Many organizations focus on deploying models, but few have an executive-level strategy for when and how to retire, consolidate, or re-scope models. This episode delivers a compact, operational playbook for C-level leaders and senior data executives to make lifecycle decisions that protect business value, reduce technical debt, and align AI investments with changing strategy. I’ll define clear signals for model retirement, explain cost-risk trade-offs across maintenance, retraining, and decommissioning, and map decision rights across product, data, and engineering leadership. Through concrete examples and governance checkpoints, the monologue covers how to measure ongoing ROI, surface hidden operational costs, and convert model sunset into a managed capability rather than an emergency. Listeners will walk away with a repeatable process, a prioritization rubric, and three immediate actions to reduce wasted spend and increase trust in their AI estate.
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.
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