Episodi

  • The Failure Envelope: Designing AI Tasks Where Mistakes Are Cheap Enough to Learn From

    The Failure Envelope: Designing AI Tasks Where Mistakes Are Cheap Enough to Learn From
    Oct 21 2026
    Most owner-CEOs stall on AI because they ask the wrong question: Can I trust it? The better question is: Can I afford the mistakes it will make while I'm learning its limits? This episode introduces the Failure Envelope, a practical framework for designing any AI task so that the worst realistic failure stays bounded, observable, and reversible. You'll learn to separate the blast radius from the task itself, set explicit failure budgets, and build a feedback loop that converts early errors into governance rules—not into crises. The episode walks through a hypothetical example of automating client follow-up, showing how to size the envelope before you grant any authority. You'll leave with a concrete next move: pick one low-stakes workflow, define its failure envelope, and run one bounded experiment this week.
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    9 min
  • The Accountability Boundary: When AI Should Advise, Not Decide

    The Accountability Boundary: When AI Should Advise, Not Decide
    Sep 20 2026
    Owner-CEOs are being told to trust AI, but when a system makes a confident mistake, the accountability lands on the human leader, not the model. This episode gives you a way to capture the upside of AI without surrendering responsibility. You'll learn how automation bias makes smart leaders defer to bad output, and why that is a leadership problem, not a technology problem. Alex introduces the Accountability Boundary framework, a simple way to classify any recurring decision into three zones: AI-autonomous, AI-assisted, and human-owned. You'll see how to apply it to real decisions like pricing, hiring, and vendor selection, and how to avoid the rubber-stamp trap that turns human review into a formality. The episode also connects the framework to Keep-Human-Own-Build-Rent decisions, so you know when owning or renting a model changes your ability to explain and defend outcomes. By the end, you'll have one concrete next move: choose a recurring decision, assign it to the right zone, and add one human confirmation step this week.
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    9 min
  • The Pre-Mortem Protocol: Stress-Testing Your AI Deployment Before You Commit

    The Pre-Mortem Protocol: Stress-Testing Your AI Deployment Before You Commit
    Oct 12 2026
    Most owner-CEOs adopt AI the way they adopt a new hire: they write the job description, pick a tool, and hope. But the highest-leverage decision isn't which AI to use — it's whether you've honestly stress-tested the deployment before it touches real work. In this episode, Alex Tran introduces the Pre-Mortem Protocol, a four-part framework that forces you to imagine your AI initiative has already failed and then work backward to identify the specific conditions that would cause that failure. You'll learn how to name the human-accountability boundary before you configure a single prompt, how to identify the data and workflow dependencies that will quietly kill your rollout, and how to turn the pre-mortem into a one-page decision document that your whole team can use. This isn't about pessimism — it's about making your AI bets more honest, more governable, and more likely to survive contact with reality. If you've ever deployed a tool that looked great in a demo and then died in your operations, this episode is for you.
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    7 min