How can product curiosity help to have more successful AI/Data teams?
summary
Only 13% of data science projects reach production, so the recommendation is concrete: build cross-functional skills, start with the voice of the customer not the dataset, and treat AI work as an ongoing product not a time-bound project. Focus on data quality (collection, security, accuracy, variety), set a long-term mission but keep tactics open, create milestones to test value, and identify early risk factors so teams can pivot. Cited impacts: 87% cancer detection vs 86% humans, faster protein design, and Google Duplex calls.
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chapters (10)
- 0:00Intro
- 3:34Why how to be curious
- 6:15Embrace the uncertainties
- 7:35Why AI data products are different
- 8:49AI data life cycle
- 10:22AI data products
- 12:55Product mindset
- 14:52Managing uncertainty
- 16:25Medical diagnosis
- 18:05Outro
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