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For 22 years, senior leaders from Fortune 1000 companies have brought their hardest problems here — and left sharper than they arrived.

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Find the peer conversation that moves your work forward.The right exchange builds better leaders, closes capability gaps, and cuts through what internal meetings can't.

Figure out if ELE is for you. Straight answers on what ELE is, who it’s built for, and how contributing members actually work together. Still have a question? Contact us.

Get answers about how Executive Learning Exchange (ELE) works — peer-powered leadership development that fixes real work issues, reverses burnout, and accelerates business value. No programs. No vendors. In the flow of work.

Real challenges. Real peers. Clear next moves.

This is where people-leaders get sharper.

Want ELE to come to you? Propose a local gathering — the community decides what gets scheduled.

Zurich North America

Executive Learning Exchange (ELE)

<blockquote>"Our 'why' is going to be about driving business value." — <a href="https://www.ele.llc/members/teri-hart" target="_blank" rel="noopener">Teri Hart</a>, Zurich North America.</blockquote>
Most AI adoption strategies start with the tools. The organizations actually moving the needle started with a different question.
That gap — between access and actual adoption — is the business problem senior talent leaders are sitting with right now. And the research is unambiguous about why it's happening: employees aren't resisting AI. They're quietly doing the math on their own relevance.
On March 13th, <a href="https://www.ele.llc/members/teri-hart" target="_blank" rel="noopener">Teri Hart</a> and <a href="https://www.ele.llc/members/jason-gallo" target="_blank" rel="noopener">Jason Gallo</a> from Zurich North America brought their real-world AI capability model into the room — and reframed what "scaling AI" actually means for HR and talent leaders.
Managers are the leverage point. Most organizations haven't treated them that way.

During the lively discussion, peers were sharing what's actually working — not only best practices from a deck. <a href="https://www.ele.llc/members/nick-allen" target="_blank" rel="noopener">Nick Allen</a> (CNA Insurance) brought one of the most replicable moves in the room. His organization started spending 5–10 minutes in team meetings calling out individual AI wins — peer-showcased, locally celebrated, not handed down from corporate or IT:
<blockquote>"Leadership is intentionally calling out different people… showcasing what they've just recently discovered… and that's turning the dial." — <a href="https://www.ele.llc/members/nick-allen" target="_blank" rel="noopener">Nick Allen</a>, CNA Insurance</blockquote>
<img class="aligncenter" src="https://wp.ele.llc/wp-content/uploads/2026/03/AI-Driven-Leaders-are-Becoming-a-Composer-and-a-Conductor-A.jpg" alt="" />

The shift from corporate mandate to something people are having fun doing together happened, in his words, in about two to three months. That's a replicable move — and it costs nothing to start.
<blockquote>"AI adoption isn't going to be about just pushing the tools… it's actually about old-fashioned change management and leading people through identity shifts." — <a href="https://www.ele.llc/members/teri-hart" target="_blank" rel="noopener">Teri Hart</a>, Zurich North America.</blockquote>
Most organizations have rolled out the tools. A small group of power users is running with them. Everyone else hasn't touched the button.

<hr />

Identity is the stall point — not the skill gap

Research surfaced in the discussion found roughly half of employees perceive AI as a threat to their professional identity — their relevance, their value, how they create worth in their work. That's not a reskilling problem. It's a change management problem.

Marty Murrillo put it in the chat as Teri was still mid-thought: "That Identity shift is huge." It landed fast because everyone in the room was feeling the same pressure their employees are navigating.
<div style="display: flex; justify-content: center;"><iframe title="YouTube video player" src="https://www.youtube.com/embed/77vXsD96zIk" width="560" height="560" frameborder="0" allowfullscreen="allowfullscreen">﻿
</iframe></div>
As Teri highlights, this shift from content delivery to business impact is already underway. For L&amp;D leaders, that's not an abstract statement. It's a line in the sand. The leaders anchoring their work to business outcomes — not program delivery or skill catalogs — are the ones building relevance right now.

