This highlight video is from January 30, 2025 session Design Charrette: Building the NextGen Talent Pipeline with AI Mentors

“What if your AI learning strategy is failing—not because of the technology—but because you’re designing for the wrong humans?”
— Tracey Wik, Co-Founder of Love Sunday Nights.

This real-time exchange offered a clear signal for talent leaders: the biggest gains in capability aren’t coming from new programs or faster rollouts. They’re coming from how learning is designed to show up inside the work itself.

Too many talent experiences are still built around tools, content, or generic personas. But what surfaced in this dialog was something more practical—and more human. Teams don’t struggle because they lack access to learning. They struggle when learning isn’t designed around how people actually think, decide, and work together under real constraints.

Three insights stood out:

1) Capability is built through how teams work—not what they consume.
When learning is treated as content to deliver, progress stalls. When it’s designed as a shared way of working—grounded in curiosity, experimentation, and problem-solving—capability compounds over time.

“Learning isn’t content. It’s an organizational stance—curiosity, experimentation, and shared problem-solving.” — Mike Hruska, Co-Founder of BARYONS.

2) Managers are the real system for learning in the flow of work.
AI shows up most powerfully when it augments manager judgment—supporting coaching, reflection, and decision-making in real moments. That’s where confidence grows: not in courses, but in practice, with feedback, under real constraints.

3) Clarity unlocks confidence—and learning velocity.
When teams don’t know what’s allowed, experimentation slows to a halt. Clear guardrails, shared expectations, and safe spaces to test ideas are what turn curiosity into real capability.

The throughline is simple—but not easy:
Reskilling isn’t something you deliver.
It’s something teams do—together—inside real work.