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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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LAK Group

CNA Insurance

Allstate Insurance

Waukesha County Technical College

Marquette University

HumanSide

<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;">"For leaders, what we need to be is responsibly curious. Ask questions, be interested, not interesting." — <a style="color: #333; text-decoration: none;" href="https://www.ele.llc/people/michael-grubich" target="_blank" rel="noopener">Michael Grubich</a>, LAK Group</blockquote>
The real work in the room
Senior talent leaders, executives, and academic advisors came together to move past the hype and get honest about what AI adoption actually requires — not just tool access, but structural redesign. The work was getting stuck where organizations were overlaying AI onto unchanged roles, skipping the governance architecture that builds real trust, and treating adoption as a communications problem rather than an organizational design problem. It matters now because the window for deliberate design is narrowing: the organizations pulling ahead aren't moving faster — they're moving more intentionally.
Leaders in the conversation
Michael Grubich, President of LAK Group and adjunct faculty at Marquette's College of Business Administration, brought a practitioner's lens on talent strategy and organizational decisions across industries. Peter Hogaboam, Director of Technical Learning and Communications at CNA Insurance, shared live pilot architecture and the nuts-and-bolts of progressive risk assessment. Jim Orheim, Program Director for Executive Education and Professional Development at Marquette University, offered the academic institution's perspective on what enterprises expect from incoming talent. Dr. Phyllis King, Chief Innovation Strategist at Waukesha County Technical College, reframed the literacy-versus-skills distinction that anchored much of the exchange. Lorna Malloy, Strategic L&amp;D Leader at Allstate, brought a Fortune 100 view of AI integration at scale — and the felt reality of how fast organizational mindset can shift. Interactive prompts from practitioners across the room surfaced the environmental friction, the silence masking resistance, and the governance gaps most organizations haven't named yet.

<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;">AI skills vs. AI literacy — and why the difference will cost you</h3>
<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;">"There's a difference between AI skills and AI literacy." — <a style="color: #555; text-decoration: none;" href="https://www.ele.llc/people/phyllis-king" target="_blank" rel="noopener">Phyllis King</a>, Waukesha County Technical College</blockquote>
AI skills mean a person can operate a tool. AI literacy means they understand the effect, the ethics, and the responsible intent behind using it. One produces tool competency. The other produces organizational judgment.
Most enterprise training programs are building the former while assuming the latter follows automatically. It doesn't. Organizations that conflate the two are producing workforces that can run AI but can't govern it — and that gap shows up in compliance failures, inconsistent use, and adoption that stalls the moment someone asks a hard question about risk.
The practical implication is structural: foundational training needs to target literacy first, tool fluency second. Jim Orheim described Marquette's College of Business already requiring all faculty and staff to complete ethics, safety, and responsible-use training this summer — not because they're behind, but because they want to set the standard before the pressure arrives from outside.

<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;">Taking the seat — redefining the job, not just the toolset</h3>
<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;">"They took an organizational approach to it versus a tactical approach." — <a style="color: #555; text-decoration: none;" href="https://www.ele.llc/people/michael-grubich" target="_blank" rel="noopener">Michael Grubich</a>, LAK Group</blockquote>
He described a client who stopped all incoming AI tools, stepped back to re-examine every role against business strategy, assessed whether the right people were in position, and then deployed. That sequence — organizational design before tool deployment — is what makes adoption stick.
The panel was direct: top-down AI messaging registers at roughly 4% on the behavior-change iceberg. The real work happens at the middle-manager and frontline-leader layer — and that's exactly where L&amp;D and HR have the deepest reach. Waiting for C-suite clarity to cascade is not a strategy.
When an organization doesn't know how to rewrite roles, update operating norms, or establish communication agreements around AI use, those are not technology problems. They are people-leader problems. The moment to act on that is now.

