エピソード

  • From Proof to Product: The Executive Playbook for AI Product Management

    From Proof to Product: The Executive Playbook for AI Product Management
    2026/07/25
    Many enterprises stall at pilots because they treat machine learning as a project, not a product. This episode gives C-level leaders a practical playbook for building AI product management as a repeatable capability: a governance-backed lifecycle that aligns discovery, data and feature ownership, model delivery, product metrics, monetization, and cross-functional funding. Mirko walks listeners through role definitions, roadmaps, success metrics that tie to business KPIs, and organizational patterns that turn prototypes into durable business units. You’ll hear concrete trade-offs—speed vs. robustness, centralization vs. embedded teams—and real decisions leaders must make when balancing risk, cost, and time-to-value. The focus is operational: how to structure investment, define SLAs for data and features, embed product managers with engineering and domain teams, and measure causal impact. Practical, executive-level guidance for turning experimentation into predictable outcomes and measurable ROI.

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    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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    9 分
  • Causal ROI: An Executive Playbook to Measure Real Business Impact of AI

    Causal ROI: An Executive Playbook to Measure Real Business Impact of AI
    2026/07/24
    Many AI initiatives report technical metrics but fail to prove business impact. This episode gives C-level leaders a practical, non-technical playbook for turning models into accountable investments by embedding causal measurement, controlled experimentation, and incremental rollout strategies into enterprise AI programs. Mirko walks listeners through real-world executive decisions: choosing causal vs. correlational evaluation, designing business-aligned A/B and quasi-experiments, instrumenting metrics and guardrails, and creating governance that insists on measurable outcomes before scale. The episode explains trade-offs between speed and statistical rigor, how to interpret heterogeneous treatment effects for different customer segments, and governance patterns that convert measurement into funding and de-risking mechanisms. Leaders will come away with concrete steps to require causal evidence, avoid common measurement traps, and structure organizations so product, analytics, and engineering jointly own ROI. Practical, tactical, and immediately actionable for executives responsible for AI investments.

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    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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    7 分
  • Model Observability for Executives: Turning Alerts into Business Confidence

    Model Observability for Executives: Turning Alerts into Business Confidence
    2026/07/23
    Executives routinely hear about model drift, data skew, and false positives, but struggle to understand which signals actually matter for the business. This episode walks senior leaders through a pragmatic, product-minded approach to model observability: how to pick the right metrics, translate technical telemetry into business KPIs, design escalation and runbooks, and fund operational controls that reduce risk and unlock value. The monologue explains concrete observability layers (data, prediction, feature, and outcome), examples of meaningful SLOs and alerts, and governance patterns that make monitoring auditable and actionable. Listeners will gain an executive checklist to hold teams accountable, a decision framework for investments in monitoring and tooling, and practical guidance on measuring ROI from reduced incidents, improved model uptime, and faster remediation. The tone is operational, strategic, and directly applicable for C-level leaders responsible for production AI.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    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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    8 分
  • Changing Behavior: A C-Level Playbook to Embed AI into Everyday Decisions

    Changing Behavior: A C-Level Playbook to Embed AI into Everyday Decisions
    2026/07/22
    Many AI projects fail not for technical reasons but because organizations don’t change the decision architecture that surrounds models. This episode is a practical C-level monologue that lays out a playbook for embedding AI into everyday business decisions: aligning KPIs, redesigning incentives, shifting governance, and operationalizing feedback loops so models influence behavior reliably and ethically. Mirko frames the conversation around a senior guest profile—an experienced Chief Data & AI Officer at a global enterprise—and walks listeners through concrete patterns: choosing the right decision boundary, converting model outputs into operable signals, building measurement and accountability, and avoiding common behavioral failure modes. Executives will get prioritized tactics for short-term wins and an organizational roadmap that moves initiatives from pilot to repeatable impact. The emphasis is actionable: metrics to track, governance guardrails, cross-functional roles, and a stepwise rollout sequence that C-suite leaders can sponsor and audit.

