Why AI Projects Fail: The Missing Link of a Strong Data Strategy
summary
Claims that corporate AI projects hit an 80% failure rate and that only 4.7% of AI projects create real innovation, traced to five root causes: unclear problem definitions, missing or poor data, picking tools before use cases, inadequate infrastructure, and tackling problems too big for ML. Examples include a hospital optimization that works technically but never deploys because surgeons reject enforced timing, and a debunked Kodak myth. Recommended fixes are job‑to‑be‑done focus, long‑term metrics, counterfactual planning and avoiding the cheering trap.
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chapters (11)
- 0:00AI project failures and data strategy
- 1:51About Data Quaza
- 3:41AI projects failure rate
- 4:24Root causes of AI failure
- 6:12Leadership failures in AI
- 10:13Kodak failure myth debunked
- 13:46Marginal thinking trap in AI
- 15:13Strong data strategy practices
- 16:41Avoiding Turing trap in AI
- 18:08Strategic advantage in AI
- 19:13Q&A
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