(00:00:00) Welcome to Data Science Dot Show

(00:00:24) The Hidden Dangers of AI System Retirement

(00:01:52) Identifying Retirement Signals in AI Models

(00:03:44) The Decision Rubric for Model Retirement

(00:05:48) Practical Blueprints for Model Transition

(00:06:48) Governance and Communication in Model Retirement

(00:08:30) Budgeting and Funding for Model Transitions

(00:09:19) Implementing a Model Retirement Process

(00:10:03) The 30-90 Day Model Retirement Playbook

(00:11:10) Closing Thoughts and Call to Action

AI lifecycles end as surely as they begin—yet most organizations lack an executive process to retire, replace, or repurpose models and datasets safely. In this focused monologue Mirko provides a decision‑first playbook that helps leaders identify retirement signals (drift, rising run-rate, opportunity cost, regulatory or contractual change), apply a pragmatic rubric balancing business value, risk, and technical debt, and run a prioritized decommissioning program. The episode covers stakeholder communication (internal owners, customers, regulators), legal and audit obligations for data retention and provenance, migration patterns (dual-run validation, phased rollback, staged sunset), and how to budget transitional costs so teams can stop subsidizing legacy systems. Listeners get a 30–90 day checklist to inventory candidates, cost ongoing run-rate vs replacement, define rollback and observability requirements, and embed retirement gates into governance. Practical, non‑technical, and action-oriented, this episode helps executives remove hidden liabilities and preserve strategic optionality.

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I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
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