AI proof of concept vs pilot vs MVP: which first step fits your project?

Teams starting an AI project use "proof of concept", "pilot" and "MVP" as if they meant the same thing, and then argue about scope, cost and success because each person means something different. For operations and IT leaders at companies of roughly 20 to 200 people, the practical question is which first step to fund, what it must prove and who has to be involved. The table below sets the three side by side.

Verdict: run a proof of concept only when feasibility is the open question. Run a pilot whenever the output will touch live work. Treat the MVP as the first production release, which for most AI automation is the pilot that passed and stayed on.

What is the difference between an AI proof of concept, a pilot and an MVP?

An AI proof of concept is a small, time-limited test of whether a specific approach can do a specific task on your data. AWS describes the aim as reducing risk and learning about an application the business intends to deploy, and our glossary entry on a proof of concept covers the basics.

An AI pilot is a time-limited trial of the system on a real workflow with a small named group, run to decide whether to scale it. It uses live data and live exceptions, and it ends with a decision.

An MVP, or minimum viable product, is the version of a new product that lets a team collect the most validated learning about customers for the least effort. MIT Orbit credits that definition to Eric Ries, and Microsoft's Dynamics 365 guidance uses the same idea when it asks which minimum viable product delivers value to users quickly.

The three are different kinds of thing. A proof of concept is an experiment, a pilot is a controlled live trial and an MVP is a release. That is why they answer different questions, and why one does not substitute for another.

Which should you run first: a proof of concept, a pilot or an MVP?

Start with the question you cannot yet answer, because that decides the stage.

1. Is it unclear whether AI can do the task at all on your data? Run a proof of concept. Examples are messy scanned contracts, unusual product codes or a legacy system whose data you have never extracted.
2. Is the technique well proven, and the unknown is how your people and process handle it? Go straight to a pilot. Reading standard supplier invoices or routing support emails usually falls here.
3. Are you building new software that people will use directly, such as a portal or an internal tool? Plan an MVP, and fold any AI feasibility test into it.
4. Is the work consequential, such as payments, legal terms or customer commitments? Skip no stage, and add human approval from the first live day.

Not every project needs all three. Most AI workflow automation needs a pilot, and a proof of concept only when the first question is open. Choose the process itself first: which business processes to automate first covers scoring candidates.

Illustrative example: Consider a distributor with about 80 employees that wants AI to read supplier invoices and propose coding for its accounts payable team. Reading standard invoice formats is well proven, so the open question is its own messy ones: handwritten amendments, credit notes and a few suppliers that send photographed paper. It runs a short proof of concept on a few hundred past invoices, including the ugly ones, and learns the photographed invoices cannot reach the target. It narrows scope to the supplier formats that pass, then pilots on live invoices with an accounts payable lead reviewing every proposed code.

What data and users does each stage need?

Each stage needs more realistic data and more real users than the one before it.

A proof of concept can run on past records because it only has to show the task is possible. Pick them to include the hard cases, not a flattering sample. A pilot needs live data with the access controls, logging and redaction that production will use, because a pilot on sanitized samples answers a different question. An MVP adds every user in scope, plus integration, training and a support route.

On people, a proof of concept needs a process expert alongside the builders, to say what a correct answer looks like. A pilot needs a named business owner, a small group who will actually use the output and an owner for the exception queue. For how to build the test set and score results, see how to test AI before deployment, which covers shadow mode, where the system runs on live inputs but its output goes nowhere.

How long should an AI proof of concept or pilot run?

No authoritative source sets a standard length for either stage, and a fixed number of weeks is more often sales positioning than evidence. AWS's guidance on proofs of concept asks for a limited scope and clear success metrics rather than a duration, and that is the right way to think about it.

Instead, these things set the length:

• Data access. Waiting for exports, permissions and a security review often takes longer than the build itself.
• Number of real cases. Run until the stage has handled enough cases, including the awkward ones, to trust the result. A pilot that has not yet met the range of cases the process produces is not finished, whatever the calendar says.
• Process rhythm. If the process peaks at month end, a pilot that never sees a month end has not been tested.
• Review cadence. The time the business owner needs to look at results and decide.

