The decision: PoC proves the AI capability works. MVP proves people want the product built around it. Building the wrong one first wastes weeks to months. Here's how to pick correctly, fast.
The one-line test
Ask: "Am I unsure if this can be done, or unsure if anyone wants it?"
Unsure if it can be done → build a PoC.
Unsure if anyone wants it → build an MVP.
If you can't answer that in 10 seconds, you don't understand your own risk yet — that's the real problem to fix first.
Build a PoC first if any of these are true
- You haven't tested model accuracy/quality on your actual data (not a benchmark, not a demo dataset).
- The task pushes into a known AI weak spot: long-context reasoning over messy docs, structured extraction from inconsistent formats, multi-step agentic tool use, numerical/spatial reasoning.
- Cost-per-call or latency at scale is unknown and could kill the economics.
- You're choosing between architectures (RAG vs. fine-tune vs. big prompt vs. small specialized model) and the choice changes months of engineering effort.
- A regulator, safety team, or domain expert requires accuracy numbers before anyone touches real users.
A PoC needs: a script, a few hundred real samples, one accuracy/latency/cost number. Nothing else. No auth, no UI, no clean code.
Skip the PoC, go straight to MVP if
- The capability is well-established (general summarization, standard RAG on clean docs, common classification) and you're not pushing into a known weak spot.
- The risk is workflow fit, not model capability — will this work integrate into how the team actually operates?
- You or a competitor already proved this capability works in a comparable context.
- Time-to-market beats squeezing out more offline accuracy.
Side-by-side
Example: AI support ticket triage
**PoC: **Pull 300 historical tickets. Run them through 2–3 model/prompt configs. Compare output to your support team's actual past routing. Measure accuracy. No ticketing integration, no UI. If accuracy < 85% on critical categories, stop — you just saved a quarter of engineering time.
MVP (after PoC passes): Wire the validated model into your real ticketing tool (Zendesk/Intercom). Add a simple confirm/override UI for agents. Add logging to track real-world accuracy over time. This is a different team, different timeline, different definition of done than the PoC.
3 mistakes that cost the most time
- PoC creep — a feasibility script quietly becomes the production backend, with no error handling, no monitoring, hardcoded keys. If a PoC graduates to MVP, rewrite it on purpose; don't let it happen by accident.
- MVP-first denial — building the full product before validating the model works, because admitting uncertainty feels like admitting you're behind. Costs more time than the PoC would have.
- PoC theater — testing on a clean, cherry-picked dataset, then being shocked when real-world MVP accuracy doesn't match. Test the PoC on data as messy as production will actually be.
Conclusion
Biggest unknown is the model → PoC first, fast and disposable, one number as output.
Biggest unknown is the product → MVP first, real usage as the feedback loop. If you want to build an AI MVP, then you need expert advice and guidelines, so talk to our AI MVP developer.
The expensive mistake isn't choosing PoC or MVP — it's building one when the project needed the other.
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