(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.

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I share practical AI leadership notes on LinkedIn — the kind you can forward internally or reuse in executive discussions.
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