Translating AI Capabilities Into Better Business Results
summary
Claims that moving from predictions to decisions raises revenue by optimizing action thresholds and modeling downstream effects using profit curves and integrated models. Uses deployed ML outputs, simple cost‑benefit math and cloud tooling like GCP Azure AWS SageMaker to pick thresholds — example: running a promotion to 50,000 people (30,000 unnecessary) yields an estimated $470,000; per-customer example shows $450 vs $498 expected profit so choosing the better action scales. Offers Decision AI software to combine models for pricing, fraud prioritization and churn.
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chapters (11)
- 0:00Intro
- 0:42Overview
- 2:48The Problem
- 5:08Example Problem
- 7:00Actionable Results
- 8:25Taking Action
- 9:35Profit Curves
- 12:40Measuring Value
- 14:32Limitations
- 18:09Example
- 21:34Where is this useful
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