People Managing People

We've all taken a workplace personality assessment that tells us something vaguely accurate about ourselves. We read the report, nod along, and promptly forget about it. So why do organizations keep investing in self-awareness tools that rarely change how people actually work?
In this episode of People Managing People, host David Rice sits down with Dr. Jay Harrison, Co-Founder and Director of Behavioral Intelligence at Align, to explore the gap between behavioral insight and meaningful change. They discuss why treating everyone the same isn't necessarily fair, how managers can better understand the people they lead, and how behavioral intelligence can help teams anticipate friction before it becomes a problem. They also confront an important question: When does understanding someone's behavior help them grow, and when does it become another label that limits their potential?
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Most executives think they understand how work gets done. They've got the org charts, process documents, and dashboards to prove it. The problem? Those neat diagrams bear little resemblance to the work employees actually do. And when organizations start automating processes they don't fully understand, they're just making flawed systems run faster.
In this episode, David Rice speaks with Heidi Farris, CEO of ActivTrak, about the gap between AI adoption and actual business impact. They explore why employee workarounds are often what keep organizations functioning, how AI-driven productivity gains can simply move bottlenecks elsewhere, and why saving time means very little unless someone decides what to do with it.
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For decades, career advice has been simple: specialize, build deep expertise, and become indispensable. But what happens when AI makes that expertise accessible to almost anyone? The value of knowing how to do something is shifting toward knowing what should be done, why it matters, and whether the result is actually any good.
In this episode of People Managing People, host David Rice speaks with Tony Castellanos, Head of People at Nextdoor, about why judgment, curiosity, and taste are becoming critical leadership capabilities. They explore how AI could invert the traditional hierarchy of specialists and generalists, why organizing teams around business problems makes more sense than defending functional boundaries, and what happens when the org chart stops dictating who gets to solve what.
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Most enterprise AI strategies start with the same question: What can we automate? It's a reasonable question if your ambition is to make the existing workplace run faster. It's a much less useful one if you're trying to build something better.
In this episode, David Rice speaks with Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce, about why human-AI collaboration demands a different approach to organizational design. They explore the hidden value of workplace friction, why employees need a seat at the table when redesigning work, and the danger of measuring AI success exclusively through efficiency. Because saving time is one thing. Preserving the judgment, relationships, and expertise that make an organization effective is another.
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Most companies are still figuring out what an AI agent should actually do. JLL has moved well past that question. With agents operating across facilities management, brand communications, and corporate functions, the company is learning that the hardest problems aren’t necessarily technical—they’re about proving value, building trust, and making sure AI solves an actual business problem instead of becoming another expensive corporate science project.
Recorded at the AI4 conference in Las Vegas—with some of the inevitable conference-floor background noise—this conversation between host David Rice and Carlin Power, Global VP of AI and Platform Technology Enablement at JLL, explores what it actually takes to deploy AI at scale. They discuss why ROI needs to live inside the P&L, how decentralized AI development can accelerate useful innovation, and why eliminating entry-level roles in the name of efficiency may be exactly the wrong lesson to take from AI.
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