<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 unspoken realities — resistance, the grid, and what's coming</h3>
The sharpest exchange in the room didn't come from the panel. It came from the floor.
A practitioner asked directly: is the environmental impact of enterprise AI data centers part of anyone's governance conversation? The panel's honest answer was largely no — and one leader named it plainly, stating that right now their organization does nothing and doesn't even have a plan yet.
What followed surfaced two signals that most organizations are not yet tracking.
The first is a psychological safety gap hiding in plain sight. Change management practitioners in the room described a dynamic that's easy to miss: people are reading the organizational climate and choosing not to surface real concerns — including environmental and ethical reservations — because they anticipate those concerns being framed as resistance to adoption. The silence looks like buy-in. It isn't. It's suppressed friction that will surface later, at a much higher cost.
The second signal is generational and incoming. Several practitioners noted that a wave of talent is arriving with environmental values baked in as non-negotiables — and that when those employees encounter mandatory AI adoption with no sustainability framework attached, the disconnect will be immediate and visible.
These aren't fringe concerns. They're governance gaps that organizations with mature people-and-culture infrastructure will be better positioned to absorb — and that organizations without it will be caught flat-footed by.


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<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;">Audit your training for the literacy gap. Pull your current AI training content and ask one question: does this teach people how to think about AI use, or only how to perform it? If the ethics, intent, and governance dimensions aren't explicitly present, close that gap before the next cohort goes through.</li>
 	<li style="margin-bottom: 15px;">Design a listening mechanism specifically for suppressed resistance. Pulse surveys and skip-level conversations with a direct question about AI concerns — anonymous, low-stakes, framed as input rather than performance assessment — will surface what people aren't saying in public. That signal is more operationally useful than any adoption metric you're currently tracking.</li>
 	<li style="margin-bottom: 15px;">Run one role-redefinition conversation before the next tool deployment. Before any new AI capability goes live on your team, gather the relevant people leaders and ask: if this tool does what it promises, what changes about this role? What stays? What needs to be renegotiated — including expectations, norms, and potentially compensation? That conversation, done once with intention, builds the muscle for doing it at scale.</li>
</ol>
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Bring your real work into the community
If this connects to work you're trying to move forward — a stalled AI rollout, a governance structure that isn't holding, roles that need redesigning, or a room that's gone quiet when it shouldn't — 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>

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Executive Cohort Panel | Framing the Why

Future of Work

Digital &amp; AI

michael.grubich

lorna.malloy

peter.hogaboam

Middle Management

jim.orheim

AI Adoption

phyllis.king

L&amp;D strategy

AI literacy

dustin.brewer

manager enablement

responsible AI use

wise2026

People Leaders

executive-cohort

insights-wise-2026

peer intelligence

role redefinition

Organizational Redesign

Governance and Trust

Change Resistance

Systemic Change

Environmental Impact of AI

West Bend Insurance

Journyz

<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;">"The managers weren't really talking about skill gaps anymore… they were uncovering things around friction." -- <a href="https://www.ele.llc/people/missy-gonzales" target="_blank" rel="noopener">Michelle Gonzales</a>, West Bend Insurance</blockquote>
People leaders across industries are asking the same question right now: how do we prepare people for AI-enabled work? In this featured in-person keynote address at ELE's Spring Conference, <a href="https://www.ele.llc/people/missy-gonzales" target="_blank" rel="noopener">Michelle Gonzales</a> (Head of Talent at West Bend Insurance) and <a href="https://www.ele.llc/people/deepa-kartha" target="_blank" rel="noopener">Deepa Kartha</a> (Founder and CEO of CultureRox &amp; Journyz) brought a critical truth to the surface: the real challenge sits one layer deeper. What their cross-organizational innovation sprint revealed is that organizations aren't just facing a skills problem. They're trying to make sense of work itself as it changes faster than anyone can formally redesign it. Workflows have drifted, decision rights are blurring, and the cognitive cost of navigating that gap is accumulating quietly—mostly in silence..
People shaping the work
The live keynote exchange drew on findings from a five-week innovation sprint conducted earlier in 2026. Participants represented a cross-organizational cohort of practitioners working across talent management, learning and development, and organizational development — each bringing direct experience of AI's pressure on their organizations. The sprint was designed and facilitated through a collaboration between West Bend Insurance and CultureRox/Journyz.

<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 problem underneath the problem</h3>
The sprint started with an assumption the field knows well: AI is creating skill gaps, and L&amp;D needs to close them.
Then the questions changed.
Instead of asking managers "what skills do people need?", the sprint asked: "what has become harder to navigate?" and "where are people hesitating?"
The answers were different. Managers stopped describing skill deficits. They started describing something harder to name — friction, ambiguity, inconsistent judgment, and a growing sense that the rules of the work had shifted without anyone officially saying so.

