AI adoption among leaders looks pretty healthy on paper. Eighty-two percent report using AI regularly, which would seem to suggest it’s becoming a meaningful part of how leadership teams work.
But regular use can mean a lot of things. The most common uses of AI among leaders are search (69%), summarization (68%), and email drafting (58%). Meanwhile, just 27% report using AI for scenario planning, and the same percentage use it for organizational design.
While there’s nothing wrong with asking AI to summarize a 40-page report so you don’t have to—frankly, that’s one of its great contributions to society—there is a big difference between using AI to get through existing work faster and using it to actually approach that work differently. And right now, most leaders appear to be doing much more of the former.
Regular AI use doesn’t always mean strategic AI use
Measuring AI adoption is important. It tells organizations whether people are actually engaging with the technology they’ve invested in and starting to incorporate it into their day-to-day work. But what it doesn’t tell you is much about AI capability.
A leader who uses AI every day to search for information, summarize documents, and polish emails can truthfully say they’re a regular AI user. So can a leader who uses it to pressure-test a business strategy, model workforce scenarios, challenge assumptions, or explore how a major decision could play out under different conditions.
Those two people may look identical in an adoption report, but their level of AI capability is very different. And if the majority of usage remains concentrated around relatively simple tasks, a high adoption rate can create the impression that an organization is further along than it really is.
The search bar is a perfectly good place to start
To be clear, search, summarization, and drafting aren’t lesser uses of AI. They’re useful, accessible, and often the easiest way for someone to begin building AI into their work. They also tend to be relatively contained, with a pretty clear idea of what a successful output should look like.
Strategic work is rarely that simple. Imagine a leader deciding how a team should evolve over the next two years. AI could help them explore several versions of that future, consider how different market conditions might affect each one, identify skills the team could need, surface risks, and question assumptions built into the original plan.
The leader still owns the decision, but instead of asking AI for an answer, they’re using it to expand the thinking that goes into reaching one. Doing that well requires a different set of skills.
The jump from productivity to strategy is bigger than it looks
It’s easy to tell leaders they should use AI for more strategic work. Actually doing it requires knowing how to structure the interaction, what context to provide, when to push further, and where AI simply shouldn’t be trusted.
Often, the difference starts with asking more of the tool. Instead of stopping at “Summarize this report,” a leader might follow with “What assumptions in this report should we challenge?” A market search could become an exploration of how three different scenarios might affect the strategy. Meeting notes could be useful for more than a recap if AI is also used to surface unresolved risks, disagreements, or questions the team hasn’t addressed.
None of those questions hand the decision over to AI. In fact, they ask more of the leader, who needs enough subject-matter expertise to recognize a weak response, spot assumptions, verify claims, ask better follow-up questions, and decide how much weight, if any, the output deserves. They also need to understand what information is appropriate to provide and what needs to stay out of the tool entirely.
That’s where the real measure of AI capability becomes less about the prompt itself and more about the judgment behind it.
Leaders need room to practice with higher-stakes work
One reason basic AI applications become common so quickly is that they’re easy to experiment with. If AI gives you a terrible first draft of an email, the consequences are usually pretty limited. You sigh, rewrite it, and perhaps question your life choices for approximately seven seconds.
The stakes are considerably different when leaders start applying AI to workforce strategy or other complex business decisions, which makes having a safe place to practice especially important. Leaders need opportunities to experiment with realistic business problems, work through ambiguity, and learn where AI is useful—and where their own judgment needs to take over.
For example, leaders can practice using AI to:
- Build and compare multiple scenarios rather than generating one recommendation
- Pressure-test a strategy from different stakeholder perspectives
- Surface questions or risks a team hasn’t considered
- Explore how changes to workflows could affect roles and skills
- Challenge their own assumptions before committing to a decision
The goal isn’t to make AI the smartest person in the room. Strong AI capability means knowing how to use AI to inform a decision while bringing the context, experience, and judgment needed to make the final call.
Give leaders a path beyond AI adoption
Getting leaders to use AI regularly is meaningful progress. But what happens next depends on whether they can move beyond the applications that are easiest to adopt and start building the capability to use AI when the work becomes more complex.
That requires practice, judgment, and enough confidence with the technology to know where it can strengthen their thinking, where its output deserves scrutiny, and where it simply doesn’t belong.
Search may be where many leaders start. It doesn’t have to be where their AI capability stops.
Help your leaders build the capability they need.