These four are not synonyms and they answer four different questions. A proof of concept asks whether something is possible. A prototype asks what it should look like. A pilot asks whether it works in your actual operation. An MVP asks whether the smallest useful version is worth keeping. Picking the wrong one is why teams end up extending throwaway code into production, and why pilots quietly run for a year.
The confusion is not academic. In our own proposal archive, requests using these words peaked in the first quarter of 2026 and have declined since as clients moved towards implementation, but the terms are still used interchangeably in briefs. A request for “a quick PoC” that actually needs real users and a production write-back is a request for a pilot, and pricing it as a proof of concept sets everyone up for disappointment.
Getting it right early matters because the alternative is well documented. Gartner expects more than forty percent of agentic AI projects to be cancelled by the end of 2027, citing escalating costs and unclear business value. Both of those describe a first step whose purpose was never agreed.
How do the four compare?
| Compare | Proof of concept | Prototype | Pilot | MVP |
|---|---|---|---|---|
| Question it answers | Is this technically possible? | What should this look like? | Does this work in our operation? | Is the smallest useful version worth keeping? |
| Who uses it | The build team | Stakeholders, in a demo | Real users, limited group | Real users, live |
| Data | Sample, often historical | Illustrative or dummy | Real, in a limited scope | Real, in production |
| Duration | 1 to 4 weeks | 1 to 3 weeks | 6 to 12 weeks | 8 to 16 weeks |
| Typical cost | 6,000 to 24,000 euros | Low, often 20 to 40 hours | Build plus supervision effort | Comparable to a first release |
| What happens to the code | Discarded | Discarded | Usually rebuilt | Extended |
| What it does not prove | That anyone will use it | That it can be built | That it scales or is maintainable | That the full scope is viable |
The bottom row is the one worth reading twice, because each of these steps is defined as much by what it deliberately leaves out as by what it demonstrates.
When is a proof of concept the right choice?
When there is a genuine technical question and a plausible answer of no. That condition is stricter than it sounds. If everybody involved already believes it will work, a proof of concept only produces a demonstration and delays the real build by a month.
Good candidates share a shape: an unusual data format, an integration nobody has tried, a required accuracy level that may not be reachable, or a task where feasibility genuinely has to be tested rather than assumed. Extracting structured data from technical drawings is a real proof of concept question, because every organisation draws differently and there is no shared convention to rely on. Extracting an invoice number from a PDF is not, because that question is settled.
Scope one to four weeks and one question. In our work a narrow technical extraction question can be answered in around forty hours, producing a structured output file and a clear yes or no. A broader use case sits at the top of that range. If a proposed proof of concept runs longer than four weeks, it has stopped being a proof of concept and become an unmanaged build.
RAND’s research on why AI projects fail, drawn from interviews with sixty-five practitioners, puts a misunderstood problem definition first among the root causes. Picking the wrong first step is that failure in its earliest and cheapest form.
Plan for the code to be thrown away, and say so out loud at the start. A proof of concept is optimised to answer a question quickly, which means shortcuts in error handling, security and structure that are entirely correct at the time and entirely wrong in production. Deciding to discard it afterwards costs more than deciding it upfront, because by then somebody has grown attached to it.
When do you want a prototype instead?
When the uncertainty is about the experience rather than the technology. What should the interface show, in what order, and what does someone do with the output? Those questions are answered faster by something clickable running on dummy data than by any specification document.
A prototype is often the cheapest step of the four and the most undervalued. On a reporting project we built a prototype dashboard on an exported data file, validated the numbers against the client’s existing report, and used it to settle what the signalling logic should be. Making that prototype available as a proper web application with single sign-on was then a separate, small piece of work, quoted at twenty hours, and it was still explicitly a prototype afterwards. Being clear about that distinction is what stops a prototype being mistaken for a product.
The failure mode here is that prototypes demonstrate well. A prototype that impresses a board is easily mistaken for something nearly finished, and the gap between it and a working system is usually larger than the gap it has already crossed.
When do you run a pilot?
When feasibility is settled and the open question is whether the thing survives contact with your operation. That means real data, real users and a limited scope, running alongside the existing process rather than replacing it.
