TL;DR
AI has made demos cheap to build, so more of them reach a funding decision with less checking. A demo shows what an AI tool can do with inputs someone chose in advance. Real work asks whether the business can depend on it day to day, as things change, and when something goes wrong.
What the paper develops
The demo runs clean: a handful of cases picked from thousands, with none of the messy ones that make up much of the real work. Nothing dishonest happens in that room. Demos exist to show what is possible. The failure comes afterward, when a good demo is treated as proof the tool will work in daily use and nobody asks what changed. AI has made that failure more common, because demos are now cheap to build. AI tools can also change after launch, and their failures are hard to see. This paper sets out the evidence the business needs before ordinary users, data, and pressure are allowed to depend on it.
What to do next
Before real work depends on an AI tool that has only passed a demo, name who owns it, test it on the hard cases, set up monitoring and a recovery plan, and agree in advance who can shut it off and how.
Inside the white paper
- What a clean demo proves, and what it quietly leaves out
- The evidence real work needs: an owner, the hard cases, monitoring, and recovery
- Who can shut the tool off, and how that gets agreed before anyone depends on it