DataScience Show Podcast

Too many AI programs fail not for lack of models but because incentives push the wrong behavior: teams optimize vanity metrics, vendors chase one‑time uplift, and business owners avoid ownership of outcomes. In this 20‑minute executive monologue Mirko lays out a compact, pragmatic playbook for designing incentives and performance systems that tie funding, career signals, and product KPIs to measurable business outcomes. The episode explains three incentive levers (funding cadence, metrics architecture, and career/accountability design), gives concrete examples of misaligned incentives and how they produced measurable harm, and presents a repeatable rubric to choose metrics that resist gaming (multi-horizon measures, cohort-based LTV, cost‑to‑serve). Listeners receive a prioritized 30–90 day checklist to audit current incentives, sample KPI translations for finance/product/data, and negotiation language to align procurement and legal. Practical, non‑technical, and immediately actionable for leaders who must turn pilots into sustained value.

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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.

Executives often demand reliability from AI but lack a shared language to measure it. In this 20-minute monologue Mirko opens with a concise vignette where unseen model latency and stale features caused revenue slippage, then delivers a compact, decision-first playbook for Service-Level Objectives (SLOs) tailored to models and data. Listeners learn how to define business-aligned SLOs (accuracy bands, latency windows, freshness, fairness thresholds), set error budgets, choose a minimal monitoring signal set that executives can read, and map SLO breaches to concrete decision gates and funding actions. Practical artifacts include a board-ready SLO template, example alert thresholds, and a prioritized 30–90 day pilot plan to embed SLOs into governance. The episode keeps trade-offs explicit and non-technical so leaders can commission measurable reliability commitments. CTA: download the Executive SLO Template and 30–90 Day Playbook at datascience.show/slo. That’s the difference between models and value.

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.

Dynamic pricing can unlock margin and responsiveness, but when algorithmic prices misalign with customer expectations or regulation, revenue wins can turn into churn, complaints, and legal risk. In this 20‑minute executive monologue Mirko presents a concise playbook for governing algorithmic pricing: translate pricing goals into board-ready SLOs (price stability, realized uplift, churn elasticity), detect economic and fairness drift, set tolerance bands and automated rollback gates, and convert technical signals into commercial decision rules. The episode opens with a short anonymized vignette where a miscalibrated model produced frequent outlier prices and measurable churn, then walks listeners through a prioritized 30–90 day audit and remediation checklist, contract and procurement clauses to insist on with vendors, and practical metrics to report to the board. Listeners leave with immediate actions and a downloadable one-page Algorithmic Pricing Governance Checklist to brief legal, product, and finance teams.

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.

Many leaders celebrate accurate models but can’t prove they change outcomes. In this 20‑minute episode Mirko opens with a vivid executive vignette: a personalization pilot that tripled engagement yet didn’t move revenue, and uses that story to frame a compact, non‑technical playbook—what he calls causal decisioning—for proving AI actually drives value. He defines causal decisioning and the term “uplift” (the measured change caused by an intervention) in plain English, explains which minimal experiment designs leaders should demand, and includes a short two‑minute worked example showing a simple uplift calculation and how to read a sample dashboard. Practical rollout patterns, governance and consent checkpoints, and a prioritized 30–90 day checklist are provided. Listeners leave with board‑ready KPI translations and a link to download a Causal Decisioning Toolkit (experiment brief, dashboard template, legal checklist) so they can commission evidence and tie funding to measurable ROI.

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.

Enterprises routinely hit practical limits: unavailable or sensitive data, rare-event gaps, and slow procurement that stalls valuable AI projects. In this focused 20‑minute episode Mirko gives senior leaders a pragmatic, decision-first playbook for using synthetic data as a strategic lever—not a silver bullet. Listeners get a short anonymized micro‑case showing measurable business impact, a plain‑language decision rubric (when to substitute, augment, or avoid synthetic data), board‑friendly ROI metrics (time‑to‑data, labeling cost delta, model performance vs baseline), and the concrete governance and contract artifacts executives must insist on. The episode closes with a prioritized 30–90 day checklist, negotiation language for procurement, and a 60–90s practitioner clip with hard lessons from a real pilot. Deliverables: a downloadable five‑item Executive Playbook and template vendor clauses to take to legal and procurement.

