This highlight video is from the June 05, 2026 live discussion Design Charrette | Simplifying Reskilling to Scale

"We are pushing all of this AI experimentation, but yet, we are not reducing our performance metrics." — Adrienne Guerrero, Positive Delta

The double bind is real — and most organizations haven't named it yet.

People leaders across industries are facing the same stuck point: despite clear business need, employees aren't keeping their skills current. The work isn't getting stuck because people don't care. It's getting stuck because organizations are asking employees to learn and perform at full capacity simultaneously — with no adjustment to what gets measured, rewarded, or protected. That gap is widening fast, and the cost of leaving it unnamed is showing up in slow AI adoption, disengaged learners, and frontline managers quietly signaling that delivery still wins over development.

Who helped move the work forward

This live interactive discussion brought together people leaders, L&D practitioners, and HR strategists from organizations including CNA Insurance, Zurich North America, Combined Insurance, Aon, Baxter, Lurie Children's Hospital, Froedtert ThedaCare Health, Spencer Stuart, UL Solutions, and others across the ELE community.

Nicole DeFalco (Upsurge Advisors) facilitated. Vilson Simon (LAK Group), Adrienne Guerrero (Positive Delta), and Mike Hruska (Baryons) led the breakout rooms. Matt Sernaker (Lake Forest Center for Leadership) produced. Baryons — an AI thinking partner — participated live, synthesizing room signals in real time as part of the working exchange itself.

The problem isn't motivation. It's the setup.

Before any solutions were on the table, the group spent time inside the problem — deliberately. Working from three perspectives (HR business partner, frontline manager, and employee), a consistent picture emerged. Employees say they don't have time. Frontline managers agree: other priorities come first. And underneath both of those responses sits something harder to name — no one has been given a clear reason why upskilling matters, what it connects to, or what's in it for them.

"Managers might be unintentionally signalling that delivery matters more than development." — Marty Murrillo, Precisely

That signal isn't always intentional. But it lands. And when employees read the environment as "perform now, learn later," they make the rational choice — they perform now.

One more layer surfaced that's easy to miss: employees are hearing "upskilling" and assuming the organization only cares whether they're using AI. If AI is taking over certain tasks, the conversation about what skills to build with that freed capacity hasn't happened yet. The void gets filled with fear.

The reframe that shifted the room

After the breakouts, the group fed their signals back into a live synthesis with Baryons. What came out stopped the conversation:

"If we reframe upskilling as a funded investment in job security, rather than a threat to productivity, we can convert employee fear into innovation." — BARYONS AI Mentor (live AI synthesis of room signals)

This wasn't a polished framework delivered from a slide. It was the room's own thinking — reflected back, sharpened, and made actionable. The smiles and thumbs-up that followed weren't just approval of a good output. They were recognition. That's what we're actually trying to say.

The opportunity hypothesis that followed gave the group something to carry forward: communicate the why in the first 30 days, adjust metrics to create safe learning zones by day 60, and formalize a framework that rewards both learning and output by day 90. That 30/60/90 scaffold moves into the AI Practical Lab on June 11 for deeper implementation work.

Change management doesn't have a finish line anymore

One of the sharpest questions to surface in the room came from Azizeh Constantinescu, UL Solutions — and it landed because nobody had a clean answer:

"When AI can do the task faster, are we cutting off human growth and development at the knees?" — Azizeh Constantinescu, UL Solutions

It's the kind of question that reframes the whole upskilling conversation. We've been focused on getting people to adopt AI faster. But if AI is absorbing the tasks that used to build judgment, pattern recognition, and strategic thinking over time — what are we actually developing people toward?

The classic change model — current state, transition, future state, refreeze — assumes a destination. AI doesn't offer one. Foundation models shift every four to six months. Workflows get disrupted quarterly. Best practices can be outdated within a year.

Any reskilling strategy built around a defined endpoint will be obsolete before it's deployed. The leaders who are getting traction are designing for iteration, not installation — building in review cadences, adjusting as they go, and treating "done" as a temporary state rather than a goal.

The AI Sandwich: a model worth testing

Mike Hruska ran the risk breakout room using a method he calls the AI human sandwich — and the room noticed.

Human framing first. AI synthesis in the middle. Human validation, challenge, and iteration at the end. Not AI replacing judgment, but AI layered into the workflow as a thinking partner — with the human holding agency at every decision point.

Marty Murrillo pushed on this directly in the debrief:

"You have to challenge it, think, and iterate. You're the director." — Marty Murrillo, Precisely

That last move — challenging the AI output, pressure-testing it against your organizational context, and iterating before you act — is itself a skill. It doesn't come automatically. And it's not in most upskilling plans yet.

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What to try next

1. Audit the double bind before you add anything new. Look at your current performance metrics alongside your upskilling ask. If the metrics haven't moved, you're asking people to do two jobs simultaneously. Identify one metric you're willing to flex for 60 days and watch what changes in learning engagement. Early evidence signal: Are managers starting to protect learning time, or are they still treating it as optional?
2. Communicate the why — specifically, not generally. The 30-day commitment that came out of this working exchange is simple: connect upskilling to job security and career growth, explicitly and repeatedly. Not a one-time announcement — a consistent signal over 30 days from leaders at every level. Early evidence signal: Can employees name why the organization is investing in their development? If not, the message hasn't landed yet.
3. Try the AI Sandwich on one real problem. Pick a low-stakes work challenge. Frame it as a human group first. Bring AI in to synthesize. Bring humans back to challenge, validate, and iterate. The goal isn't a better output — it's building the habit of human agency over AI-assisted work before the stakes are higher. Early evidence signal: Are team members challenging AI output, or accepting it? The first challenge is the signal that the skill is developing.