The gap between an impressive AI demo and a useful AI feature is mostly engineering. Production AI needs evaluation, monitoring, guardrails and a rollback path — the same discipline you’d apply to any critical service.

Start with a narrow, high-value problem: a repetitive workflow, a slow manual review, a support queue. Wire a model behind clear metrics, measure its real-world impact, and only then expand. Retrieval-augmented assistants and workflow agents shine when grounded in your own data.

Used this way, AI becomes leverage: fewer manual steps, faster cycles, and capacity freed for the work only humans can do.