Leader AI readiness has a strange new problem. Executives are using AI more than the people they lead, and at the same time a meaningful share of them admit they are pretending to understand it better than they do.

Those two facts sound like they contradict each other. They do not. Using a tool and understanding what it is doing to the work are different things. And when the person at the top cannot say which one they have, the whole organization starts guessing.

This week’s research points to a simple, uncomfortable idea. The most useful thing a leader can say about AI right now might be: “I am still learning this. Here is how we will learn it together.”

Key takeaways

  • Recent research reported by HR Dive found executives use generative AI more often than employees do, and are far more likely to use agentic AI.
  • Separate research from Emergn found a share of senior leaders admit to pretending they understand AI better than they actually do, and fewer than half report substantial ongoing AI training.
  • A Wiley survey found that manager time to develop people is strongly linked to whether employees plan to stay.
  • Leader AI readiness is less about knowing every tool and more about credibility: naming what is still unclear, defining standards and protecting time to learn.

What’s happening

On October 6, HR Dive reported that executives may use AI more than their workers do. Citing a Channel V Media report based on a June 2026 survey of 7,675 U.S. adults, the article says 53% of executives use generative AI compared with 38% of employees, and executives are 2.7 times more likely to use agentic AI (18% versus 7%). Executives mostly use AI for research, while employees lean on it for internet searches. Channel V Media described it this way: “Leaders appear to be moving past AI as a search tool toward AI-assisted creation.”

The same HR Dive piece pointed to a separate Emergn study of senior leaders in which 18% admitted to “pretending to understand more about AI than they actually do,” 27% worried their careers would suffer without better AI skills, and 20% said they felt “out of their depth.”

ITPro’s coverage of the Emergn research, also published October 6, framed the UK findings even more sharply: nearly one in four UK business leaders pretend to know more about AI than they actually do, and only 43% of UK senior leaders said they had taken part in substantial, ongoing AI training. (The two outlets report the pretending figure differently, so I would treat it as a range rather than a precise number.) Emergn CEO Alex Adamopoulos put it plainly: “Pretending leaves you with the same unanswered question tomorrow. Employers need to make it easier for people to say where they are stuck and get useful help.”

The third piece of the story is about managers. HR Dive also reported on a Wiley Workplace Intelligence survey of roughly 1,500 employees finding that the biggest predictor of learning and development success is how much time managers can spare. When managers had time to develop people, 84% of employees said they were likely to stay, compared with 41% when managers lacked that time. Wiley’s recommendation: “protect manager time and treat the availability of their managers as a retention metric, not a culture nicety.”

Put those together and you get one picture. Leaders are using the technology. Many are not sure they understand it. And the people who most need to translate it for their teams are the people with the least time to do so.

Why leader AI readiness is a credibility problem, not a usage problem

In Chapter 6 of The Next Turn, “AI Is Not a Tool,” I argue that AI is becoming infrastructure, not a tool. Leaders who see a tool tend to delegate it downward. Leaders who see infrastructure start asking different questions: How does work flow now? Where does effort go? Where does judgment sit?

Those are leadership questions, not software questions.

The Next Turn

That is why the gap in this research matters. Usage is a tool question. Understanding is an infrastructure question. A leader can use AI every day to summarize research and still have no clear answer to what AI’s role should be in their team’s work, what has to stay human, or how quality will be judged once the process changes.

This is the second turn in the book, the structure of work: how tasks actually get done, by whom, and with what tools. When the structure shifts faster than leaders can explain it, people fill the silence on their own. Some hide their AI use. Some overuse it. Some distrust anything that comes back too fast.

In Chapter 12, “Defining Work Again,” I make the case that AI readiness is largely manager readiness, and that leaders need to define AI’s role in the work before employees define it informally. One line from that chapter fits this week’s news almost too well:

The leader who can approve tools but cannot define standards is not leading the work. They are standing beside it.

The Next Turn

Pretending is a form of standing beside the work. It looks like leadership from a distance. Up close, it leaves the same questions unanswered.

I do not read this research as a story about leaders who do not care. Most of the leaders I work with care a great deal. The pressure the Emergn findings describe, worry about careers, anxiety about change, feeling out of depth, is the pressure of people who think they are supposed to have the answer already.

That expectation is the real problem. It belongs to an older definition of leadership, where authority came from knowing more than the people you led. AI is making that harder to sustain for everyone.

In Chapter 14, “The Leader as Translator,” I describe what replaces it:

This is where credibility becomes more important than confidence.

The Next Turn

Credibility means naming tension honestly, defining what matters, explaining tradeoffs and admitting what is still being learned. Confidence without understanding does the opposite. It signals certainty the organization cannot build on.

There is a standards-and-signals lesson here too. Fluent talk about AI is a signal. Sound judgment about where AI belongs in the work is the standard. Organizations that reward the signal will get more of it, from leaders and employees alike.

Your next turn

If you lead a team, a department or an organization, here are a few moves for this week:

  • Say where you are. Tell your team, in one or two sentences, what you understand about AI in your work and what you are still figuring out. Credibility starts there.
  • Define AI’s role in one workflow. Pick a single process and write down where AI should help, where it should not, what must be disclosed and what remains fully human responsibility.
  • Separate usage from understanding. In your next leadership meeting, ask: “What have we decided about how AI changes this work?” not just “Who is using it?”
  • Protect manager time to learn and to teach. If managers are the translators, they need time inside the working day to do it. Treat that time as a business decision, not a perk.
  • Make it safe to be stuck. Create one regular, low-stakes place where anyone, including senior leaders, can bring an AI question they cannot answer yet.

Frequently asked questions

Why are executives using AI more than employees?

According to research reported by HR Dive, executives are more likely to use generative and agentic AI and tend to use it for research and creation, while employees use it more for search. Usage alone does not mean leaders have defined how AI should change the work.

What should a leader do if they do not fully understand AI yet?

Say so, and pair that honesty with a plan to learn. Leaders build more trust by naming what is still unclear and setting clear standards for AI use than by projecting confidence they do not have.

How does manager time affect AI adoption and retention?

A Wiley survey reported by HR Dive found employees were far more likely to stay when managers had time to develop them. Managers are often the people who translate AI changes for their teams, so without time to learn and coach, adoption tends to drift.

The question underneath the tools

No leader is going to be ahead of AI on every front. That was never the job. The job is to make the work clear enough that people can do it well while the tools keep changing.

Pretending buys a little time. Clarity builds something people can stand on.

If you want more on leading through both turns at once, you can find other posts on the Next Turn blog.

So here is the question I would sit with this week: what is one thing about AI in your work that you have been acting certain about, and what would change if you said out loud that you are still learning it?

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