Leading Through AI Hype
How executives separate signal from noise, set real ROI, and drive AI adoption without burning out their teams
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How to Get and Keep Executive Roles in the AI Economy
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In this talk, you will learn how to separate AI hype from real business value with practical pilots and ROI thinking and how to lead AI adoption with the human skills that matter most — discernment, empathy, and judgment — so your team moves faster without burning out.
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Key Takeaways
AI is both hype and real — your job is to straddle both the unrealistic expectations and the people who still resist. Signal urgency to match CxO expectations, but channel energy into practical pilots that produce measurable outcomes (not chaos), while managing both “move 3x faster” pressure and pockets of resistance. Key questions to ask:
Where does AI solve actual problems?
What outcomes are measurable?
Where is the ROI realistic?
What does your team need now?
The AI bottleneck is not coding speed…it’s organizational alignment. AI may accelerate task execution, but product shipping still depends on stakeholder alignment, documentation, go-to-market, pricing, and cross-functional decisions — “soft skills” work that increasingly defines executive impact.
The key job for executives today: Can you generate that organizational movement and alignment?
AI adoption is a change management problem disguised as a tech rollout. A sound AI adoption plan creates room to experiment (and for your team to fail safely), defines clear uses cases (assign internal owners to lead them), and sets clear guardrails for governance and ethics. Your role is the empathetic change agent, partner with your technical leaders and builders to teach and coach the broader org to adopt new habits.
Leading with “AI Fluency” is not enough, you must lead with discernment, empathy, and creativity. The leaders who stand out combine strong judgment (e.g. what to automate vs. what needs human nuance) with patience and coaching — meeting people where they are instead of shaming, rushing, or brushing off concerns.
Model the behavior: you are the driver, not the passenger. Evaluate where you are (e.g. AI curious → functional → fluent → native), keep learning publicly, and use your own experimentation to set the tone. Humans must lead these shifts, not tools.
If you have not started your AI learning journey, use the tools themselves (e.g. Claude, ChatGPT) as your personal tutor, ask them to “Explain it like I’m five” (ELI5), and keep drilling with follow-ups until the concepts and action steps are clear.
C-suite spots like CTO, CPO, and CMO are available right now to strong leaders who can turn AI investment into actual company results and profits.
Current research indicates that most companies are not seeing actual speed or profit gains from AI. They are investing in adoption and some narrow functions like coding are reporting back big gains. But there is no sign yet that this is turning in to higher total productivity or profit for most companies.
At the same time, driven by Wall Street and the press, every CEO is talking about AI and pushing their companies to show actual results.
Sue Bethanis (Executive Coach & CEO/Founder, Mariposa Leadership) and I have updated our course, Cracking the C-Suite, to educate current and aspiring C-suite leaders in how to approach *useful* AI adoption that will actually deliver the business impact that the CEO and Board want to see.
We also focus on the time-tested skills of executive leadership (the ones that AI is not automating) creativity, communication, decision making, and influence. Skills that leaders need even more as AI-generated output comes at you faster and faster.
Below are the four growth areas and the specific skills we will cover within each role.
You will also be invited to ongoing alumni reunions with leaders from all cohorts to expand and continue deepening your network.
Here’s what alumni shared:
“This is the most condensed set of leadership advice I’ve ever seen in any format (books, classes, articles - you name it). I will re-read the course materials a lot because it really gives you everything you need to succeed as an exec.” — SVP Product at Stellantis
“Loved the presenters and found the content, discussions and frameworks to be highly applicable to my current and future leadership roles.” — VP Global Product Marketing at Visa
“The course gave a great crash course that’s relevant to leadership, lot of ah-ha moments and hungry for more. It’s definitely super valuable and really enjoyed everything, a weekend well spent.” — Senior Director at Dutch Bros
“This course is a masterclass in C-suite leadership, combining both theory and practice in a fast-paced, engaging way. The real-world case studies make every lesson relevant and immediately applicable. The Q&A sessions were full of invaluable insights and wisdom—Ethan and Sue’s deep expertise really shines through.” — Director at Amazon
Class starts May 2nd.
Enroll now for Cracking the C-suite in the AI Economy.
FYI.
In the next newsletter, we will take you inside an executive coaching conversation with a senior technical leader whose promotion case looks strong on paper:
Expanding scope.
Increased leadership attention.
Leading a major, company-wide initiative.
And yet…
They are still not being recognized for promotion opportunities.
We will unpack what’s really happening, share the questions that pinpoint the root cause, and outline clear, actionable next steps.
Keep an eye on your inbox.
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The organizational alignment point is underrated. I've watched technically strong teams get stuck because leadership hadn't decided what they actually wanted from AI - not technically, but strategically.
The change management layer is messier than the tech layer. Models are predictable. People aren't.
What I notice: the discernment piece is the hardest. Knowing which tasks to hand to AI and which to keep human isn't obvious until you've gotten it wrong a few times. That's hard to teach without giving people space to experiment and fail safely.
This is the right framing. The AI bottleneck is rarely “can the model do the task?” anymore. It is usually: can the organization agree on the use case, trust the output, change the workflow and measure the ROI?