Model Retirement: An Executive Playbook for Responsible End‑of‑Life in Enterprise AI

Most enterprises obsess about model build and deployment—far fewer plan for model retirement. This episode gives C-level leaders a practical, executive-focused playbook to treat model end-of-life as a strategic discipline. Mirko walks listeners through why planned decommissioning reduces risk, saves operating costs, preserves auditability, and prevents technical debt from turning into business exposure. Through clear decision criteria, governance checkpoints, legal and data-retention considerations, and step-by-step operational steps—from observability triggers to stakeholder communications and archival strategies—leaders will learn how to embed retirement into the ML lifecycle. The monologue includes real-world decision rules for when to patch, retrain, shadow, or retire models; cost‑benefit heuristics for replacement versus refactor; and governance patterns that align product, legal, and engineering stakeholders. Executives will leave with a concise checklist to operationalize model retirement across finance, risk, compliance, and engineering so AI programs stay sustainable, auditable, and aligned to business goals. Subscribe to stay informed.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.