<hr />

The confidence gap runs in one direction

Teri opened with a data point that stopped the room: 76% of executives believe their employees are enthusiastic about AI adoption. Ask the individual contributors? It's 31%.

The more senior you are, the more confident you are that things are going well. That disconnect doesn't close with more content or more tools. It closes when leaders consistently model the why — and make it visible at the team level, not just the enterprise level.
<blockquote>"It's technically available, but in many companies, it's optional. It's behaviorally optional." — <a href="https://www.ele.llc/members/teri-hart" target="_blank" rel="noopener">Teri Hart</a></blockquote>
That phrase — behaviorally optional — named something the room had been circling. Deployment isn't adoption. And the people who aren't engaging aren't confused about the technology. They're uncertain about what it means for them.

<hr />

What to try next

Ask three frontline managers to explain the AI "why" in their own words — unprompted. If the answers vary significantly, you don't have a skill problem. You have a messaging alignment problem. That's the faster fix.

Put one AI win on the next team meeting agenda — peer-led, 5 minutes, no deck. Early evidence signal: does someone volunteer a story the following week without being asked?

<hr />

If any of these insights reflect the business challenges you're navigating right now — bring them into the room. ELE builds its event experiences around the real problems members are wrestling with. <a href="https://www.ele.llc/faqs/share-top-of-mind-talent-challenges">Submit My Business Challenge Now</a>

&nbsp;

Design Charrette: From AI Adoption to Shared Practice at Scale

Future of Work

Digital &amp; AI

dirk.tussing

tracey.wik

nick.allen

teri.hart

linh.lawler

Change Management

adrienne.guerrero

psychological safety

jason.gallo

shonda.cromwell-rashad

tamora.martin

peter.hogaboam

yolanda.mangram

david.scherer

rachel.kaberon

AI Adoption

susan.c.guest

michael.hruska

capability building

cindy.pinkstaffmiller

deepa.kartha

alaska.fu

1Q2026

megan.schuette

manager enablement

human-centered ai

Confidence in AI

behavioral adoption gap

identity shift

AI capability building

business value storytelling

talent leader strategy

L&amp;D relevance

shared practice at scale

baryons.aimentor

Alter Domus

<blockquote>"AI is cosmically stupid if it doesn't have context." — <a href="https://www.ele.llc/members/christopher-lind" target="_blank" rel="noopener">Christopher Lind</a></blockquote>
That line set the tone for everything that followed on March 12.

<a href="https://www.ele.llc/members/anton-maletich" target="_blank" rel="noopener">Anton Maletich</a> brought a real problem to the room: Alter Domus is scaling fast, hiring junior officers straight out of university, and by week four those employees can't explain what the company does — let alone speak credibly with clients. Not a motivation gap. A mental model gap. The business needed a repeatable way to close it.

So the room built one. Live.

The live discussion brought together a sharp mix of senior talent and L&amp;D leaders — from Alter Domus, Precisely, and Learning Sharks anchoring the conversation, to practitioners from Lurie Children's, PepsiCo, Discover, Zurich North America, Aon, CNA Insurance, insightsoftware, SunSource, FranklinCovey, Reynolds Consumer Products, and Waukesha County Technical College in the mix. The kind of room where the chat is as good as the main conversation — and it was.

<hr />

The brief matters more than the tool.

Marty walked through The CRIT Model live — Context, Role, Interview, Task — and built a 30-day onboarding framework for Alter Domus in real time, no proprietary data shared. The output was specific because the input was specific. The move most people admitted skipping: asking the AI to ask you clarifying questions before it responds.
<blockquote>"Full disclosure: The CRIT Model was new to me in that room too. Marty pulled it from Geoff Woods' <a href="https://www.ele.llc/members/bookshelf/the-ai-driven-leader" target="_blank" rel="noopener">The AI-Driven Leader</a> — and watching him run it live was one of those lightbulb moments where you think, why haven't I been doing this the whole time? Context, Role, Interview, Task. Four steps. Completely changes what comes back." — <a href="https://www.ele.llc/members/dirk-tussing" target="_blank" rel="noopener">Dirk Tussing</a>.</blockquote>
[caption id="" align="aligncenter" width="822"]<img title="The CRIT Model infographic" src="https://wp.ele.llc/wp-content/uploads/2026/03/Gemini_Generated_Image_CRIT-model.jpg" alt="Infographic of the CRIT model for AI leading, breaking down Context, Role, Interview, and Task with icons and descriptions for each step." width="822" height="448" /> The CRIT Model for AI-driven leadership, from Geoff Woods. (Credit: Google Gemini Banana)[/caption]

