エピソード

  • Jidoka for Agents: Building AI That Knows When to Stop

    Jidoka for Agents: Building AI That Knows When to Stop
    2026/08/31
    Sakichi Toyoda's breakthrough was not a loom that could handle a broken thread. It was a loom that noticed the thread had broken and stopped itself — which meant one operator could run dozens of machines instead of standing guard over one. That principle became jidoka, and Chris Bradley argues it is the most useful idea available to anyone deploying AI agents in an enterprise today.
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    24 分
  • Context, Loops, and Graphs

    Context, Loops, and Graphs
    2026/08/17
    Chris Bradley argues the churn is hiding something worth understanding. Those terms don't replace each other; they stack. And each one names a layer of the same discovery: that the model is not what separates an AI program that works from one that doesn't.
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    21 分
  • The ROI Reckoning

    The ROI Reckoning
    2026/08/03
    Chris Bradley takes on the number that reset the enterprise AI conversation — a widely-cited MIT study finding 95% of pilots delivered no measurable return — and locates the real cause. Much of what is sold as agentic AI is generative AI wearing an agent title. It advises. A person still completes the work. And the promised time savings were never structurally possible.
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    24 分
  • The Art of Rightsizing AI

    The Art of Rightsizing AI
    2026/07/20
    In this episode, Chris Bradley makes sense of a landscape that shifted fast in June and July. A new most-capable model. A family that ships in three deliberate sizes. And a wave of open-weight models from the US and China that companies can run inside their own walls. His frame for all of it is rightsizing: matching the model to the work the way a manager matches a person to a task. He sorts the work into three tiers, the Clerk for high-volume routine work, the Operator for the reliable execution that runs the business, and the Expert for the hard, high-stakes reasoning.
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    19 分
  • Build vs. Buy in the Age of Agentic AI

    Build vs. Buy in the Age of Agentic AI
    2026/07/06
    In this episode I turn that gut check into a decision you can defend. The key is a better question — not “should we build or buy?” but “how much orchestration does this work actually need?” That question sorts every AI decision into three tiers. Work that lives inside one app — buy the AI already there. Work that crosses standard systems — bridge them with an orchestration platform. Work that runs through your customized core, where no pre-built connector reaches — build it.
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    20 分
  • The Nine Wastes of AI

    The Nine Wastes of AI
    2026/06/22
    Most teams look at a slow process and ask one question — can AI do this faster? It’s the wrong first question. The better one is harder: should this work exist at all? In this episode I borrow a seventy-year-old lens from the Toyota Production System — the wastes of Lean — and point it at knowledge work, where waste hides far better than it ever did on a factory floor.
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    26 分
  • Kaizen + Agentic AI: The Enterprise AI Transformation Playbook

    Kaizen + Agentic AI: The Enterprise AI Transformation Playbook
    2026/06/08
    In this episode, Chris Bradley makes the case that Continuous Improvement is the non-negotiable core of agentic AI. Recorded from Veritiv's Customer Service center in Jacksonville — the day before a three-day Kaizen — he lays out a simple model for bringing AI to any process: three levels, Automate, Improve, and AI-Native Rebuild. Level one paves the cow path. Level two strips the waste with Lean before automating. Level three rebuilds the process from scratch around what agents can now do — and you can't earn it without doing level two first.
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    23 分
  • Your Agentic DNA — The Portable Layer That Travels With You

    Your Agentic DNA — The Portable Layer That Travels With You
    2026/05/25
    This episode is about the fix — building a portable layer of identity, memory, and skills that travels with you across every environment. I call it your Agentic DNA. A small set of files you own, that capture the version of you AI needs to know about, that deploy into any new harness in minutes instead of months.
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    23 分