AI proof of concept
What is AI proof of concept?
A small, time-boxed build that tests one question: can this AI actually do the job on real data? It proves technical feasibility before you commit to production. The trap is treating it as the finish line. A working demo is not a deployed system, and by IDC's count most proofs of concept never make the jump.
Why it matters
A proof of concept exists to kill bad ideas cheaply, and the teams that use it well are as content to fail one as to pass it. The real danger is emotional, not technical. Once a demo works and people have seen it, momentum builds to declare the job done and move on, even though the hard parts, such as real data volumes, security, integration and the users who never behave like the test, have not been touched. A proof of concept that skips a clear success criterion set in advance is worse than none, because it will always look like a success to whoever wanted it to.
In practice
A team builds a two-week prototype to test whether a model can pull data from messy supplier PDFs. It works on 10 clean samples, and someone declares victory. The honest version runs it on a few hundred real documents, the scanned and the badly formatted included, and sets a pass mark up front: below 90 percent accuracy, the idea goes back on the shelf.
