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ELE brings leaders together across companies and industries to work through the messy stuff: decisions, risk, handoffs, execution drag, and what it takes to move from idea to action.

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Lurie Children's

Precisely

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;">"There were lawsuits because we didn't have human judgment in the loop around governance." — <a style="color: #333; text-decoration: none;" href="https://www.ele.llc/people/marty-murrillo" target="_blank" rel="noopener">Marty Murrilo</a></blockquote>
The real work in the room
Coaches and people leaders came into this working exchange carrying a question that doesn't have a clean answer yet: where exactly does AI make coaching sharper, where does it surface the biases we can't see in ourselves, and where does it need to be kept out entirely to protect the professional integrity that makes coaching worth paying for?
The work was getting stuck in the middle of all three. Not because the tools aren't capable — but because most practitioners and leaders haven't yet built the discipline to tell the difference between those three zones in real time. And with low-cost AI coaching products already in market, and hiring algorithms already producing lawsuits, the window for getting this right is narrowing.
People shaping the work
This live interactive discussion was facilitated by Marty Murrilo and Mike Hruska, joined by Mary Ann Kennedy, alongside a room of approximately 20 certified practitioners, people leaders, and learning professionals, all navigating the AI-in-coaching question from inside active practice.

<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;">Where the conversation started: human or reverse centaur?</h3>
Mike opened with a question the room kept returning to all morning.

<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 you working for it, or is it working for you?" — <a style="color: #555; text-decoration: none;" href="https://www.ele.llc/people/michael-hruska" target="_blank" rel="noopener">Mike Hruska</a></blockquote>
He framed it through what he called the Technology Centaur model: in a healthy human-AI relationship, the human stays in the lead and AI functions as a power multiplier. The reverse — where the algorithm manages the human, the way a delivery driver is routed and monitored by an app — is already happening in workplaces, often without people noticing.
The practical question for coaches and people leaders isn't whether AI is useful. It's whether you're directing it or being directed by it. Most people in the room were somewhere in between, which is exactly where the blind spots live.

<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 context problem: why most AI output is mediocre</h3>
Mike Hruska brought a live example that reframed how the room was thinking about AI quality. He described a group of executives at a global company who asked AI for ideas about AI in manufacturing. They got generic output and assumed the tool was the problem.
It wasn't. The problem was context.
Mike asked them to start over — this time giving the AI a name, a role, a personal backstory, and specific details about the people in 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;">"Pretend a 20-year-old intern named Sam. Tell Sam your name, age, degree, tenure — and ask Sam to just ask you questions." — <a style="color: #555; text-decoration: none;" href="https://www.ele.llc/people/michael-hruska" target="_blank" rel="noopener">Mike Hruska</a></blockquote>
The same group came back with 157 ideas. People said they had done more forward-looking strategic thinking in that working exchange than they typically did in dedicated planning time.
The signal for people leaders: the quality gap most teams are experiencing with AI isn't a tool problem. It's a context investment problem. The people getting the best output are the ones who spend time upfront building rich personal and organizational context before asking for anything.

<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;">AI as a mirror: what forensic transcript analysis actually reveals</h3>
Vilson Simon introduced the concept that generated the most sustained discussion of the morning — using AI not to summarize a coaching conversation, but to forensically analyze what the coach was missing during 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;">"AI is augmenting my capacity to listen — and giving me feedback on what I'm missing." — <a style="color: #555; text-decoration: none;" href="https://www.ele.llc/members/vilson-simon" target="_blank" rel="noopener">Vilson Simon</a></blockquote>
He described a specific case: a CFO client he was ready to give up on after her second coaching exchange. His own judgment bias against the client was too strong, and she knew it. With the client's permission, he ran the transcript through Manus AI and asked for a forensic analysis of what was said and, more importantly, what wasn't being said clearly.
The analysis surfaced something he had filtered out entirely: the client was carrying unresolved childhood trauma, and was unconsciously treating the coaching relationship as therapy. Vilson knew he wasn't the right resource for that. But he couldn't see it in the room — because his bias was in the way.
The broader signal: coaches and managers have blind spots that live precisely in the places they're most confident. AI forensic analysis doesn't replace the judgment call. It creates the data that makes a better judgment call possible.
Vilson also named a subtler version of this problem — one rooted in language and culture rather than personal bias. As a native Portuguese speaker practicing in English, she discovered through AI analysis that he had been using a phrase that translates as gentle reassurance in Portuguese but lands as confrontational in English. The tool caught the drift his own fluency had hidden from him.
This matters for any people leader working across cultures, languages, or demographic contexts where the same words carry different social weight.