When the AI pressed Anton on where his junior officers actually break down, he got precise: it's not the legal structure — it's how accounting flows through it. That answer changed everything the AI produced. The diagnosis landed correctly. The room noticed.

<hr />

You own the output.

Speed is real. So is the risk of using it uncritically. Christopher drew a hard line: if you can't answer detailed follow-up questions about what the AI produced, you haven't done the work.
<blockquote>"If you just use the results without analysis or reflection, you're not using it correctly." — <a href="https://www.ele.llc/members/david-scherer" target="_blank" rel="noopener">David Scherer</a></blockquote>
And AI won't tell you when you're wrong — it's built to keep you engaged, not challenge you. You have to ask for the pushback explicitly.

<hr />

Design the workflow, don't improvise it.

The practical split that landed: use outside-firewall tools for structure and frameworks, then move inside your approved environment to add sensitive detail.
<blockquote>"I'm looking for this tool outside your firewall to give structure. I can take that and put it inside the firewall and flesh out the detail." — <a href="https://www.ele.llc/members/marty-murrillo" target="_blank" rel="noopener">Marty Murrillo</a>, Precisely</blockquote>
Christopher added: build custom GPTs, Gems, or Claude Projects for work your team repeats — stakeholder interviews, onboarding design, needs assessments. Pre-load the context once. Anyone picks it up without starting cold.

<hr />

What to try next

Use The CRIT Model on one real work product this week. Add the Interview step — ask it to ask you up to three clarifying questions before responding. Early evidence: fewer revision cycles, more specific output.

Split your AI workflow deliberately. Map which parts happen outside the firewall (structure, options, frameworks) and which stay inside (proprietary detail). Make it a decision, not an accident. Early evidence: you can explain your process to InfoSec without hesitating.

Ask it to push back before you finalize. Prompt it directly: "What's wrong with this? What am I missing?" It won't do this unprompted. Early evidence: you'll catch at least one assumption you'd been treating as settled.

<hr />

If any of these insights reflect the business challenges you're navigating right now — bring them into the room. ELE builds its event experiences around the real problems members are wrestling with. <a href="https://www.ele.llc/faqs/share-top-of-mind-talent-challenges">Submit My Business Challenge Now</a>

✨ AI Practical Lab: Context Is the Job

christopher.lind

anton.maletich

colin.hahn

keri.kersten

nancy.dome

marty.murrillo

Performance Improvement

ana.bedard

audra.pinter

human AI collaboration

prompt engineering

L&amp;D strategy

AI governance

AI Workflow Design

Context Setting

CRIT Model

Onboarding Effectiveness

Product Knowledge Training

Junior Talent Development

Clarity in AI Use

Senior Talent Leaders

HumanSide

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:
<blockquote>“If the C-suite won’t read, they have to see it first.” — <a href="https://www.ele.llc/members/dustin-brewer" target="_blank" rel="noopener">Dustin Brewer</a>, HumanSide.</blockquote>
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:
<blockquote>“It still has to land—human to human.” — <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a>.</blockquote>
AI can speed the draft. It can’t replace the relationship.
<h5>Three insights for leaders designing team-based learning experiences</h5>
<h6>1) Legibility is a capability strategy</h6>
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?
<h6>2) Story isn’t fluff — it’s the operating system for alignment</h6>
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.
<h6>3) AI changes roles — and raises the bar for human value</h6>
A helpful reset surfaced as a throughline in the February 12th lab: the real shift isn’t “AI is going to take my job.” It’s that AI changes how value gets created—and makes the human worker more indispensable when the work matters.
Ed brought in a quote from Shyam Sankar (CTO of Palantir) that set a new standard for how we talk about AI and work: “The American worker will wield AI to do more with less—and become more productive and valuable as a result.” That’s a stronger standard for how to view AI’s role in the workforce: not as a replacement narrative, but as a multiplier—where humans become higher-impact by focusing on judgment, clarity, credibility, and connection.
The lab reinforced what that looks like in practice. AI can accelerate drafts and options, but humans still own the parts that create trust and movement—especially verification and delivery.

