What does AI due diligence involve for investors?

Full Explanation

AI due diligence has emerged as a distinct discipline within the broader investment due diligence process. As AI-driven valuations have increased, so has the need to verify that AI claims are substantive rather than superficial. A thorough AI due diligence process covers seven key areas. Technology assessment examines the actual AI/ML systems: What models are in production? What is their accuracy, latency, and reliability? How are they monitored for drift? Is there meaningful technical differentiation, or is the company using off-the-shelf models with minimal customisation? Data assessment evaluates the quality, volume, uniqueness, and legal provenance of training data — often the most defensible AI asset. Talent and organisational assessment determines whether the company has the team to maintain and improve its AI systems, and critically, whether key knowledge is concentrated in a few individuals (key-person risk). Technical debt assessment uncovers accumulated shortcuts: hard-coded rules masquerading as ML, models running on outdated infrastructure, or test coverage gaps. Governance and compliance review checks for ethical AI practices, regulatory compliance (EU AI Act, sector-specific regulations), and bias testing procedures. Competitive moat analysis evaluates whether the AI advantages are defensible through data network effects, proprietary architectures, or switching costs. Finally, financial impact analysis quantifies the actual contribution of AI to revenue and margins versus what could be achieved with conventional methods. Investors who skip AI due diligence risk overpaying for AI washing or inheriting significant technical and regulatory liabilities.

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