AI Proof Of Concept: How To Test An AI Solution Before You Commit

An AI proof of concept (POC) is a short, controlled test that checks whether an AI solution can do a specific task in your business well enough to be worth building or buying. It uses your own real examples, success criteria written down before the test starts, and a clear decision at the end: go ahead, adjust or stop.

Getting this step right saves money. In July 2024, Gartner predicted 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 (Gartner (opens in a new tab)). Research by IDC with Lenovo, reported by CIO.com in March 2025, found that for every 33 AI proofs of concept a company launched, only four reached production (CIO.com (opens in a new tab)). A well-designed AI proof of concept brings those problems to the surface while they are still cheap to fix, or to walk away from.

This guide explains how to run an AI POC from start to finish. It covers how a proof of concept differs from a pilot, prototype and MVP; how to set success criteria and a stop line; how to build a test set from your own cases; what to agree with a vendor before you share any data; how testing differs for generative AI and AI agents; and a worked example. It is written for owners and managers of businesses with roughly 10 to 500 people who want a decision they can defend, not a demo that looked impressive.

What Is An AI Proof Of Concept?

An AI proof of concept answers one narrow question: can this AI approach do our kind of task, on our kind of data, well enough to justify the next step? It is deliberately small. It tests one workflow, for a fixed period, with a fixed budget, and it does not need to be production-quality software.

The Australian Government's Digital Transformation Agency (DTA), which published guidance on moving AI from proof of concept to scale (opens in a new tab) in March 2026, describes a proof of concept as an "early, experimental build to test concepts, feasibility, or specific technical components" that ends with a decision on whether the idea "has merit" (DTA (opens in a new tab)). The UK government's service manual describes the similar alpha phase as building things to test ideas, "not production quality code" (GOV.UK (opens in a new tab)).

What a proof of concept is not matters just as much. A vendor demo on the vendor's own examples is not a proof of concept, because it tells you how the product performs on work chosen to make it look good. A useful AI proof of concept is scored on your work, against numbers you set in advance. That also makes it the most practical way to evaluate an AI vendor before a full rollout. For a lighter check to run on every shortlisted vendor first, see our guide to how to vet an AI vendor.

AI Proof Of Concept Vs. Pilot Vs. Prototype Vs. MVP

These terms are often used loosely, and vendors sometimes mean different things by them. The proof of concept and pilot rows below draw on the DTA's stages; prototype and MVP are common industry terms. Each row shows the plain-language question that stage answers.

The DTA suggests synthetic data (artificial records made to look like real ones) at the proof of concept stage to reduce privacy risk. This guide recommends real past cases with personal details removed instead, shared only once data terms are signed, because results on artificial examples often fail to match real work. The DTA frames the outcome of a proof of concept as "proof of feasibility" and the outcome of a pilot as "proof of viability", meaning a decision on whether to scale to production (DTA (opens in a new tab)). You may also hear vendors use "proof of value". It has no standard definition, so ask what the vendor will measure and how. In practice, a good AI proof of concept should include at least one business measure, such as time or cost per task, not just a technical accuracy score.

Why So Many AI Proofs Of Concept Never Reach Production

When an AI proof of concept fails to lead anywhere, the cause is rarely that the AI was incapable. More often, the test was set up so that it could not produce a clear answer. RAND's 2024 study of AI project failures, based on interviews with 65 data scientists and engineers, found that the first of five root causes is that people "misunderstand or miscommunicate" what problem AI needs to solve (RAND (opens in a new tab)). RAND also notes that, by some estimates, more than 80% of AI projects fail, which is about twice the rate of IT projects without AI.

The patterns below come up repeatedly:

• No baseline. Nobody measured how long the task takes or how often it goes wrong today, so there is nothing to compare the AI against.

• Success defined after the results. The goalposts move to fit what the AI happened to do well.

• Demo data instead of real data. The test used clean, typical examples and missed the messy cases that make up real work.

• Only accuracy measured. The AI was accurate, but too slow, too expensive per task or too hard to fit into the way people work.

• No path beyond the test. Nobody budgeted for connecting the AI to your systems, training staff or running it safely, so a successful test stalls.

OpenAI's own evaluation guidance warns against "vibe-based evals", which it describes as using "it seems like it's working" as an evaluation strategy (OpenAI (opens in a new tab)). Every step in the process below is designed to replace that impression with evidence.

Before You Start: Is The Use Case Worth Testing?

An AI proof of concept is only worth running on a task that matters. Before planning one, check that the task meets four conditions:

1. 1

   It happens often. A task done a few times a month rarely justifies the cost of building and running AI.

