The Useful Unkown

Berk Bayri
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A podcast about strategy, AI, innovation, and the messy space between knowing and doing. I explore how better questions, sharper thinking, and useful uncertainty can lead to better decisions, products, and work.

Episodes

  1. 23h ago

    The handoff nobody designed

    “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.html Cloudera, “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.html NIST, 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.pdf

  2. 6d ago

    A pilot is a question, not a small product

    The best pilot is not the smallest product you can build. It is the smallest experiment that still exposes the uncertainty capable of killing the idea. Pilots become dangerous when they acquire a second job: prove that the project deserves to scale. Episode 3 reframes the pilot as a decision instrument rather than a miniature product. Berk Bayri looks at polished demos, dirty data, whole-workflow measurement, stop criteria, governance and the politics of sponsorship. The central idea is simple: a successful pilot is one that leaves the organization knowing something important it did not know before — even when that knowledge is “do not continue.” Featuring a short excerpt from Jeff Bezos on the difference between experimental failure and operational failure. References UK Government / Behavioural Insights Team, AI-Assisted vs Human-Only Evidence Review: Results from a Comparative Study (published 2025). The AI-assisted review was completed 23% faster overall, with large time savings in analysis and synthesis but more time required for revisions. The authors explicitly caution that this is a case study and not broadly generalisable.https://www.gov.uk/government/publications/ai-assisted-vs-human-only-evidence-review/ai-assisted-vs-human-only-evidence-review-results-from-a-comparative-study NIST, AI Risk Management Framework. The voluntary framework is designed to incorporate trustworthiness considerations into the design, development, use and evaluation of AI systems. NIST’s current site notes that AI RMF 1.0 is under revision and that a Critical Infrastructure Profile concept note was released in April 2026.https://www.nist.gov/itl/ai-risk-management-framework NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST-AI-600-1). Used as background for lifecycle risk, evaluation and governance considerations specific to generative AI.https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf

  3. 6d ago

    Stop looking for AI use cases

    The use-case workshop feels productive because it produces visible output. But a spreadsheet of AI ideas can become a substitute for understanding where the organization is actually paying for friction, uncertainty and bad decisions. Most companies do not have an idea shortage. They have a problem-selection problem. In Episode 2, Berk Bayri takes apart the familiar “AI use-case” workshop and replaces it with a more demanding question: where is the organization repeatedly paying for friction, uncertainty or poor decisions? Through sales, content and productivity examples, the episode shows why the same AI tool can create very different outcomes depending on task, expertise and workflow. It also explains why specificity matters more as software gains more autonomy. Featuring a short excerpt from Andrew Ng on the advantage of concrete, falsifiable ideas. References Stanford HAI, 2026 AI Index Report — Economy. 88% organizational AI adoption; 70% of organizations using generative AI in at least one function; agent deployment remained in the single digits across nearly all business functions.https://hai.stanford.edu/ai-index/2026-ai-index-report/economy Deloitte AI Institute, State of AI in the Enterprise 2026. Survey of 3,235 business and IT leaders across 24 countries. 30% said they were redesigning key processes around AI; 37% reported relatively surface-level usage with little or no change to underlying processes; only 21% reported mature governance for agentic AI.https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html Erik Brynjolfsson, Danielle Li and Lindsey R. Raymond, Generative AI at Work, Quarterly Journal of Economics 140(2), 2025. AI assistance increased customer-service agent productivity by 15% in the studied firm, with heterogeneous effects across workers.https://academic.oup.com/qje/article/140/2/889/7990658 METR, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity. Randomized controlled trial: in this specific setting, AI access increased task completion time by 19%.https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ METR, We are Changing our Developer Productivity Experiment Design, February 2026. METR says late-2025 tools likely provide greater speedups, but selection effects make the newer estimate unreliable.https://metr.org/blog/2026-02-24-uplift-update/

  4. 6d ago

    Solving the wrong problem brilliantly

    The most dangerous transformation failure is not always a bad solution. Sometimes it is a very good solution aimed at a problem that never deserved the effort. This episode is about how leaders can separate technological momentum from actual strategic value. AI can make teams faster, more productive and more convincing before anyone has proved that the work matters. In this episode, we look at the organizational reflex behind “we need a website,” “we need an app,” and now “we need AI” — and why the better starting point is evidence, consequence and the quality of the problem itself. The episode moves from support automation and UX research to executive pressure, innovation fatigue and the hidden cost of spending organizational belief on the wrong bet. Featuring a short external excerpt from Tim Brown on the importance of starting with the right question. References Stanford Institute for Human-Centered Artificial Intelligence, 2026 AI Index Report — Economy. Organizational AI adoption reached 88% of surveyed organizations in 2025; generative AI was used in at least one business function at 70%; agent deployment remained in the single digits across nearly all business functions.https://hai.stanford.edu/ai-index/2026-ai-index-report/economy Deloitte AI Institute, The State of AI in the Enterprise: The Untapped Edge (2026). Survey of 3,235 business and IT leaders across 24 countries. Deloitte reports that 34% of companies described AI as being used to deeply transform their business; 37% reported surface-level use with little or no change to underlying processes.https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html NIST, AI Risk Management Framework. Used as background for the emphasis on context, measurement, governance and lifecycle thinking rather than technology-first deployment.https://www.nist.gov/itl/ai-risk-management-framework

  • 4 Episodes

About

A podcast about strategy, AI, innovation, and the messy space between knowing and doing. I explore how better questions, sharper thinking, and useful uncertainty can lead to better decisions, products, and work.

Information

  • Creator
    Berk Bayri
  • Years Active
    2026
  • Episodes
    4
  • Rating
    Clean
  • Copyright
    © Berk Bayri
  • Show Website
    The Useful Unkown