This highlight video is from February 12, 2026 session ✨ AI Practical Lab: From Insight to Influence — Telling Stories with Business Impact
In last week’s ELE hands-on AI Practical Lab, a simple truth kept surfacing: when a team can’t see the work clearly, they can’t change the work confidently.
That’s why so many learning initiatives stall—not because the idea is wrong, but because the story is unreadable. The value stays invisible. Leaders move on.
Dustin Brewer put it bluntly in a way most talent leaders will recognize immediately:
“If the C-suite won’t read, they have to see it first.” — Dustin Brewer, HumanSide.
In other words: if your “insight” can’t travel in a glance, it won’t survive the week.
But the lab didn’t stop at getting attention. It pushed into the harder question: what makes people trust the message enough to act on it? That’s where Nicole DeFalco grounded the group with the reminder that capability doesn’t transfer through outputs alone:
“It still has to land—human to human.” — Nicole DeFalco.
AI can speed the draft. It can’t replace the relationship.
Three insights for leaders designing team-based learning experiences
1) Legibility is a capability strategy
Teams don’t adopt what they can’t quickly grasp, repeat, and share. A crisp one-page visual, a clean infographic, even a “cover story” mock—these aren’t marketing flourishes. They’re capability tools, because they compress complexity into something a team can align around.
If your experience design ends with “we trained them,” you’re done too early. The real question is: what will the team use together next Tuesday when the pressure hits?
2) Story isn’t fluff — it’s the operating system for alignment
In the lab, “story” wasn’t treated as creativity. It was treated as structure: What changed? What’s at stake? What choice are we asking leaders to make? What proof would they trust?
That’s what makes work decidable in the room. And that’s what makes a team-based learning experience feel like real work—not a field trip.
3) AI speeds drafts — but trust is still earned
The skeptic thread mattered. AI can hallucinate. It can produce outputs that look polished but are wrong in ways that create reputational risk. The strongest practice wasn’t “trust the tool.” It was design the verification loop, then make sure a human owns the final story and delivery.
Because the goal isn’t more content. It’s more confident action—anchored in credibility.
✨ What to do Monday: four moves that build team capability in real work
Run a 45-minute story prototype sprint. Pick one live initiative—new workflow adoption, manager friction, AI integration—and build a one-page artifact plus a 30-second talk track that answers one question: what decision should a leader make differently after seeing this?
Use a simple rhythm to keep the work moving: Clarify → Co-Create → Customize → Commit. Clarify the decision and audience. Co-create a first draft with peers and AI. Customize it to stakeholder reality. Commit by piloting it in the flow of work and capturing what changed.
Make iteration explicit. Your first version should be fast—and imperfect—so people can react to something real instead of debating abstractions. Two quick feedback loops (peer + end-user) will teach your team more than a “perfect” deck ever will.
Treat prompting and verification as part of the experience design. Prompting isn’t a personal party trick; it’s stakeholder translation with constraints. Verification isn’t optional; it’s what makes speed safe. Give your team a repeatable checklist (sources, SME review, human sign-off) so credibility scales with adoption.
Continue the conversation
If you’re working on the toughest talent challenges—and you want capability that shows up in real work (not just course completions)—join the continuing thread on the ELE Idea Exchange:
