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.
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.
Podden och tillhörande omslagsbild på den här sidan tillhör
Mirko Peters. Innehållet i podden är skapat av Mirko Peters och inte av,
eller tillsammans med, Poddtoppen.
