Why feasibility is usually the fastest win

Industrial AI projects fail less from “bad modeling” and more from unclear success criteria, brittle data, or evaluation that does not match the operating reality. A feasibility study compresses months of uncertainty into a short, evidence-based decision.

Step-by-step checklist

1. Define the decision: What should the system decide, and who acts on it?
2. Define costs: What is the cost of a false alarm vs. a missed detection?
3. Audit data reality: coverage across shifts, machines, operators, materials, weather, sensors, etc.
4. Pick a baseline: simple rules or classical models as a reference point — avoid “AI-first” bias.
5. Design evaluation: splits that respect time, location, machine, or batch boundaries to avoid leakage.
6. Stress-test robustness: mismatch (new sensor), drift (new process), noise (factory floor variability).
7. Decide next action: (a) proceed to PoC, (b) collect/label data, or (c) stop.

Deliverables that make the result actionable

• Problem statement + measurable KPI
• Data report (coverage, quality, gaps, risks)
• Baseline results + error analysis
• Plan: PoC scope, timeline, and required dataset work

Note: This is general guidance and not a substitute for a project-specific consulting agreement.