GLN 2026 Solving the Learning Transfer Gap Plus-BYOL-Plus Panel

[Paul Terlemezian]

Wednesday, October 28, 2026 12:30 - 1:45     Solving the Learning Transfer Gap        Plus-BYOL-Plus

https://us02web.zoom.us/j/84574765868?pwd=dvMJMaijB3WFpuyYJsCh1Nabk...

• Meeting ID: 845 7476 5868
• Passcode: 174553

The panelists will address the massive learning transfer gap in L&D and will represent their perspective on how technology is poised to enable L&D to fill that gap. Sound learning science is preferable to opportunistic tech play and yet not necessarily easy to prove when faced with entrenched alternatives. This conversation will allow for us to hear the perspective from each panelist and then dig deeper with them into the science, solution, gaps, and opportunities the participants discuss in the breakout. We will come back together to share insights and determine next steps.

Humorists

• Five-Minute University

Panelists

• Lee Dale
• David Grad
• Pete Mastin
• Kim Summerlot
• Katie Warvel

Curiosity Catalysts

Remarkable "Speaker"

• Michelle Prince

[Paul Terlemezian]

The questions on my mind

1. What technologies have been created (vs. adapted) or need to be created to solve this gap for the training industry?
2. What processes have been improved or need to be improved?
3. What is it about human nature that makes it difficult to solve this gap - from the provider and the consumer perspective?

[Paul Terlemezian]

We are prepared with five panelists and a remarkable speaker to have a traditional Plus-BYOL-Plus Georgia LEARNS panel as described on the Georgia LEARNS 2026 page.

An alternative approach is to have a facilitated conversation focused on the challenges associated with "entrenched" alternatives.

Panelists are encourage to reply to this discussion with:

1. A self-intro
2. Their Point-of-view on Solving the Learning Transfer Gap
3. Their perspective on "entrenched" alternatives
4. Authentic questions (i.e., ones for which they do not have an answer) related to this conversation

[Lee Dale]

Thanks so much, Paul.

I’m Lee Dale, CEO of Say Yeah! I’ll be discussing Shine Performance, which enables an ongoing practice to measure and grow the capability of every participant. With Shine Performance, each participant gets a personalized practice program that gives coaches meaningful signals and focuses practice on what matters most to the organization.

My point of view on the learning transfer gap
Transfer usually breaks down after training, when people go back to work and wait for the right situation to apply what they learned. The research calls this “opportunity to perform.” The fix is practice that doesn’t wait for the opportunity. By the time the situation comes along, recall may have faded, or the capability may never have been built in the first place.

Instead, we’ve built a platform around short, realistic scenarios drawn from each person’s role and based on the situations that most affect their organization, so each participant can grow the skills they need to be ready for the moments that matter.

Before LLMs, personalizing practice like this was impractical at scale. But as an industry, our understanding of what makes skills development effective isn’t new. The ability to deliver it at scale is.

On entrenched alternatives
The most entrenched alternative is the status quo. L&D is boxed into workshops and periodic coaching, with no clear path to measuring and growing capability. That creates a catch-22: training has never been able to prove its value to leadership, so it’s treated as a cost, and because it’s treated as a cost, it rarely gets the chance to prove its value. We can now measure capability at scale, which breaks that loop. The work ahead is helping leaders across each organization see what’s now possible.

The second challenge is telling tech-first solutions apart from those built on learning science. There’s a glut of AI-first L&D technology. Much of it isn’t grounded in learning science, isn’t designed to support facilitated training programs, and isn’t built to show meaningful organizational impact.

As with any digital transformation, the answer is never technology first. It starts with understanding the processes, the effort, and the human impact, then asking how technology can best enable them.

We’re finally able to offer ongoing, personalized practice at scale, and to quantify capability in the workplace. To deliver on that promise, we have to follow the science first, then connect capability to business impact, so L&D can grow from a checkbox cost into a driver of capability, retention, and growth.

