From Prototype to Production AI Workflow

A step-by-step path: demo → pilot → hardening → monitoring.

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Why this matters now

Teams are adopting automation quickly—but the winners are the ones who build reliable systems with clear ownership and measurable outcomes.

A practical approach

Start with one workflow tied to revenue or customer experience. Add logging, retries, and a simple dashboard. Then scale across teams.

Common mistakes to avoid

Skipping error handling, mixing data sources without validation, and letting AI write directly into critical systems without review are the fastest ways to create chaos.