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ELE is a peer-powered community where people leaders don't just reframe challenges — they leave with decisions, next moves, and 30/60/90-day commitments to move real work forward.&nbsp;For 22 years, leaders from Fortune 1000 companies have brought their hardest problems here — and left sharper than they arrived.

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This is where people leaders get sharper — and their teams get smarter.ELE is a peer-powered network of people leaders who accelerate business results through shared intelligence and real-work collaboration. Learn more below about why ELE has been the trusted choice for leaders building high-performing teams for over 20 years.

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Bring a real talent challenge. Name what's at stake, what you've tried, and why it matters now.

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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.

Where people leaders share what's working, ask what's hard, and learn from peers navigating the same talent challenges right now. Search before you post. When referencing a resource, paste the key insight directly into your response with a link to the source.

Real conversations from people leaders tackling today's toughest talent challenges -- captured live, ready to apply with your teams when it fits your schedule, and connected to what's happening right now.

Insights on Talent Challenges | Captured Peer Conversations | ELE

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Pick your path — each plan reflects a different commitment to accelerating business value.

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

LAK Group

Executive Learning Exchange (ELE)

BARYONS

<div style="font-family: 'Calibri', 'Candara', 'Segoe UI', 'Optima', Arial, sans-serif; color: #333; line-height: 1.6; max-width: 800px; margin: auto; font-size: 1.1rem;">
<blockquote style="border-left: 4px solid #990000; margin: 20px 0; padding: 10px 20px; background-color: #f8f9fa; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"They just gave people tools and said figure it out — and that's not going to happen." — <a href="https://www.ele.llc/people/susan-c-guest" target="_blank" rel="noopener">Susan Guest</a></blockquote>
The pattern is everywhere: AI declared a priority, tools deployed, upskilling on the strategic plan. And the work still isn't moving.
The problem isn't awareness or budget. Organizations are treating a transformation challenge like a technology rollout — and the people side keeps getting left behind.
Who helped move the work forward
This live interactive discussion brought together people leaders, L&amp;D strategists, organizational development practitioners, and AI adoption specialists from organizations across healthcare, insurance, financial services, manufacturing, and consulting. Facilitating the work: Adrienne Guerrero (Positive Delta), Vilson Simon (Ágora &amp; LAK Group), Marty Murrillo (Precisely), and Mike Hoyt (ELE), with the Baryons AI Mentor participating as a live synthesis partner.
Participants worked through role-specific AI prompts — taking on the perspectives of senior leaders, frontline managers, and HR business partners — to build execution-ready action plans for unsticking upskilling in their own organizations.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 30px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">The signals and the incentives aren't aligned</h3>
Everyone in the room could name it. Leaders say upskilling matters. The reward system says hit your numbers. And the manager layer — the people who actually control whether employees get time to learn — is stuck in the middle with no incentive to open the door.

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"If you can't get through the door to get coffee, you can just look in the side window and watch the machine — does you no benefit." — <a href="https://www.ele.llc/people/anton-maletich" target="_blank" rel="noopener">Anton Maletich</a>, Alter Domus</blockquote>
The answer the group kept returning to: give managers explicit permission, then tie it to how you evaluate and reward them. One without the other doesn't move.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 25px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">"Deploy the tool" is not a transformation strategy</h3>
This was the sharpest shared signal of the discussion — and it surfaced across every breakout group.
Organizations are procuring AI as a SaaS product, tracking token usage, measuring productivity gains, and calling it transformation. They're missing the point.

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"This isn't about technology — this is about transformation, rethinking business processes, workflows." — <a href="https://www.ele.llc/people/marty-murrillo" target="_blank" rel="noopener">Marty Murrillo</a>, Precisely</blockquote>
<a href="https://www.ele.llc/people/karen-waterlander" target="_blank" rel="noopener">Karen Waterlander</a> named what that gap looks like on the ground: people know the tools exist, they know they're embedded in some systems, but there's no clear direction on when, how, or why to use them. The result is people "floundering."
The reframe that emerged: stop measuring whether people are using AI. Start asking what they're doing with it.

