Applied AI Deployment Specialist

Clarify workflow pain points, affected users and constraints.

Evidence: An interview record separates needs from requested technology.

Set a baseline, measurable outcome and excluded scope.

Evidence: The sponsor brief includes acceptance and stop criteria.

Audit access, quality, provenance and operational availability.

Evidence: A readiness report identifies blockers and remediation owners.

Compare a deterministic baseline and AI-enabled alternatives.

Evidence: An option decision records cost, security and maintenance tradeoffs.

Break the solution into milestones with dependencies.

Evidence: A plan includes test evidence and a realistic fallback.

Build the smallest useful workflow in the project sandbox.

Evidence: The prototype meets a narrow acceptance test without external side effects.

Connect mock enterprise systems through validated contracts.

Evidence: Integration tests cover timeout, duplicate and unauthorised requests.

Measure held-out task performance and reviewer effort.

Evidence: Results compare against the original baseline and expose failures.

Check permissions, monitoring, cost limits and rollback.

Evidence: All critical readiness checks pass or block release.

Run a scripted acceptance session with a reviewer.

Evidence: Defects are recorded, prioritised and retested.

Prepare user guidance, operational documentation and ownership.

Evidence: A reviewer completes a task and recovery exercise from the materials.

Demonstrate the solution and explain limitations and next steps.

Evidence: The panel can reproduce outcomes and trace evidence to requirements.