<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 governance gap: where human judgment isn't optional</h3>
Marty grounded the bias discussion in a harder, more consequential example. Several companies — Workday among those named in legal proceedings — fed years of successful hiring data into AI talent acquisition systems without auditing what values and assumptions were embedded in that data. The result was algorithms that systematically downgraded candidates based on gender markers in their applications, producing real legal liability.
The lesson isn't that AI can't support talent decisions. It's that AI will amplify whatever is already in your data — including the biases you haven't examined. Human judgment in the governance layer isn't a nice-to-have. It's what keeps the amplification pointed in the right direction.
The same logic applies to coaching. AI can surface patterns, flag language, and analyze what was missed. It cannot determine whether a client needs a coach, a therapist, or a direct conversation with their manager. That call belongs to the human in the room.

<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;">Protect or evolve: where the profession is actually heading</h3>
The most direct tension in the working exchange was the one between coaches who feel the pressure to protect professional standards and those who have already moved through that question toward deliberate integration.
One participant — a veteran coach approximately three years from what she expected to be retirement — described the shift plainly:

<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'm offloading the stuff I don't like doing — and I am completely re-engaged."</blockquote>
She had been planning to coast. Instead, by automating administrative work she disliked — feedback synthesis, session prep documentation, review drafts — she freed herself to do more of the high-value human work she'd entered the profession to do. She didn't know anymore if she'd retire on the schedule she'd planned.
The room's consensus, stated plainly by another participant: "Part of the way that we protect is by evolving."
That's the practical frame. Not protectionism. Not passive adoption. Deliberate integration — where AI handles the work that doesn't require you, and you bring full human judgment to the work that does.


<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>
The closing of the working exchange used a Head / Heart / Pocket structure — one new thought, one new feeling, one new piece of tradecraft. Here's how that maps to practical moves worth testing this week.
Head — change how you set context before the first prompt. Before your next AI interaction — whether it's a coaching prep note, a feedback synthesis, or a planning prompt — spend three minutes building context first. Name the role, the person, the history, the outcome you're working toward. Don't start with the request. Start with the setup. Measure whether output quality changes.
Heart — run one piece of your own work through forensic analysis. With appropriate permissions in place, upload one coaching transcript, team debrief, or feedback conversation to an AI tool and ask it to surface what was not said clearly — not just a summary of what was. Look specifically for where your own framing or language may have shaped the conversation in ways you didn't intend. The early evidence signal: you find at least one thing you would have handled differently.
Pocket — map where human judgment is non-negotiable in your current workflow. Draw a simple line for your team or practice. On one side: the work AI can handle (admin compression, feedback drafts, prep notes, pattern analysis). On the other: the work that requires human judgment (cultural nuance, imposter syndrome, governance calls, relationship repair). If you can't draw that line clearly right now, that's the first piece of tradecraft to build.

<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 work into the ELE community
If you're navigating where AI fits in your coaching practice, your manager development work, or your talent decisions — and you're not sure where the line is — that's exactly the kind of real work the ELE community exists for.
Bring 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>

