Everyone is talking about AI right now. And not in a calm, reflective way.
It feels urgent. Sometimes anxious. Sometimes overconfident. Every few weeks there’s a new tool, a new promise, a new warning that if you don’t move now, you’ll be left behind. Against that backdrop, the Author Talks conversation on Rewiring to Outcompete with AI felt oddly grounding.
Not because it offered shortcuts or bold predictions, but because it slowed things down just enough to say something uncomfortable and true: most AI struggles have very little to do with AI itself.
As Kate Smaje, Robert Levin, and Eric Lamarre explain in their discussion of the second edition of Rewired, real AI advantage does not come from chasing technology. It comes from rewiring how organizations think, decide, learn, and work together (Smaje, Levin, & Lamarre, 2026).
That stayed with me.
The more I reflected on the conversation, the clearer it became that this is not really a story about artificial intelligence. It’s a story about people facing change at a pace they were never trained for.
The Question Everyone Is Quietly Asking: “Is This Even Worth It?”
At some point, every leader wrestling with AI reaches the same private question: Is this actually worth all the trouble?
AI transformation is disruptive. It’s expensive. It forces uncomfortable conversations about skills, roles, and relevance. And despite the hype, many organizations have little to show beyond pilots and dashboards.
This is why Lamarre’s comments on returns stood out. Studying companies that executed AI transformations exceptionally well, McKinsey found an average 20 percent uplift in EBITDA, with payback typically within one to two years (Lamarre et al., 2026). Those are not abstract benefits. Those are results that would justify almost any strategic investment.
What mattered more to me than the numbers, though, was how those results were achieved.
Not by doing everything at once.
Not by blanketing the business with AI tools.
But by focusing intensely on a small number of economic leverage points where improvement actually changes outcomes.
It made me rethink how often “digital transformation” fails not because leaders aim too high, but because they aim everywhere.
The Quiet Discipline of Focus
One line from the conversation keeps resurfacing in my mind: value usually comes from two or three places, not twenty.
That idea runs counter to how many organizations behave under pressure. When uncertainty rises, activity explodes. New initiatives. New committees. New roadmaps. AI becomes another layer of noise.
The companies described in Rewired resist that impulse. They narrow their field of vision. They choose where AI will matter most and ignore the rest—for now.
There’s something deeply human about that discipline. It requires restraint. It requires saying no while others are experimenting wildly. And it requires trust that depth beats breadth.
Levin reinforces this by pointing out that the biggest gains often come not from cutting costs, but from changing how organizations grow revenue—through pricing, customer experience, and better decisions (Levin et al., 2026). That only happens when AI is embedded where business judgment already lives.
Speed Isn’t About Moving Faster. It’s About Friction.
A lot is said about speed in the interview, but it isn’t the breathless “move fast or die” version we’re used to hearing.
Smaje describes something more subtle: organizations today feel frenetic, but not necessarily fast. There is motion everywhere, yet progress feels strangely slow (Smaje et al., 2026).
The distinction that helped me was this: speed is not acceleration—it’s the absence of drag.
Rewired organizations have removed the friction that slows everything else down. Decisions are closer to the work. Resources move more frequently. Learning loops are shorter. Not because people are rushing, but because fewer things get in the way.
That kind of speed feels calm rather than chaotic. And it’s hard to fake.
When AI Starts to Feel “Normal”
One of the most compelling descriptions in the conversation is what a rewired company actually feels like.
It feels normal.
Not heroic. Not experimental. Not like a special project run by a few experts in a corner. Innovation becomes routine. Managers expect to improve their part of the business with data and technology. AI stops being “the thing” and starts being part of how work gets done (Levin et al., 2026).
That image stuck with me because it explains why so many transformations never stick. They remain exceptional. Impressive demos. Poster‑worthy successes. But not normal.
Culture doesn’t change when something works once. It changes when something works so often that no one thinks to celebrate it anymore.
The Line That Cuts Through Everything: “This Is a People Transformation”
The most repeated—and most important—idea in the conversation is also the simplest: AI transformations are people transformations (Smaje et al., 2026).
This is easy to nod at and hard to live out.
It means projects don’t fail because models underperform. They fail because:
- incentives stay the same,
- workflows don’t change,
- leaders don’t model new behavior, and
- people don’t feel safe learning in public.
AI exposes organizational weaknesses. It doesn’t cause them.
When people resist AI, it’s often described as fear of technology. More often, it’s fear of uncertainty. Fear of not knowing. Fear of being reshaped without being supported.
The conversation doesn’t romanticize this challenge—and that honesty matters.
Leadership Can’t Delegate This Away
Another point the authors make bluntly is that AI transformation cannot be delegated down the hierarchy.
It isn’t an IT initiative. It isn’t an innovation portfolio. It is an organizational design problem, and that puts it squarely in the hands of the CEO and top team (Lamarre et al., 2026).
What struck me here was how different this is from the way many leaders were trained. Reorganizations? Yes. New strategies? Absolutely. But rewiring how an organization works at speed is newer, harder, and far less tidy.
It demands sustained attention, not grand announcements.
Learning Is No Longer Optional—It’s the Strategy
Toward the end of the conversation, the authors lean hard into learning, and for good reason.
Everyone is learning right now. No one has had time to establish mastery. That levels the field—and raises the stakes.
The idea of learning quotient (LQ) stayed with me. The ability to learn, unlearn, and relearn is no longer a personality trait. It’s an organizational requirement (Smaje et al., 2026).
What I appreciated most was the reminder that learning is collective. It’s not about individual brilliance; it’s about whether people can learn together without shame, defensiveness, or hierarchy getting in the way.
The companies that win with AI will not be the ones with the smartest tools. They will be the ones that adapt the fastest.
Why This Isn’t Overhyped—It’s Understood Late
One might read all this and think: This sounds slow. Careful. Almost conservative.
That’s exactly why it’s powerful.
The transformations described in Rewired take years. They are hard. And precisely because of that, they are difficult to copy. Competitors can buy the same tools, but they cannot quickly replicate culture, capability, and trust.
That’s the real competitive moat.
Final Reflection
What I appreciated most about Rewiring to Outcompete with AI is that it resists the temptation to dramatize technology. Instead, it dignifies the harder work: aligning people, priorities, and pace.
AI does not demand that organizations become something entirely new. It demands that they become honest about how they already work.
And that might be the hardest transformation of all.
Reference
Smaje, K., Levin, R., & Lamarre, E. (2026). Author Talks: Rewiring to outcompete with AI. McKinsey Global Publishing.