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"Are they reimagining their workflows? Are they reinventing new lines of business? Not just are they using it to increase productivity." — <a href="https://www.ele.llc/people/marty-murrillo" target="_blank" rel="noopener">Marty Murrillo</a>, Precisely</blockquote>
<a href="https://www.ele.llc/people/adrienne-guerrero" target="_blank" rel="noopener">Adrienne Guerrero</a> shared a client example: hackathons built around AI process refinement surfaced workflow problems that had nothing to do with AI. Some of the biggest wins came from simply stopping things nobody had questioned in years.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 25px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">The metrics aren't measuring what matters</h3>
Most organizations are tracking consumption — LMS completions, hours logged, token usage. None of that tells you whether work actually changed.
Vilson Simon framed the stakes: 39% of worker core skills will be transformed or obsolete by 2030, and 63% of employers already name skills gaps as the top barrier to business transformation. If that's true, the measurement has to match.
Anton Maletich's group moved toward more useful signals: learning hours alongside client satisfaction scores, manager stress indicators, and lines-of-business comparisons to see where investment is actually shifting outcomes.


<hr style="border: 0; border-top: 1px solid #eee; margin: 40px 0;" />

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-bottom: 15px;">What to try next</h3>
Run all three role prompts — then synthesize. Senior leader, frontline manager, HR business partner. Run them independently, then feed the outputs into a single thread. If the three contradict each other, you've found where your org is misaligned.
Give managers the permission in writing — and tie it to their evaluation. One to two hours per week, explicit from the top. Without the incentive attached, the mandate stays theoretical.
Use AI to focus learning time, not just deliver content. Before the learning hour, run a quick AI diagnostic on where to focus. Anton Maletich's team is piloting Outlook MCP integration with Claude to surface development signals directly from work context.

<div style="background-color: #f1f4f9; padding: 20px; border-radius: 8px; margin-top: 40px; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">
Bring your version of this into ELE
If your org is staring at the same gap — tools deployed, managers unsure, strategy still fuzzy — bring it in. This is exactly the kind of stuck work the ELE community works through together.
Shape what gets on the calendar. Submit your challenge, then vote for the ones worth solving first.
Submit Your Challenge: <a href="https://www.ele.llc/calendar/submit" target="_blank" rel="noopener">https://www.ele.llc/calendar/submit</a>
Vote for what matters most: <a href="https://www.ele.llc/calendar/vote" target="_blank" rel="noopener">https://www.ele.llc/calendar/vote</a>

