Leaders often ask why high-performing models don’t translate to measurable business outcomes. This episode presents a focused playbook for metric engineering—the discipline of mapping business KPIs to model objectives, evaluation metrics, and measurement plumbing so AI efforts reliably move the needle. Mirko walks through concrete patterns: decomposing top-line metrics into decisionable signals, designing offline proxies that correlate with live impact, aligning loss functions with commercial value, building attribution and experiment plans, and establishing measurement SLAs. The episode addresses common traps—misaligned incentives, surrogate metrics that mislead, and measurement latency—and offers governance and organizational practices to embed metric ownership. Designed for C-suite and senior data leaders, the monologue gives practical steps to reduce uncertainty, prioritize investments, and create an end-to-end measurement discipline that turns models into accountable business levers.
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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