Agree the end condition before the stage starts, as a number of cases or a business cycle, and put a date next to it as the latest acceptable finish.

How do you write success criteria before you start?

Success criteria are the measures and targets that decide whether a stage passed, agreed in writing by the business owner before any results exist. Without them, a proof of concept ends in "that's really cool". AWS's own advice is that a proof of concept needs a clear definition of success, and it lists "no clear definition of success" among the reasons proofs of concept fail to reach production. Microsoft's cloud guidance likewise says to define what success looks like for each goal and to get stakeholders to agree to it.

Use this template and fill it in on one page:

1. Business question. One sentence the stage will answer.
2. Baseline. Today's handling time, error rate and volume, measured before the build.
3. Target. The result that means proceed, for example an error rate no worse than the current process on routine cases.
4. Failures that matter. The specific mistakes that are worse than average errors, such as a wrong payment amount, with a stricter target for each.
5. Sample. How many real cases, drawn from which period, including exceptions.
6. Stop condition. The result that ends the work, written down in advance.
7. Decision owner and date. Who decides, and by when.

What does skipping a step cost?

Skipping a stage moves the risk to a more expensive moment rather than removing it.

• Skipping the proof of concept when feasibility is unknown means paying for integration, security work and training before finding out the task cannot hit the target. The expensive discovery arrives late.
• Skipping the pilot means the first real exposure to live exceptions is the full launch. Microsoft's cloud guidance is blunt that you should not release a system to all users on day one without some real-world validation, and recommends exposing it first to internal users or a pilot group.
• Treating the proof of concept as the product carries a proof of concept's gaps into production: no error handling, monitoring, access control or training.
• Running a proof of concept with no intent to deploy is its own failure. AWS observes that many companies are not actually committed to deploying the proof of concept, and that the work can end up testing the model but not the business case. Gartner predicted in 2024 that at least 30% of generative AI projects would be abandoned after the proof of concept stage by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value. Treat it as a forecast, not a measured result.

Should you roll out in phases or all at once after a pilot?

For most AI workflows, roll out in phases, because each go-live then stays small enough to reverse. A phased rollout releases by process, team or location, and a big bang releases to everyone on one date. Microsoft's Dynamics 365 guide lists the trade-offs.

The same guide says big bang suits small and simple implementations, or organizations with a high risk appetite. A small, low-consequence workflow with one team can be a reasonable big bang candidate. An approval or payment workflow across several teams is not. Whichever you choose, keep the previous way of working available until the new one has earned trust, and agree who decides to go back.

For the checks between a passing pilot and live operation, see why AI pilots stall and how to move one into production.

What are the signs a pilot should be stopped?

Stop a pilot when more time will not change the answer. These signs justify it:

• The target is out of reach on the cases that matter. The failures sit in the most common or most costly case types, not in rare edge cases.
• Fixes break other things. Every improvement to one case type damages another, which suggests the task is not stable enough to automate.
• The data does not exist in usable form. The pilot keeps stalling on missing fields, inconsistent records or access that is never granted.
• No one owns it. Nobody on the business side will take responsibility for exceptions and results.
• Reviewers spend longer than the old process took. Checking the output costs more time than doing the work.
• People work around it. The pilot group keeps falling back to the old method.
• Scope keeps growing. New requirements arrive while the original criteria stay unmet.

Write the stop condition in the success criteria so stopping is a planned outcome. A pilot that shows a process is not ready has done its job, and the next question, whether the problem is data, process or tool, is worth a note for the business owner.

How Kastling approaches choosing the first step

Kastling begins with a free discovery call to understand the work before the technology. Where the project is large or unclear, we scope a separate paid audit that looks at the process, the data and the systems, and that is where the open question gets named: whether it is feasibility, adoption or integration. Success is agreed before the build, measured against a baseline, and development is tested against real business scenarios with the people who will use the result involved.

Where feasibility is the unknown, a narrow test comes first. Where it is not, the first build runs on the live workflow, with a named person approving consequential actions, so the pilot can become the first release. Integration, training and optional maintenance follow under separate agreements. Read more about AI Integration & Automation.