</div>
</div>

✨ AI Practical Lab: AI in the Loop Coaching

Future of Work

Digital &amp; AI

learning culture

Mike.Slepikas

chris.olson

anton.maletich

karen.waterlander

adrienne.guerrero

marty.murrillo

Workforce Reskilling

ai practical lab

Workforce Transformation

Skills Gap

susan.c.guest

AI Coaching

mike.hoyt

2Q2026

manager enablement

AI adoption strategy

People Leaders

vilson.simon

learning measurement

peer intelligence

baryons.aimentor

upskilling at scale

AI tools without strategy

frontline manager accountability

execution readiness

Discover

<div style="font-family: 'Calibri', 'Candara', 'Segoe UI', 'Optima', Arial, sans-serif; color: #333; line-height: 1.6; max-width: 800px; margin: auto; font-size: 1.1rem;">
<blockquote style="border-left: 4px solid #990000; margin: 20px 0; padding: 10px 20px; background-color: #f8f9fa; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"It's not going to be by developing courses. It's going to be those human-centered skills." — <a href="https://www.ele.llc/people/patricia-ouzounian" target="_blank" rel="noopener">Patti Ouzounian</a></blockquote>
The real work in the room wasn't abstract. L&amp;D professionals and people leaders were trying to reframe a moment of genuine disruption — AI reshaping job markets, role definitions, and hiring expectations — into a concrete career opportunity they could act on before the window closed. The work was getting stuck in the same place for most people: they knew their value was shifting, but they didn't have a repeatable method for mapping what to protect, what to let go, and how to communicate what they bring now. That's what this live in-person working exchange was built to move forward.
Who was in the room:
Andrea Nunez (Discover/Capital One), serving as the Internal Client — anchoring the challenge in the real business pressure L&amp;D professionals face when AI disrupts both their work and their market position. Mike Hoyt (ELE), as the AI Strategist — guiding the technical workflow live, step by step. Patti Ouzounian (ELE), as the Real-World Skeptic — testing what would actually work, not just what sounded promising. Alongside them: a room of practitioners from L&amp;D, talent management, OD, and knowledge management — some in active transition, some navigating rapid internal scope expansion, some trying to build an internal business case for the first time.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 30px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">Designing the CRIT Prompt Loop</h3>
Most people in the room had used AI for routine tasks. What the lab exposed was the gap between using AI and briefing it well.
The framework at the center of the working exchange was the CRIT Model — from Geoff Woods' The AI-Driven Leader — distributed as a physical card in the room. Four components: Context (give AI everything about your role, function, team, industry, company), Role (tell AI it's your career advisor and playbook builder), Interview (instruct AI to ask you exactly three clarifying questions before it builds anything), Task (specify the deliverables — a 30/60/90-day action plan, a stakeholder map, objection handling).
Mike Hoyt walked the room through each component live, then made the critical distinction: the only section most people needed to customize was Context. And context, it turned out, was where most people were underinvesting.

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"The more specific you are about your role and function, the more effective this playbook will be." — <a href="https://www.ele.llc/people/mike-hoyt" target="_blank" rel="noopener">Mike Hoyt</a></blockquote>
The proof came from the tables. One participant — Natalie — had her actual job description on hand and pasted it directly into the prompt. Her playbook came back precise and usable on the first pass. Others who described their role in general terms got general output and had to restart. The room named it cleanly during share-out:

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"If you're not heavy on context, you have to go back." — [Community Voice, table share-out]</blockquote>
The Interview step — the "I" in CRIT, and the step Mike noted most people skip — turned out to be where the real diagnostic work happened. AI's three follow-up questions didn't just fill gaps. They surfaced things participants hadn't consciously mapped. One practitioner shared two of the questions her AI generated. The second one stopped the room:

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"Where are you currently spending time that feels necessary but you suspect is not actually where you create your highest value?" — [Community Voice, participant sharing AI-generated interview question]</blockquote>
That's not a course-building question. It's a career architecture question — and it came from the prompt, not the facilitators.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 25px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">Safeguarding Highest-Value Energy</h3>
The 30/60/90-day playbooks that came out of the live exercise weren't all pointing in the same direction — and that was the signal.
An OD consultant in the room described what her playbook reframe actually looked like: "It's not how do I grow my consulting business — it's how do I design a business that protects and amplifies my highest value energy." The shift she named was from change management executor to organizational redesign architect in the era of AI. Bigger market position. Cleaner scope. Different conversation with clients.
Another participant described a role with significant legacy creep — doing operations work inside HR, manual processes that had accumulated over time. Her playbook gave her the 30/60/90 structure she needed to make the business case for what belongs where. Not a career development document. A stakeholder presentation with a path to executive sign-off.
A third came in planning to use the playbook for an external job search. She left thinking about how to use it to pitch her CHRO on an internal AI initiative — building the business case, anticipating objections, framing the ROI.
Same tool. Three completely different applications. All grounded in the same core move: stop describing what you do and start protecting what only you can do.

