What if Efficiency Isn’t the Only Goal?
For years, technology has promised to make work more efficient.
And in many ways, it has.
We can connect instantly with colleagues across the country or the world. For many people, remote and hybrid work have eliminated commutes and made geography far less relevant. Collaboration tools allow us to share information and make decisions without everyone being in the same room. And now generative AI can help us research, analyze, brainstorm, summarize and produce work in a fraction of the time it once took.
I use many of these tools myself. I see their value.
But I’ve also been thinking about something else I’ve experienced and hear in conversations with leaders:
As we’ve gotten faster at doing the work, the work itself seems to have gotten faster too.
An efficiency gain doesn’t necessarily create more space. Often, it simply creates capacity for something else.
The meeting we eliminated becomes three more things on the to-do list. The hour saved becomes an expectation for another deliverable. The ability to respond from anywhere can quietly become an expectation that we respond from everywhere.
And now AI is raising the possibilities and the expectations again.
At the same time, the world around the work we do is also changing faster.
New technologies, expectations, workforce dynamics, risks, and ways of communicating and collaborating. Leaders are increasingly being asked to make decisions in environments where the information is incomplete, the variables keep moving and today’s answer may not work tomorrow.
I’ve seen this tension before.
During my years at FEMA, urgency was the norm. Disasters didn’t wait for us to have perfect information or ideal systems. People needed help, and that need often required us to adapt quickly -- to create new processes, remove barriers, use technology differently, shift people and resources, and sometimes make decisions while the conditions around us were still changing.
There was good reason for that urgency. Sometimes moving faster really was the goal.
But speed had a cost, too.
When you are moving from one urgent problem to the next, there isn't always time to understand the longer-term implications of the adaptations you are making. A workaround that solves today's problem can become tomorrow's process. A temporary demand can become a permanent expectation. Something that helps people absorb a surge for a few weeks can become the way the organization expects them to work indefinitely.
We adapted. But I’m not sure we always had enough space to ask what those adaptations were teaching us, or what they were costing us.
I see echoes of that dynamic in workplaces today.
The source of the pressure may be different, but the pattern feels familiar. Technology allows us to do more and respond faster. Remote and hybrid work have changed where and when work happens for many people. AI is dramatically accelerating what we can produce. At the same time, the environment itself is changing faster.
Once again, we are adapting while moving.
We often talk about the capacity this requires as adaptability, but lately I’ve been wondering about the capacity underneath adaptability.
What actually allows us to adapt well?
I think learning is a big part of the answer.
And perhaps an even more important question is:
How do we make sure we are learning as fast as we are adapting?
Adaptation Without Learning Is Just Reaction
When conditions change, we can respond quickly without necessarily learning anything.
We can move faster, work harder, change the process, adopt the new technology, reorganize the team, and even produce a different output.
But adapting well requires us to notice what is changing, to question assumptions that may no longer apply, to recognize when something that worked before isn’t working now, to listen to perspectives that challenge our own, and to try something, pay attention to what happens and adjust.
Sometimes, it requires us to admit that we were wrong.
That is learning.
And it raises an interesting tension for leaders: At precisely the moment when the pace of change makes our capacity to learn more important, could our pursuit of efficiency be crowding out some of the experiences that help us learn?
What Happens When We Remove the Friction?
That question was sharpened for me recently by Lynda Gratton’s Harvard Business Review article, AI Is Changing How We Learn at Work.
Gratton explores a challenge organizations are only beginning to confront: AI doesn’t simply change how we perform work. It may change how people develop expertise, judgment, empathy and professional identity.
Much of that development has historically happened through experiences we might easily label as “inefficient.”
Struggling with a problem, watching someone more experienced navigate a difficult situation, writing the first draft ourselves, having a conversation that doesn’t go exactly as planned, making a decision without being completely certain, getting something wrong and figuring out why, or listening to someone whose interpretation differs from ours.
