Why it matters for your business
A POC is the cheapest way to learn whether an AI idea holds up against your actual data, which is often messier than any demo. A useful POC has success criteria written in advance, such as a minimum extraction accuracy or a maximum cost per item, uses real rather than hand-picked samples, and ends with a clear go, adjust or stop decision. POCs that skip these steps become endless pilots. A POC is not production software, so budget separately for integration, security, monitoring and training if it succeeds.
Example
A title company wants to extract key terms from scanned purchase agreements. Before funding a full build, it runs a three-week POC on 150 past agreements, measuring field-level accuracy against what staff entered and the model cost per document. Results meet the target on typed agreements but not on handwritten addenda, so the production scope leaves handwriting to staff.