Many organizations spin up impressive prototypes but struggle to capture sustained value from machine learning. In this 23-minute episode Mirko lays out a compact, actionable playbook for executives to close the gap between experimentation and production impact. The episode explains who should own model outcomes, how to structure incentives and cross-functional teams, which governance checkpoints actually reduce business risk, and how to measure ROI with operational metrics instead of vanity KPIs. Drawing on real enterprise patterns—team design, deployment guardrails, monitoring, lifecycle finance, and vendor vs build trade-offs—this session gives leaders a prioritized roadmap that fits typical executive time horizons and governance constraints. Listeners get concrete decision points, a simple responsibility matrix, and three immediate moves they can make in the next 30–90 days to increase the likelihood that models deliver measurable business outcomes.
I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions. Follow Mirko on LinkedIn if you want decision-ready frameworks, not hype.
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