AI proof of concept (PoC): what it should prove before you invest

Name the assumption to be tested

Every AI project rests on assumptions: that the necessary information is in the documents, that a model can extract it reliably, that reviewers will trust the result. A PoC should target the assumption that would be most expensive to disprove late. In an invoice-review workflow, that might be whether line items can be matched to orders despite varying formats; for a knowledge assistant, whether answers can be traced back to approved sources.

Phrase the assumption as a question with a measurable answer and a deadline. “Can we automate accounts payable?” is not a PoC question. “For the invoice formats that make up most of our volume, can matching proposals be prepared so that case handlers confirm most of them without rework?” is one. The narrower question makes the result interpretable, including a negative one.

Agree success criteria before the build

Google’s Rules of Machine Learning recommend designing and implementing metrics first, and keeping the first model simple while getting the surrounding infrastructure right. For a PoC, that means agreeing measures and thresholds before the first result is visible. Which accuracy, which share of cases handed back for manual processing and which review time per case would justify a next step?

The same source advises considering a launch without machine learning as well. A credible PoC therefore includes a baseline: today’s manual process or a simple rule-based approach. If a simpler method already meets the criteria, that is a valuable finding. It saves building and operating a model where it adds little.

Sources: Google for Developers · Rules of Machine Learning

Test with representative cases, not a showcase

A demo built on a few clean examples says little about daily work. Assemble a test set from real, anonymised cases: ordinary ones, incomplete ones and known exceptions. Hold part of it back for the final assessment so that the approach is not tuned to the very cases it is measured on. Where data may not leave the organisation, the PoC can run locally or in a private environment from the start.

Anthropic’s guide to evaluating agents distinguishes the steps an agent records from the outcome it actually achieves. A PoC should check the outcome: was the case created correctly, is the cited passage the right one, did the case reach the right team? The specialists who judge these results need time in the plan, because their assessments are the evidence the PoC delivers.

Sources: Anthropic · Demystifying evals for AI agents

Decide what the result means

At the end, separate three kinds of statements: what the tests demonstrated, what they suggest without establishing it and what remains open. A PoC run on selected documents does not demonstrate performance across all suppliers; a prototype without system integration does not demonstrate stable operation. Naming these limits explicitly prevents a promising PoC from being mistaken for a production decision.

Three outcomes are legitimate: continue with a pilot that adds real integration and review, narrow the scope to the cases that worked, or stop. NyxAI’s Agent Pilot brings these steps together for one workflow: prototyping, evaluation and handover, with local, private or hybrid operation. It does not guarantee production readiness; it delivers the evidence for that decision.