AI proof of concept development

Start with one question

A good proof of concept tests one thing: can AI classify our support emails into these eight categories accurately enough? Can it extract these six fields from our supplier invoices? Can it draft replies our team would send with light editing? Broad goals such as 'see what AI can do for us' produce interesting demonstrations but little evidence for a decision.

The narrower the question, the faster and cheaper the answer, and the clearer the decision afterwards.

Agree success criteria first

Thresholds are set by you before testing, based on what would make the task worth automating.

How a proof of concept runs

1. 01

   Define

   Question, success criteria and test data agreed.

   Output: Test plan

2. 02

   Prepare data

   Representative examples collected and anonymised, with correct answers noted.

   Output: Test set

3. 03

   Build minimally

   Just enough to run the AI step on the test set.

   Output: Working prototype

4. 04

   Test and report

   Results measured against criteria; failures examined.

   Output: Results and recommendation

Three honest outcomes

• Go

  Criteria met; plan a pilot with review steps.

• Adjust

  Close, with specific failure types that rules, better data or a narrower scope could fix.

• Stop

  AI is not reliable enough for this task on this data; money saved by finding out early.

A proof of concept is not a product

Proofs of concept are built to answer a question, not to run a business process. They usually lack the integrations, error handling, logging and review screens a live workflow needs. Treating a successful proof of concept as finished software is a common mistake. A positive result should lead to a properly built pilot, using what was learned.

Real data, handled carefully

Testing on invented or tidy examples tells you little. The test set should include your awkward real cases. Where data is personal, anonymise or mask what the test does not need, check the AI provider's terms before sending anything and delete test data when the work ends.

Compare with how it is done today

Results mean more when compared with the current process. If people currently make mistakes on a share of items, or take several minutes per item, the proof of concept should be judged against that, not against perfection. An AI step that is slightly less accurate than a careful person but flags its uncertain cases may still be worth pursuing; one that is confidently wrong in ways nobody would notice probably is not.

Recording the current time and error rate before testing makes this comparison possible.

Measure running costs too

A proof of concept is a good moment to measure how much AI usage each item needs, so the running cost of a full workflow can be estimated from evidence. A technically successful result that would be too expensive at your volumes is an important finding in its own right.

Who should be involved

The people who do the task today should help choose the test examples and judge the results. They know which cases are difficult and whether an AI suggestion would actually save them time. A short review session where they look at a selection of results, including failures, often tells you more than the summary numbers alone.