Executive Summit Keynote: How to Ensure ML Project Success
summary
Executives get concrete actions: start with business pain not algorithms, pick KPIs that measure end‑user actionability (avoiding the common 60% failure rate statistic), deploy human‑in‑the‑loop designs like agent‑assist instead of full automation, and time‑box R&D when off‑the‑shelf ASR or models fail. Build cross‑disciplinary squads, host a central forecasting repo that surfaced one surprising transfer win, and be willing to pivot from complex models when simpler forecasts already meet constraints. human-AI collaboration time-boxed R&D
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chapters (12)
- 0:00Welcome and event intro
- 2:15Sponsor acknowledgment
- 5:44Event program overview
- 7:07Interview with Fran Bell
- 10:02Why ML projects fail
- 14:55Aligning ML with business
- 17:03Organizational design for ML
- 20:49Talent strategy at BP
- 22:56Success vs disguised failure
- 28:35Pivoting in ML projects
- 31:03Interdisciplinary teams
- 34:13Final advice for executives
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