<blockquote>“We’re not putting the person out of the loop. I’m going in there and verifying.” — <a href="https://www.ele.llc/members/edward-desrosiers" target="_blank" rel="noopener">Edward DesRosiers</a></blockquote>
AI increases speed. Human ownership increases responsibility. And that’s exactly what makes the human worker more valuable, not less.
<h5>✨ What to do Monday: four moves that build team capability in real work</h5>
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.

<img class="aligncenter wp-image-44484" src="https://wp.ele.llc/wp-content/uploads/2025/12/Four-Step-Model-for-AI-Workflows-ELE-4-Cs.jpg" alt="ELE's 4 C Model for AI Workflows" width="781" height="439" />

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.
<h5>Continue the conversation</h5>
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:

<a href="https://community.ele.llc/t/2026-feb-ai-lab-pre-chat-post-chat/5767">2026 FEB: AI Lab Pre-chat &amp; Post-Chat</a>

✨ AI Practical Lab: From Insight to Influence — Telling Stories with Business Impact

jean.ryan

edward.desrosiers

leadership development

missy.gonzales

business impact

stakeholder alignment

AI tools

AI in HR

ai practical lab

Workforce Transformation

nicole.defalco

performance enablement

roberto.seif

talent development strategy

AI in L&amp;D

dustin.brewer

AI reskilling

executive communication

team capability

learning in the flow of work

Storytelling for Influence

Clarity to Commit

Co-Creation

Practical Learning Design

Culture of Experimentation

Journyz

<blockquote data-start="772" data-end="868">
“Most of what surfaced here has nothing to do with learning—and everything to do with how work is designed.” — <a href="https://www.ele.llc/members/brian-gold" target="_blank" rel="noopener">Brian Gold</a>, Acrisure.
</blockquote>
The ELE Ideation Sprint offered a timely reminder that many of today’s capability challenges do not show up as obvious skills gaps. Instead, they surface quietly in everyday work: in manager workarounds, ambiguous decisions, delayed conversations, and teams absorbing more risk than expected just to keep things moving.

What leaders heard during the sprint was not a call for more training. Managers were not asking for new programs or tools. They were describing work that had changed without being formally redesigned. Roles were stretching beyond their original scope. Expectations were shifting without explicit conversation. Decision rights were becoming less clear.

That friction isn’t noise. It’s signal.
<img class="aligncenter" src="https://wp.ele.llc/wp-content/uploads/2026/01/What-Managers-Actually-Surfaced.jpg" alt="" width="779" height="466" />
What surfaced across manager conversations

As participants compared real manager moments across organizations, a consistent pattern emerged. Reskilling needs are showing up first as judgment, risk, and decision challenges, not as requests for learning. Capability gaps appear as hesitation, uncertainty, and overload long before they show up in performance metrics, engagement surveys, or development plans.

Managers are navigating higher stakes work with faster timelines, tighter scrutiny, and greater system dependence, often without updated guardrails or shared definitions of “good work.” In response, many compensate quietly by stepping in, redistributing work, or softening expectations. Over time, these adaptations normalize risk and hide deeper structural gaps.

This reframes how leaders should think about reskilling altogether.

