AI Investment Portfolio: A C-Level Playbook to Prioritize and Fund AI Initiatives

Executives face a steady stream of AI proposals but rarely a disciplined method to prioritize, fund, and scale the ones that produce measurable business value. This episode introduces a pragmatic AI investment portfolio framework for C-level leaders: define expected value and risk profiles, adopt stage-gated funding, balance short-term operational wins with strategic bets, and align capacity across data, engineering, and governance. I unpack concrete metrics—expected value, time-to-impact, cost-to-production—and a simple scoring model plus an executive review cadence that converts pilots into a diversified portfolio. Through concise, real-world examples I show common trade-offs (double down, pivot, or sunset), resource reallocation strategies, and how to avoid “pilot trap” churn. The monologue closes with governance templates, scoring pitfalls to avoid, and a repeatable 90-day playbook for prioritization and funding decisions that help leaders maximize ROI and institutionalize sustained AI value.Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.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.