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-top: 25px; margin-bottom: 10px; border-bottom: 1px solid #eee; padding-bottom: 5px;">Beyond the Prompt: Building the Personal Agent</h3>
Patti Ouzounian closed the working exchange with a move most of the room hadn't considered yet.
After the previous Design Charrette in this series, she'd taken the playbook output and done something with it: she built a custom ChatGPT agent. Not a new chat. An agent — with persistent instructions, a defined role, and an automated task running in the background.

<blockquote style="border-left: 4px solid #990000; margin: 15px 0; padding: 5px 20px; font-style: italic; color: #555; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">"I have it scanning 35 to 40 job posting sites — it ranks positions according to the descriptors I put in." — <a href="https://www.ele.llc/people/mike-hoyt" target="_blank" rel="noopener">Mike Hoyt</a></blockquote>
The agent monitors the market continuously, ranks opportunities against explicit priority descriptors, and surfaces alignment without manual searching. The setup, it turned out, didn't require a tutorial. Mike Hoyt framed it: "I didn't need a video. I just had to follow the instructions. It's like a recipe."
The room immediately surfaced the next question: what makes some agents work well and others not? Same answer as the playbook — precision of the brief. Vague instructions produce vague agents. Explicit descriptors, a clearly named role, and a specific task structure are what separate a useful agent from one that needs constant correction.
The prompt builds the playbook. The playbook briefs the agent. The agent does the scanning while you focus on the work only you can do.


<hr style="border: 0; border-top: 1px solid #eee; margin: 40px 0;" />

<h3 style="font-family: 'Montserrat', sans-serif; font-weight: bold; color: #1a1a1a; font-size: 1.3rem; margin-bottom: 15px;">What to try next</h3>
<ol style="padding-left: 25px; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">
 	<li style="margin-bottom: 15px;">In the next 30 days — load your context and run the full CRIT prompt. Don't describe your role. Paste your actual job description, your team size, your industry, and your company context. Let AI ask its three questions. Answer them slowly — especially the ones you didn't expect. The playbook you get back is only as precise as what you put in.</li>
 	<li style="margin-bottom: 15px;">In the next 60 days — define the ROIs your business actually cares about and map your stakeholders. The base prompt doesn't automatically generate financial framing. Add an explicit instruction: "Factor in the objections I'm likely to receive and the ROI indicators my business cares about." Whether you're making an internal case or an external one, the business outcome language is what gets you in the room — and keeps you there.</li>
 	<li style="margin-bottom: 15px;">In the next 90 days — brief a personal agent on your highest-value descriptors. Take the playbook output and turn it into agent instructions. Be explicit about the type of work, organizations, and opportunities you want it to track. Start with one agent. Test it. A well-briefed agent running in the background frees you to focus on the diagnostic and design work that no prompt can do for you.</li>
</ol>
<div style="background-color: #f1f4f9; padding: 20px; border-radius: 8px; margin-top: 40px; font-family: 'Calibri', sans-serif; font-size: 1.1rem;">
Bring your work into the room
If this connects to real work you are trying to move forward — a career repositioning, a business case that keeps stalling, or a role that has accumulated more scope than it should — bring it into the ELE community. Share the challenge, compare signals with trusted peers, and leave with practical next moves you can use.
Submit My Challenge Now: <a style="color: #0056b3; font-weight: bold; text-decoration: underline;" href="https://www.ele.llc/faqs/share-top-of-mind-talent-challenges">https://www.ele.llc/faqs/share-top-of-mind-talent-challenges</a>

