This is the same blueprint my team applies at AutoKon when we turn construction site issues into computer vision pilots. It keeps stakeholders synced while protecting engineering focus.
1. Diagnose and define
- Shadow end users for a full shift to capture friction and manual exceptions.
- Translate observations into a measurable north star, e.g. “reduce punch list cycle time by 25%.”
- Validate baseline data availability early—video quality, labelling effort, integration touchpoints.
2. Prototype with discipline
- Build a simulation dataset first. We blend recorded walkthroughs with labelled still frames to stress-test inference.
- Wrap models inside feature flags so supervisors can opt in without breaking existing QA steps.
- Instrument outcomes from day one: false positive rate, resolution time, and resubmission frequency.
3. Launch, compare, iterate
- Run pilots in shadow mode. Every AI suggestion is compared with the human-generated record.
- Hold weekly retros with field leads to interpret discrepancies together.
- Promote once the pilot sustains the agreed KPI shift for two consecutive cycles.
Keeping a 90-day horizon forces clarity. It balances experimentation with accountability, and it builds a repeatable muscle for future AI investments.