AI in HR is finally moving out of the pitch deck and into the messy reality of day-to-day operations. In this conversation, Tim Fisher sits down with Josh Rod from HiBob to unpack what’s actually changing—and what’s still just noise. The headline? Most organizations aren’t chasing some agentic, fully automated future. They’re trying to make today’s workflows less painful, faster, and marginally more effective.

But underneath that pragmatic adoption sits a deeper shift: the structure of work itself is being quietly rewritten. When every employee becomes a “manager” of AI, the old hierarchies start to wobble. HR isn’t just implementing tools anymore—it’s being asked to design the operating system for how humans and machines collaborate. And that’s where things get interesting.

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You don’t need to be using AI constantly to be hooked on it—you just need to feel relief when you do. That’s the uncomfortable premise at the heart of this conversation with psychologist and conflict expert Dr. David Zierk. AI doesn’t just give you answers; it removes uncertainty. And in doing so, it quietly rewires how you think, learn, and connect.

What starts as convenience can quickly become dependency. Leaders, in particular, are at risk of trading away the friction that produces insight, the curiosity that fuels growth, and the empathy that sustains relationships. The result? Faster answers, thinner thinking, and a growing gap between knowing what to do and actually doing it.

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AI is supposed to free people up for “higher-value work.” Fine. But what, exactly, is that work? In this episode, David Rice talks with cyberpsychology researcher and psychotherapist Dr. Rachel Wood about the part of AI adoption most organizations keep skimming past: the human cost of automating too much, too quickly, without a real philosophy for what should remain deeply, stubbornly human.

Their conversation cuts through the usual AI optimism and gets to the more uncomfortable truth. Some friction should absolutely go away. Nobody needs to spend an hour copying and pasting spreadsheet data. But some friction is the job: disagreement, discernment, hard conversations, learning by getting things wrong, and figuring out who you are when your expertise is suddenly easier to imitate. This episode is really about that distinction, and why leaders need to stop treating AI as a software rollout and start treating it as a human development challenge.

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Leaders love to frame AI transformation as a technology problem. It’s cleaner that way—tools, roadmaps, implementation plans. But what Anouk Brack lays out here is less flattering and far more consequential: this is a biological stress test, and most leadership teams are quietly failing it.

Under constant uncertainty and pressure, your nervous system defaults to survival mode. That means the very capabilities you’re counting on—strategic thinking, self-reflection, sound judgment—start to degrade. Not dramatically. Subtly. You keep moving, keep deciding, keep “leading.” But you’re doing it with a shrinking field of view and a growing pile of bad bets.

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Your leadership team doesn’t have a strategy problem—it has an execution problem disguised as one. The offsite went great, the vision is crisp, and the slides look expensive. But somewhere between “bold initiative” and “Tuesday morning,” nobody translated strategy into what people should actually do. That gap? That’s where most organizations quietly stall.

Tom Healy argues that L&D is the missing link leaders keep ignoring. Not because it’s ineffective—but because it’s unglamorous. CEOs will talk endlessly about growth, retention, and performance, yet fail to connect those outcomes to how people are trained, onboarded, and supported day-to-day. Meanwhile, AI is making content creation trivial. The real work—the uncomfortable, strategic clarity about culture, behavior, and expectations—is still being skipped.

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When turnover is low, leadership loves to call it stability. Jay Caldwell makes the more uncomfortable point: sometimes it is just fear with better optics. In this conversation, he and David unpack why “quiet staying” can become a serious organizational liability in an AI era—especially when people are still hitting goals, still showing up, and still slowly draining the place of experimentation, risk-taking, and fresh thinking.

They also get into the deeper workforce consequences of AI adoption: why broad rollouts often create anxiety instead of momentum, why the most AI-engaged employees may be the most likely to leave, and why cutting entry-level hiring might solve a short-term budget problem while quietly wrecking your future talent pipeline.

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If your organization looks successful on paper but feels strangely tense in practice, there’s a good chance fear—not excellence—is quietly running the show. In this episode, David Rice talks with Brave Together author Chris Deaver about how fear disguises itself in high-performing workplaces: polished presentations, perfect metrics, and meetings where nobody laughs—and nobody challenges anything either.

Their conversation explores what happens when organizations reward superhero behavior, visibility over collaboration, and certainty over curiosity. The result is brittle excellence and teams that quietly fracture under pressure. The alternative, Deaver argues, is building cultures of co-creation—where leaders shift from being the smartest person in the room to becoming integrators, connectors, and context builders. In a world reshaped by AI and constant disruption, the real advantage isn’t information anymore—it’s shared wisdom, deep empathy, and the courage to build together.

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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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