Three insights felt especially important for leaders designing team-based learning experiences:

First, manager conversations are the earliest capability sensor.
When leaders look across manager input not one story at a time, but in patterns they can detect role drift, uneven adaptation, and system strain early. These signals surface in everyday decisions, not in formal development requests. By the time learning demand is explicit, the organization is often already carrying risk.

Second, many “reskilling” challenges are actually design challenges.
Unclear workflows, broken handoffs, ambiguous decision ownership, and outdated role definitions cannot be trained away. The sprint helped leaders slow down long enough to diagnose whether an issue was rooted in skill, system, or structure and to respond intentionally rather than reflexively. This distinction proved critical for avoiding well-intended but misdirected learning interventions.

Third, learning works best in the flow of real work.
A key shift for participants was learning how to ask open-ended, work-centered questions that surfaced real problems instead of defaulting to one-track training conversations. When L&amp;D leaders resisted the urge to jump to solutions and instead invited managers to describe what felt hard, risky, or unclear, the conversation changed.

Rather than narrowing quickly to “what course do we need,” discussions opened up into a clearer understanding of how the work itself had changed and what kind of response was actually required. This approach consistently elevated L&amp;D’s role with the business.
<blockquote data-start="1289" data-end="1362">
“This gives L&amp;D a way to go back to the business with real value—not just more programs.” — <a href="https://www.ele.llc/members/brian-gold" target="_blank" rel="noopener">Brian Gold</a>, Acrisure.
</blockquote>
A different role for L&amp;D

One of the most important outcomes of the sprint was how it reframed the role of learning leaders. Instead of acting primarily as program designers or solution providers, participants left better equipped to serve as sense-makers and partners in diagnosing change.

Even when the answer was not training, L&amp;D leaders could guide their business partners toward clearer problem definition, better decisions, and more appropriate responses. That shift alone changes the credibility and impact of learning in the organization.

The bigger move is not about faster deployment or better content. It is about designing experiences that help teams see what has changed in the work, make smarter decisions together, and adapt in real time.
That’s the kind of capability-building work that will matter most in 2026.
𝐀𝐁𝐎𝐔𝐓 𝐓𝐇𝐄 𝐒𝐏𝐑𝐈𝐍𝐓: The ELE Ideation Sprint focuses on reskilling closer to real work. Rather than starting with large-scale frameworks or tools, the sprint centers on practical manager–employee conversations and the early signals that reveal where capability is actually breaking down. The goal is not to jump to solutions, but to identify and validate the real problem space worth solving.

𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒? Explore the <a href="https://www.ele.llc/faqs/innovation-sprint-reskilling" target="_blank" rel="noopener">ELE Ideation Sprint FAQs</a> or reach the ELE team at help@ele.llc.

ELE Ideation Sprint — Reskilling Closer to Real Work

future of work

Leadership

azizeh.constantinescu

organizational capability

Performance Consulting

brian.gold

manager effectiveness

work design

Talent Leadership

manager coaching

ali.glaser

Ideation Sprint

workforce adaptation

work clarity

reskilling signals

manager moments

elevated HR

root cause analysis

rapid learning

AI practicality

scaling capability

AI and talent

manager conversations

Session Focus: Build on a  3-step AI workflow model presented by<a href="https://www.ele.llc/members/julia-meyer"> Julia Meyer</a> and <a href="https://www.ele.llc/members/rebecca-benson">Becca Benson</a> at our October 2025 conference by validating the underlying business problem with human context and committing to practical next steps that stick.
This hands-on AI Practical Lab builds directly on last month’s AI workflow challenge, shifting the focus from generating better outputs to ensuring we’re solving the right problem—before accelerating with AI.
Using the same challenge from last month’s <a href="https://www.ele.llc/insights/ai-practical-lab-ai-powered-workflows" target="_blank" rel="noopener">✨ AI Practical Lab: AI-Powered Workflows</a>, talent learners in this lab will refine the 3-step model, surface root causes, and apply ELE’s Four-Step Model to clarify why a problem exists—and what action is truly worth committing to. The emphasis is on human judgment, shared context, and practical decision-making so AI-enabled work translates into outcomes that actually stick.
This lab introduces ELE’s Four-Step Model as a practical way to move from clarity to commitment—keeping humans at the center of AI-enabled work.
<img class="aligncenter wp-image-44484" src="https://wp.ele.llc/wp-content/uploads/2025/12/Four-Step-Model-for-AI-Workflows-ELE-4-Cs.jpg" alt="ELE's 4 C Model for AI Workflows" width="900" height="506" />
&nbsp;
<blockquote>"AI gives us amazing options, but I need to see the big picture. How is this actually moving the needle for the business?" -- <a href="https://www.ele.llc/members/keri-kersten" target="_blank" rel="noopener">Keri Kersten</a></blockquote>
This discussion offered a timely reminder: most capability gaps don’t start with a learning problem—they start with a clarity problem.
As <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a> put it,

