Data, AI, Hype and Fear - Recipe for Disaster?
summary
Crisp claim: 85% fail to deliver and only 53% reach production, backed by practical experience at a large insurer where a fraud detection project at a global insurer is two months in with measurable early savings. Discussion names real tools — transformers, GPT‑3 style models, AutoML, data engineering and MLOps — and shows what succeeds: small, business‑led pilots, domain experts embedded in crossfunctional teams, clean reliable data pipelines and pragmatic platform choices rather than hype.
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chapters (7)
- 0:00AI hype and fear
- 10:08AI hype cycle
- 21:17Building AI teams
- 31:44Data project principles
- 41:45Organizing AI projects
- 47:13Key takeaways
- 50:26Q&A
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