</div>
</div>

AI Practical Lab | AI In The Loop: Coaching

Digital &amp; AI

Coaching

marty.murrillo

maryann.kennedy

AI in Coaching

AI Adoption

michael.hruska

AI governance

coaching tools

wise2026

human judgment

Context Setting

People Leaders

Baryons.AI

insights-wise-2026

vilson.simon

peer intelligence

baryons.aimentor

Coaching Practice

Technology Centaur

Forensic Analysis

Algorithmic Bias

Cultural Nuance

Manager Span of Control

Professional Evolution

CNA Insurance

Alter Domus

Scout

<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 treat it like we're rolling out a piece of tech — just trying to get people to use and adopt it."</blockquote>
Most organizations are managing AI like a software rollout. The real work people leaders are trying to move forward is harder: bridging the gap between rapid deployment and actual human alignment — before hidden risks like policy conflicts, content sprawl, and false oversight signals compound into something harder to fix. The work is getting stuck because the governance, accountability, and data structures needed to support AI at scale weren't built before the tools went live. That gap is now showing up in real operations, real delays, and real exposure.
Perspectives shaping the work
This live interactive discussion brought together L&amp;D leaders, change management practitioners, and talent executives from across enterprise organizations — each navigating their own version of the same underlying challenge: AI is already running in their organizations, and the management infrastructure hasn't caught up. Before the discussion opened, a live poll asked the room where AI was already creating the most risk. The top answer: AI tools used without shared practice. Not a future concern. A current reality.
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;">Blind Spot 1: Deploy Then Govern</h3>
The instinct is understandable. Tools are ready, the tech team has invested, employees are waiting. So organizations launch — and then discover that existing data protection policies, infosec requirements, and compliance frameworks weren't written with AI in mind.
The result isn't just a policy update. It's a delayed rollout, a scramble to rebuild employee trust about what's actually safe to put into a tool, and the realization that governance should have been the first conversation, not the cleanup.
There's a second layer: the endorsed company tool is never the only tool in play. Vendor platforms have AI embedded. Employees have personal devices. If the company tool doesn't meet their needs, they'll find one that does — and that ecosystem of shadow usage is where unmanaged risk accumulates.

<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;">"Don't assume the tool you give them is the only tool they have."</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;">Blind Spot 2: Content Governance Sprawl</h3>
GenAI makes people more productive. More productive people produce more. That's the paradox nobody is talking about loudly enough.
As highlighted during the working exchange:

<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 makes people more productive. Isn't that wonderful? Well, maybe."</blockquote>
When output volume rises faster than attention can scale, organizations end up managing content sprawl instead of content strategy. Duplicate efforts multiply. People with no L&amp;D background use AI to produce polished-looking deliverables — and the finish quality that once signaled expertise and care no longer does.

<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;">"AI is good at making things with fit and finish — what we'd typically call a human marker of trust."</blockquote>
When that heuristic breaks down, so does the ability to quickly assess quality, credibility, or strategic alignment. The fix isn't slowing down output. It's aligning content strategy directly to organizational strategy — and being willing to tell people no, even when what they've made looks good.

<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;">Blind Spot 3: Confidence Theater vs. Actual Oversight</h3>
Audit logs and dashboards create the feeling of control. That feeling is the problem.

<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 lot of confidence theater — making you feel you have control because you get a report."</blockquote>
Real oversight requires the ability to intervene before a workflow at autonomous scale compounds an error — not document it afterward. The gap between those two is where significant operational risk lives. An extreme example from the discussion: an AI agent that deleted an entire company's codebase in nine seconds, with no mechanism in place to stop it in time.
The data layer compounds this. Agentic AI operates from the context it's given. Bad data doesn't slow agents down — it directs them. Organizations that haven't addressed underlying data quality issues before layering agents on top aren't getting AI efficiency. They're getting accelerated bad outcomes.
The reframe that emerged: stop treating AI like software to deploy and maintain. Start treating it like a workforce to hire, onboard, set expectations for, give feedback to, and hold accountable.