2. 2

   It has a measurable result. You can tell whether each case was handled correctly, such as an invoice matched, an email routed or a field extracted.

3. 3

   Examples exist. You have, or can collect, at least a hundred real past cases with known correct answers.

4. 4

   Someone owns it. A named person is responsible for the workflow and will decide what happens after the test.

If the task fails any of these, fix that first. Google's first rule of machine learning is "Don't be afraid to launch a product without machine learning" (Google (opens in a new tab)), and some tasks are better solved with simple rules or automation. Our AI readiness assessment helps you check whether your data, systems and people are ready, and which workflow to test first.

How To Run An AI Proof Of Concept In Six Steps

The six steps below work whether you are testing a vendor's product, a custom build by a development partner or a tool built by your own team. The order matters: the measuring and the rules come before anyone builds anything, so the results cannot shape the test.

Step 1: Choose One Workflow And Measure How It Runs Today

Pick a single workflow, such as sorting customer emails, matching invoices or extracting data from forms. Then measure it as it runs now: how many cases a week, how long each takes, how often mistakes happen and what a mistake costs. Google's guidance on machine learning makes the same point: "track as much as possible in your current system" before building anything (Google (opens in a new tab)).

This baseline is the yardstick for every later decision. Without it, a result of 88% accuracy means nothing, because you don't know whether your team achieves 80% or 98% today.

Step 2: Write Success Criteria And A Stop Line

Success criteria are the numbers the AI must reach for the proof of concept to pass. Write them down and agree them with everyone involved, including any vendor, before the test begins. Anthropic's guidance on testing AI says good criteria are specific, measurable, achievable and relevant, and contrasts a weak criterion ("The model should classify sentiments well") with a precise one that names a score, a test set and an improvement over the current baseline (Anthropic (opens in a new tab)).

One number is rarely enough. As the same guidance puts it, "Most use cases need multidimensional evaluation along several success criteria." For most business tasks, set a target for each of the measures below, plus a stop line for the ones that matter most. A stop line is the result below which you walk away rather than keep adjusting.

The examples are illustrations only. Your targets should come from your baseline and from what the task is worth. For tasks where the AI predicts or classifies, our guide to evaluating machine learning models explains which measures fit which decisions.

Step 3: Build A Test Set From Real Cases

A test set (also called an evaluation set) is a collection of real past cases with the correct answer written down for each. It is the heart of any AI proof of concept, and it is where most published guidance stays vague. Anthropic advises designing tests that "mirror your real-world task distribution" and not forgetting edge cases (Anthropic (opens in a new tab)), while OpenAI recommends including "typical cases, edge cases, and adversarial cases" (OpenAI (opens in a new tab)), meaning everyday work, unusual situations and inputs designed to trick the system.

A practical starting point for a business task is about 100 recent cases. Ask the person who knows the work best to record the right answer for each one. Make sure the set includes the awkward cases: unclear requests, missing information, unusual formats and examples of things the AI should refuse or hand to a person.

Then split the set. Share about half with whoever is building the solution so they can develop and adjust against it, and keep the other half hidden until the final scoring run. This matters because any AI system can be tuned to do well on examples it has already seen. A score on hidden cases is the closest you can get to how it will perform on next month's work. Remove names, account numbers and other personal details before sharing anything. SensViz's guide on what to check before launching an AI application (opens in a new tab) shows how to record each test case so results can be compared fairly between versions.

Step 4: Agree The Terms Before You Share Data

If a vendor or partner is involved, agree the commercial and data terms in writing before any of your cases leave the business. These include who owns your data and the outputs, whether the vendor may train its models on your data, what happens at the end of the test and what it costs to continue. The section on vendor terms below covers each one.

Step 5: Run The Test And Score It

Run the solution on the hidden half of the test set and score every case against the answers your expert recorded. Record quality, serious errors, speed and cost per task, exactly as defined in step 2. Where possible, have the scoring done by your own team or by simple automated checks, rather than relying on the vendor's report.

Look at the failures as well as the score. Ten errors spread evenly across the test set mean something different from ten errors that are all the same kind of case. Grouping failures by type tells you whether a problem can be fixed with clearer instructions or better data, or whether the approach is wrong for the task.

Remember that small test sets give rough scores. With 50 hidden cases, each case is worth two percentage points, so treat a difference of a few points with caution, and add more cases if a decision depends on it.

Step 6: Decide Whether To Go Ahead, Adjust Or Stop

Compare the results with the criteria and stop line you wrote in step 2, and make one of three decisions. Keep the decision with the workflow owner, not the vendor.