<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;">"We started with questions and used the questions to understand the scenario." -- <a href="https://www.ele.llc/people/deepa-kartha" target="_blank" rel="noopener">Deepa Kartha</a>, Journyz</blockquote>
That shift in question design changed everything that followed.

<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 4 signals that surfaced</h3>
What emerged across organizations wasn't a unified skill gap. It was four distinct patterns — each one a signal that work had changed faster than organizations could interpret it.
Risk inflation. The perceived cost of a wrong decision increased — even when actual risk hadn't changed. More escalation. More "just checking" behavior. Slower approvals. Growing dependency on leadership sign-off for calls that used to be made at the front line.
Workaround behavior. When formal workflows stopped fitting reality, people invented unofficial ones. Shadow approvals, undocumented shortcuts, side-channel decisions. A key signal shared during the keynote: people were no longer doing what their job descriptions said they were doing. The work had changed. The official process hadn't caught up.
Uneven adaptation. Some teams moved quickly. Others slowed dramatically — not because of capability differences, but because of structural ones. How a manager interpreted the changes. How clearly expectations had been reset. How much risk tolerance existed on that team. Treating uneven results as a talent problem misses the actual driver entirely.
Decision confidence erosion. Managers began second-guessing decisions they previously made without hesitation. Execution slowed. Escalation inflated. Review cycles multiplied. And beneath all of it, a reframe emerged on stage that stopped people:

<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;">"It is not change management. It is slow digestion of change." -- <a href="https://www.ele.llc/people/deepa-kartha" target="_blank" rel="noopener">Deepa Kartha</a>, Journyz</blockquote>
<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 moment that named what nobody had said out loud</h3>
One of the sharpest observations from the live interactive presentation wasn't a data point — it was an image.
Managers aren't failing to adapt. They're straddling.
Running old workflows and new ones at the same time. Without clarity on which rules apply where. Without organizational acknowledgment that the cognitive load of that dual reality is real, accumulating, and costly.

<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;">"We're kind of here in the old world and we're here in the new world." -- <a href="https://www.ele.llc/people/missy-gonzales" target="_blank" rel="noopener">Michelle Gonzales</a>, West Bend Insurance</blockquote>
The output looks the same. The execution reality does not. And most organizations are detecting that gap late — after performance problems have already appeared, after escalation has already inflated, after the workarounds have already become the unofficial standard.
A collective perspective emerged around what this actually requires: not faster training, not another framework. Something closer to organizational permission — to name what's hard, slow down before designing a response, and treat friction as a signal worth exploring rather than a problem to eliminate.

<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;">A new possibility for L&amp;D</h3>
Here's what the sprint surfaced that most L&amp;D strategies aren't accounting for yet.
L&amp;D practitioners — because of their proximity to people, behavior, and organizational patterns — are already positioned to detect these signals before they become performance problems.
Not by abandoning what they do well. By expanding what they're listening for.
The sprint pointed toward an expanded role: organizational sensing, signal interpretation, behavioral pattern recognition, operational listening. These aren't foreign capabilities for L&amp;D. They're an extended aperture on skills the function already holds — applied earlier, upstream of the intervention.

<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;">"We're uniquely positioned in L&amp;D to notice where subtle shifts are in the organization." -- <a href="https://www.ele.llc/people/missy-gonzales" target="_blank" rel="noopener">Michelle Gonzales</a>, West Bend Insurance</blockquote>
The question the keynote left open: is L&amp;D being asked to do this work? And if not — should it start without being asked?