<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;">Run a policy conflict audit before the next AI tool launch. Pull your current data protection, infosec, and compliance policies and map them against planned AI workflows. Identify the conflicts before employees do. Early evidence signal: How many existing policies technically prohibit workflows your team is already using?</li>
 	<li style="margin-bottom: 15px;">Build one human intervention checkpoint into your highest-volume AI workflow. Identify the autonomous workflow running at the greatest scale in your organization and define the specific trigger that requires a human to stop or redirect it — before it compounds. Early evidence signal: Can your team describe that trigger point today, or does the workflow run without one?</li>
 	<li style="margin-bottom: 15px;">Audit your content ecosystem against your organizational strategy. Take the last 30 days of AI-assisted content produced in your org and ask: how much of this maps to what leadership has said people should be seeing and doing? Early evidence signal: Where is content being produced because someone can, rather than because the organization needs it?</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, 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>

The AI Blind Spots Costing You More Than You Think

suzanne.sherry

Operations

chris.olson

anton.maletich

Talent Strategy

peter.hogaboam

AI blind spots

content governance

deploy then govern

confidence theater

AI oversight

agentic AI

AI sprawl

data hygiene

L&amp;D executives

workforce integration

enterprise AI

shadow AI

policy conflict

content strategy

AI risk

human intervention

AI as workforce

Gainwell Technologies

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;">"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>
<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, 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>

The Easy Button Problem| The catch-22 at the heart of AI adoption.

Performance Improvement

L&amp;D strategy

jenny.massoni

dustin.brewer

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

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

colin.hahn

keri.kersten

adrienne.guerrero

nancy.dome

ana.bedard

shonda.cromwell-rashad

audra.pinter

david.scherer

human AI collaboration

capability building

prompt engineering

1Q2026

AI Workflow Design

CRIT Model

Onboarding Effectiveness

Product Knowledge Training

Junior Talent Development

Clarity in AI Use

Confidence in AI

Senior Talent Leaders

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

Future of Work

tracey.wik

upskilling

azizeh.constantinescu

talent development

talent management

learning &amp; development

matt.donovan

marsha.love-morrow

employee experience

reskilling

workforce planning

matt.sernaker

megan.bickle

HR transformation

HR leadership

Business Alignment

sara.fitzgerald

AI &amp; Workforce

angelica.janina.stilling

4Q2025

cindy.pinkstaffmiller

organizational design

terresa.bubbers

ali.glaser

sam.amelio

2026 planning

chris.dunford

manager readiness

job architecture

executive learning

2026 priorities

Zurich North America

Journyz

Theme: Turning AI adoption into a culture of practice and shared learning.

<blockquote data-start="487" data-end="574">
“Practice, training, and sharing wins—that’s how we build momentum.” — <a href="https://www.ele.llc/members/kate-haney" target="_blank" rel="noopener">Kate Haney</a>, Zurich North America.
</blockquote>
This lab reminded us that the real differentiator in AI maturity isn’t the number of tools—it’s how people learn to use them, together.
As L&amp;D leaders, we often focus on technology readiness. But as the discussion showed, cultural readiness is what truly moves the needle. Momentum comes when teams have space to experiment, share early wins, and learn from one another’s prompts and missteps.

<blockquote data-start="984" data-end="1074">
“Make it OK to say things like, ‘I have no idea how to get started.’” —<a href="https://www.ele.llc/members/laura-ruff" target="_blank" rel="noopener"> Laura Ruff</a>
</blockquote>
That simple idea captures what every organization needs to unlock AI fluency: psychological safety. Before we can scale responsible AI use, we have to normalize not knowing—and create environments where questions are as valued as answers.

<blockquote data-start="1322" data-end="1439">
“L&amp;D’s role isn’t making content anymore—it’s enabling the organization’s adoption journey.” — <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a>
</blockquote>
The takeaway is clear: in this new era, L&amp;D is the architect of adoption. Our work isn’t just to teach tools—it’s to cultivate curiosity, reinforce responsible experimentation, and connect peer learning into enterprise workflows.
When learning becomes daily practice, AI stops being “another initiative” and starts becoming how work gets done.


