An AI PoC is a short, fixed-scope experiment that tests whether a technical hypothesis works with the company's data and constraints before becoming a project. Most never leave the slide deck because they start without a success criterion, a baseline or an owner for the decision: they end as a demo, not as a decision to scale, adjust or discard.
What an AI PoC is, in practice
PoC stands for proof of concept. In AI, it answers one specific technical question: can the model extract the right fields from these contracts? Does the assistant answer from the internal knowledge base without making things up? Does the demand forecast miss less than the current spreadsheet?
It is not a product, not a pilot and not a sales demo. It is a controlled test, with real or simulated data, in a separate environment, that ends with a written recommendation. An idea goes in, a decision comes out.
Three traits separate a PoC from any other prototype:
- An explicit hypothesis. A sentence like "if we do X with this data, we get Y with accuracy Z".
- A small scope. One use case, one data source, one flow. Anything that does not help answer the question stays out.
- A fixed deadline. It ends on an agreed date, with or without an answer. A PoC that never ends has become a project with no budget.
Why do most AI PoCs never leave the slide deck?
The problem is almost never the model. The PoC stalls for reasons that existed before the first line of code. Gartner's projection on generative AI projects abandoned after the PoC points the same way: high cost, value that is hard to measure and weak data. The patterns we see most:
It starts without a question
"Let's test generative AI" is not a hypothesis. Without a question that can be answered with no, the PoC cannot fail, and therefore cannot prove anything. It ends when the enthusiasm runs out.
It runs on data that does not exist in production
The team assembles a small, clean dataset for the test. The result looks great. Then someone runs it on the real data, full of empty fields, scanned PDFs and duplicate names, and accuracy collapses. The PoC proved the model works in a world that is not the company's.
There is no baseline
Is 87% accuracy good or bad? It depends on what the company does today. If the person classifying the emails gets 95% right, the PoC failed. If the current process takes three days and the PoC answers in minutes at 87%, it may be an easy call. Without measuring the before, the after means nothing.
Nobody owns the decision
The PoC ends, the report goes out by email, and the question "now what?" has no owner. Scaling requires budget, integration and someone who signs. When that was not agreed at the start, the result becomes a slide in the quarterly review.
The corporate environment gets in the way
Data access, security approval, a cloud account, an API key. In a large company, each of these can take weeks. Many PoCs die in the ticket queue before they run. That is why PoC Factory builds in a separate environment, on AWS or Google Cloud, and sorts out access before kickoff.
What does a good AI PoC need to contain?
A useful PoC is defined by what it delivers at the end, not by the technology it uses. The minimum:
- A success criterion agreed before day one. A number, a threshold and who judges. "Extract the 12 fields with at least 90% accuracy on 200 real contracts, evaluated by legal."
- A measured baseline. How the process works today: time, cost, error rate. That is what the PoC will be compared against.
- Representative data. A real sample, with the real mess. If the data is sensitive, anonymized or simulated with the same characteristics.
- Technical and business metrics. Accuracy, latency and cost per call on one side. Hours saved, tickets resolved and rework avoided on the other.
- A Decision Package. An executive report with what was tested, what was measured, what was missing and a clear recommendation: scale, adjust or discard. With an estimate of what scaling would require.
We published a checklist of what a good AI PoC needs to contain that works as the agenda for the first meeting.
PoC, pilot or MVP: what is the difference?
The three terms are often used as synonyms, and that creates the wrong expectations. The table below uses the PoC Factory formats as the reference for timelines.
| Format | Question it answers | Where it runs | Timeline | Deliverable |
|---|---|---|---|---|
| PoC (Starter) | Does the hypothesis work with our data? | Separate environment, AWS or Google Cloud | 3 weeks | Decision Package |
| PoC (Advanced) | Does it work integrated with real operational data? | Separate environment, integrated with real sources | 4 to 6 weeks | Decision Package |
| Pilot | Does it work with real users, at limited scale? | Production, with a restricted group | Case by case | Usage metrics and rollout plan |
| MVP | Does anyone use it and pay for it? | Production, for the customer | Paced by the fundraising round | Minimum product in use |
A PoC proves technical feasibility. A pilot proves adoption. An MVP proves market value. Skipping steps usually costs more than doing each one well. We wrote about this in PoC vs MVP vs product.
How long does an AI PoC take and how much does it cost?
Time, at PoC Factory, is 3 to 6 weeks, from kickoff to the Decision Package. The difference between the shortest and the longest format lies in the integration with real data, not in the size of the model.
On cost, the honest answer is: it depends on the hypothesis, the data source and the level of integration, which is why we do not publish a price list. What we guarantee is the format. Scope and timeline are fixed in writing before the first day, in a proposal that describes the hypothesis, the success criterion, the data required and what the company receives at the end. No overtime, no scope that grows halfway through. If the hypothesis does not fit in 6 weeks, it is too big for a PoC and needs to be broken down.
What a PoC should not cost is the price of an entire project. That is the point: the big money only comes in after the idea has shown it works.
Next step
If there is an AI hypothesis sitting idle in your company for lack of a cheap way to test it, PoC Factory was designed for that. Tell us the hypothesis and we reply with scope, timeline and success criterion.