PoC, MVP and product answer three different questions. A PoC asks whether it can be done. An MVP asks whether anyone uses it and pays for it. A product asks whether it holds up at scale with quality and margin. When a company asks for "an MVP" but really wants to know whether the technology works, or approves "a PoC" while expecting real users in production, the trouble starts before the first line of code: budget, timeline and expectations were built for the wrong question.
In AI projects the confusion is more expensive, because the gap between a convincing demo and a reliable system is wider than in traditional software. A model that nails 20 test questions in front of the executive team looks ready. It isn't.
PoC: can it be done?
A proof of concept answers a tightly scoped technical and business question: does this approach work with our data, at an acceptable cost? It doesn't need a polished interface, a login or the capacity for a thousand users. It needs real (or representative) data, a success criterion written in advance and an honest estimate of what production would cost.
What a PoC delivers is a decision, not a system. The right answer can be "discard," and that's good news: finding out in four weeks that the idea won't pay off is far cheaper than finding out in a year. In 2024 Gartner predicted that at least 30% of generative AI projects would be abandoned after PoC by the end of 2025, because of poor data, weak risk controls, escalating costs and unclear value. A good PoC exists to surface those four problems early.
Typical length: 3 to 6 weeks. Who uses it: the project team and a few business experts. Common mistake: treating the PoC demo as a nearly finished product.
MVP: does anyone use it and pay?
Eric Ries, who popularized the term, defines the MVP as the version of a new product that lets a team collect the maximum amount of validated learning about customers with the least effort (Startup Lessons Learned, 2009). The key word is customers. An MVP assumes technical feasibility is settled and tests behavior: do people adopt it, come back, pay, change how they work?
That requires real users outside the project team, and just enough product for the experience to be honest. In AI, that includes things the PoC ignored: handling the cases where the model doesn't know the answer, a way for users to correct the system, and basic cost and quality monitoring.
Inside a company, the MVP's "customer" can be internal: the credit team, the support team. The question is the same: do they use it without being told to?
Typical length: 2 to 4 months. Who uses it: a real, limited group of users. Common mistake: loading the MVP with features and delaying the learning it was supposed to produce.
Product: does it hold up at scale with quality and margin?
A product assumes technology and demand have been validated. The question now is operational and financial: does it work for ten or a hundred times more users the way it worked for a few? Does the margin hold when model costs grow with volume?
This is where most of the work nobody sees in the demo shows up. Hidden Technical Debt in Machine Learning Systems, a paper by Google engineers (NeurIPS 2015), shows that the machine learning code is a small part of a real system, surrounded by data collection and validation, configuration, monitoring, serving infrastructure and process management. Generative AI adds continuous answer evaluation, per-token cost control, prompt injection defenses and an audit trail.
Typical length: ongoing. Who uses it: every intended user. Common mistake: scaling the PoC code instead of rebuilding what needs to be rebuilt.
What about the pilot?
Many companies use a fourth word: pilot. In practice, a pilot is usually the MVP of an internal initiative, with real users from one area or business unit and a fixed timeline. The name matters less than the question. If the pilot has real users relying on it in their actual work, treat it as an MVP, with adoption and quality criteria. If it's only the project team testing with real data, it's a PoC under another name, and it should end in a decision, not an automatic rollout. The most common risk is the pilot that never ends: it stays live for months with a handful of users while nobody decides whether it becomes a product or gets switched off.
Hypothetical example: contract review in the legal department
The scenario below is a hypothetical example built to show the difference between stages. It is not a client.
A legal department reviews about 150 supplier contracts a month. The idea is to use AI to extract risk clauses (penalties, price adjustments, jurisdiction, limitation of liability) and flag deviations from internal policy.
In the PoC, the team takes 100 contracts already reviewed by lawyers and measures whether the model finds the same clauses. Agreed criterion: find at least 90% of the risk clauses at an estimated cost below $2 per contract. No lawyer uses the system day to day. In four weeks, there's a decision.
In the MVP, three lawyers start receiving the AI's analysis before reviewing each new contract, for two months. The question changes: do they trust it, review faster, ask to keep it? If they go back to reading every contract from scratch, the MVP has shown something the PoC never could.
In the product, the system serves the whole legal department and is integrated with the contract management tool, with every analysis logged for audit, accuracy monitored by contract type and a monthly model budget tracked by finance.
Notice that the same "contract review system" has three scopes, three budgets and three different success criteria. Approving the product budget before the PoC would be a blind bet. Asking for a PoC timeline for the product would guarantee a late project.
How to tell which stage you're in
Answer in order. The first "no" tells you where you are:
- Has it been shown, with real data and a criterion written in advance, that the approach works and that production cost is acceptable? If not, you're in the PoC.
- Have real users outside the project team been using the system voluntarily for at least a few weeks? If not, you're in the MVP.
- Does the system have quality and cost monitoring, a named owner, an audit trail and a plan for ten times the volume? If not, you're building the product, and you're not there yet.
Scoping meeting checklist
- The question this stage must answer is written in one sentence.
- The success criterion has a number and was agreed before starting.
- Budget and timeline fit this stage, not the next one.
- What happens if the result is negative is defined.
- Whoever approves the next stage knows they'll have to decide, and when.
Next step
In your next meeting about an AI project, before discussing price, write down the question the stage needs to answer. It sets the right scope.
That's why we offer the stages as separate services. The Cyborg Forge tests a hypothesis in 3 to 6 weeks and ends with a Decision Package. MVP As a Service builds a lean MVP for founders who have validated the idea and need disciplined execution. And our methodology shows how one stage hands off to the next.