The checklist has changed, and most generalist reviews miss the new items
Technical due diligence used to be mostly generic: architecture quality, technical debt, scalability, key-person risk. Those checks haven't gone away, but AI-specific diligence has added a layer most generalist reviewers don't know to look for: whether there's a versioned evaluation set with regression tests, whether prompts and models are version-controlled the way code is, whether every model call is logged, how failures are handled when a model gets something wrong, what inference actually costs per user, and whether the company genuinely has rights to the data it trains or retrieves on. Investors increasingly treat a private, versioned eval suite as the clearest available proof that an AI system actually works, as opposed to working in the one demo everyone saw.
The numbers behind why diligence got stricter
The scrutiny isn't paranoia, it's a response to real data. A widely cited Stanford study found hallucination rates above 17% for some legal AI tools, and an MIT-affiliated 2025 report found that roughly 95% of enterprise generative-AI pilots showed no measurable bottom-line impact. Those figures explain the current investor posture precisely: enough AI pitches have failed to deliver that "it's AI" no longer answers any question on its own, and reviewers now need to see the evidence a specific system works, not an assurance that AI in general does.
Where deals actually stall
The general red flags haven't changed much, excessive technical debt, outdated architecture, cybersecurity weaknesses, scalability bottlenecks, key-person dependencies, but one new category has become a real gate rather than a checkbox: data ownership and training rights. One account from a diligence advisor describes a round stalling entirely over a single unresolved question, who actually owns the data the model was trained on. That's not a hypothetical edge case anymore. If your training or fine-tuning data has any ambiguity about rights, consent, or licensing, that ambiguity will surface in diligence, and it will surface at the worst possible time, after term sheet, during the review that determines whether the deal closes.
How to actually prepare, before you need to
The founders who move through diligence fastest treat these checks as engineering hygiene from day one, not a scramble before a raise. That means: maintain a versioned eval set as you build, not after an investor asks for one; log every model call with enough detail to reconstruct what happened; document exactly where your training and fine-tuning data came from and what it was licensed or consented for; and have someone on the technical side who can answer hard questions about inference cost and failure handling in the room, not in a follow-up email three days later. This is precisely the kind of readiness a CTO Partnership exists to build in, because by the time the data room opens, it's too late to retrofit it.