Proof of value vs proof of concept: what’s the difference?
| Proof of concept | Proof of value | |
|---|---|---|
| Question | Can it work? | Does it pay? |
| Data | Samples or a sandbox | Live work |
| Success measure | It runs | A business number moves against a baseline |
| Owner | IT or the vendor | The business leader whose number it is |
| Ends with | A demo | A go or no-go decision |
A proof of concept proves the technology; a proof of value proves the business case. Most AI pilots that never reach production were proofs of concept that everyone treated as proofs of value.
Why are companies moving from pilots to proofs of value?
Because pilots rarely produce a decision. In a 14 October 2026 piece on procurement, Bain argues that “proof-of-concept pilots rarely generate the organizational learning or the executive conviction required to scale,” and describes leading firms running proofs of value on a 12-week timeline with real outcomes and real money at stake (Bain, read 9 November 2026). A trial without a pre-agreed threshold can only end in “promising.”
How do you design an AI proof of value?
Pick one process and one number: cost per invoice, hours per claim, days to close. Measure the baseline before the system arrives. Agree on the threshold that means go, and the one that means stop, in writing. Run it on live work with a person checking outputs, and track cost per task as well as quality, since a system that is right but expensive fails the test too. Write the decision rule before you see the result. The proof of value is finished when the owner can sign a yes or a no, not when the budget runs out. The method behind the number is covered in AI ROI measurement.
What happens after a successful proof of value?
The business case is already written: the trial produced its inputs. What remains is the work a trial skips, such as integration with every system of record, failure handling, monitoring and the cost of running at full volume. Use the result to rank the next use cases, as in AI use case prioritization, and budget for the gap between a supervised trial and an unattended system. Our piece on Bank of America’s AI returns shows what that gap looks like at scale.