Everyone’s Busy, But We’re Going Nowhere

This weekend, I went on a guys' trip with some friends from business school. Unsurprisingly, when we talked about work, the conversation kept coming back to the impact of generative AI technology on our companies,  society at large, and our own careers. There was a lot of speculation about the future, but no one questioned whether we’re in the midst of a huge change or whether they needed to take action. 

I was most struck—and inspired—by one friend who reported that he’s responding to the moment by spending a couple of hours each night building with AI tools. It’s a big investment of time and energy, but it’s what he thinks is required to build new skills and close the competitive gap with colleagues. 

It’s not that he doesn’t have anything better to do—he’s got a full life with his wife, kids, and the thousands of things calling for his attention at work each day. Yet, he still makes the investment in learning. 

In contrast, I’ve met people who recognize that we’re at a moment in which their individual and organizational learning is critical, but they nonetheless allow today’s to-do list to completely crowd out investment in tomorrow. 

The calendars are full, but without the strategic learning, they might just be running on a treadmill. Work is getting done, but they are going nowhere. 

By the way, I’m not above this issue. After a very productive 45-minute creative session this past Monday morning, I allowed meetings, conferences, and travel to disrupt my schedule the rest of the week. The tactical work got done, but the stuff that could be really impactful three months from now stalled. 

What can I do to make sure I don’t fall victim to it in the future? A few things come to mind.

1. Creating dedicated time for learning—and showing it off.

Assigning time to learning and innovation is the easy part. Protecting that time is somewhat harder, but it can still be done because most people don’t ask questions when you tell them, “I have a conflict with that meeting.” 

But I realize that in not clearly sharing with others, I might fail to send a clear signal that they should also be comfortable carving out time to build their skills. That means sharing what I’m learning openly. What was inspiring about my friend’s experience is that even though he focuses on building AI tools in the evenings, during the workday, his team shares their results and swaps knowledge. It creates a dynamic in which they can create team productivity improvements that are even more powerful than if they worked alone.    

2. Asking “How did you use AI to make this better or more efficient?” whenever I review people’s work.

As a leader, I want people to believe that experimenting with new tools and looking for ways to continuously improve should be the default behavior—not a nice-to-have. A stronger version of that message: It’s always worth spending 15 minutes seeing how AI can help before jumping into a task, and it’s always worth doing an after-action review for our projects. Skipping those learning routines is short-sighted.

It will also require telling people they won’t be judged negatively for a slight decrease in performance. “It’s OK to get a few fewer things done today to increase our capacity for tomorrow. Let’s talk about the tradeoff.”

3. Asking, “How many ‘good mistakes’ did we make?”

Learning requires some “wasted” effort—some of the things we’d try won’t work or won’t be as good in reality as we thought. That’s a feature of learning, not a bug. But if people think everything has to be right the first time, they’ll stick to whatever’s worked in the past. Hence, I want to make sure I’m clearly stating expectations and giving permission. Asking about “good mistakes” underscores that if we’re getting everything right, it's a sign we weren't pushing ourselves.

This Isn’t Just About AI

These are fundamental dynamics of learning, not new ones. Learning requires time, space, and permission to experiment—and it is never convenient. AI just makes the cost of skipping it more visible.

The real question isn't whether your team has learned about AI. It's whether you've built an environment where learning—of any kind—is actually possible.