<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;">Change the first question. Before any upskilling or reskilling initiative, replace "what skills do people need?" with "what has become harder to navigate?" Run that question with five to ten managers. If the answers surface hesitation, inconsistent judgment, or workarounds rather than skill gaps — you're looking at the real problem. That's the early evidence signal.</li>
 	<li style="margin-bottom: 15px;">Map the workarounds before designing the intervention. When people are inventing unofficial processes, the official workflow no longer fits reality. Spend two weeks documenting where shadow approvals, duplicated checks, or undocumented shortcuts are happening. That map is more useful than any needs assessment built on stated skill gaps.</li>
 	<li style="margin-bottom: 15px;">Build a short discovery sprint before committing to a solution. Five weeks of structured inquiry — open-ended questions, behavioral observation, rapid pattern synthesis — can reframe the entire problem definition. The investment is small. The cost of skipping it is designing the wrong solution at full speed.</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 version of this into the ELE community
If this connects to real work you are trying to move forward — the escalations that shouldn't be escalating, the workarounds that have quietly become the process, the uneven adaptation nobody has officially named yet — bring it into the ELE community. Share the challenge. Compare signals with trusted peers. 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>

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Live Insights | What the Innovation Sprint Is Revealing

missy.gonzales

Future of L&amp;D

deepa.kartha

innovation sprint

AI and Work

organizational listening

risk inflation

decision confidence

Decision Friction

Workflow Drift

Workaround Behavior

Uneven Adaptation

Discovery Framing

Organizational Sensing

Gainwell Technologies

<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;">"We're giving people the most powerful tool of their careers — and rewarding them for using it the laziest way."</blockquote>
The real work on the table: Most organizations have already answered the easy question: are we using AI? The harder question — are our people using it well, and do they even know the difference? — is the one almost nobody is answering. For L&amp;D and people leaders, defining what good AI use actually looks like is critical before cognitive atrophy, skill degradation, and gradual disempowerment quietly erode the human judgment organizations depend on.
Who was in the room: This working exchange brought together L&amp;D executives, people leaders, and talent strategists from across the ELE community — professionals actively navigating enterprise AI licenses, workforce capability questions, and the pressure to show ROI on tools their teams may or may not be using well.

<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 system is rewarding the wrong thing</h3>
<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;">"That was not a tech failure. It was a structural failure."</blockquote>
The easy button problem isn't a discipline problem. It's a design problem. At most organizations, the professional who uses AI carelessly outperforms the one who uses it carefully — at least on the metrics that currently get measured: license usage, login rates, output volume, and speed.
When the system rewards the easy button, people press it. That's not laziness. That's the system working exactly as designed — toward the wrong outcome. Until L&amp;D leaders redefine what gets measured, training alone won't change behavior. The incentive structure has to change first.

<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;">What erodes when we stop doing the hard thinking</h3>
<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;">"Higher confidence in AI correlated with less critical thinking."</blockquote>
Three peer-reviewed studies surfaced during the working exchange aren't reassuring. MIT showed measurable cognitive debt in writers using AI, including memory retention issues and a reduced ability to integrate personal understanding with completed work. The Lancet tracked endoscopists using AI assistance for three months and found measurable skill degradation after tools were removed. Microsoft and Carnegie Mellon found that higher confidence in AI correlated directly with less critical thinking. This is the research backdrop behind the eight capacities of human agency — identifying what AI structurally cannot replace, but can degrade through disuse.

<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 two capacities L&amp;D must protect</h3>
<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;">"When we give away our critical decision-making, we give away more than we realize."</blockquote>
Two of the eight capacities are especially at risk, and especially relevant to the clarity and confidence people leaders need to act well in an AI-assisted environment:
The Weight Bearer is the human capacity to have skin in the game — to feel the stakes because your name is on the work. When people hand AI their ideation, structure, and key decisions, they don't just save time. They quietly surrender the ownership instinct that makes their work credible and defensible.
The Course Corrector is the meta-capacity: noticing when something is off and doing something about it. It's the capacity that catches AI's errors before they ship. It's also the one most directly degraded by over-reliance on the easy button.
Both capacities are skills. They're built through use and eroded through avoidance — which means L&amp;D has direct leverage over whether they survive AI adoption or quietly disappear.

<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 investment gap hiding in plain sight</h3>
<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;">"Trained humans don't follow your AI strategy. They become it."</blockquote>
Boston Consulting Group's framework for AI investment suggests 70% should go to people and process, 20% to data architecture, and 10% to tools and algorithms. Most organizations have that ratio flipped. The distinction produces fundamentally different outcomes. Systems — policies, gates, validators — are necessary. They're the floor, but they only produce compliance. People — trained judgment, ownership, the ability to catch what AI gets wrong and reinvent what it can augment — produce capability. And capability compounds in ways compliance never will.


