Teams often treat AI like a demo feature: wire up an API, ship a chat box, and hope responses stay useful in the wild. Production AI needs the same discipline as payments or auth. The model is one component in a system that must fail safely.
Start with a narrow job
Pick one workflow with a clear success metric. Summarize support tickets, draft release notes from changelogs, or classify inbound leads. A narrow scope makes evaluation honest and keeps latency predictable.
Build an evaluation loop before you optimize prompts
Collect real inputs from staging or anonymized production data. Define expected behaviors: format, tone, refusal cases, and edge inputs. Run the suite on every prompt or model change. Green in staging is a gate, not a guarantee, but it beats guessing.
Add guardrails at the boundary
Validate outputs with schema checks where possible. Strip or redact sensitive fields before they reach the model. Rate-limit per user and per workspace. Log prompts and responses with retention policies your legal team can sign off on.
Ship behind flags with rollback
Feature flags let you enable AI for internal users first, then a percentage of customers, then everyone. Keep a non-AI fallback path until error rates and cost curves look stable for a full release cycle.
Operate it like software
Monitor latency, token cost, error rate, and human override frequency. Assign an owner. Schedule reviews when the underlying model or vendor changes.
AI features earn trust when they behave predictably under load, not when they produce the flashiest demo on slide one.