it compounds product differentiation, accelerates development, or improves margins. It’s a liability when it introduces black-box risk, unvetted data pipelines, or overreliance on automation.
Financial diligence tells you what the company is worth today. Technical diligence, particularly AI diligence, tells you whether it will stay that way tomorrow.
For operating partners, CTOs, and portfolio executives, that’s the new reality: AI fluency is now essential for investment confidence.