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    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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    8 分
  • Internal Pricing for AI: How Chargebacks and Product Pricing Turn Models into Sustainable Business Units

    Internal Pricing for AI: How Chargebacks and Product Pricing Turn Models into Sustainable Business Units
    2026/07/21
    Many enterprise AI efforts stall not because models fail but because incentives, visibility, and funding are misaligned. This episode gives C-level leaders a pragmatic playbook for designing internal pricing and chargeback models that make AI costs transparent, encourage responsible consumption, and drive repeatable ROI. I introduce a senior AI product leader as the guest profile and walk through real-world design patterns: usage-based pricing for model inference, fixed subscription for data products, value-based pricing for decision automation, and hybrid approaches that balance experimentation with cost control. You’ll hear concrete governance rules, billing telemetry to collect, how to avoid perverse incentives, and sample KPIs that translate to executive budgets. The goal is practical: help leaders decide when to subsidize, when to charge, and how to use pricing as a lever to productize AI, prioritize scarce engineering capacity, and measure economic impact.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    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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    9 分
  • AI Investment Portfolio: An Executive Playbook to Prioritize, Fund, and De‑risk AI Initiatives

    AI Investment Portfolio: An Executive Playbook to Prioritize, Fund, and De‑risk AI Initiatives
    2026/07/20
    Most organizations run AI projects as isolated bets: promising pilots, scattered budgets, uneven governance, and inconsistent outcomes. This episode delivers a practical, exec-level playbook for treating AI initiatives as a coherent investment portfolio that aligns with strategy, risk appetite, and measurable ROI. I walk leaders through portfolio segmentation (core vs. exploratory vs. platform), stage-gated funding, risk-adjusted valuation, go/kill criteria, and mechanisms to surface technical debt and delivery risk early. You’ll get decision-ready tools for prioritization, cross-functional accountability, capacity planning, and executive dashboards that move teams from experiments to sustained, measurable value. Real-world trade-offs, common failure modes, and governance patterns are examined with an eye toward pragmatic adoption at enterprise scale. By the end, listeners will have a repeatable framework to allocate scarce resources, accelerate winners, and limit costly pilots that never scale.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    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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    8 分
  • The Responsible AI Executive Scorecard: KPIs That Turn Ethics into Business Outcomes

    The Responsible AI Executive Scorecard: KPIs That Turn Ethics into Business Outcomes
    2026/07/19
    For executives the language of ethics and governance often feels disconnected from balance sheets. This episode delivers a practical, executive-focused playbook for defining, measuring, and governing Responsible AI through a compact scorecard that drives decisions. Mirko walks listeners through selecting a minimal set of KPIs—covering performance, fairness, safety, explainability, data quality, cost, and adoption—that map directly to business risks and objectives. You'll hear how to set thresholds, assign ownership, embed metrics into product and investment gates, and create an executive dashboard that supports audits, regulatory requests, and board reporting. The episode emphasizes trade-offs, common measurement traps, and how to keep the scorecard lean and action-oriented so it scales with the organization. Intended for CEOs, CTOs, CDOs, heads of analytics, and senior data leaders, this monologue translates Responsible AI from abstract principles into operational controls that preserve value while managing risk.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    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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    9 分
  • Scaling Human-in-the-Loop AI: Executive Design Patterns for Reliable Collaboration

    Scaling Human-in-the-Loop AI: Executive Design Patterns for Reliable Collaboration
    2026/07/18
    Enterprises increasingly depend on systems where humans and automated models collaborate—fraud review queues, content moderation, clinical decision support, and assisted sales. This episode gives C-level leaders a pragmatic playbook for turning isolated HITL experiments into reliable, auditable, and cost-effective operational systems. Mirko lays out strategic decision points—when to automate, when to route to people, and how to allocate human effort for maximum marginal value. The episode covers concrete design patterns (triage, confidence-based routing, human review as a feature), measurement and KPIs that translate to ROI, governance and accountability for mixed decision workflows, and operational scaling levers including staffing models, tooling, and continuous training loops. Listeners walk away with an executive checklist to evaluate HITL use cases, reduce false positives and churn, and embed human oversight without creating bottlenecks or hidden costs.

    Become a supporter of this podcast: https://www.spreaker.com/podcast/datascience-show-podcast--6817783/support.

    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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    8 分