The three artifacts answer different questions
POC, prototype, and MVP are not universally standardized names; some organizations use them interchangeably. This guide defines them by the decision each artifact must support, so teams can agree on quality, users, data, and evidence before building.
Start with the decision. If the largest uncertainty is technical feasibility, a polished interface wastes attention. If the largest uncertainty is whether a user understands the workflow, production infrastructure is premature. If both are sufficiently understood and the team needs operating evidence, an MVP becomes appropriate.
| Artifact | Primary question | Typical evidence | Not expected to prove |
|---|---|---|---|
| AI POC | Can the difficult capability work well enough on representative inputs? | Working critical flow, evaluation cases, limits, latency and cost observations | Market demand, production reliability, or a complete user experience |
| Prototype | Can users understand and complete the proposed experience? | Observed usability, comprehension, flow gaps, content and interaction feedback | Technical feasibility, integration reliability, or scalable operation |
| MVP | Will a small complete product create repeated value in real use? | Adoption, completion, retention, operational load, support and quality signals | Every future feature, broad scale, or final architecture |
Choose by the most expensive unknown
List what must be true for the investment to make sense. Then rank those assumptions by consequence and current evidence. The first artifact should attack the assumption that is both important and poorly understood.
- Choose an AI POC when model behavior, retrieval quality, data availability, integration, latency, or unit cost could invalidate the idea.
- Choose a prototype when workflow, information hierarchy, trust cues, human review, or user comprehension is the central risk.
- Choose an MVP when feasibility and the critical interaction are understood enough to test repeated value with real users and operations.
- Combine a thin prototype with a technical spike when experience and feasibility are equally uncertain, but keep the two evidence questions distinct.
What a useful AI POC contains
A useful POC is narrow, but it is not vague. It names the workflow, representative inputs, expected output, evaluation criteria, unacceptable failures, and the decision the result will support.
For example, testing an assistant should include approved knowledge, realistic questions, expected answers or behaviors, failure categories, and human review. A single impressive prompt is a demo, not enough evidence for an investment decision.
Four common selection mistakes
The artifact has done its job when the decision changes. It may justify a larger build, reveal a narrower opportunity, identify another test, or show that the idea should stop. A stop decision can be a valuable outcome when it prevents a larger unsupported commitment.
- Calling a prototype an MVP and judging it by production standards it was never designed to meet.
- Polishing a POC until weak feasibility evidence looks more convincing than it is.
- Launching an MVP before permissions, evaluation, exceptions, and ownership are safe enough for real operation.
- Continuing because something was built, even when the original uncertainty remains unresolved.
A practical decision sequence
If feasibility is the unknown, a bounded Rapid POC is the sensible first move. If the product direction is unclear, start with product discovery and design. If both are understood and the goal is a usable release, move into AI product development.
- Write the decision in one sentence.
- Name the assumption that could most easily make the decision wrong.
- Define the cheapest credible evidence that would change confidence.
- Choose POC, prototype, MVP, or a deliberately separated combination.
- Set the exit criteria before building.
Sources and further reading
- Microsoft: Conduct a proof of concept : Narrow scope, real data, unresolved assumptions, and terminology that varies by organization.
- GOV.UK: How the alpha phase works : Prototype the riskiest assumptions without treating alpha work as production software.
- UK Government: Digital, Data and Technology Playbook : Limited-group testing, wider release, and continued evaluation across delivery stages.