<blockquote data-path-to-node="3,0,1">
"Clients don’t come to us with problems—they come to us with solutions wrapped in assumptions."
</blockquote>
That framing resonated because it reflects the daily reality talent leaders face. We’re often handed a fully formed request—train this, roll that out, scale it fast—and rewarded for speed, not scrutiny. But when we rush to deliver, we risk solving the wrong problem beautifully.
The most powerful moments came from slowing down and asking harder questions:

<ul data-start="873" data-end="1111">
 	<li data-start="873" data-end="917">
What business outcome is actually off?
</li>
 	<li data-start="918" data-end="1030">
What evidence says this is a skills issue—and not incentives, workload, role design, or market conditions?
</li>
 	<li data-start="1031" data-end="1111">
What would people need to do differently in real work for results to change?
</li>
</ul>
That shift—from order-taker to problem framer—is where team capability actually starts.
Another insight landed hard: training can’t compensate for an environment that discourages the very behaviors we’re trying to build. If KPIs, job design, time pressure, or incentives pull people in the opposite direction, no amount of content will stick. Capability has to be designed into the work itself.
The group also challenged the instinct to “train everyone.” Instead, the conversation moved toward targeted pilots with high-impact cohorts—designing with the people closest to the outcome, learning fast, and scaling only once behavior change is visible. That’s not slower; it’s smarter.
AI showed up not as a content engine, but as a thinking partner—helping leaders rehearse stakeholder conversations, pressure-test assumptions, and surface blind spots before committing resources. Used this way, AI didn’t replace judgment; it sharpened it.
And as <a href="https://www.ele.llc/members/angelica-janina-stilling" target="_blank" rel="noopener">Angelica Stilling</a> summed up succinctly,

<blockquote data-path-to-node="3,0,1">
"Clients don’t come to us with problems—they come to us with solutions wrapped in assumptions."
</blockquote>
That line captured the discipline required of leaders right now—not more activity, not faster delivery, but clearer thinking.
For talent leaders, the takeaway is straightforward but demanding: Building team capability isn’t about delivering better learning. It’s about designing the conditions where learning shows up as different decisions, actions, and results.
If your team is busy building programs but struggling to move the needle, the most impactful move may be to pause—and ask better questions before you build anything at all.
&nbsp;

&nbsp;

&nbsp;

✨ AI Practical Lab: Humans Solving the Right Problem — ELE’s Four-Step Model

learning strategy

performance improvement

mike.kester

diana.anderson

future ready teams

angelica.janina.stilling

Behavior Change

Strategic L&amp;D

AI-enabled learning

organizational performance

human-centered learning

amy.p.wilkins

talent experience design

trusted advisor mindset

problem framing

AI as thought partner

confidence capability clarity

design thinking for talent

evidence-based learning

The Regis Company

The traditional L&amp;D operating model is obsolete. Simply rolling out more content won't solve the capability gaps driven by rapid technological change. In this crucial keynote, Mike Vaughan delivers critical, data-backed research on the irreversible shift from a content-first to a skills-first learning architecture, driven by AI and comprehensive talent data.