</div>
</div>

AI Practical Lab | Career Opportunity in a Time of Disruption

patricia.ouzounian

andrea.nunez

wise2026

CRIT Model

Personal Playbook

insights-wise-2026

AI Career Strategy

Career Repositioning

Custom AI Agents

Context-Driven Prompting

L&amp;D Professional Development

Legacy Role Creep

Highest-Value Energy

30-60-90 Day Action Plan

Stakeholder Influence

People Leaders in Transition

AI-Enabled Work

<blockquote class="ml-2 border-l-4 border-border-300/10 pl-4 text-text-300">
"I went from a blank to having a pretty good rough draft that I can go then and edit, and makes my time much more productive." — <a href="https://www.ele.llc/members/dirk-tussing" target="_blank" rel="noopener">Dirk Tussing</a>
</blockquote>
Here's the business problem that kicked this off: small and mid-sized companies know leadership development isn't working. Classroom training isn't building AI-ready leaders. And the people who do understand AI can't always translate it into what a 500-person manufacturing company actually needs to do differently on Monday morning.
That was the challenge Mike Grubich brought to ELE's April 10th <a href="https://www.ele.llc/insights/design-charrette-stop-talking-start-building-ai-leaders" target="_blank" rel="noopener">Design Charrette — </a><a href="https://www.ele.llc/insights/design-charrette-stop-talking-start-building-ai-leaders" target="_blank" rel="noopener">Stop Talking, Start Building Leaders</a>. The charrette generated raw peer insight and a partially filled action planning worksheet. April 16th was about turning that raw material into something a leadership team could actually use.


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
This one was open to anyone — members, guests, and curious colleagues alike. No slides, no formal structure. Just Dirk walking through the live workflow with a small group willing to watch AI do the heavy lifting in real time.


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
The workflow: Charrette → Action Plan → Playbook
ELE's new sprint model is a three-step sequence. The Design Charrette starts with a real business problem and generates peer-tested insight. That becomes the input for an AI Action Planning hour — where AI tools help complete Aaron Olson's Action Planning Worksheet (from Strategy and Change), a structured tool designed for exactly this kind of high-disruption, hard-to-focus moment. The completed worksheet then feeds an AI Practical Lab, where the community co-creates customized implementation playbooks.
As <a href="https://www.ele.llc/members/patricia-ouzounian" target="_blank" rel="noopener">Patti Ouzounian</a> put it during the discussion:

<blockquote class="ml-2 border-l-4 border-border-300/10 pl-4 text-text-300">
"You're taking what was done in the charrette and all of the transcript items and pieces to come up with a new and improved, updated action planning worksheet — so it can stand as the premise for the AI practice sessions, and then ultimately the playbook."
</blockquote>
That's the architecture. And the April 16th hour was proof it works.


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
AI doesn't replace the thinking — it accelerates it
Dirk walked the group through the live workflow: drop in the Zoom transcript, the group chat, and the partially filled worksheet; run a CRIT prompt (Context, Role, Interview, Task); let the AI interview you with three targeted questions to sharpen the focus; then watch it fill out the worksheet with situation framing, insights, choices, risks, opportunities, and measurable actions.
The output wasn't perfect. It wasn't supposed to be. The point was to go from blank page to credible rough draft — fast — and then scrub it before sharing.

<blockquote class="ml-2 border-l-4 border-border-300/10 pl-4 text-text-300">
"Senior leaders need to stop treating AI leadership as a content rollout and start treating it as an execution choice."
</blockquote>
That line came out of the AI-assisted worksheet completion, drawing directly from Mike's business challenge. It's the kind of reframe that takes a room an hour to land on. The AI surfaced it in minutes — because the charrette had already done the real work of naming the problem.


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
The part that surprised the room
<a href="https://www.ele.llc/members/david-scherer" target="_blank" rel="noopener">David Scherer</a> raised something worth flagging:

<blockquote class="ml-2 border-l-4 border-border-300/10 pl-4 text-text-300">
"I love these features — I really appreciate how we can leverage ELE in so many different ways outside of the organization."
</blockquote>
That's not just a compliment about the platform. It's pointing at something real: when you contribute to an ELE Design Charrette, you're building a documented, shareable artifact of your thinking. One you can actually point to. For senior leaders navigating career transitions or gig work, that matters.


