Building the AI Runway: Executive Capacity Planning to Sustain AI at Scale

Many AI initiatives stall not because the models are weak but because organizations run out of runway: data availability, compute, talent, or governance capacity. This episode gives C-level leaders a concise, operational framework to build a multi-year AI runway that aligns strategy, budget, and operational reality. Mirko walks through how to quantify dataset velocity, forecast feature engineering throughput, size compute and storage for production workloads, plan hiring and skill shifts, and bake governance and compliance into capacity decisions. The approach focuses on decision-driven metrics, cross-functional slos, and sanity checks that separate optimistic experiments from fundable, repeatable programs. Listeners will get an executive checklist, three realistic forecasting templates, and example trade-offs—so you can present a defensible three-year AI capacity plan to your board or executive committee.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.