Picture a churn model that scores well on a cleaned sample of last quarter’s records, and a steering committee that asks for a production date. Nobody in the room can say whether the result will hold on next month’s data.
An AI proof of concept (PoC) is a short, scoped test of whether an AI approach can do one specific task on an organization’s own data well enough to justify further investment. A useful PoC also records the data, evaluation set and acceptance criteria behind its result, so the result can be checked again before production.
CUBIG’s UCI Bank Marketing experiment (October 2026) shows why the record matters. Random forest F1 rose from 0.4526 to 0.6054 after data preparation on the same 824 evaluation records, while false positives rose from 16 to 39. The inputs also included call duration, which is known only after a call ends. The write-up states that the experiment evaluates classification using call information and does not demonstrate pre-call targeting.
Key takeaways
- A PoC answers whether an approach can work on your data for one task. A pilot and production ask different questions.
- A result is only as strong as its conditions: which data, which evaluation records, which inputs were available at decision time, and which criteria counted as a pass.
- Report more than one metric. F1 can rise while false alarms rise too.
- Keep the data version, evaluation set and criteria with the result, so the next stage can check it rather than repeat it.
What is an AI proof of concept?
An AI proof of concept tests feasibility on a narrow task. It often runs for weeks rather than months, uses a sample or a historical extract of the organization’s data, and ends with a decision: stop, adjust, or move to a pilot.
| Stage | Question it answers | Data it usually runs on | What it should leave behind |
|---|---|---|---|
| Proof of concept | Can this approach do the task on our data? | A sample or historical extract | A result tied to its data version, evaluation set and criteria |
| Pilot | Does it work for real users in a limited setting? | Live data for one team, region or process | Evidence that the PoC result held, and the exceptions it missed |
| Production | Does it keep working as data and rules change? | All live data in scope | A way to recheck results after each change |
What should an AI proof of concept prove?
An AI PoC should prove that the approach meets agreed criteria on data that resembles what the task will meet. That breaks down into five things the PoC report should be able to state.
- The task and the decision. What the model or agent produces, who uses it, and what a correct result looks like.
- The acceptance criteria. Which metrics count, at which threshold, and which errors are too costly, agreed before the results come in.
- The evaluation records. A fixed evaluation set, kept apart from training and covering the cases that matter, including rare outcomes and known exceptions.
- Inputs available at decision time. No field the real decision would not have, which is a form of data leakage. A review of machine-learning-based science published in Patterns in 2023 found 17 fields where leakage had been reported, collectively affecting 294 papers.
- The data version. Which extract, with which preparation steps, produced the result.
A PoC that cannot state these can still be promising. The result just cannot be checked by the next stage.
Why do AI proofs of concept stall before production?
A common reason is a positive result that nobody can trust beyond the PoC. CUBIG’s glossary entry on pilot purgatory describes the pattern: the next step needs budget, a security review and a decision that the result will hold, and the third is the one nobody can give.
The PoC data is often part of the reason. Google’s Rules of Machine Learning use the term training-serving skew for the difference between performance during training and performance during serving. A sample can be cleaner, smaller or older than live data, or missing the exceptions that make the real process hard. Inputs can include information the live decision will not have, as call duration did in the bank example. Neither problem shows in a single headline score.
Google engineers made a related point in a NeurIPS 2015 paper on technical debt in machine learning: only a small fraction of a real-world ML system is the ML code, and the surrounding infrastructure, from data collection and verification to serving and monitoring, is vast and complex.
Results also need more than one metric. In the UCI Iranian Churn experiment (October 2026), logistic regression found 78 of 99 churners instead of 42 after data preparation, while customers flagged by mistake rose from 8 to 41. Whether that trade works depends on what a missed churner and an unneeded retention call cost, which the PoC criteria should settle in advance.
How do you run an AI proof of concept on your own data?
A PoC on your own data can follow a short sequence that keeps the result checkable.
- Write the task and criteria first. Name the output, the decision, the metrics and the threshold before looking at results.
- Choose data that resembles the task. Check the extract against the channels, periods and exceptions the live task will meet, and note what is missing.
- Fix the evaluation set. Set aside evaluation records before any preparation, and keep them unchanged for every comparison. The scikit-learn guide to common pitfalls likewise advises splitting the data before any preprocessing and fitting preprocessing on the training data only.
- Remove inputs the decision will not have. Check each field against the moment the decision is made.
- Compare under the same conditions. If you test data preparation, keep the model settings, evaluation records and threshold fixed, and read F1 with recall, precision and false alarms.
- Record the result with its conditions. Keep the data version, evaluation set, criteria and limits, such as a single split, with the result.
The five data preparation experiments CUBIG published in October 2026 apply step 5 to public datasets: the same model settings, evaluation records and threshold before and after preparation. Each write-up states its limits, including the call-duration input in the bank experiment. They are a reason to run the same comparison on your own task, not evidence about your data.
What should an AI proof of concept report include?
A PoC report should let someone who was not in the project decide whether to move on. At minimum, the report states the task and criteria, the data version and how it was prepared, the evaluation records and their size, the result on each agreed metric, the trade-offs between errors, and the limits of the test.
The limits are part of the result. A single train and test split, a small number of positive cases, or inputs that will not exist at decision time each narrow what the result supports. A report that names them gives the pilot team a list of what to check first, the same discipline described in AI model evaluation.
Where does Syntitan fit in an AI proof of concept?
Syntitan, CUBIG’s AI-ready data platform, keeps the original dataset unchanged, starts each use case as a Project from a copy, and keeps preparation and validation results as Project Versions. Ready for Model prepares the training data for a specific model task and compares results before and after preparation under the same evaluation conditions.
Ready for Model fits the comparison step of a PoC: the same model settings and evaluation records, with the data version recorded. Syntitan does not decide the acceptance criteria or whether a trade-off is acceptable; those stay with the team that owns the task. Try Syntitan on one task and its data.
