People Managing People

Recorded live at the AI4 conference in Las Vegas, this conversation puts David Rice and Dr. Lilian Ajayi Ore—AI lead faculty and lecturer in strategic communication at Columbia University and co-author of The Power of the Learning Mindset—in the middle of one of the biggest questions surrounding workplace AI: Are companies gaining efficiency while quietly weakening their people’s ability to think? With the energy of a live conference comes some background noise, but the discussion cuts through it.
David and Dr. Ore explore cognitive debt, skill decay, and why the usual AI metrics—seats, logins, prompts, adoption rates—say very little about whether workforce capability is actually improving. They discuss the risks of outsourcing judgment and critical thinking, the loss of institutional knowledge when organizations replace people rather than reskill them, and why AI transformation needs to be treated as a talent priority alongside a technology priority. Because faster work isn’t much of a win if the people doing it are slowly losing the skills that made them valuable in the first place.
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AI may be changing the work, but the bigger risk is what happens when organizations optimize so aggressively for today’s efficiency that they stop building tomorrow’s capability.
Recorded live at the AI4 conference in Las Vegas, host David Rice sits down with Dr. Jana Brown, Chief Human Resources Officer at eHealth, to unpack the long-term consequences of shrinking entry-level pipelines, why HR needs “transformation fluency” rather than technical expertise alone, and what separates useful AI initiatives from expensive corporate hobbies. They also explore the human and operating systems that have to move together if transformation is going to stick—and why the technology itself is rarely the thing that sinks an AI project. Listeners may hear a little more conference-floor noise than usual, but that’s the price of capturing these conversations where they’re actually happening.
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AI in HR has a credibility problem: the technology is getting better at spotting patterns, summarizing data, and automating work, but none of that guarantees a better employee experience. In this conversation, Motivosity Chief Product Officer Jesse Dowdle and VP of Engineering Dano Gillette make the case for “appreciation intelligence”—using AI to remove administrative friction without automating the human moments that actually make recognition meaningful.
The bigger opportunity isn’t an AI-generated thank-you note. It’s giving HR and frontline managers a richer picture of employees, surfacing signals before problems become crises, and turning data into useful action. But there’s an important line running through the conversation: AI can recommend, filter, moderate, and nudge. Humans still need to own the judgment.
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When people stay quiet at work, organizations lose more than participation. They lose ideas, warnings, customer insight, process improvements, and market intelligence that never make it into the room. Seema Bhansali calls this the silence tax—the hidden cost companies pay when their environments make self-censorship the safer option.
In this episode, Seema joins David Rice to unpack why “speak up” advice misses the real problem. Employee voice isn’t simply a confidence issue; it’s shaped by systems, social risk, management behavior, and the environments leaders create. They also explore why this matters even more as companies adopt AI, and why communication needs to be treated less like a soft skill and more like an operational capability tied to performance.
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AI transformation has a funny way of becoming an organizational X-ray. The companies getting real value from it aren’t necessarily the ones buying the most tools or burning the most tokens. They’re the ones that understand what they’re trying to accomplish, what excellence looks like, and which problems are actually worth solving.
In this episode of People Managing People, David Rice speaks with Zapier’s Chief People and AI Transformation Officer, Brandon Sammut, about why AI ROI starts with organizational clarity, how to build experimentation that survives contact with actual workloads, and why adoption eventually needs to give way to impact. They also explore AI coaching, the growing value of judgment and wisdom, and why the best employers may increasingly be defined by how much more capable people become while working there.
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