Seven Steps,
Each with Deliverables
You Can Accept or Reject
No black-box delivery. Every step ends with a concrete output you review — including the final step, which may legitimately conclude "stop."
The full chain from diagnosis to production
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Discover
Interview records, process observation, samples & source inventory
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Define
Owners, goals, baseline, scope, and non-goals
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Design
Workflow design, system boundaries, risks & acceptance criteria
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Pilot
A minimal workflow in a test environment
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Evaluate
Failure-sample logs, human corrections, edge cases
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Deploy & Handover
Authorized deployment, documentation, monitoring & rollback
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Review & Expand
Metric review — continue, adjust, or stop
Why it works this way
These principles combine 20+ years on manufacturing floors with AI engineering practice — see the problem clearly before acting, and keep critical judgment with people.
- Understand the real business before deciding whether AI is needed; when automation does not fit, I say so explicitly
- Start with one clearly scoped scenario at a time
- Agree goals, scope, deliverables, non-goals, and acceptance criteria before work begins
- Validate with approved samples and test environments first
- Keep human approval on critical actions: outbound email, quotes, orders
- Deliver documentation, run instructions, limitations, and handover materials
Want to see this method applied to your process?
In a 30-minute call, I map the seven steps onto your workflow and point out where it starts paying off.