Senior leaders will move beyond generalized trends to understand the specific L&amp;D 3.0 framework necessary for enterprise transformation. You will gain actionable insights on:
<ul>
 	<li>The Skills-First Imperative: Why merely having a skills taxonomy isn't enough. Learn how to leverage AI-driven skills data for proactive talent planning, enabling true talent mobility and a responsive learning ecosystem.</li>
 	<li>From Reactive Training to Proactive Capability: Discover how high-performing L&amp;D functions are using data to anticipate skill needs before they become business-critical gaps, moving L&amp;D from a service center to a strategic capability builder.</li>
 	<li>The Power of AI-Enabled Personalization: Review compelling research that validates the exponential increase in learner engagement and performance when AI is used to deliver personalized and relevant career journeys, not just generic training paths.</li>
 	<li>Measuring Business Impact: Focus on the metrics that matter. Understand how to connect skills development directly to key business outcomes (like customer experience and innovation) to finally prove the strategic ROI of your talent investment.</li>
</ul>
This keynote provides the research and the roadmap to future-proof your L&amp;D strategy, positioning it as the indispensable engine of organizational agility and growth.

How AI is Changing L&D: Research | Skills Data

Integrated Talent Management

talent mobility

Analytics

michael.vaughan

organizational agility

CHIE2025

workforce planning

skills taxonomy

HR Strategy

Data-Driven L&amp;D

enterprise transformation

skills gap analysis

AI personalization

skills first architecture

L&amp;D 3.0

talent data

proactive talent planning

learning ecosystem

measuring business impact

ROI of talent

future proofing L&amp;D

This live discussion with experienced talent executives, mostly ex-Accenture, made one thing clear: skills are more than a talent metric—they’re the language of business capability. When viewed as data, skills connect individual growth to enterprise agility, shifting the conversation from headcount to capability management.
<a href="https://www.ele.llc/members/craig-friedman" target="_blank" rel="noopener">Craig Friedman</a> reminded us that:

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“Skills are both people data and business data. That shift takes us from counting heads to managing capabilities.”
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That single shift reframes how talent leaders partner with the business. Instead of defending headcount, we’re managing capabilities that drive agility, growth, and performance.
But, as Heber cautioned:

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“Skills data only matters if it’s accurate. Decisions are only as strong as the confidence you have in the data.” -- <a href="https://www.ele.llc/members/heber-sambucetti" target="_blank" rel="noopener">Heber Sambucetti</a>, Skillsoft
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Confidence in your data determines whether leaders trust skills intelligence to shape pay, succession, and workforce planning.
And as Craig warned, complexity is the enemy:

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“If your taxonomy is exploding, step back. Keep the skill the skill—and separate the context.”
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The conversation didn’t just redefine skills—it revealed what leaders must prioritize to make skills strategy work in practice

<h3 data-start="1972" data-end="1991">Key Takeaways:</h3>
🔹 Skills are strategic currency, bridging individual growth with enterprise capabilities. 🔹 Data quality is non-negotiable—without it, business decisions crumble. 🔹 Start where the business hurts; pilots anchored in real challenges prove value fast. 🔹 Simplicity wins—overbuilt taxonomies collapse under their own weight. 🔹 AI is an accelerant, not a replacement; governance keeps it usable.


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<h3 data-start="2405" data-end="2447">Practical Actions for Talent Leaders:</h3>
✅ Frame conversations with executives around capability, not headcount. ✅ Audit skills data before using it to drive staffing, comp, or workforce plans. ✅ Launch a pilot tied to a pressing business problem to win support and credibility. ✅ Establish governance guardrails early: clear definitions, owners, and refresh cycles. ✅ Use AI smartly—let it speed tagging and content, but apply governance filters.

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Skills strategy isn’t about boiling the ocean. It’s about building trust, proving value, and scaling what works—step by step.
Which of these actions feels most urgent in your organization right now? Let’s continue this conversation in the <a href="https://www.ele.llc/idea-exchange" target="_blank" rel="noopener">ELE Idea Exchange</a>.

Skills Are Data: Designing a Strategy for Agility and Growth

Strategy

janice.simmons

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