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
What to try next

<ol class="[li_&amp;]:mb-0 [li_&amp;]:mt-1 [li_&amp;]:gap-1 [&amp;:not(:last-child)_ul]:pb-1 [&amp;:not(:last-child)_ol]:pb-1 list-decimal flex flex-col gap-1 pl-8 mb-3">
 	<li class="whitespace-normal break-words pl-2">Run your own CRIT prompt on a current challenge. You don't need ELE's full workflow to start. Take a business problem you're stuck on, give an AI tool the Context, assign it a Role, ask it to Interview you (max 3 questions), then name the Task. Notice how different the output is from a generic prompt.</li>
 	<li class="whitespace-normal break-words pl-2">Use the worksheet as a pre-read filter. Before your next leadership team conversation about AI adoption, run the Action Planning Worksheet on the situation yourself — even partially filled. It forces you to separate symptoms from signals before you walk in the room.</li>
 	<li class="whitespace-normal break-words pl-2">Identify whose "business why" is missing. In most organizations, AI adoption stalls because senior executives haven't articulated why it matters for this business, this team, this year. Name one executive in your organization who should own that framing — and who isn't yet.</li>
</ol>
Early evidence signal: Within two weeks, can you get one senior leader to articulate AI adoption as a business execution decision — not a training initiative?


<hr class="border-border-200 border-t-0.5 my-3 mx-1.5" />
If any of these insights resonate — and you've got a top-of-mind talent business problem you'd like the ELE community to work on — send it our way.
Members can submit their business challenge here: <a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.ele.llc/faqs/share-top-of-mind-business-challenges">Submit My Business Challenge Now</a>
Looking to turn these ideas into action? Access the <a href="https://community.ele.llc/t/pre-chat-post-chat-leadership-development-drives-ai-transformation/5860/2" target="_blank" rel="noopener">Idea Exchange Post-chat</a> for an updated Action Planning Worksheet.

Co-create Live with ELE | From Charrette to Action Plan: Watching AI Do the Heavy Lifting

Leadership

dirk.tussing

onboarding

deborah.lenchard

leadership development

peer-learning

HR and learning trends

david.scherer

clarity

networking

AI Adoption

organizational learning

knowledge sharing

ELE Office Hour

idea exchange

fresh ideas

new members

re-engagement

Q&amp;A

Design Charrette

confidence

capability

ELE 2026

action planning

business problem

CRIT prompting

senior leaders

execution strategy

Alter Domus

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

tracey.wik

jean.ryan

edward.desrosiers

missy.gonzales

colin.hahn

business impact

stakeholder alignment

AI tools

AI in HR

nicole.defalco

performance enablement

capability building

roberto.seif

talent development strategy

1Q2026

AI in L&amp;D

dustin.brewer

AI reskilling

executive communication

team capability

learning in the flow of work

human-centered ai

Storytelling for Influence

Clarity to Commit

Co-Creation

Practical Learning Design

Culture of Experimentation

<blockquote>
“What if your AI learning strategy is failing—not because of the technology—but because you’re designing for the wrong humans?” — <a href="https://www.ele.llc/members/tracey-wik" target="_blank" rel="noopener">Tracey Wik</a>, Co-Founder of Love Sunday Nights.
</blockquote>
<iframe title="YouTube video player" src="https://www.youtube.com/embed/XcY2TdkIGg8?si=wjp4LrU6NafVzXiJ" width="560" height="315" frameborder="0" allowfullscreen="allowfullscreen"></iframe>
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:
These insights didn’t emerge from a presentation—they were shaped through ELE’s Design Charrette approach.

<blockquote>Borrowed from architecture, a design charrette brings experienced practitioners together to solve complex problems quickly.
<ul>
 	<li>Design Charrettes—introduced by <a href="https://www.ele.llc/members/alaska-fu">Alaska Fu</a>, ELE’s Experience Designer—are focused, peer-powered working conversations that help leaders co-create and refine practical approaches in 50–90 minutes.</li>
 	<li>In ELE design charrettes, peers work through challenges using three complementary lenses—internal client, AI learning guide, and real-world skeptic—to strengthen judgment before committing to action.</li>
</ul>
</blockquote>
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.