There is friction in all of those experiences. Technology is extraordinarily good at removing friction.
Usually, that’s a good thing. There is plenty of friction in our organizations that serves no useful purpose. Few of us want to return to slower processes simply because they were slower.
But perhaps we need to distinguish between friction that gets in the way of work and friction that develops our capacity to do the work.
Emerging research on AI and learning makes that distinction increasingly important. Researchers are examining what happens when we “offload” cognitive work to AI. The picture is not as simple as AI making us smarter or making us think less. AI can scaffold thinking, expose us to ideas and help us learn. But when it substitutes for meaningful cognitive engagement, we may get the immediate benefit of a better or faster output without developing the same underlying capability.
That makes me wonder whether our definition of efficiency is too narrow.
Efficient for What?
We tend to measure efficiency at the level of the task.
How quickly did we produce the report?
How many meetings did we eliminate?
How many transactions did we process?
How much more can one person accomplish?
But leaders are responsible for more than today's output. We are also shaping the capability people and organizations will have tomorrow.
Imagine two employees facing the same unfamiliar problem. One uses AI to explore possibilities, challenge assumptions, identify gaps in their reasoning and test their emerging conclusions. Another asks AI for the answer, accepts the output and moves on.
Both may finish faster than they would have without the technology. But did the same learning happen? Probably not.
For me, that suggests we may need another way to think about efficiency. Something can be efficient for the task without necessarily being efficient for human development. The reverse can also be true. A technology that removes low-value work may give someone more capacity for deeper thinking, creativity, relationships and judgment.
The goal, then, isn’t to preserve difficulty for difficulty’s sake.
It is to become much more intentional about what we automate, what we accelerate and what we protect.
What Is Worth Protecting?
I don’t think we know all of the answers yet. The technology is evolving too quickly, and so are we.
But I think there are some questions worth asking.
Are we protecting enough space for people to think, and not simply produce?
Are people still getting opportunities to wrestle with difficult problems before the answer is handed to them?
Are we creating enough interaction for people to learn from one another and not just exchange information?
Are people developing judgment, or simply becoming more skilled at accessing someone or something else’s judgment?
Do our workplaces make it safe to question, experiment, get something wrong and change course?
And perhaps most importantly:
Are the ways we are making work more efficient also helping our people become more capable of navigating what comes next?
Because what comes next will almost certainly require adaptation, and adaptation requires more than speed.
It requires curiosity, reflection, judgment, experimentation, and feedback. The willingness to question what we think we know and remain open long enough to discover something new.
It requires learning.
That is the tension I want to explore this month through the theme Learning in Motion.
Because learning can no longer be something we reserve for the classroom, the annual training, the leadership retreat or the after-action review.
The pace of change won’t give us that luxury.
We have to learn while we lead.
We have to learn while the conditions are changing.
We have to learn while new tools are changing the work itself.
And as AI gives us extraordinary new ways to move faster, leaders may need to become equally intentional about knowing when faster is the goal, and when something more important is at stake.
The leaders who grow are the ones who stay open, even when pressure pushes them to close.
Affirmation for This Month
I can move with urgency without losing curiosity.
I can use new tools without giving away my judgment.
I can create space to learn, even while the work keeps moving.
What is worth protecting?
This month, I invite you to pay attention to how you and the people you lead are adapting to the changing pace of work.
Where is greater efficiency genuinely creating value? Where might speed, technology, or the pressure to produce be crowding out opportunities to think, question, experiment, connect, or learn?
Not all friction is valuable, but not all friction is waste, either.
As we explore Learning in Motion this month, I’ll be thinking about what it takes to keep learning while we lead, not after the change is over, but while we are in the middle of it.
The question is whether we are learning as fast as we are adapting.
If this month’s theme resonates with you, I’d love to continue the conversation through coaching, leadership development, or workshops focused on helping leaders and organizations build the human capacity to navigate change with curiosity, judgment, and intention.
