How to operationalize AI
summary
Most AI projects stop at prototypes — roughly 5–10% reach enterprise outcomes — because teams build things like a customer-segmentation recommendation model, run it as a prototype, then hit POC hell with 20–30 POCs and no production data pipeline. Common fixes described: hire data engineers (applications show ~28:1 scientist-to-engineer), enforce data governance and GDPR-sensitive sourcing, pick cloud platforms (Google Microsoft IBM) deliberately, set upfront value metrics, and treat the initiative as an AI molecule of tech people data culture and governance.
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chapters (18)
- 0:00Intro
- 1:25Concepts to outcomes
- 3:38Data collection
- 4:35Model
- 5:42POC
- 6:45Data
- 8:12The biggest problems
- 9:05The most important pieces
- 11:22Data Engineers
- 13:09Culture
- 14:12Process
- 16:05Acceptation
- 17:25Governance
- 17:50Ideas
- 19:13AI molecule
- 19:50Conclusion
- 20:13Learnings
- 21:15Outro
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