AI PoC: prove feasibility before you commit.
A proof of concept exists to make a decision, not to impress a room. Here is how to scope, run and close an AI PoC that ends in a written go or no-go, in one to three weeks.
A PoC is not a demo.
An AI PoC answers one closed question with numbers: can this approach reach a defined score, on a real dataset, at an acceptable cost, in a bounded time. The output is a decision, not a slide.
The problem with most AI PoCs is not the model, it is the missing scaffolding around it. No baseline, no evaluation harness, no stop rule. The team spends four months polishing prompts and cannot say whether the case is viable.
PoC, MVP, pilot, production.
Four stages, four different questions. Collapsing them is the source of most enterprise AI overruns.
AI PoC
Answers: can this technically work on our data. One capability, offline evaluation, no live users. Days to two weeks.
AI MVP
Answers: do real users get value. Working product on one flow, live traffic, telemetry. Weeks.
Pilot
Answers: does the value hold at small scale. Limited rollout, SLOs, cost model. Weeks to a quarter.
Production
Answers: does it survive real load, security, drift and ownership. Delivered with ZeroFive.AI. Quarter and beyond.
Six rules for a PoC that ends in a decision.
One question, in writing
The PoC exists to answer one closed question. If it needs a paragraph to explain, it is a project, not a PoC.
A dataset that matters
Real, anonymized, representative of the corner cases. Synthetic data on a PoC is a way to lie to yourself for free.
An evaluation harness
A labeled set of 50 to 200 cases with a scoring rule agreed in advance. The harness is the deliverable, the model is a byproduct.
A baseline
The current process, a rule-based system, or a smaller model. A PoC that has nothing to beat cannot fail, therefore cannot inform a decision.
A budget for the PoC itself
Model calls, data prep, engineering. Capped up front so a PoC never becomes a stealth MVP.
A go or no-go rule
Written before you start. What score, on which set, at what cost, in what time, triggers the next stage.
What a real AI PoC hands back.
Evaluation report
Score of the candidate approach vs the baseline on the labeled set. Not a slide, a document with numbers and reproducible runs.
Cost per execution
Real unit cost projected to expected volumes. The dimension that most often flips a go into a no-go.
Failure catalog
Where the approach breaks, on which cases, with which frequency. This is what pilot and production have to design around.
Recommendation
Ship as MVP, iterate the PoC once, or stop. No fourth option. Signed by the team that ran the PoC.
Five ways AI PoCs waste a quarter.
PoC that never ends
Without a written stop rule, a PoC becomes a research project. Time-box in weeks and enforce it.
No baseline
A PoC that only measures itself cannot inform a decision. Anything, even the current manual process, is a baseline.
Cherry-picked examples
Ten good outputs on a slide is not evaluation. A labeled set with adversarial cases is.
Confusing PoC with MVP
PoCs answer feasibility, not desirability. Real users only enter the picture at MVP stage.
No cost projection
A prompt that works at ten requests will bankrupt you at ten thousand. Cost is part of feasibility.
AI PoC, straight answers.
The questions enterprise teams ask before starting.
- What is an AI proof of concept?
- A time-boxed experiment that answers whether an AI approach can technically work on your data, at acceptable cost, against a baseline, with a documented evaluation rule.
- AI PoC vs AI MVP, what is the difference?
- The PoC answers can this work. The MVP answers do real users get value from it. PoC lives in an eval harness, MVP lives in production with telemetry.
- How long should an AI PoC take?
- One to three weeks for a focused case. If the PoC needs longer, either the question is too broad or you are actually building an MVP.
- How much does an AI PoC cost?
- A well-scoped PoC lands in a low four to low five-figure euro range depending on data prep and integrations. The real cost is the decision it lets you avoid making blindly.
- What deliverables should an AI PoC produce?
- An evaluation report against a baseline, a real cost per execution, a failure catalog and a written go or no-go recommendation. No PoC without an evaluation harness.
- Can we skip the PoC and go straight to the MVP?
- Yes when feasibility is not in doubt and the risk is user value, not technology. Skip is the default for well-known capabilities, PoC is the default for anything novel on your data.
- Do we need a data scientist for an AI PoC?
- You need someone who owns the evaluation harness. It is often a product engineer with strong evals discipline, not necessarily a data scientist.
- What kills most AI PoCs?
- No baseline, no stop rule, no owner, and evaluation done on the same handful of examples the team used to build the prompt. Fix these four and the PoC produces a real decision.
Bring the question, we hand back a decision.
Thirty minutes to scope the PoC, agree the stop rule, and lock a start date.
Book a discovery call