“The pilot was successful” is not the end of the story. Episode 4 looks at the difficult transition from protected experiment to ordinary operations: budgets, permissions, support, monitoring, training, governance, workflow redesign and the context that has to travel with the artifact. Berk Bayri argues for co-ownership before transfer and asks a practical scaling question: what must become true for this system to remain valuable when the pilot team stops protecting it? Featuring a short excerpt from Satya Nadella on why AI adoption at enterprise scale is fundamentally a change-management and process-change problem.
References
Deloitte AI Institute, State of AI in the Enterprise 2026. In a survey of 3,235 business and IT leaders across 24 countries, 25% said they had moved 40% or more of AI pilots into production; 54% expected to reach that level in the following three to six months. Deloitte also reports that only 30% were redesigning key processes around AI.
https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.htmlCloudera, “The Great AI Re-Architecture” press release, August 11, 2026. Based on a survey of 1,500 enterprise architects, cloud infrastructure leads and data architects. 95% reported delaying or canceling AI initiatives in the prior year because of data governance, compliance or regulatory challenges; 72% said their data architecture required significant overhaul. This is vendor-sponsored research and should be described as such if quoted on air.
https://www.cloudera.com/about/news-and-blogs/press-releases/2026-08-11-ninety-five-percent-of-enterprises-have-delayed-ai-projects-as-infrastructure-limitations-spark-the-great-ai-re-architecture.htmlNIST, AI Risk Management Framework and Generative AI Profile. Background for lifecycle governance, monitoring, evaluation and risk management.
https://www.nist.gov/itl/ai-risk-management-frameworkhttps://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdfGet new essays as they publish.
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