How does AI change deal due diligence in private equity?
AI changes deal due diligence by automating document review, cross-referencing claims across data sources, and identifying patterns that human analysts might miss. However, research shows that unsupervised AI deployment introduces significant risks: AI systems can confirm rather than challenge sponsor claims, hallucinate supporting evidence, and produce overconfident assessments that create a false sense of rigour.
Where AI adds genuine value in deal analysis
<p>AI is most effective in due diligence when used for tasks that are high-volume, pattern-based, and verifiable: extracting key terms from legal documents, comparing financial projections across time periods, identifying entity relationships across corporate registries, and flagging internal contradictions within a data room.</p> <p>These are tasks where speed matters, where human fatigue creates real error risk, and where the AI's output can be checked against objective criteria. The value is operational — it compresses timeline, not judgment.</p>Where AI introduces new risks
<p>The risk emerges when AI is used for judgment-dependent tasks without appropriate oversight. A 2026 study of real-world coding agent interactions found that fully autonomous AI operation produced output with nine times the defect rate compared to human-supervised operation, and cost three times more per unit of accepted work.</p> <p>In deal diligence, the equivalent risk is an AI system that produces a confident-sounding analysis that the analyst accepts without interrogation. The output looks rigorous — it has structure, citations, and quantified assessments — but the underlying reasoning may be circular, the citations may be hallucinated, and the confidence may be uncalibrated.</p>Fully autonomous AI coding produced output with 9× the security vulnerability rate and 3× the cost per committed line versus human-supervised work. Baumann et al. (2026), arXiv:2604.20779