<hr data-start="1797" data-end="1800" />

<h3 data-start="1802" data-end="1859">🔑 6 Key Takeaways for Talent Development Leaders</h3>
<ol data-start="1861" data-end="2736">
 	<li data-start="1861" data-end="1980">
Culture beats capability. True AI maturity grows from a culture of experimentation, not from a stack of tools.
</li>
 	<li data-start="1981" data-end="2101">
Psychological safety is the new prerequisite for learning. Curiosity thrives where leaders model vulnerability.
</li>
 	<li data-start="2102" data-end="2247">
Governance drives confidence, not control. Clear use policies and shared frameworks reduce fear while accelerating responsible adoption.
</li>
 	<li data-start="2248" data-end="2397">
Peer learning multiplies ROI. When employees share their AI experiments and “quick wins,” adoption scales faster than formal training alone.
</li>
 	<li data-start="2398" data-end="2564">
Iteration is the new instruction. Learning leaders should design systems that evolve—updating playbooks, prompts, and governance as the organization matures.
</li>
 	<li data-start="2565" data-end="2736">
L&amp;D’s role is shifting from teacher to orchestrator. We’re no longer just building courses; we’re building ecosystems for continuous learning and safe innovation.
</li>
</ol>

<hr data-start="2738" data-end="2741" />

<h3 data-start="2743" data-end="2773">⚙️ 6 Practical Actions</h3>
<ol data-start="2775" data-end="3619">
 	<li data-start="2775" data-end="2907">
Create an internal “AI Practice Lab.” Dedicate space for employees to test tools, share results, and surface new use cases.
</li>
 	<li data-start="2908" data-end="3040">
Host quarterly “Show &amp; Share” sessions. Celebrate practical wins from across teams to make learning visible and contagious.
</li>
 	<li data-start="3041" data-end="3178">
Develop a Responsible AI Playbook. Combine governance guardrails, example prompts, and decision frameworks to simplify adoption.
</li>
 	<li data-start="3179" data-end="3322">
Launch a manager toolkit. Equip people leaders with talking points, FAQs, and reflection prompts to lead AI conversations confidently.
</li>
 	<li data-start="3323" data-end="3460">
Build micro-learning paths. Deliver quick, contextual lessons on prompt writing, bias checks, and validation for real workflows.
</li>
 	<li data-start="3461" data-end="3619">
Measure adoption through stories. Track momentum not just by tool usage, but by examples of efficiency, creativity, and collaboration improved by AI.
</li>
</ol>

<hr data-start="3621" data-end="3624" />
Momentum doesn’t come from mandates—it comes from moments of learning shared across a community. And that’s exactly where L&amp;D leaders have the power to turn whiteboard ideas into real-world workflows.
👉 Continue the conversation: share your own AI experiments, prompts, or success stories with fellow ELE members in our <a href="https://drive.google.com/drive/folders/1-BgyQdBV4ixZKIZlUS6_KFq0Q1slyg2s?usp=drive_link" target="_blank" rel="noopener">AI collaborative workspace</a>.


<hr data-start="3621" data-end="3624" />

Skillsoft's AI Maturity Model: A Roadmap to Transformation outlines four distinct phases that organizations navigate as they move from initial learning to fully integrated, value-delivering AI operations.

Their approach emphasizes that achieving the full transformative potential of AI requires a concerted, strategic effort across four key dimensions: people, platforms, processes, and policies. By following this roadmap, organizations can chart a course to successfully embed AI into the core of their business.
<h3>Which Phase Describes Your Organization?</h3>
<ul>
 	<li>Phase 1 - Exploration: Nascent phase focused on learning and discovery; understanding AI applications with an opportunistic approach.</li>
 	<li>Phase 2 - Experimentation: Testing AI technologies through pilot projects and proofs-of-concept to validate use cases.</li>
 	<li>Phase 3 - Innovation: Moving beyond pilots to extensive implementation; AI becomes an integral part of business processes, actively driving innovation.</li>
 	<li>Phase 4 - Realization: AI is fully embedded across operations, delivering measurable value and driving strategic decisions; efforts are governed, measured, and optimized.</li>
</ul>
<img class="aligncenter" src="https://wp.ele.llc/wp-content/uploads/2025/10/Skillsoft-Phases-of-AI-Maturity.jpg" alt="" />

<a href="https://www.skillsoft.com/blog/understanding-your-organizations-ai-maturity-a-roadmap-to-transformation">Understanding Your Organization’s AI Maturity: A Roadmap to Transformation</a> (Source: Skillsoft)

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