AI Economics: An Executive Playbook for Budgeting, Measuring, and Optimizing AI Spend

Enterprises routinely underestimate the ongoing costs of production AI: cloud inference, retraining, data pipelines, model ops, and organizational overhead. This episode gives C‑level leaders and senior data executives a compact, actionable playbook to align AI spend with measurable business outcomes. In a solo monologue, Mirko walks through how to define AI unit economics, set budget guardrails, create chargeback or internal showback models, prioritize high-value features, and measure engineering productivity tied to value delivered. The episode balances financial rigor with technical realities—covering cost-aware model design, tradeoffs between latency and expense, vendor procurement levers, and governance to prevent runaway spend. Listeners will get concrete steps to build an annual AI budget, short-cycle experiments to validate cost assumptions, metrics to present to the board, and organizational practices that preserve innovation while containing cost risk.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.