<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;">Ask the governance question this week. Find out whether your organization has a written, org-wide standard for what good AI use looks like — and who owns the answer. Early evidence: If three people give you three different answers, the gap is confirmed and you have the business case to close it.</li>
 	<li style="margin-bottom: 15px;">Add one qualitative signal to your AI measurement. Ask managers to include a "walk me through how you used AI on this" question in their next round of 1:1s — before reviewing the output. Early evidence: If people struggle to explain their process or can't identify what they changed, the Weight Bearer and Course Corrector capacities are already at risk on your team.</li>
 	<li style="margin-bottom: 15px;">Map your AI investment ratio against the BCG framework. Make a rough honest estimate: what percentage of your current AI spend is going to systems versus people development? Early evidence: If the ratio is inverted — heavy on tools and compliance, light on trained judgment — that number becomes the opening line of your next budget conversation.</li>
</ol>
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Bring your work into the room
If this connects to real work you are trying to move forward, 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>

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The Easy Button Problem| The catch-22 at the heart of AI adoption.

Performance Improvement

jenny.massoni

AI governance

Executive Learning Exchange

AI fluency

human agency

easy button problem

workforce capability

Cognitive Debt

Gradual Disempowerment

People Development

AI Measurement

Systems vs People Investment

Skill Degradation

Alter Domus

Executive Learning Exchange (ELE)

<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

dirk.tussing

anton.maletich

colin.hahn

keri.kersten

adrienne.guerrero

nancy.dome

marty.murrillo

ana.bedard

shonda.cromwell-rashad

audra.pinter

david.scherer

human AI collaboration

capability building

prompt engineering

1Q2026

AI Workflow Design

Context Setting

CRIT Model

Onboarding Effectiveness

Product Knowledge Training

Junior Talent Development

Clarity in AI Use

Confidence in AI

Senior Talent Leaders

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.

Co-Creating Human-Centered Transformation in the Age of AI

Leadership

mike.kester

edward.desrosiers

liza.fernandez

guillermo.gutierrez

tamora.martin

organizational capability

L&amp;D

matt.sernaker

AI in HR

rachel.kaberon

Workforce Transformation

erin.joslin

nicole.defalco

Future Ready Leadership

cindy.pinkstaffmiller

manendra.bhugra

mike.hoyt

AI and learning

AI reskilling

AI Era Leadership

leadership judgment

learning in the flow of work

talent experience design

confidence capability clarity

AI-enabled leadership

team capability building

human-centered ai

critical thinking at work

AI and culture

team-based learning

Rothy's

This live discussion provided a necessary strategic pause—reframing 2025, a year when AI acceleration outpaced governance and skills into a clear, action-oriented agenda for 2026, shaped by some of the brightest minds in talent leadership.
The session, which focused on turning 2025's lessons into 2026's impact, moved beyond a simple recap to identify the structural gaps holding organizations back: unclear roles, inconsistent manager readiness, and a lack of skills and governance to match the speed of AI experimentation.

<h6 data-path-to-node="2">The Core Narrative: Addressing Structural Crisis and Strategic Mandate</h6>
The key insight is that AI has exposed profound flaws in organizational design, making the chief priority for 2026 a fundamental overhaul of how we define and manage work itself.

<blockquote data-path-to-node="3,0,1">
"Work is evolving faster than roles, causing heartburn around antiquated job structures." -- <a href="https://www.ele.llc/members/megan-bickle" target="_blank" rel="noopener">Megan Bickle</a>, Rothy's
</blockquote>
This structural crisis demands that the talent function lead the charge in strategic redesign—not as a support function, but as the architect of how work, skills, and AI come together.

