What if the AI gets a deal wrong (and who is liable)?

Nothing acts on a wrong output, because a human reviews and approves before anything is sent or acted on. Systems are built to fail safe: rule checks and human checkpoints sit in front of every sensitive step (underwriting assumptions, investor comms, financials), and error handling plus alerting surface a problem immediately rather than letting it pass silently.

The design principle is that the review gate is not optional. In an investment-committee workflow, the machine drafts and the analyst edits; the committee reads an analyst-approved document, never raw AI. That is how the memo-automation system we build works: upload the offering memo, get a structured deal snapshot, and every figure in the draft traces back to a snapshot field a person has already inspected. See the investment committee memo automation case study and how we architect trust into agents in AI agent trust systems.

On liability, and this is not legal advice: the operator remains accountable for their own decisions. AI is decision-support, not a party that assumes your legal liability. A well-built system reduces the chance of an error reaching a decision, but it does not transfer responsibility for the decision away from your firm. Treat any AI output the way you would treat a junior analyst's first draft: useful, fast, and reviewed before it counts. Your engagement and confidentiality terms should say this plainly, and your counsel should confirm it for your jurisdiction.

The honest version wins here: no system is infallible, so the whole point of the architecture is that a single wrong number cannot silently become a committee decision. NextAutomation builds the review gates, fallbacks, and traceability so errors surface early and a person always signs off. To map exactly where those checkpoints belong in your workflow, start with an Operations Audit; to embed the discipline in your team, the AI Team Program does that. See the AI Underwriting Copilot for the reviewed-output pattern in practice.