Choose a use case, run a controlled pilot, apply human review, and measure risk and value before scaling.
Choose a recurring task with describable inputs and outputs. Define what the system may suggest and what remains a human decision. Avoid beginning with a vague promise to “automate everything.”
Classify public, internal, confidential, and personal data; retention, processing location, and usage rights. Do not enter sensitive data into a tool before reviewing terms, settings, and the authorized path.
Create realistic examples, reference answers or evaluation criteria, and a threshold for unacceptable errors. Evaluate accuracy, completeness, consistency, bias, and user impact instead of relying on a first impression.
Define who reviews, what evidence they see, when to escalate or reject, and how corrections are recorded. Human review is not a policy phrase; it is an operational step with time and ownership.
NIST frames AI risk management across the system lifecycle and context. Compare time, cost, and quality gains with error, privacy, dependency, and monitoring risks, then scale, modify, or stop.
The review used the following primary sources, with independent wording focused on business decisions.
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