<h6 data-path-to-node="6">Key Takeaways for Senior Talent &amp; Learning Leaders:</h6>
<ul data-path-to-node="7">
 	<li>
The Structural Work/Role Disconnect is the New Crisis: Work is evolving faster than job architecture, creating real tension around compensation, accountability, and career paths.
</li>
 	<li>
Operational AI Exposes Manager Readiness as the #1 Governance Gap: As AI moves into daily work, inconsistent manager capability—not technology—has become the primary bottleneck.
</li>
 	<li>
L&amp;D's Identity Must Pivot to 'Designer of the Blended Workflow: The talent function must intentionally design how human judgment and AI systems integrate—moving people up the value chain by design, not accident.
</li>
 	<li>
The Future of Work is a Mental Health Design Challenge: The focus is shifting from reactive wellness programs to proactively redesigning work in ways that protect psychological well-being.
</li>
</ul>

<hr data-path-to-node="8" />

<h6 data-path-to-node="9">The Strategic Mandate: Linking AI to Business Value</h6>
The ultimate goal for 2026 is harnessing AI to maximize competitive advantage. That requires moving past technological enthusiasm to disciplined, strategic deployment. As Chris Dunford emphasized, this is the constraint talent leaders must enforce:

<blockquote data-path-to-node="3,0,1">
"Don't just follow the tech for the tech's sake; it must be linked to a business need." -- <a href="https://www.ele.llc/members/chris-dunford" target="_blank" rel="noopener">Chris Dunford</a>, Aon
</blockquote>
This quote serves as the central mandate for any new AI-driven capability investment.
When that constraint is applied, the value proposition becomes clear. 
As <a style="font-size: 16px; background-color: #ffffff;" href="https://www.ele.llc/members/matt-donovan" target="_blank" rel="noopener">Matt </a><a href="https://www.ele.llc/members/matt-donovan" target="_blank" rel="noopener">Donovan </a>reinforced, this is the outcome talent leaders should design for:

<blockquote data-path-to-node="3,0,1">
"AI takes on tasks to enable humans to move up the value chain."
</blockquote>
This is not a philosophy—it’s the benchmark against which every AI-enabled role redesign, learning investment, and manager capability should be measured.


<hr data-path-to-node="15" />
Practical Actions for 2026 Strategic Planning (Where to Start)
To generate the necessary Talent Development Intelligence and address these strategic and human gaps, talent and learning leaders can start with the following actions:

<ol start="1" data-path-to-node="18">
 	<li>
Launch a Cross-Functional "Deconstruction of Work" Sprint: Start by deconstructing 3–5 mission-critical roles. Map which tasks are best handled by AI versus high-value human judgment to inform Q1 job architecture and skill redesign.
</li>
 	<li>
Measure Manager Readiness for AI Governance (Not Just Usage):  Assess managers on their ability to govern, embed, and coach responsible AI use—not just whether tools are adopted.
</li>
 	<li>
Reallocate L&amp;D Resources to the "Three C's": Use AI to clarify role expectations, co-curate third-party content, and customize individual learning journeys—embedding learning directly into the workflow.
</li>
</ol>
These actions shift talent teams from reacting to AI disruption to intentionally redesigning how work actually gets done.
ELE Outlook for 2026: Why Incremental Change Isn’t Enough for How Work Gets Done
As ELE heads into 2026, one signal is already clear: incremental improvements won’t keep pace with the human, structural, and leadership challenges created by AI at scale.  The mandate for talent and learning leaders is to shape how work evolves—clarifying job architecture, building practical manager capability for AI governance, and ensuring every technology investment is explicitly tied to business value. By focusing on these priorities, talent and learning leaders can turn the disruption of 2025 into a scalable impact in the year ahead.

2025 in Review, 2026 in Focus: Defining Talent Priorities for the AI Workforce

future of work

Strategy

tracey.wik

upskilling

azizeh.constantinescu

talent development

talent management

learning &amp; development

matt.donovan

Talent Strategy

marsha.love-morrow

employee experience

reskilling

workforce planning

megan.bickle

HR transformation

HR leadership

Business Alignment

sara.fitzgerald

AI &amp; Workforce

angelica.janina.stilling

4Q2025

organizational design

terresa.bubbers

ali.glaser

sam.amelio

2026 planning

chris.dunford

manager readiness

job architecture

executive learning

2026 priorities

This AI practical lab provided a crucial blueprint for Talent and L&amp;D leaders looking to move past simple AI experimentation and toward disciplined, enterprise-grade workflows. The core theme was this: Generative AI is not a strategy replacement, but a force multiplier that radically redefines team capacity. The lab demonstrated a focused, three-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—Clarify, Create, Customize—which ensures human expertise remains in the critical stages of problem definition and cultural refinement. The implication is profound: AI promises to unlock unprecedented productivity, allowing HR to finally operate at the speed of the business, provided leaders enforce a "human-in-the-loop" quality standard.

