The Debrief: What We Learned About Leading Through AI Transformation
Lessons from the Reflection & Reset series
Before you read this, take a big, deep breath. You did it. You survived 2025!
I hope you found time this month to pause and look back on the year before the January sprint begins. If you’re still racing to close out the month, it’s not too late!
Over the past three weeks, we explored the hidden costs of rushing through AI adoption, the framework for resetting how your team works, and the five ongoing practices that turn your reset into sustained change.
If you missed any part of the series or read them all but want synthesized takeaways, I’ve summarized what matters most as you head into 2026.
This debrief includes lessons on leading through a major transformation when the pressure is to move fast, and the cost of moving in the wrong direction is your team’s trust, confidence, and performance.
Here are the 5 key learnings that will shape how you lead in 2026.
Learning #1: Your Team’s Silence Is the Crisis
From Week 1: “Your Team Is Quietly Cracking”
The most dangerous problems accumulate quietly:
Your senior designer is redoing every AI-generated mockup from scratch but telling no one
Your content lead can’t articulate why AI outputs feel “off-brand” and is losing sleep over it
Your best people are questioning why they’re still employed if AI can do their work
The research that matters:
Manager engagement dropped from 30% to 27% in 2024, and Gallup explicitly attributed this to leaders feeling unprepared for AI-era challenges.
What this means for you:
Stop waiting for your team to tell you they’re struggling. They won’t. Create structured opportunities (like 15 minute weekly check-ins) where problems can surface while they’re still small and before they compound into quiet quitting or project failures.
The shift: From reactive firefighting to proactive problem-surfacing
Learning #2: You Can’t Build New Systems on Old Assumptions
From Week 2: “The Reset Framework”
Most teams adopted AI without resetting how they work. They bolted new capabilities onto old workflows, old role definitions, old ways of making decisions.
That’s why AI usage metrics look good on paper while team anxiety builds in the background.
The pattern we see:
Teams using AI tools don’t equate to teams working effectively with AI. To achieve the latter, leaders need to provide clarity about what’s changed:
What work should humans still own vs. what AI should handle?
How do we evaluate AI output quality?
Who owns decisions about which tools to use and when?
What does “good work” look like in an AI-native workflow?
What this means for you:
Before you optimize for speed, optimize for clarity. Your team needs to know: What’s our new operating system? How do we work together when AI is in the mix? What are we each responsible for?
The shift: From tool adoption to workflow redesign
Learning #3: Reflection Without Action Is Pointless
From Week 3: “5 Practices That Keep You Moving”
Insight without implementation doesn’t drive change. Most leaders do the reset—they reflect, they identify what needs to change—and then other priorities take over and nothing changes. Your team will think that the reset was just another item on your to-do list and quietly vent about wasting valuable time they could have spent completing their to-do list.
The truth about transformation:
It doesn’t happen through willpower. It happens through rhythm. Practices you run on repeat turn one-time insights into ongoing behavior. It takes time to build a habit, but once you do, this will be ingrained in your team and become effortless.
The five practices that sustain momentum:
The Huddle - Surface problems early
The Review - Create accountability without micromanaging
The Retrospective - Zoom out to strategy
The Learning Drop - Build shared knowledge
The Leadership Mirror - Keep yourself honest
What this means for you:
Don’t treat the reset as a one-time event in December. Build it into your operating rhythm for 2026. The teams that thrive are the ones who continuously recalibrate.
The shift: From a one-time event to an ongoing system
Learning #4: AI Literacy is More Than Training
The common thread:
One-time AI training has a very short shelf-life. Two-day workshops don’t create sustained behavioral change. What works is continuous, practice-based learning where teams capture what they’re discovering through trial and error.
The research that backs this up:
92% of marketing leaders believe AI literacy will be a must-have skill in the next 2-4 years. But AI literacy isn’t about knowing how to use tools, it’s about understanding when to use them, why they work (or don’t), and where human judgment remains essential.
What creates real AI literacy:
Regular debriefs where teams share what they learned
Peer-to-peer knowledge sharing that scales
Permission to experiment, fail, and surface challenges without penalty
Frameworks that evolve based on what the team discovers
What this means for you:
Stop sending your team to more AI training. Instead, create the conditions where they teach themselves, and each other, through doing, reflecting, and sharing.
The shift: From training workshops to practice-based learning
Learning #5: The Same Behaviors That Drive Engagement Enable AI Adoption
The common thread:
This is the insight that connects everything: The leadership behaviors that drive team engagement (acknowledgment, recognition, clarity, reflection, modeling) are exactly the same behaviors that enable effective AI adoption.
Why this matters:
To successfully guide your team through AI transformation, you need to do what good leaders have always done—just more intentionally, more consistently, and with more urgency.
The behaviors that matter most:
Acknowledgment: See the struggle, name it, don’t minimize it
Clarity: Define what success looks like, what’s changing, what stays the same
Reflection: Create space to pause and assess, not just execute
Modeling: Show your own learning process, don’t pretend you have it figured out
Psychological safety: Make it safe to say “I don’t know” and “this isn’t working”
What this means for you:
If your team is struggling with AI adoption, the fix isn’t better tools or more training, it’s better leadership. It’s creating the conditions where people can learn, experiment, fail, and surface problems without fear.
The shift: From managing AI tools to leading humans through transformation
What This Means for January 2026
December was about reflection. January is about action.
You’ve done the work of identifying what needs to change. Now comes the harder part: sustaining that change when urgency builds, when deadlines hit, when it feels easier to slip back into old patterns.
Here’s what you know:
Your team’s silence is dangerous. Create structured ways to surface problems.
You can’t build new systems on old assumptions. You must redesign workflows, not just adopt tools.
Reflection without action is pointless. Build ongoing practices, not one-time events.
AI literacy comes from doing. Create conditions for continuous learning.
Good leadership is good leadership. The fundamentals haven’t changed, just the context
The question for January:
Will you build the rhythm to sustain your reset? Or will you let your busy calendar erase the clarity you just created?
A Special Invitation
In January, I’m shifting from reflection to implementation. I’ll be sharing the frameworks, playbooks, and EQ-driven approaches I use with clients to redesign how they lead through AI transformation in 2026. And for the first time, I’m opening a small group of coaching spots for leaders who want hands-on support making these changes stick.
If you want to be among the first to know when spots open, reply to this email with “Interested.”
Until then: take what you learned in December and turn it into practice in January.
Your team is watching to see if the reset was real, or if it was just another good idea that faded when work got busy.
Next week: We kick off January with “The Leadership Transition: From AI User to AI Leader”—exploring what changes when you shift from learning to use AI yourself to leading a team that uses it.