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Why Generative AI Could Make Commercial Due Diligence Less Reliable --
July 3, 2026
Generative AI is rapidly becoming a staple of commercial due diligence. From market sizing and competitor analysis to customer research and report drafting, AI promises to make diligence faster, cheaper, and more scalable.
But what if the biggest risk isn't that AI gets things wrong?
What if the real risk is that AI makes flawed analysis look convincing?
Unlike traditional research tools that simply retrieve information, Generative AI synthesizes data, identifies patterns, and generates conclusions. As a result, its outputs often appear polished, structured, and authoritative—even when the underlying assumptions are incorrect.
Consider a diligence team assessing an investment opportunity. An AI-generated market overview overestimates industry growth, overlooks emerging competitors, or misinterprets customer sentiment. The output looks credible, gets incorporated into the analysis, and ultimately influences the investment thesis. The diligence process becomes faster, but not necessarily better.
Key challenges include:
- Hallucinated insights presented as factual market intelligence.
- Limited source transparency, making validation difficult.
- False confidence created by professional-looking outputs.
- Inconsistent conclusions across models, prompts, or datasets.
- Reduced critical thinking when teams rely on AI-generated summaries instead of challenging assumptions.
In commercial due diligence, the cost of being wrong is measured in millions. The challenge with Generative AI is not that it makes mistakes—it is that those mistakes can look investment-ready.
The firms that create lasting advantage will not be those that use the most AI. They will be those that combine AI-driven efficiency with rigorous validation, domain expertise, and human judgment.