People Managing People

AI is speeding up people decisions at exactly the moment those decisions require more care, more context, and frankly, more humility. In this episode, Matt Poepsel from The Predictive Index joins David Rice at Transform to unpack the growing gap between what AI can do and what organizations actually understand about the people data they’re feeding into it. Because while everyone is obsessed with automation, agents, and productivity gains, most managers still don’t know how to ask the most important question: “Do we actually have the right context for this decision?”
The conversation digs into the uncomfortable reality underneath modern workplace AI adoption. Companies are compressing timelines because AI can generate outputs faster, but they’re still operating from old assumptions about productivity, management, and collaboration. The result? Burnout, shallow decision-making, and what Matt calls “work slop” — endless AI-generated summaries, presentations, and outputs that create distance between people while pretending to improve teamwork. The deeper challenge isn’t technological. It’s behavioral. And most organizations still aren’t equipped to deal with that.
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Only 10% of senior leaders admit to using AI. Which raises an awkward question: if leadership isn’t really using the tools, who exactly is teaching everyone else how to work with them? Increasingly, the answer is peers. In this conversation from Transform, David Rice sits down with returning guest Kamaria Scott to unpack why AI adoption is becoming less of a top-down transformation initiative and more of a global peer-learning experiment happening in real time.
They explore the growing tension between organizational pressure to adopt AI and the complete lack of capacity many employees have to actually experiment with it. Along the way, they dig into manager enablement, skill atrophy, learning agility, performance management, and the uncomfortable possibility that companies are automating away the very expertise employees need to judge whether AI output is any good in the first place. The result is a candid discussion about why managers may become the bottleneck in AI adoption—and why organizations that failed to build real learning cultures before this moment are now paying for it.
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Most leaders are making AI decisions in the dark—restructuring roles, cutting headcount, and chasing use cases without understanding how work actually gets done. Not the org chart version. The real, messy, task-level reality. And that’s a problem, because when you don’t understand what creates value, automation becomes guesswork dressed up as strategy.
Victoria Pelletier joins the show to challenge the prevailing top-down approach. Instead of starting with AI capabilities, she argues for working backwards from business strategy to task-level workflows, skills, and human contribution. The result? A more grounded, more human, and frankly more effective way to redesign work in an AI-driven world.
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Everything is urgent—until it isn’t. When every ticket is a fire, teams don’t move faster; they burn out. In this episode, Barbara Nicholas (CEO at Polly) borrows a lesson from search and rescue: urgency only matters when it actually changes outcomes. Most white-collar work isn’t life or death, but we’ve built cultures that pretend it is—and people are paying for it in cognitive overload and constant distraction.
Barbara walks through how she operationalizes a triage system across her company—embedding shared language into tools like Slack, Notion, and Jira, and empowering teams to challenge urgency instead of blindly accepting it. From AI experimentation to customer demands and internal comms, this is a conversation about cutting through noise, making better calls under pressure, and remembering what actually matters.
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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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