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AI is not rolling out in a smooth curve. It is moving in step changes, where what felt like strong performance a few months ago quietly becomes the baseline, often without any clear signal that the bar has moved. And it has moved again since I started writing this article.

The pace of this shift is catching most teams off guard. Just in the last few weeks, we’ve gone from AI tools that help with individual tasks to systems that can operate autonomously across your entire computer. Agentic platforms like OpenAI’s Operator, Perplexity’s computer use, OpenClaw and Anthropic’s Claude Cowork are now executing complex from start to finish. The implications for how we think about work are immediate and hard to overstate.

Many tasks that used to take hours can now be done in minutes. A workflow that requires coordination across teams can be handled by one person. The volume of AI innovation shipped in March alone outpaced anything we’ve seen before, and Anthropic is scaling revenue at a pace no company has hit before.

Today, a lot of the conversation still focuses on adoption: who is using AI, who is not and how quickly teams are rolling it out. But that framing misses what is actually happening.

Capability is no longer evenly distributed

Some people are fully engaged with these tools. They are testing ideas, building custom GPTs, creating skills in Claude and developing real instinct for where AI actually helps. Others use it more cautiously, keeping it at the edges of their work.

There is also a third group emerging. These are the people going much deeper, using open-source tools, pushing on the edges of what is possible and running “scary” experiments — , for example — sometimes faster than the organization can keep up with.

All three groups are using AI, but they are not operating at the same level. That difference compounds quickly by changing how work gets done, how fast it moves and how people experience their roles day to day.

The work itself is changing

For years, most roles were built around execution. The focus was on delivering the work — following the process and getting the output across the line. AI is now compressing much of that layer.

The work is shifting toward evaluating, connecting and deciding what matters. You can get to a first version quickly now, which changes where people spend their time and how they approach the work.

In some cases, it goes further. You can begin to build systems that reflect how you think, how you write and how you approach problems. That starts to change how individuals operate, not just how fast they move. But most organizations are still structured around the previous version of the job. That raises broader questions about how organizations are structured and what roles are actually needed, which I’ll come back to in a future article.

What actually helps teams keep up

When companies think about AI enablement, the instinct is to standardize quickly — creating rules, forming committees, defining best practices and rolling out a consistent approach. That approach breaks down. It assumes you can define how new capabilities should be used and then push them into the organization. In practice, that model no longer holds up. A more effective approach is to build structured time into how the company operates so every operational group can engage with these tools directly and regularly, within the context of their own work. The premise is simple: people know their own jobs best.

At our company, we started with “AI Days” — a small group would step away from their day-to-day to focus on their own work and explore how AI could drive efficiency or create new value for the business. Over time, we realized everyone should be able to participate, so we introduced more frequent sessions we call “AI Fridays.”

These are small, facilitated groups, usually no more than ten people, with an AI implementation expert in the room the entire time. People are expected to commit fully. No multitasking, no checking email. We block off a minimum of three hours because anything less isn’t enough to engage meaningfully. We close with a short, structured share-out: What did you try? What worked? What didn’t?

During these share-outs, you start to see how differently people approach the same tools. People from different operational groups are working toward the same goal, but with very different backgrounds and ways of thinking, and that tends to produce outcomes you would not get otherwise. Simply seeing how others approach their work can be surprisingly inspiring and starts to shift how you think about your own. It also helps normalize failure in a very real way. This work takes time, and people do get stuck or go down paths that do not lead anywhere useful right away. When that happens in a group setting, it becomes part of the process rather than something to avoid. Seeing others push through it, or even laugh it off, makes it easier to stay engaged instead of stepping back too early.

There is also a broader shift happening. Most people are used to tools like Google, where every interaction starts fresh. AI builds context over time and responds differently as you continue working with it. Getting comfortable with that iterative loop — where you refine, adjust and build on what came before — takes practice.

Over time, these sessions create a rhythm where people update their sense of what is possible and get comfortable with how quickly things change. It’s common to spend hours building something and then see a new capability replace part of it not long after, and that becomes part of how the work evolves.

Where leadership makes the difference

Leading in this environment means paying attention to how quickly expectations are shifting, including your own.

I’ve seen this happen inside our own business. You spend time with the tools and start to see the work differently. The issue is when that shift stays in your head. The team ends up trying to catch up to a standard they cannot see, and that is where the disconnect starts. There is still a baseline expectation that does not change. If something is being shared or used to make a decision, it needs to be understood and owned. That applies regardless of how it was produced.

There are a few behaviors that tend to make the difference:

• Own it. If expectations are shifting, leadership needs to take responsibility for driving that change.
• Create real space for experimentation. This work requires time and focus, not something squeezed in between other priorities.
• Set clear, meaningful milestones. The goals should be ambitious enough to push teams to actually change how they work.
• Tie outcomes to incentives. People need to see that leaning into this shift leads to real upside, not just more work.

There’s a shift that comes with this. Something you spent years learning can now be done differently, and sometimes faster, than you would have done it yourself. That takes a bit of getting used to. You work through it, figure out what works and what does not, then push that back into the team. That’s when it starts to change. People pick it up, build on it and take it further than you would have on your own.

That’s where it starts to get interesting, and where it actually becomes pretty exciting.