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.

(00:00:00) Welcome to Datascience Dot Show

(00:00:23) The Hidden Risk of Embedded AI in M&A

(00:01:52) Pre-Deal AI Due Diligence Checklist

(00:03:33) Fast Signals for Model and Data Health

(00:05:15) Translating Findings into Deal Mechanics

(00:06:46) Legal and IP Red Flags to Watch Out For

(00:08:13) 30-90 Day Integration Playbook

(00:10:58) Case Study: AI Integration Challenges

(00:11:53) Three Executive Actions for AI in M&A

(00:12:43) Mitigating AI Risks in Deals

Mergers and acquisitions routinely misprice or miss downstream costs of embedded AI: tangled data lineage, undocumented models, unenforceable IP claims, or regulatory exposures can turn strategic acquisitions into recurring liabilities. In this 20‑minute executive monologue Mirko delivers a decision‑first playbook for buyers and integration sponsors. He walks through focused AI due diligence (what to ask in 30–90 minutes of executive interviews), a lightweight technical checklist for validating model and data health without deep engineering work, legal and IP red flags to surface, and a prioritized post‑close integration plan that preserves optionality and reduces run-rate. Listeners get board‑ready metrics to translate technical findings into price adjustments and escrow triggers, negotiation levers to allocate remediation costs, and a 30–90 day integration roadmap to onboard models, align SLAs, and retire redundant pipelines. Practical, non‑technical, and immediately actionable for deal teams and executives. That’s the difference between models and value.

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.

(00:00:00) Welcome to Data Science Dot Show

(00:00:24) The Hidden Dangers of AI System Retirement

(00:01:52) Identifying Retirement Signals in AI Models

(00:03:44) The Decision Rubric for Model Retirement

(00:05:48) Practical Blueprints for Model Transition

(00:06:48) Governance and Communication in Model Retirement

(00:08:30) Budgeting and Funding for Model Transitions

(00:09:19) Implementing a Model Retirement Process

(00:10:03) The 30-90 Day Model Retirement Playbook

(00:11:10) Closing Thoughts and Call to Action

AI lifecycles end as surely as they begin—yet most organizations lack an executive process to retire, replace, or repurpose models and datasets safely. In this focused monologue Mirko provides a decision‑first playbook that helps leaders identify retirement signals (drift, rising run-rate, opportunity cost, regulatory or contractual change), apply a pragmatic rubric balancing business value, risk, and technical debt, and run a prioritized decommissioning program. The episode covers stakeholder communication (internal owners, customers, regulators), legal and audit obligations for data retention and provenance, migration patterns (dual-run validation, phased rollback, staged sunset), and how to budget transitional costs so teams can stop subsidizing legacy systems. Listeners get a 30–90 day checklist to inventory candidates, cost ongoing run-rate vs replacement, define rollback and observability requirements, and embed retirement gates into governance. Practical, non‑technical, and action-oriented, this episode helps executives remove hidden liabilities and preserve strategic optionality.

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.

