Something I’m realizing more and more:
The pace of AI development is getting relentless.
Aside from running the business — meetings, strategy, operations, team management — I now have to intentionally carve out time just to stay updated. New models. New tools. New use cases. New risks. If I don’t deliberately schedule time to learn, the week gets consumed by urgent tasks and I fall behind on what matters most.
And I don’t think I’m alone in this.
The learning model we inherited was built for a slower era
Most leaders were trained to front-load their learning. Study hard, build expertise, then apply it for years. Learning was something that happened before the career, or in periodic bursts: a certification, a conference, a book on a long flight. Between those moments, you managed on what you already knew.
That model worked well enough when industries moved slowly enough for accumulated expertise to stay relevant. The window between “new development” and “competitive pressure from it” was wide. You had time.
That window has closed.
AI is not releasing one significant capability per year. It is releasing meaningful changes across models, tools, platforms, deployment patterns, and risk profiles on a cadence that most leaders were never designed to track alongside everything else they carry. What was true about your deployment options six months ago may not be true today. What felt like an experiment in your industry last year may be a baseline expectation by next quarter.
The leaders I work with through PAIBA and in the programs we run at Olern are not struggling because they lack intelligence or drive. Many of them are falling behind because their learning rhythm was built for a different era. The pace changed. The habit did not.
What happens when learning falls out of the week
Here is what I observe most often: the week starts with good intentions. There is a vague plan to read something, watch a demo, catch up on what’s new. But urgent tasks expand to fill the time. The client needs a response. The team needs a decision. The operations fire needs putting out.
By Friday, the learning has not happened. It was never scheduled. It was only hoped for.
This plays out across months and then years. The leader keeps running their current business effectively. But they are quietly missing the second job: understanding how AI is shifting their industry’s cost structures, customer expectations, talent dynamics, and competitive positions. And by the time the gap becomes visible — when a competitor launches something, when a client asks why you are not doing what your peer is doing — the distance is harder to close.
The problem is not willingness. Most leaders I know want to stay current. The problem is that staying current was never made into a structural habit. It was left as aspirational overflow from an already-packed week.
Two tracks, not one
I have started thinking about this as a two-track problem.
Track one is running the business. Delivering on today’s commitments, managing the team, protecting the culture, keeping the operation healthy. This is where most of a leader’s week goes, and it should. This is the job everyone can see.
Track two is rebuilding the business for what is coming. Understanding which processes in your organization could be radically better. Watching how AI is shifting the economics of your industry. Identifying where the next competitive threat is building before it arrives. Deciding what your team needs to know, and when.
Track two requires awareness. Awareness requires learning. And learning requires time you have to actively protect — because nothing in your calendar will protect it for you.
Right now, most leaders are running hard on track one and hoping track two happens in the gaps. It does not. The gaps do not exist, or they appear at random, which is not how strategic awareness gets built.
The result is a compounding gap. Every week without deliberate AI learning is a week where the distance between where you are and where the frontier is grows a little wider. It does not feel dramatic in the short term. Over twelve months, it becomes visible.
What scheduled learning actually looks like
This is not about reading every newsletter or following every AI account on LinkedIn. That path leads to noise and anxiety, not clarity and action. What I mean by “scheduled learning” is deliberate, protected time with a specific focus.
Here is what that looks like in practice:
Block time before the week fills in. One focused ninety-minute block early in the week does more than scattered reading across five days. Put it in the calendar the way you would a client commitment, because it is a commitment — to your own future-readiness. If it is not in the calendar, it will not happen.
Pick one question and go deep. Not “learn about AI.” That is too broad to make progress in ninety minutes. Pick one focused question instead: How is AI changing the customer service model in my industry? What does the new generation of coding tools mean for my team’s output? What are companies similar to ours actually deploying right now, and what happened when they did? One question per session builds more understanding than surface-level scanning across twenty topics.
Apply what you learn to something real before the session ends. Reading alone does not transfer. At the end of each learning block, name one thing you will test, try, or change based on what you read. Even a small experiment — running one prompt on a task you normally handle a different way, sharing one finding with your team — closes the gap between concept and capability. In the workshops I run at Olern, the teams that apply something small immediately retain and build on it far better than the teams that accumulate information without a point of use.
Make your learning visible to the people around you. When your team sees you learning, they learn that it is safe to learn. Share what you found this week. Mention when something changed your thinking. This is not about performing curiosity — it is about signaling that awareness-building is valued work, not a distraction from real work. The culture shift that follows often surprises leaders who try this. It is faster than any formal training program, and it costs nothing except the willingness to be seen learning in public.
The discomfort is the point
It may feel like slowing down in the short term.
Protecting learning time means saying no to something else in the same week. That is real. It can feel irresponsible when the urgent pile is always tall and the expectations on your calendar are high. But the two tracks do not compete with each other. Track two awareness is what makes track one decisions better. The leader who understands how AI is changing their industry’s cost structures makes better resourcing decisions. The leader who understands what their competitors are testing makes better prioritization decisions. Awareness is not a detour from the job. It is part of the job.
The discomfort of protecting that learning time is the cost of the discipline. And the discipline, once built, compounds. The leaders who are building this habit now will not look obviously ahead by next month. In two years, when the gap between those who kept learning and those who only kept meeting becomes visible, the compounding will be clear.
In the AI era, the leaders who schedule learning will outpace those who schedule only meetings.
If you are building a learning rhythm for your leadership team, I would be curious to hear what has worked — and what has gotten in the way.
Frequently Asked Questions
Why do leaders struggle to keep up with AI development?
Most leadership schedules were designed for a slower-moving environment where expertise accumulated over years stayed relevant for years. AI is releasing meaningful changes to tools, models, and competitive dynamics at a pace that was not part of that design. Learning falls out of the week because it was never structurally scheduled — it was left as aspirational overflow from an already-packed calendar.
What does the two-track model of AI leadership mean?
The two-track model separates the work of running the business today (track one) from the work of rebuilding the business for what AI makes possible tomorrow (track two). Most leaders run effectively on track one but leave track two to happen in the gaps, which means it rarely happens. Both tracks require deliberate time allocation.
How much time do leaders need to schedule for AI learning?
One focused ninety-minute block per week is a practical starting point for most leaders. The goal is not to cover everything, but to go deep on one question per session. Consistent, focused learning compounds faster than sporadic, broad scanning.
What should leaders focus on during scheduled AI learning time?
Pick one focused question per session rather than trying to track everything. Useful starting questions include: how AI is changing your industry’s cost structure or customer expectations, what competitors in your space are deploying, and what your team’s current skill gaps look like relative to the tools becoming standard in your function.
How does a leader’s learning habit affect their team?
When leaders learn visibly — sharing what they found, mentioning when something changed their thinking — it signals that awareness-building is valued work, not a distraction. Teams that see their leaders learning publicly tend to build the habit themselves. This culture effect is faster and more durable than most formal training programs.
What is the risk of not scheduling AI learning time?
The risk compounds quietly over time. Each week without deliberate learning widens the gap between where a leader’s awareness sits and where the competitive frontier is moving. It does not feel dramatic in the short term, but over twelve months the distance becomes visible — often when a competitor launches something unexpected or a client raises expectations the leader was not tracking.
