This step is intentionally built only from the existing Artificial Intelligence content library.

Build enablement that survives scale

AI adoption breaks when success depends on a few experts answering questions and fixing edge cases manually.

A practical approach:

• Document the core workflow (how to use the AI product safely).
• Create a small set of example questions and “gold standard” answers.
• Teach teams how to report failures (what to capture, where to escalate).
• Use simple feedback signals to drive improvements.

Make adoption measurable

Even lightweight measures help you spot where adoption is stuck:

• % of target users who tried the tool weekly
• top recurring questions or failure modes
• cases that require escalation

What to capture when something fails (so the team can fix it)

If users only report “it gave a wrong answer,” the system will not improve.

A simple, repeatable failure report should include:

• the exact user question or request,
• the answer the system gave,
• what the user expected instead (one sentence is enough),
• which source or system of record should have been used (if known),