Reviewed and updated 20 October 2026.
Trust in an enterprise generative AI tool comes from observable behavior and accountable operation. Confident marketing language cannot replace evidence that the tool is appropriate for a particular task.
Explain the data handling
Describe what data enters the system, who can access it and how long it is retained. Distinguish your own controls from the provider’s current terms and settings. Do not promise that data is anonymous merely because obvious identifiers have been removed.
Give customers a clear point of contact for questions. Where obligations depend on location or industry, involve the appropriate privacy, security and legal specialists.
Evaluate realistic work
Test representative tasks and difficult cases, including incomplete instructions and information the system should not disclose. Record material errors, unsupported claims and failures to follow boundaries.
Report what the evaluation covers and what it does not. A score from one dataset is not proof that the tool will perform equally well across every customer, language or workflow.
Make human responsibility visible
- Identify which outputs need review before use.
- Give reviewers access to the underlying evidence where appropriate.
- Provide a way to report an incorrect or harmful result.
- Explain when the system should stop and refer the task to a person.
- Keep a workable fallback for important business processes.
Treat updates as operational changes
Models, prompts and connected data can change behavior. Re-evaluate important tasks after material changes and keep records that help investigate incidents. Avoid treating a model upgrade as an automatic improvement for every workflow.
A practical customer-facing explanation should cover intended use, known limitations, data handling, review responsibilities and support. It should avoid unsupported claims that the system is always accurate, unbiased or fully compliant.
Reference: NIST AI Risk Management Framework. Its generative AI profile provides a framework for identifying and managing risks; it is not a product certification.
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- Generative AI Copilots: Useful Tasks and Limits
- Inclusive Product Design: A Practical Equity Checklist
Cover photograph: Vitaly Gariev / Unsplash.