What should AI be responsible for?
Start with a bounded task where a person can review the result. The goal is not to make the company look like an AI company; it is to improve one consequential loop without losing authority or recovery.
- Name the repeated task.
- Name the human who owns the decision.
- List the sources the system may use.
- Define what requires review before action.
Write one sentence: ‘The system may help with ___, but ___ remains responsible for ___.’
How do you question a confident output?
Ask what is known, where it came from, what is inferred, and how the record can be corrected. A useful AI interface makes those boundaries visible instead of hiding them behind fluent language.
- Mark sourced facts.
- Mark owner-reported claims.
- Mark assumptions and unknowns.
- Define the correction path.
Take one AI answer you use at work and annotate every sentence as fact, claim, inference, or unknown.
What is a trustworthy first win?
Choose a result that matters but remains reversible: prepare a draft, surface a missed item, route a request, or summarize a record for review. Earn trust before widening the system.
- Choose one measurable loop.
- Keep a manual fallback.
- Log the before and after state.
- Review failure cases before expansion.
Define one two-week pilot and the exact evidence that would justify continuing it.