(00:00:00) Welcome to Data Science Dot Show

(00:00:27) AI on the Balance Sheet: A Boardroom Perspective

(00:00:45) The Six Million Dollar Model Error

(00:01:18) Translating Model Risk into ERM Language

(00:02:15) Unpacking the Mini Case: A Step-by-Step Analysis

(00:03:06) Mapping AI Risk to ERM Categories

(00:03:51) Key Metrics for AI Risk Assessment

(00:05:11) Mitigating AI Risk: Three Levers

(00:05:58) Leadership and Decision Rights in AI Risk Management

(00:06:44) A Prioritized Playbook for AI Risk Management

Boards often treat AI as a technical issue rather than a balance-sheet exposure. In this 20-minute executive monologue Mirko reframes AI as an enterprise risk that must be managed inside ERM. The episode opens with a concrete mini-case — a hypothetical pricing-model error that shaved 3% off a quarterly revenue number (for example, roughly $6M on a $200M quarter) — to show how model failures translate to dollars and timelines. Mirko then walks executives through mapping model, data, vendor, and operational risks to standard ERM categories and translates key metrics into plain English (e.g., loss-velocity = how fast an error becomes a financial loss). Listeners receive a prioritized 30–90 day playbook and a downloadable AI-ERM Board Pack: one-page PDF heatmap, Excel metric template, and checklist to use with audit committees. Tone is pragmatic and executive-first: convert technical gaps into budget asks, insurance choices, and clear decision rights.

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.

(00:00:00) Welcome to Datascience Dot Show

(00:00:29) The Hidden Costs of AI Pilots

(00:02:30) Mapping Outcomes to Sourcing Strategies

(00:03:52) Four Common Approaches to Foundation Models

(00:04:37) Legal and Procurement Checklist

(00:05:18) Total Cost of Ownership Considerations

(00:06:41) Data Rights and Exit Clauses

(00:07:29) Operational and Security Considerations

(00:08:05) Organizational Implications and Procurement Cadence

(00:08:39) 30-Day Checklist for Procurement

Large language and multimodal foundation models offer capability leaps but introduce complex procurement, cost, and legal trade-offs that routinely stall enterprise adoption. In this monologue Mirko lays out a pragmatic executive playbook for buying—versus building—foundation models responsibly. The episode covers how to scope business outcomes, compare licensing models (hosted API, private deployment, fine-tuning), map true TCO (compute, data ops, monitoring, latency/SLA costs), assign contractual risk (data ownership, IP, reverse-engineering, security), and design exit and portability clauses before signing. Mirko uses concise, anonymized vignettes to show common procurement pitfalls and executive negotiation levers that protect margin and compliance. Listeners receive a prioritized 30–90 day checklist to assess current contracts, power conversations with procurement and legal, and a simple decision rubric to choose the model sourcing approach that aligns with strategy and risk appetite. Practical, non-technical, and board-ready guidance for leaders who must buy capability without buying long-term surprise 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.

(00:00:00) Welcome to Datascience Dot Show

(00:00:26) The Importance of AI Incident Simulations

(00:02:22) Four High-Impact AI Scenario Families

(00:03:32) Designing Effective Tabletop Exercises

(00:04:05) Defining Decision Gates and Roles

(00:05:52) Measuring Readiness and Post-Mortems

(00:07:28) Implementing a 90-Day Rollout Plan

(00:08:13) Actionable Steps and Closing Remarks

(00:08:53) Downloadable Resources

(00:09:26) Subscription and Next Episode

Organizations prepare for cyber incidents but rarely rehearse AI-specific failures—model drift, hallucinations in customer agents, pricing errors, or biased automated decisions. In this monologue Mirko delivers a practical C‑suite playbook for designing and running AI incident simulations and tabletop exercises that make abstract risks operationally manageable. He explains how to choose scenario scope and severity, create realistic triggers, assign clear decision gates and escalation paths, coordinate legal/comms/regulatory playbooks, and measure readiness with board-ready metrics. Through concise anonymized vignettes Mirko highlights trade-offs (high-impact/low-probability vs frequent operational faults) and shows how to convert exercise outcomes into governance changes, funding requests, and measurable SLA improvements. Listeners receive a prioritized 30–90 day rollout plan, a tabletop script template, and guidance for turning simulations into continuous improvement. This episode is for executives who need AI systems that are resilient, auditable, and decision-ready without adding bureaucracy.

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.