Implementation checklist
Manufacturing AI Implementation Checklist for Operational Workflows
A practical checklist for selecting, scoping, governing, and measuring a manufacturing AI workflow before production deployment.
1. Choose one operational outcome
Avoid starting with “use AI in manufacturing.” Select one repeated outcome with a visible beginning and end.
- Supplier delay resolved
- Maintenance exception coordinated
- Quality deviation prepared for review
- Customer-impact update assembled
- Weekly operations briefing completed
2. Map the current work
Document what people actually do, including the unofficial steps that live in email and experience.
- Trigger and required inputs
- Systems read and records changed
- Owners and handoffs
- Policies and exceptions
- Approvals and evidence
- Expected final system state
3. Define the autonomy boundary
Separate actions into permitted, approval-required, and prohibited categories. Apply the least authority needed for the workflow.
- Read-only context retrieval
- Internal record preparation
- Internal task creation
- External communication
- Financial or contractual changes
- Deletion, publishing, and irreversible actions
4. Prepare data and integrations
Name the system of record, data owner, authentication method, permitted fields, retention requirement, and fallback for every integration.
5. Build an evaluation set
Test the normal case and the real exceptions. Record expected outcomes before running the workflow so evaluation is not adjusted after the fact.
6. Measure operational evidence
Measure completion, intervention, error, retry, escalation, and elapsed-time outcomes. Report the baseline, sample, period, and calculation method with every claim.
Start with one workflow and prove the boundary.
Map the event, systems, permissions, approvals, exceptions, and evidence before granting access. The result is a pilot scope your operations and technical teams can evaluate together.
Book a workflow mapping