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AI Proof of Concept vs MVP: What Should You Build First?
Proof of concept, prototype, pilot or MVP? This guide defines each stage for AI projects, the decision it supports, the data and engineering it needs, three example scenarios and the exit criteria for continuing, revising or stopping.
TopicsAI and Machine LearningProduct Design and DiscoverySoftware Costs and Budgets
Key takeaways
- Each stage answers a different question: can it work (PoC), will people understand it (prototype), does it work in real conditions (pilot), will people keep using it (MVP).
- Engineering effort rises at each stage: integration, security, evaluation and operations become essential by the pilot.
- Define exit criteria before starting so the result leads to a decision: continue, revise or stop.
On this page
AI projects fail expensively when teams build the wrong kind of experiment: a polished product before knowing the model can do the task, or an endless demo that never meets real users. Proofs of concept, prototypes, pilots and MVPs each answer a different question. This guide defines them for AI work and helps you choose where to start.
Define PoC, prototype, pilot and MVP
There is no single industry standard for these terms. In this article we use them as follows:
| Stage | Question it answers | Typical form |
|---|---|---|
| Proof of concept (PoC) | Can the AI perform the task well enough on our data? | Scripts or notebooks run against a test set |
| Prototype | Will users understand and trust the experience? | Clickable or lightly functional interface |
| Pilot | Does it work in real conditions with real users and data? | Working system for a limited group, with monitoring |
| MVP | Will people keep using it and get value? | Minimal production product for real customers |
Working definitions used in this article.
What decision each stage supports
- PoC → technical feasibility: is accuracy acceptable on representative examples, including hard ones?
- Prototype → usability: do users understand outputs, know when to trust them and how to correct them?
- Pilot → operational fit: does it handle real volume, edge cases, permissions and failures; how much review is needed?
- MVP → product value: do customers adopt it, return and pay?
Skipping a stage is fine when its question is already answered. If a model clearly handles the task, you may go straight to a prototype or pilot.
Compare data and engineering requirements
| Requirement | PoC | Prototype | Pilot | MVP |
|---|---|---|---|---|
| Representative data | Test set | Sample data | Real data, limited scope | Real data |
| Integration with systems | None | Minimal or mocked | Required for the workflow | Required |
| Security and permissions | Test data only | Basic | Required | Required |
| Evaluation | Core activity | Usability testing | Ongoing quality monitoring | Ongoing |
| Human review design | Considered | Designed in UI | Operating | Operating |
| Operations and monitoring | None | None | Required | Required |
| Cost tracking | Estimate | Estimate | Measured | Measured |
Requirements grow at each stage.
Use three example scenarios
| Scenario | Unknown | Suggested first stage | Why |
|---|---|---|---|
| Invoice data extraction | Can a service extract our critical fields reliably from our document mix? | PoC on a sample of real invoices | Accuracy on your documents is the main risk |
| Internal knowledge assistant | Will staff trust cited answers, and are our documents good enough? | Prototype plus small retrieval PoC, then pilot with one team | Both usability and content quality are uncertain |
| AI summary feature in an existing app | Will customers use summaries, and at what cost? | Pilot behind a feature flag for some users | Feasibility is well known; adoption and cost are not |
Experiment-type decision matrix applied to three scenarios.
Want help choosing the smallest useful experiment?
Define exit criteria
Agree before you start what evidence means continue, revise or stop.
Stage-exit checklist
PoC
- Accuracy on critical outputs meets the agreed threshold on the test set
- Failure cases are understood and have a plausible mitigation
- Estimated cost per task is acceptable at expected volume
Prototype
- Users complete key tasks without help
- Users can tell when to verify or correct outputs
- Feedback identifies no fundamental trust or usability barrier
Pilot
- Quality holds on real data over several weeks
- Review effort and cost per task are within targets
- No permission or data-handling issues found
- Users want to keep using it
MVP
- Adoption and retention meet targets
- Operating model (support, monitoring, updates) is sustainable
If criteria are not met, decide deliberately: revise the approach, narrow the scope or stop. Stopping after a cheap PoC is a good outcome compared with discovering the same problem after a full build.
Budget for the next decision
Budget each stage separately and fund the next one only when exit criteria are met. For PoC and pilot planning, our guide How to scope an AI pilot before development begins explains how to choose a workflow and define checks. For product budgets, see MVP development cost.
Frequently asked questions
How long should a PoC take?
As short as possible while testing representative data. The goal is a decision, not a product.
Can a PoC become the product?
Rarely as-is. PoC code usually lacks integration, security and monitoring. Reuse what you learn, not necessarily the code.
Who should be involved in a pilot?
Real users, a business owner who will make the decision, and the people who will operate and review the system.
Do we need a PoC if we use a well-known model?
If the task is common and low-risk, you may start with a prototype or pilot. Test on your data either way.
Sources and methodology
Facts in this article were checked against the sources below on 5 November 2026. Directory data (ratings, review counts, rate bands and team sizes) changes over time and is shown as published on the date accessed. Illustrative examples are labelled as such and are not quotes.
- PoliteTech Insights: How to scope an AI pilot — accessed 5 November 2026. Pilot scoping method
- PoliteTech: MVP Development — accessed 5 November 2026. MVP approach
- PoliteTech: AI Consulting — accessed 5 November 2026. Discovery and pilot definition
Related reading
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