Allow at most one or two rounds of adjustment, each with a fixed end date. Once you have studied the errors on the hidden cases, score the next round on a fresh batch of recent cases, so the new result is still a fair test. The UK government's service manual is explicit that stopping is a legitimate outcome of an early test: if the team is not confident, it "could stop altogether" (GOV.UK (opens in a new tab)). A proof of concept that ends in a clear stop has done its job, because it saved you from paying for a full build that would not have worked.

What To Agree With A Vendor Before An AI Proof Of Concept

Most guides to AI proofs of concept skip the commercial side, but this is where many businesses lose control of their data or get locked into a product that only half works. Public bodies have already set out what to look for. The UK government's AI procurement guidelines say a procurement process "can include proof-of-concepts to test the technologies before the solution goes live", and advise buyers to consider who should own any new intellectual property (GOV.UK (opens in a new tab)).

Before a vendor proof of concept starts, agree the following in writing:

Watch for two clauses that favor the vendor. Guidance written for AI companies by a US law firm advises vendors to own "the platform and all improvements, configurations, and know-how developed during the pilot" and to restrict customers from publishing benchmark results or sharing them with competing vendors (Andrew S. Bosin LLC, September 2026 (opens in a new tab)). Neither is unusual, but you should notice them and decide whether they are acceptable. The same guidance makes a point worth borrowing: "Every pilot should end on a date, not an event."

If you are still choosing between vendors, our guide to AI vendor selection covers the wider evaluation criteria, including security, integration and exit planning.

Testing Generative AI And AI Agents: What's Different

Generative AI tools, which write text, summarise documents or answer questions, add two complications to an AI proof of concept.

First, they can give different answers to the same input. Run each test case more than once, or at least a sample of them, and check whether the results stay consistent. An answer that is right two times out of three is not reliable enough for most business tasks.

Second, their output is harder to score than a yes-or-no answer. Many teams use another AI model to grade the answers, which is fast but has known weaknesses. OpenAI notes that AI graders can show "position bias" (being swayed by the order in which answers appear) and "verbosity bias" (preferring longer answers) (OpenAI (opens in a new tab)). If you use an AI grader, have a person grade a sample of the same cases and check that the two agree before trusting the automated scores.

AI agents, which take actions such as updating records or sending messages, need one more layer of testing. Score the whole run, not only the final answer: did the agent use the right tools, in a sensible order, without taking actions it should not? Test the cases where it should stop and ask a person. Our guide to the anatomy of a production AI agent explains how to evaluate complete agent runs and where human approval belongs.

How Long Does An AI Proof Of Concept Take, And What Does It Cost?

There is no authoritative standard for either. Published estimates vary from a few days to several months, and the cost ranges quoted online are rarely backed by any method. The closest official reference is the UK government's service manual, which says that alpha phases, its equivalent of an early test, "tend to last between 6 and 8 weeks" (GOV.UK (opens in a new tab)).

For a single workflow in a business of 10 to 500 people, four to eight weeks is a reasonable planning range. A week or two goes on measuring the baseline and building the test set, a few weeks on building and adjusting, and a final week on the hidden-set scoring run and the decision. Tests that need complex integrations or sensitive data take longer.

Most of the cost of an AI proof of concept is people's time: your own staff gathering cases and checking answers, plus the vendor's or developer's time building and adjusting. Model usage during a test is usually small. Plan for what comes after, too. Once a proof of concept is complete, the DTA's readiness checklist asks whether there is "a clear pathway to deploy to production and adequate funding available" (DTA (opens in a new tab)). It is worth knowing the answer before you start. If the proof of concept involves an AI agent, our guide to AI agent cost covers what an agent costs to run.

From Proof Of Concept To Pilot To Production

Passing an AI proof of concept means the approach can work. A pilot then tests whether it does work in daily operations. A pilot uses live or near-live data, a limited group of real users, and the real workflow, including the hand-offs to people when the AI is unsure.

The questions change at this stage. Do staff trust the output? How much time does checking it take? What happens when the AI is wrong? Our guide to human-in-the-loop AI design explains where people should review and decide, and how to keep review from becoming a bottleneck. To turn a validated test into a monitored production release, our guide on moving from AI demo to production in six weeks sets out a focused plan.

Common AI Proof Of Concept Mistakes

• Testing the vendor's demo, not your work. Always score on your own cases.

• Letting the builder see the whole test set. Keep part of it hidden for the final score.

• Testing too many things at once. One workflow per proof of concept gives a clear answer.

• No stop line. Without one, a test drifts into months of adjustment.

