The Invisible 90%: Why AI Projects Fail After the Demo
- Demos have never been easier to build. Production has never been harder to reach. AI projects fail twice as often as traditional IT, and 95% of enterprise GenAI pilots return nothing measurable. The demo is the easy 10%.
- This talk covers what demos structurally hide: data that isn’t AI-ready, the team production actually needs, and the bill that arrives after the pilot.
- It looks at what billion-dollar failures like IBM Watson Health and UnitedHealth’s nH Predict share with a rescue engagement I lived through, where world-class ML talent met every KPI and the project still failed.
- It closes with the five root causes of AI failure, ranked, and a four-step recovery plan executives can apply to their own AI portfolio the next day.