<img class="aligncenter size-full wp-image-44448" src="https://wp.ele.llc/wp-content/uploads/2025/03/Three-Step-Model-for-AI-Workflows-ELE.jpg" alt="Three-Steap Model for AI Workflows (ELE)" width="690" height="356" />

👂ELE’s POV: Ed gets into the core tension: Is the accelerated adoption of GenAI in L&amp;D a necessary breakthrough for speed—or a reckless path that risks bypassing trust?

<audio controls="controls" data-mce-fragment="1"></audio>
<h3 data-path-to-node="2">Key Takeaways for Senior HR Leaders</h3>
<ul data-path-to-node="3">
 	<li>
The 10x Capacity Shift is Real: The most striking finding was the demonstrable power of an AI-augmented team. As <a href="https://www.ele.llc/members/edward-desrosiers" target="_blank" rel="noopener">Ed DesRosiers</a> revealed during the lab’s case study:

<blockquote data-path-to-node="3,0,1">
"With AI, our team of two created 10 times the deliverables of an 8-person team working the traditional way." This forces a re-evaluation of current capacity plans and resource allocation.
</blockquote>
</li>
 	<li>
AI Demands Higher-Level Thinking: AI elevates your team from producers to strategic partners. As <a href="https://www.ele.llc/members/marty-murrillo" target="_blank" rel="noopener">Marty Murrillo</a> stated:

<blockquote data-path-to-node="3,1,1">
"AI amplifies you, it doesn't replace you. You are the thought leader; AI is simply your thought partner." The focus shifts entirely to defining strategy and ensuring quality.
</blockquote>
</li>
 	<li>
Strategy Must Precede Speed: The "Clarify" step of the 3C model is non-negotiable. You must define the audience, challenge, and success metrics before generating content to avoid creating high-volume noise.
</li>
</ul>
<h3 data-path-to-node="4">Practical Actions to Take Immediately</h3>
<ul data-path-to-node="5">
 	<li>
Mandate the "Customize" Quality Gate: Stop content from going to the client or employee without a human refining the tone, ensuring cultural fit, and applying nuance.
</li>
 	<li>
Challenge "Solutioneering": Before a team asks an AI tool to "Create" a solution, demand proof that they completed the strategic diagnosis. If they can’t articulate the business need, halt production.

<blockquote data-path-to-node="5,1,1">
"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>
</li>
 	<li>
Invest in an AI Stack, Not Just a Single Tool: Audit your tool ecosystem (e.g., NotebookLM for research, Copilot for drafting, Canva for design) to ensure it supports the entire Clarify-Create-Customize workflow seamlessly.
</li>
</ul>
The era of the AI-powered workflow demands a new kind of leadership—one that is disciplined, strategic, and focused on impact. By implementing the "Clarify, Create, Customize" structure, you can harness the staggering productivity gains of generative AI while ensuring your Talent and L&amp;D functions remain grounded in human-centered design and strategic business alignment. This isn't about working harder; it’s about leading smarter.
The urgency of this evolution is echoed by other experts; as <a href="https://www.ele.llc/members/michael-vaughan" target="_blank" rel="noopener">Mike Vaughan</a> recently argued on LinkedIn, <a href="https://www.linkedin.com/posts/michaelsvaughan_ai-wont-help-ld-unless-we-let-go-of-what-share-7404581161945214976-b3-P?utm_source=share&amp;utm_medium=member_android&amp;rcm=ACoAAAAyK2cBgiwsMpK9fDqZM0mahKsHVCHm_Y8" target="_blank" rel="noopener">AI Won't Help L&amp;D Unless We Let Go of What Got Us Here.</a>
Let's continue this conversation on the ELE <a href="https://www.ele.llc/idea-exchange" target="_blank" rel="noopener">Idea Exchange</a> and share the non-negotiable quality controls you're putting in place for AI-generated content!

✨ AI Practical Lab: AI-Powered Workflows

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