How to bridge the gap between Data Scientist & Decision Makers?
summary
Argues that success means shipping models into workflows, not just accuracy, and recommends starting with workshops, pairing with product/BA/UX, and building a one‑page demo (5–10 minute front end + API) to prove value. Gives concrete warnings: a reinforcement‑learning logistics prototype works on a 5‑node hub but fails at 1,500 hubs; demos overfit, so map is not the territory. Notes infra needs (GPU required), names a quick prototype Locus demo and lists measurable business KPIs to track.
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chapters (8)
- 0:00Introduction
- 3:44Why bridging the gap matters
- 7:57Communication breakdowns
- 14:19Data science as business effort
- 22:47Designing demos for decision makers
- 27:26Workshop to align AI with goals
- 32:46Five takeaways for AI delivery
- 33:17Q&A
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