Vision AI PoC Design Guide: Timeline, Data, and Success Criteria
Blog post from Superb AI
A Vision AI proof of concept should progress through a technical stage that tests whether a model can perform a task and an operational stage that evaluates its fit with real workflows, people, and production constraints. Success should be measured across model performance, including recall for high-cost misses and precision for burdensome false alarms, as well as manual review rates, processing speed, system throughput, and staffing effort rather than accuracy alone. The guidance recommends using production-representative data that captures variation in lighting, camera angles, seasons, and products, while noting that foundation models can enable initial validation with limited task-specific data. Technical PoCs commonly take two to four weeks and operational pilots four to eight weeks, with data preparation and internal personnel costs considered upfront. A cited electronics manufacturer used this approach to assess an AI-assisted inspection workflow by tracking automated processing and manual reinspection needs before moving toward deployment.
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