1. Define the decision the system is helping with
Write down the user, the task and the real-world outcome that the software should improve. A support assistant may aim to reduce search time, but that is different from automatically issuing refunds. Keep assistance and autonomous action distinct.
Practical takeaway: Questions: Who is accountable for the result? What happens when the model is uncertain? Which users are excluded?
2. Map data and permissions
Identify every source of information and its owner. Separate public content from confidential customer records. The AI integration must not grant broader access than the underlying application already allows.
Practical takeaway: Checks: purpose limitation, consent, credential scope, storage location, retention rules, deletion request process.
3. Build task-specific evaluations
Generic benchmarks rarely reflect your users. Create realistic prompts from expected workflows, known edge cases and harmful failure scenarios. Record the answer or outcome that a trained reviewer would consider acceptable.
Practical takeaway: Checks: factual grounding, safety, bias, refusal quality, multilingual accuracy, missing data and tool failure.
4. Control every action
Actions that change orders, billing, accounts or permissions should be authenticated, authorized and appropriately confirmed. Use typed tool schemas, idempotency safeguards and logging for important transitions.
Practical takeaway: Checks: least privilege, approval points, duplicate prevention, rollback, escalation and explicit consent.
5. Plan the operating model
A launch is the beginning of model lifecycle management. Assign owners for monitoring, incident handling, customer feedback, evaluation refresh and provider changes.
Practical takeaway: Checks: latency, cost budgets, failure alerts, runbooks, dependency inventory, backup and recovery.
6. Publish only verified claims
Marketing, legal and security statements must match the deployed service. Do not publish ISO, SOC 2, availability or accuracy claims without evidence relevant to the scope.
Practical takeaway: Outcome: an honest release note, reviewable deployment decision and a list of known limitations.
Where to go from here
A useful next step is to translate these principles into a written product decision: who owns the workflow, what data may be used, how success will be evaluated and what happens when the system is unsure.