What is an AI proof of concept?

After a successful PoC, you typically move to a production pilot with a limited user group, then to full deployment. The PoC findings inform the implementation plan, budget, and timeline for the production system.

A PoC that shows the approach will not work is still valuable. It prevents a much larger failed investment and provides insights that can guide alternative approaches. The cost of a failed PoC is typically 10-15% of what a failed full implementation would cost.

This depends on the use case, but generally a representative sample of 100 to 1,000 records is sufficient. The data should reflect the real variety and complexity of your production data to produce meaningful results.

Define a specific, bounded problem that can be validated within 4 to 8 weeks. Identify clear success criteria that are measurable and agreed with stakeholders. Use a representative sample of real data. The PoC should answer a specific question about feasibility and value, not try to build a complete system.

Yes. Involving a small group of end users provides essential feedback on usability and practical value that technical testing alone cannot reveal. User input during the PoC also builds early advocacy that supports broader adoption after full deployment.