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

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

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

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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

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 | AI In The Loop: Coaching

✨ AI Practical Lab: Context Is the Job

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