Alter Domus

ELE Group, LLC

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"Senior leaders are not crafting the why. So people aren't really being expected to use it to drive performance." — <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a>, Upsurge Advisors
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The business problem hiding inside your AI rollout
Most organizations have launched AI tools. Fewer have launched leaders who know how to use them.
That's the gap this live interactive discussion set out to close. The business problem, as Anton Maletich (Alter Domus) put it plainly from the Internal Client chair: his organization had hundreds of agents built by a fraction of its workforce, productivity targets declared but unmeasured, and no shared standard for what AI-enabled work actually looks like. The tools were live. The strategy wasn't.
Who was in the room: Senior HR, L&amp;D, and business leaders including <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a> (Upsurge Advisors) as facilitator, <a href="https://www.ele.llc/members/anton-maletich" target="_blank" rel="noopener">Anton Maletich</a> (Alter Domus) as Internal Client, <a href="https://www.ele.llc/members/marty-murrillo" target="_blank" rel="noopener">Marty Murrillo</a> (Precisely) as AI Strategist, and <a href="https://www.ele.llc/members/christopher-lind" target="_blank" rel="noopener">Christopher Lind</a> (Christopher Lind Co.) as Real-World Skeptic, alongside a cross-section of ELE members spanning healthcare, financial services, manufacturing, and consulting.


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It's not an adoption problem. It's a leadership framing problem.
Anton grounded the room in what the Internal Client actually experiences: a tool mandate from the top, fragmented usage below, and no shared definition of success. Agents were being built in silos — some redundant, some ungoverned — and no one was asking whether they addressed productivity that actually mattered.

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"The agent marketplace is now the nightmare that the LMS has been. We've just replaced the LMS with an agent marketplace." — <a href="https://www.ele.llc/members/nicole-defalco" target="_blank" rel="noopener">Nicole DeFalco</a>
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The tools proliferated. The strategy didn't.


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AI output isn't neutral — and neither is your judgment
This is where the Real-World Skeptic earned his moment. Christopher Lind named the tension the room needed to hear: AI output that looks polished on the surface is often structurally empty underneath — no performance-tied behaviors, no measurable outcomes, no accountability built in.

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"It looks good on the surface to a seasoned expert, it actually is usually slop." — <a href="https://www.ele.llc/members/christopher-lind" target="_blank" rel="noopener">Christopher Lind</a>
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He also introduced the "Grandeur Effect" — AI doesn't just affirm ideas, it presents them as grand, unique, and never-before-seen. The risk isn't bad output. It's confident-looking output that leaders ship without reading closely.

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"It can make you look better than you are, and you don't really know." — <a href="https://www.ele.llc/members/dirk-tussing" target="_blank" rel="noopener">Dirk Tussing</a>, ELE
</blockquote>

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The CRIT Model: From assistant to thought partner
Marty Murrillo — in the AI Strategist role — offered the reframe: the problem isn't that leaders are bad at prompting. It's that they've never been asked to treat AI as a thought partner rather than an answer machine. The CRIT Model changes that architecture.
We live-tested Marty's demonstration of the CRIT Model (Context, Role, Interview, Task). The breakthrough? The Interview step. By forcing the AI to ask us questions before generating an answer, we surface blind spots and pressure-test assumptions.
<img class="wp-image-44940 aligncenter" 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="835" height="455" />The CRIT Model for AI-driven leadership, from Geoff Woods. (Credit: Google Gemini Banana)
The insight wasn't about the tool itself — it was about leadership capability. If a leader can't answer the AI's interview questions, that gap is the actual business problem to solve.


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A moment that mattered
When AI pushed back on a vague prompt — asking the group to name a specific business outcome, not just "improve productivity" — the room landed on something honest: most organizations don't actually know what AI-driven performance looks like in their context.
Dirk Tussing put it plainly: "If we just ask for general stuff, we're gonna get general stuff back."
The CRIT interview step didn't just improve the AI output. It exposed the strategic ambiguity that was already there.


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What to try next
Start with one decision, not one tool. Have senior leaders identify a single business decision — something that already happens this quarter — where AI will be used to inform or accelerate it. Name the outcome. Define what "better" looks like before anyone touches a prompt.
Run the CRIT interview on yourself. Before building any AI-enabled leadership development, use the CRIT model's Interview step with a real organizational challenge. If the senior leaders in the room can't answer the clarifying questions, that's your curriculum.
Baseline before you build. Christopher Lind's challenge to the group: if you want to improve productivity, what's your baseline today? Without it, any change is just a gut feeling. Ask your internal client to name the data they have — and flag what's missing.


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If any of these insights resonate — and you've got a top-of-mind talent business problem you'd like the ELE community to work on — send it our way. Members can submit their business challenge here: <a class="underline underline underline-offset-2 decoration-1 decoration-current/40 hover:decoration-current focus:decoration-current" href="https://www.ele.llc/faqs/share-top-of-mind-business-challenges">Submit My Business Challenge Now</a>

✨ AI Practical Lab: Framing the Why — Leadership Development for AI at Scale

Strategy

Digital &amp; AI

Leadership

christopher.lind

dirk.tussing

anton.maletich

matt.donovan

adrienne.guerrero

marty.murrillo

patricia.ouzounian

audrey..hackley

rachel.kaberon

AI transformation

ai practical lab

AI Adoption

susan.c.guest

nicole.defalco

angelica.janina.stilling

2Q2026

manager enablement

confidence capability clarity

talent strategy 2026

CRIT Model

christie.wereski

leadership development for AI

AI framing the why

AI at scale

prompt engineering for leaders

AI execution gap

senior leader alignment

frontline manager coaching

<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.

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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.

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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.

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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.

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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.

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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: Framing the Why — Leadership Development for AI at Scale

✨ AI Practical Lab: Context Is the Job

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