Most organizations run AI projects as isolated bets: promising pilots, scattered budgets, uneven governance, and inconsistent outcomes. This episode delivers a practical, exec-level playbook for treating AI initiatives as a coherent investment portfolio that aligns with strategy, risk appetite, and measurable ROI. I walk leaders through portfolio segmentation (core vs. exploratory vs. platform), stage-gated funding, risk-adjusted valuation, go/kill criteria, and mechanisms to surface technical debt and delivery risk early. You’ll get decision-ready tools for prioritization, cross-functional accountability, capacity planning, and executive dashboards that move teams from experiments to sustained, measurable value. Real-world trade-offs, common failure modes, and governance patterns are examined with an eye toward pragmatic adoption at enterprise scale. By the end, listeners will have a repeatable framework to allocate scarce resources, accelerate winners, and limit costly pilots that never scale.
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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