Leading in the Age of AI: From Doing the Work to Designing the System
AI hasn’t changed what leadership is, but it has made the gap between doing work and designing how work happens impossible to ignore.
Leading in the age of AI isn’t about knowing the newest tool or writing the best prompt. It’s about how leaders shape the system around the work. What gets automated, what gets elevated, and what humans are expected to focus on instead.
One question now sits at the center of this shift:
Am I doing value-add work, or am I just very efficient at busy work?
AI makes this question unavoidable. If a task can be automated, augmented, or delegated to a machine, leaders have to decide not just whether to do that, but what replaces it. Is it learning? Judgment? Strategy? Human connection?
This is where many organizations are getting stuck.
Value Creation vs. Work Slop
AI was sold as an efficiency unlock. In practice, many teams are experiencing something else: more output, lower quality, and hours spent cleaning up what employees now openly call “AI slop.”
Leaders need to be clear about this. AI isn’t value by default. When used badly, it just shifts effort from creation to cleanup.
You hear it when teams start asking:
“Is it faster to spot-check AI output, or would I be quicker just doing this myself?”
That question means the system isn’t working, and it’s why clarity matters. What do you actually want AI to do? Speed up drafts? Reduce admin? Improve decision-making? Without clear intent, teams drift into overuse, underuse, or misuse - all of which create friction instead of value.
AI also exposes learning gaps inside organizations. People approach it with very different levels of confidence, shaped by role, experience, and skill. AI doesn’t fix gaps in judgment or role clarity; it amplifies them. And when people don’t fully understand their work, AI output becomes harder, not easier, to trust.
The Real Constraint: Time and Permission to Learn
Most of this pressure lands on leaders.
In coaching conversations, leaders keep asking the same thing: How am I supposed to learn AI when I’m in meetings all day?
I’ve seen teams where the CEO has time and space to experiment, but middle managers are still measured on the same outputs, on the same timelines, with nothing removed from their plates. Learning becomes something people are expected to do in their own time and then execute perfectly at work. But if leaders want adoption, they have to design for it.
That means changing the system, not just the message:
making room to experiment, even when things break
protecting time to explore
redefining success away from “running the same process” toward “doing less work through automation”
preparing managers for what will fail, and how to respond without blame
Psychological safety is non-negotiable. If people believe AI use will be monitored, penalized, or used to justify role cuts, curiosity collapses. No amount of encouragement fixes that.
One practice I’ve seen work: a standing one-hour weekly block with no meetings, dedicated to learning or experimentation. No deliverables, just space.
It sends a clear signal that learning is part of the job.
Incentives Matter (and Often Backfire)
Some organizations are now tying bonuses to AI usage or “AI impact.” This usually backfires. When you incentivize activity, you get gaming behavior - more prompts, more tools, more noise, but not necessarily more value. What teams actually need is shared ways of thinking:
Which use cases genuinely save time?
Which improve quality or decision-making?
Which create more work downstream?
Leaders who stay close to these questions learn faster than those who mandate adoption from the top.
What Leadership Looks Like Now
AI is raising the stakes for leadership development. The question is no longer whether someone can do the job today, but whether they can keep redesigning the job as the work changes. Leaders who do well in this environment tend to:
default to “let me learn” instead of “don’t we have someone for that?”
stay close enough to the work to see how AI is actually affecting it
think in trajectories - not just today’s performance, but where the team needs to be in two or three years
model curiosity and visible learning, rather than polish
This matters most for leaders who’ve moved far away from execution. In the age of AI, distance from the work is a liability. Admin may be automated, but ownership, judgment, empathy and problem-solving aren’t.
If Learning Only Happens in Training, You’re Already Behind
AI is moving too fast for quarterly training or one-off initiatives. Learning has to become part of how work happens.
In practice, that looks like leaders sharing what worked and what didn’t. Managers talking about growth, not just delivery. Teams sharing experiments openly. Protected time for exploration. And people empowered to decline meetings where they add no value.
When learning isn’t part of the job, the people who want to grow leave, and the ones who stay stop experimenting. Improvement becomes accidental instead of designed.
The Human Edge
As AI accelerates and the job market tightens, many people are wondering where human work fits in, and what will still matter.
What I’m seeing so far points in one direction: deeply human skills are becoming more valuable, not less. Empathy, judgment, negotiation, teaching, and ethical decision-making aren’t nice-to-haves; they’re differentiators.
As AI takes over more execution, humans become the conductors. Engineers may write less code, but they solve harder problems. The same shift is coming to many roles. The work doesn’t disappear; it moves up a level.
The Real Task of Leadership
Leading in the age of AI isn’t about control, it’s about design. Designing roles, incentives, learning environments, and psychological safety so people can do their best work alongside machines.
Leaders have to show that growth is possible. Individuals still have to take ownership of it. When both meet in the middle AI becomes a lever rather than a threat.
And the most important leadership question becomes:
What kind of system am I creating and what does it say about me?