<blockquote data-start="1038" data-end="1155">
“Learning isn’t content. It’s an organizational stance—curiosity, experimentation, and shared problem-solving.” — <a href="https://www.ele.llc/members/michael-hruska" target="_blank" rel="noopener">Mike Hruska</a>, Co-Founder of BARYONS.
</blockquote>
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.

Design Charrette: Building the NextGen Talent Pipeline with AI Mentors

future of work

azizeh.constantinescu

carole.widmer

matt.donovan

tamora.martin

organizational capability

matt.sernaker

michael.hruska

actionable insights

AI-enabled learning

cindy.pinkstaffmiller

alaska.fu

learning experience design

NextGen Talent Pipeline

AI Mentors

talent experience design

confidence capability clarity

team capability building

team-based learning

manager led learning

capability over content

real work learning

learning as a system

talent strategy 2026

skills and talent initiatives

peer driven learning

adaptive learning design

Lead Belay

This real-time exchange surfaced a truth many talent leaders are quietly wrestling with: AI isn’t just changing how work gets done—it’s revealing whether our teams have the confidence, clarity, and shared judgment to learn together in the flow of work.
The conversation didn’t drift toward tools or training catalogs. Instead, it stayed grounded in what leaders are seeing inside their organizations right now: uncertainty, acceleration, and unspoken fear. As Michael named directly:

<blockquote data-start="772" data-end="868">
“Great leaders don’t hide fear. They help people face it together.” — <a href="https://www.ele.llc/members/mike-kester" target="_blank" rel="noopener">Michael Kester</a>, Lead Belay.
</blockquote>
That idea reframed the role of talent leaders in this moment. Before teams can build new capabilities, leaders have to design spaces where fear is discussable—where learning isn’t confused with performance, and curiosity isn’t penalized.
<img class="aligncenter wp-image-44497" src="https://wp.ele.llc/wp-content/uploads/2025/11/WISE2024-Kester-and-Lead-Belay.jpg" alt="Mike Kester facilitating a small-group breakout discussion with senior talent and business leaders." width="759" height="441" />
From there, three insights stood out for leaders designing team-based learning experiences:
First, AI doesn’t fix weak systems—it exposes them. As Deepa put it:

<blockquote data-start="1289" data-end="1362">
“AI amplifies everything—structure and chaos.” — <a href="https://www.ele.llc/members/deepa-kartha" target="_blank" rel="noopener">Deepa Kartha</a>.
</blockquote>
Teams aren’t struggling because they lack tools. They’re struggling where roles, decision rights, and ways of thinking were already unclear. AI simply makes that visible—fast.
Second, the capabilities that matter most aren’t new. In a moment that brought relief to many in the discussion, Nicole DeFalco reframed the skills conversation:

<blockquote data-start="1714" data-end="1811">
“The new skills aren’t new. They’re the ones we stopped practicing.” — <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a>.
</blockquote>
Critical thinking, sensemaking, and judgment are no longer “soft skills.” They are the infrastructure that allows AI to be useful rather than risky.
Third, clarity—not access—is the real gap. Nearly everyone has access to AI. Far fewer teams have shared norms for when to trust it, how to validate outputs, and when human judgment must lead.
<img class="aligncenter wp-image-44471" src="https://wp.ele.llc/wp-content/uploads/2025/12/AI-Transformation-Upskilling-in-AI-Maturity-e1766111773634.png" alt="Upskilling in AI Maturity" width="609" height="585" />
The takeaway for talent leaders is clear: this isn’t about delivering more training or scaling content faster. It’s about designing experiences where teams practice thinking together—inside the work that actually matters.
Because in the age of AI, learning doesn’t live in programs. It lives in the moments when leaders slow the work down just enough for teams to learn forward—together.