Most organizations treat analytics as projects; product-minded leaders treat them as repeatable products. This episode gives C-level listeners a practical playbook for converting insights, models, and data services into reliable data products with clear customers, SLAs, and unit economics. It covers how to define product-market fit for internal consumers, when to monetize externally, the roles and funding models that make products sustainable, and the engineering and governance practices required for scale (APIs, versioning, contracts, and observability). You’ll hear an outcome-first approach to prioritization, trade-offs between speed and reliability, and measurable success metrics leaders can use to hold teams accountable. The monologue focuses on decisions executives must own—investment criteria, ROI guardrails, product leadership, and legal/compliance implications—so organizations move from one-off proofs to repeatable product value.
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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