Models degrade for predictable reasons—seasonality, shifting customer behavior, pipeline changes, and calendar-driven promotions—but executives rarely fund sustained freshness practices until revenue drifts. In this 20‑minute monologue Mirko opens with a concise vignette where a forecasting model missed a seasonal peak and cost inventory millions, then lays out a non-technical, decision-first playbook: define board-ready freshness signals (performance decay curves, cohort slippage, feature drift rate), map business calendars and cadence dependencies (promotions, fiscal cycles, product launches) to recalibration policies, and create a financed 'retraining runway' with explicit budget buckets for routine recalibration, emergency retrains, and validation sampling. Listeners get a 30–90 day pilot to inventory top models, set trigger thresholds, run a controlled recalibration, and present a single-page funding request to finance. Practical governance language and procurement clauses are included so leaders convert model upkeep from an invisible technical cost into a funded strategic capability. Visit datascience.show/model-freshness to download the Freshness Checklist. That’s the difference between models and value.

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