There’s a version of the leadership conversation that happens in conference rooms, on earnings calls, and in published frameworks. It’s polished, forward-looking, and full of confidence. And then there’s the version that happens when the agenda is loose, the wine is open, and people feel genuinely free to say what’s true.
I got the second version in March, hosting a group of senior leaders from across industries—financial services, precision manufacturing, civil engineering, technology—for a wine tasting and discussion. Different sectors, different org structures, but the same essential situation: they’re managing teams through a period of artificial intelligence (AI) transformation that is so fast and layered that traditional playbooks aren’t holding.
What they shared wasn’t panic. But it wasn’t false confidence either. It was something more useful—the honest, grounded perspective of experienced leaders trying to figure this out in real time. A few things they said are worth repeating.
The core problem isn’t resistance. It’s ambiguity.
One leader from the manufacturing sector described it clearly: when people feel uncertain about the future, they don’t become obstinate. They become conservative. They retreat to the habits and relationships that have worked before, because those are the things they can control.
The instinct to blame resistant employees for “not wanting to change” misses something important. The real driver isn’t stubbornness—it’s the absence of a clear signal about where the organization is going and what role each person plays in getting there. When change accelerates without a corresponding increase in clarity, people protect themselves the only way they know how.
This means the leadership response can’t just be more urgency. It has to be more clarity. Clear goals, communicated not once but on a cadence. Explicit line of sight from individual contributions to organizational outcomes. Regular touchpoints that keep people anchored when everything else is moving.
Goal setting isn’t static. It has to keep up with the speed of AI.
Something that came up repeatedly was the limitation of annual goal-setting cycles in a moment when business conditions can shift materially within a quarter. McKinsey’s 2025 State of AI survey found that 88% of organizations now regularly use AI in at least one business function—a figure that was a fraction of that just two years prior. The pace of adoption has not given planning cycles time to catch up.
Several leaders had moved to a model that sets annual direction but builds in structured 90-day check-ins—both individually with team members and collectively with the team—to reassess priorities, reallocate energy, and make sure people are working on the things that actually matter now, not the things that mattered six months ago.
One leader from the financial services sector named the underlying tension directly: the volume of possible work has expanded dramatically, particularly with the addition of AI tools that surface new ideas and unlock new capabilities. But the capacity to execute hasn’t grown at the same rate. The result is that prioritization—always a leadership skill—has become a leadership crisis. Leaders who can’t help their teams focus are inadvertently creating the very paralysis they’re trying to avoid.
The practical answer isn’t a different framework. It’s a different rhythm. More frequent recalibration, more deliberate conversations about what you are not going to chase, and a shared understanding across the team of what “done” looks like for the quarter.
AI adoption is a trust problem, not just a skills problem.
The conversation inevitably turned to AI, because it’s impossible to talk about leading teams through change right now without talking about AI. And here the group was honest.
The fear is real. A 2023 EY survey of 1,000 employed U.S. workers found that 71% were concerned about AI—a finding that tracks closely with Gallup data from this year showing 18% of U.S. employees believe it is very or somewhat likely their job will be eliminated within five years, rising to 23% among those in organizations that have already adopted AI. For many frontline employees—contact center agents, engineers, operational staff—that fear isn’t abstract. It’s visible in the work changing around them. It doesn’t go away because a leader holds an information session or issues a policy.
What the leaders in the room were finding effective wasn’t reassurance. It was participation. Several described building in structured, low-stakes opportunities for teams to experiment with AI tools—hackathons, sandbox time on the calendar, small group exercises where the goal was exploration rather than output. The point wasn’t to produce anything. The point was familiarity. And with familiarity came the thing that actually drives adoption: reduced fear.
One participant leading AI transformation for a marketing organization put it well. The culture he’s built around AI adoption is one where failure is expected, shared, and even celebrated—because when the AI makes an obviously wrong call, it humanizes the technology and gives people permission to see it as a tool to be guided rather than a force to be submitted to. Creating that psychological safety, he argued, is what separates organizations making real progress from those where AI adoption is mandated on paper and resisted in practice.
I shared something from our own experience at ExecOnline. At a company offsite, we embedded someone with strong coding skills on each team and had them use AI to build a prototype of how we might update our website based on our strategy. Nobody shipped a website. But the exercise collapsed the gap between our AI-forward teams and those that were lagging—and it did it through experience rather than instruction.
The lawyer who still prints emails
I’ve told this story before, but it bears repeating. When I was a law associate, email was displacing the fax and the BlackBerry was arriving. Some partners at the firm refused to engage. They had their assistants print their emails, wrote responses in the margins, and handed the pages back to be typed up and sent.
Those partners weren’t bad at their jobs. They were experienced, capable lawyers. But they chose to opt out of a technology that was becoming the infrastructure of professional life—and that choice had costs.
AI is not email. It’s more consequential, more disruptive, and more ambiguous. But the dynamic is the same. The leaders in this moment who help their teams build early familiarity—not perfect mastery, just genuine engagement—are building durable organizational capacity. The ones who wait until the path is clear may find that the window for catching up has narrowed considerably.
What this moment asks of leaders
The leaders I spoke with weren’t just mandating AI adoption and telling their teams that everything is going to be fine. They’re too experienced for that. What they were doing—and doing well—was thinking carefully about the structures, rhythms, and communication patterns that could give their teams the stability to navigate uncertainty and rapid change.
A few principles emerged from the conversation that are worth holding onto:
Clarity is a leadership output. When people are anxious, the antidote is not motivation. It’s information—clear goals, visible priorities, explicit expectations. The leader’s job is to reduce ambiguity, not just acknowledge it.
Cadence matters more than comprehensiveness. You can’t set perfect annual goals in a dynamic environment. What you can do is build in regular moments of recalibration. The 90-day check-in isn’t a hack; it’s a structural response to a faster world.
Adoption follows safety. Teams don’t adopt new technologies because they’re told to. They adopt them when they have room to experiment, fail without consequence, and build genuine confidence through use. Creating that environment is a leadership decision, not an HR initiative.
Equip people for the future they’re actually entering. The skills being built right now—learning to work alongside AI tools, developing the judgment to guide them—are portable. They travel with the individual, regardless of employer. Leaders who invest in developing those capabilities are doing right by their organizations and by their people.
There’s one more thing the conversation surfaced, and it’s the thing I’ve been thinking about most since. Every insight that emerged that evening was about how individual leaders can help their teams navigate change. But leadership development has historically been designed for individuals—courses, coaching, certifications, all of it aimed at the person, not the team. The team, which is the actual unit of execution, is largely left to figure out how to apply what one person learned.
That gap—between individual learning and team-level change—is the structural problem underneath everything the leaders in the room were describing. It’s why we recently acquired Teamraderie, whose approach to expert-facilitated team development is built to close exactly that gap. Combined with our academic partnerships with Berkeley, Columbia, Stanford, and Wharton, we can now connect what a leader learns to what a team actually does—and measure the difference.
The pace of change isn’t slowing down. But the leaders who approach this moment with clarity, humility, and a genuine commitment to developing their teams are the ones who will bring their organizations through it.
The genie is out of the bottle. The only question is who’s prepared to work with it.
Stephen Bailey is the Co-Founder and Chairman of ExecOnline, an enterprise leadership development platform partnered with top business schools.
Follow ExecOnline on LinkedIn. Visit execonline.com to learn more.
