People Managing People

Businesses are pouring millions into generative AI—chatbots, copilots, “agents”—while quietly ignoring the other half of the AI stack that’s been delivering measurable value for decades. Predictive AI doesn’t write poetry. It predicts who’s going to churn, which transaction is fraud, and which customer is worth contacting. It calculates probabilities and helps you act on them at scale. Not glamorous. Just effective.
In this conversation, Eric Siegel—author of The AI Playbook and founder of Machine Learning Week—makes a subversive claim: most organizations should be investing at least as much in predictive AI as generative AI. The problem isn’t the math. It’s the gap between tech and business. Companies celebrate models as value. But the model isn’t the value. Acting on predictions is.
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You were told AI would clear your calendar. Instead, you’re answering 800 chats a day and wondering what, exactly, you accomplished. Productivity is up. So is the volume. You 10x your output and somehow inherit 10x the work. Welcome to the hamster wheel.
In this episode, Eliza Jackson, COO at ButcherBox, and I unpack the real transformation behind AI at work. It’s not about learning a new tool. It’s about unlearning how you work. It’s about rethinking what you own versus what you delegate. And it’s about building the kind of resilience and mindset that don’t show up on a résumé—but determine whether your team can survive what’s coming.
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Most employees are using about 1% of what AI can actually do. Not because they’re lazy. Not because they lack access. But because no one has shown them how to think with it. Meanwhile, somewhere in Silicon Valley, a 23-year-old is running a startup like they’ve got 28 PhDs sitting beside them—for a penny a minute. That gap isn’t theoretical. It’s operational. And it’s widening by the hour.
In this conversation, Kevin Surace and I dig into what that gap really means—for your productivity, your profession, and your relevance. From three-paragraph prompts to million-dollar consulting projects replicated in minutes, we explore why this wave looks familiar (desktop computers, the internet, Excel) and why it’s moving faster than all of them. Resistance isn’t noble. It’s career-limiting.
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So yeah—your dashboards look great. Your team’s shipping faster, summarizing more, “getting leverage” with AI… and all the while you might be quietly trading away the one asset you can’t buy back on a subscription plan: human judgment.
In this episode, I’m joined by Dr. Vivienne Ming—neuroscientist, entrepreneur, and an “AI realist” who has zero patience for utopian hype or Skynet fan fiction. Vivienne lays out a clean fork in the road: cognitive automation (AI does the thinking for you) vs. cognitive augmentation (AI makes you think better—often by making the work harder). If your AI strategy is mostly about convenience, this is your gentle-ish warning that convenience is not a strategy. It’s a sedative.
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AI didn’t replace your job—it replaced your value proposition. In this episode, we sit down with Taylor Blake, SVP of AI Labs at Degreed, to talk about the uncomfortable truth facing L&D teams: if your job is framed as delivering content, unblocking employees, or feeding answers in the flow of work, AI is already doing it better, faster, and without your calendar invite.
But where AI stops short is precisely where L&D’s future begins. Taylor shares how her team at Degreed lives as “customer zero,” using their own tools before shipping them to clients—which means they’re embedded in the mess, not just pitching the promise. From readiness over responsiveness to the emotional toll of relentless efficiency, this conversation explores what it really means to build capability in a world where one employee now has the power—and pressure—of ten.
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