The conversation opens with a crucial distinction: most workforce initiatives are focused on re-skilling individuals, but few organizations are rethinking how teams collaborate or how workflows are structured in light of AI. AI isn’t just a tool for individual productivity. It’s rapidly becoming a larger force, embedded across systems like Microsoft Teams, Google Workspace, and Slack. This calls for a rethink of how teams share information, make decisions, and even conduct meetings.

Upskilling efforts often rely on formal learning paths or top-down mandates. But what’s driving real behavior change, according to the guests, is peer sharing, where colleagues show each other how they’re actually using AI to get work done.

Springboard hosts weekly “AI show-and-tells” across internal departments where employees share with each other new use cases or advancements using AI. At Wharton and Penn, faculty and staff share custom GPTs and tools during monthly syncs. This kind of informal, community-based learning creates a culture of experimentation and accountability.

AI anxiety is pervasive. Employees fear automation, irrelevance, or even that they’re “training” the AI that will replace them. But as Keith and Gautam point out, the key to navigating this moment is reframing AI as a tool for agency, not obsolescence. Leaders play a role here too, not just by talking about efficiency, but by connecting AI to growth, innovation, and purpose.

Rather than thinking narrowly about how a job will adapt to AI, Keith encourages companies to step back and rethink entire functions and processes. A hiring example came up: AI can now write job descriptions, post them, screen applicants, schedule interviews, and even manage onboarding, all with minimal human input.

Even as new opportunities emerge, millions of workers will be displaced — not just call center staff or warehouse employees, but also middle- and upper-tier knowledge workers. The job market won’t evolve fast enough for everyone to reskill in time. Gautam argues that we’ll need new forms of societal scaffolding: universal basic income, tuition-free upskilling programs, and job transition support. Kartik adds that AI design itself should include human-alignment goals — not just efficiency metrics.