AI Project Proof of Concept Development
PoC answers one question: "Does this technically work on our data?" Not "will this scale" and not "is it production-ready" — only technical feasibility. A properly conducted PoC saves months of development and hundreds of thousands in investments.
PoC Structure
Scope Definition (Day 1–2): Concrete task with measurable success criterion. "AI will classify support tickets with >85% accuracy on our data" — good PoC scope. "AI will improve customer service" — that's not PoC, that's a vision.
Data Audit (Week 1): Client's real data: volume, format, quality, presence of labels. If data doesn't exist — define minimum dataset for validation. PoC is meaningless without real data.
Baseline (Week 1): Simple solution: rule-based system, keyword matching, linear regression. Baseline answers: "Do we even need ML?" If baseline gives 80% — maybe ML isn't needed.
ML Solution (Weeks 2–3): Quick experiment with minimal toolset. Goal — not optimal solution, but representative result.
Evaluation and Decision (Week 3–4): Comparison with baseline. Error analysis — which cases are complex for the model. Assessment: "What's needed for production?" — data, compute, time.
Typical PoC Results
| Result | Frequency | Next Step |
|---|---|---|
| Metric achieved | ~40% | MVP development |
| Metric partially achieved | ~35% | Approach or data review |
| Technically infeasible | ~15% | Task redefinition |
| Need more data | ~10% | Data collection plan |
Duration and Scope
Typical PoC: 2–4 weeks, 1–2 ML engineers. Deliverable: Jupyter notebook with experiments, report with metrics, recommendation document (Go/No-Go + why).
PoC is not production-ready code. It's research. After successful PoC, production rework is needed: tests, monitoring, API, documentation.