Start with friction, not with the model
The best starting point is usually a recurring technical task: collecting inputs, comparing cases, checking consistency, documenting assumptions, or navigating a large body of engineering evidence.
AI has value when it reduces that friction and gives engineers more time to investigate the result. It has less value when the use case depends on authority the model does not possess.
- Name the user and the decision
- Define the baseline time, error, or rework
- Select a bounded workflow before selecting a platform
Keep four controls visible
A supervised workflow separates what the tool may accelerate from what the engineer must approve. The boundary should be designed before the prototype is built.
- Authoritative inputs: which sources are accepted
- Physics checks: which relationships and limits must hold
- Review gates: who challenges and approves the output
- Decision trace: how the final recommendation is reconstructed
Prove usefulness against the real workflow
A polished demonstration is not proof of operating value. Compare the prototype with the current method on speed, technical quality, review effort, and usability.
The right conclusion may be to adopt, iterate, narrow the scope, or stop. A disciplined stop is a successful R&D decision when it prevents a weak workflow from scaling.
- Use representative asset data
- Record exceptions and failure modes
- Include the engineers who will own the workflow