Two design decisions determine whether a pilot is useful. The first is duration: long enough for the exceptions to appear, which for most business processes means six to twelve weeks. A two-week pilot sees only the ordinary cases, which are the ones you were never worried about. The second is the exit condition, agreed before the pilot starts. Without one, a pilot becomes a permanent parallel process, and running two processes is more expensive than either finishing or stopping.
A pilot should run with a human in the loop inside the department that will eventually own the process, not as a demonstration for management. That difference matters because the people doing the work notice things a demonstration never surfaces, such as output that is technically correct but arrives at the wrong point in their day.
Expect to rebuild afterwards. Pilot code carries compromises made to get something in front of users quickly, and the point of the pilot is the learning rather than the artefact.
When is an MVP the right first step?
When feasibility is not in doubt and the real question is whether a narrow version delivers enough value to justify the full one. Unlike the other three, an MVP is built to be extended and goes live for real, which means it carries production requirements from day one: exception handling, monitoring, access control and an owner.
The discipline is in choosing the slice. On a contract processing project we scoped this year, the approach was to start with the most structured and straightforward contract types, deliver that successfully, and only then grow towards the full solution covering every contract type and language. That sequencing is the whole point. An MVP that covers the easiest twenty percent of cases and works reliably teaches you more, and costs less, than a full build that covers everything unreliably.
The common mistake is calling something an MVP when it was scoped as a proof of concept. If real users depend on the output, it needs production properties regardless of what it is called, and skipping them because of the label is how throwaway code ends up carrying a business process.
How do you choose between them?
If the answer is “if it turns out to be impossible”, you want a proof of concept. If it is “if people find it confusing”, you want a prototype. If it is “if it does not hold up on our real work”, you want a pilot. If it is “if the value is not there even when it works”, you want an MVP.
If the answer is “nothing would make us stop”, none of these is the right step. What you have is a decision that has already been made, and the honest next move is to plan the build rather than to dress it as an experiment. That is not a criticism. It is considerably cheaper than spending four weeks proving something to an audience that was never going to change its mind.
Conclusion: Start with the right question, not the right label
A proof of concept, prototype, pilot and MVP each serve a different purpose. Choosing the right one comes down to understanding what you need to learn, what success looks like and what happens next. Getting those decisions right early prevents unnecessary development costs and helps turn promising AI ideas into solutions that deliver real business value.
At DataNorth AI, we help organizations navigate this process from initial idea to implementation. Our AI Strategy services help identify opportunities and define a clear direction. With a focused AI Proof of Concept, we validate technical feasibility before larger investments. And through our Custom AI Development services, we help turn validated concepts into reliable solutions integrated into your daily operations.
Not sure whether your project needs a PoC, prototype, pilot or MVP? Get in touch with DataNorth AI to discuss your use case and determine the most effective next step.
Because the goal is not simply to prove that AI works. It is to build something worth using.
Frequently asked questions
What is the difference between a proof of concept and a pilot?
A proof of concept asks whether something is technically possible and runs on sample data without real users. A pilot asks whether it works in your actual operation, so it uses real data with real users on a limited scope, running alongside the existing process rather than replacing it.
How much does an AI proof of concept cost?
For a scoped single-process use case, between roughly 6,000 and 24,000 euros depending on whether the build takes one week or four. A narrow technical question can be answered in forty hours. Anything quoted below that range is usually a demonstration rather than a proof.
When should you build an MVP instead of a proof of concept?
When feasibility is not in doubt and the real question is whether a narrow version delivers enough value to keep. An MVP is built to be extended and goes to real users, so it carries production requirements a proof of concept deliberately skips.
Can you turn a proof of concept into production code?
Rarely, and attempting it is a common and expensive mistake. A proof of concept is optimised to answer a question fast, which means shortcuts in error handling, security and structure. Plan for it to be discarded, because deciding that afterwards costs more than deciding it upfront.
How long should an AI pilot run?
Long enough to see the exceptions, which for most business processes means six to twelve weeks. Shorter and the awkward cases never appear. Longer and the pilot becomes a permanent parallel process, which costs more than either finishing or stopping.
The sentence worth putting in the proposal
Whichever of the four you pick, write down what result would make you stop, and what happens to the code afterwards. Those two sentences prevent most of the confusion this article describes, because they force the label to match the intent.
A step whose failure condition is undefined is not an experiment. It is a build with a smaller budget and a more optimistic name.