Questions we continue to research

1. What evidence linking demonstrated capability to business results would leadership accept, and would it raise the role of L&D in your organization?
   
   
2. How would leadership define your organization’s most effective employees? Do you have a model for growing every employee to that level and proving it to leadership? What does it look like?
   
   
3. How might we better bridge the gap from practice scenarios to observed on-the-job behaviour?

[Paul Terlemezian]

Thanks, Lee. I look forward to introducing you to some opportunities where we can get the answers to your three questions.

[David Grad]

Thanks, Paul, and thanks to Lee for getting us started. I'm looking forward to the conversation!

A brief introduction

I'm David Grad, co-founder and CEO of elevator9, a learning reinforcement and impact measurement platform I built with my co-founder, Dr. Page Chen with significant input and guidance by one of our key board advisors, Dr Clark Quinn.

I'm also an executive coach, leadership development facilitator, and Senior Lecturer at IE New York College, where I teach Values-Based Leadership. So I've experienced the learning transfer challenge from both sides: helping people develop new capabilities and trying to understand what happens when they return to their everyday work.

My point of view on solving the learning transfer gap

I think we need to reconsider when we decide that training has been successful.

Is it when participants complete a workshop, pass an assessment, or tell us they had a great learning experience?

Or is it when they're actually applying what they learned and doing something differently in their work?

We know from learning science that lasting change requires opportunities to revisit what we've learned, reflect, practice, and apply new skills over time.

Yet we often evaluate training at precisely the moment when the real work of applying it is just beginning.

Live training doesn't fail. It fades.

At elevator9, we start with the assumption that organizations may already have excellent training. Our focus is what happens next.

We use brief, spaced learning reactivations called LIFTs to help people recall what they learned, prepare to use it in real workplace situations, and reflect on what happened when they tried.

Our approach to AI is particularly important to me. Rather than having AI do the thinking for learners, we use it to ask thoughtful, sometimes challenging questions that require learners to do the thinking themselves. 

But there's another opportunity here that I find equally exciting: measurement.

Every response to a prompt in a LIFT generates information about how people are engaging with their learning, the depth of their reflections, their expressed confidence and commitment, and how they're describing its application to their work.

We also invite managers or workplace sponsors to assess relevant behaviors before and after the learning journey, providing another perspective beyond what participants report themselves.

We're careful to distinguish these indicators of learning and reported application from independently verified changes in performance. But taken together, they can provide a much richer understanding of what's happening after training than attendance records or satisfaction surveys alone.

My perspective on entrenched alternatives

I don't think our biggest obstacle is a lack of knowledge about how people learn. It's that we've built training processes, budgets, and measures of success largely around the training event itself.

Following up in the weeks after takes planning, resources, and organizational commitment. And measuring what happened can be uncomfortable if the results aren't what we hoped for.

We've experienced this ourselves. In one of our leadership development engagements, getting learners to reflect on and apply their learning was only part of the challenge. Getting their managers to participate in assessing what happened afterward proved more challenging.

That taught us something important: We can't simply assume managers will reinforce learning. We have to design their involvement into the process from the beginning.

And when the evidence shows that learning isn't transferring as we'd hoped, I believe we should view that as an opportunity to improve, not an indictment of the training or the people who delivered it.

Questions I'm genuinely interested in exploring

1. Who should own the weeks after a workshop: the training provider, the L&D team, the learner's manager, or some combination? How do we make that shared responsibility work?

2. What's the right balance between practicing skills in simulated situations and applying them in real workplace situations? Does that balance change depending on the skill?

3. As AI becomes more capable of generating convincing responses, how do we distinguish evidence of thoughtful reflection from evidence of actual behavior change?

I'm excited to explore these questions with the other panelists and participants. I suspect solving the learning transfer gap requires us to rethink not just how we deliver training, but how we support, observe, and measure what happens afterward.

[Paul Terlemezian]

Thank you David. Your questions are excellent. What I think about often is how our business models may prevent or enable the answers we wish to experience.