• Ignoring cost and speed. A highly accurate result that costs too much per task is still a fail.

• Sharing data before the terms are signed. Agree ownership, training and deletion first.

• Treating a pass as a finished product. A proof of concept is not production software. Budget for the pilot and the build.

An Example AI Proof Of Concept (Illustrative)

This example is hypothetical. The business, figures and results are invented to show the method, not taken from a Fossilite client.

A 120-person heating and air conditioning service company receives about 900 customer emails a week. Two coordinators read each one, route it to the right team (bookings, billing, warranty, sales or urgent repairs) and write a first reply. Measuring for two weeks showed that routing takes about three minutes per email and about 12% of emails go to the wrong team first, so the coordinators route about 88% correctly. Missing an urgent no-heat case is the costliest mistake.

The company wants to test an AI tool that routes each email and drafts a reply. Before contacting vendors, the operations manager pulls 200 recent emails, and the senior coordinator records the correct team and an acceptable reply for each. The set includes 15 urgent cases (about half of them in the hidden set), angry and unclear messages, and emails with attachments only. Personal details are removed. Half the set is shared with the vendor; half is kept hidden.

The vendor signs an agreement covering no training on the company's data, deletion within 30 days and an agreed price if the test passes. The proof of concept runs for four weeks. The routing target is set above the coordinators' 88%, so the AI has to do better than the current process. On the hidden 100 emails, the scores against the written criteria are:

The decision is to adjust. With 100 hidden emails, 90% means ten were misrouted, two more than the target allows. Grouping the routing errors shows that most confuse warranty and billing, because the company's own definitions overlap. The team writes clearer category rules and runs one more two-week round, scored on a fresh batch of 100 recent emails labelled the same way, because the first hidden set has now been studied. Routing reaches 93%. The company moves routing to a pilot. Drafting stays out of scope until a further round reaches the 70% target.

Without a baseline, a hidden test set and written targets, the first round's 90% might have been called a success or a failure depending on who was in the room. With them, the next step is clear.

How Fossilite Approaches An AI Proof Of Concept

We treat a proof of concept as a decision tool, not a sales step. We start by looking at how the workflow runs today and measuring it, then agree success criteria and a stop line with you before building anything. We build the test set with the people who do the work, keep part of it hidden for the final score, and report results against your numbers in plain business terms.

If the results don't justify going further, we say so. If they do, we plan the pilot and the path to production together, including integrations, human review and running costs.

Frequently Asked Questions

What Is An AI Proof Of Concept?

An AI proof of concept is a short, controlled test of whether an AI solution can perform a specific business task well enough to justify further investment. It uses real past cases, success criteria set before the test begins and part of the test set kept hidden, and it ends with a clear decision to go ahead, adjust or stop.

What Is The Difference Between An AI POC And A Pilot?

An AI POC tests whether an AI approach can do a task, using past cases and a small team, outside daily operations. A pilot tests whether it delivers value in real work, with live or near-live data and a limited group of staff. A POC proves feasibility; a pilot proves the solution is worth scaling.

How Long Should An AI Proof Of Concept Take?

For one workflow, four to eight weeks is a reasonable planning range: time to measure the baseline and build a test set, a few weeks to build and adjust, and a final scoring run. There is no official standard, but the UK government's service manual says its similar early-test phase usually lasts six to eight weeks.

How Much Does An AI Proof Of Concept Cost?

The cost of an AI proof of concept is mostly people's time: your staff collecting cases and checking answers, plus the builder's time. Model fees during a test are usually small. Some vendors offer free proofs of concept; a paid one credited against the contract can encourage more serious effort on both sides. Budget for the pilot and production too.

What Success Criteria Should An AI POC Use?

AI POC success criteria should be written before the test starts and cover quality on hidden test cases, serious errors, speed, cost per task and the effort staff still spend checking. Compare each result with how the work performs today. Set a stop line for the most important measures, so the decision does not depend on opinion.

Should We Use Real Data In An AI Proof Of Concept?

Yes, where you safely can, because results on made-up examples rarely match real work. Remove names, account numbers and other personal details, and share cases only after the vendor agrees in writing not to train on them and to delete them afterwards. Australia's DTA suggests synthetic data, meaning artificial records, at this stage to reduce privacy risk.

Should We Let A Vendor Run The Proof Of Concept?

A vendor can build and run the solution, but you should own the test. Write the success criteria, build the test set, keep part of it hidden and check the scoring yourself or with an independent partner. Otherwise the vendor decides what success looks like for its own product.

If you are comparing vendors before a proof of concept, our guide to AI vendor selection is a useful next read.