How Do You Lead When AI Unsettles the Expert’s Identity?

“AI is currently perhaps the single most powerful factor testing identity and the experience of agency in organisations.”

The leadership team meeting is reviewing progress on the use of AI. The tools have been acquired, the training sessions held, and adoption is already under way. The leadership team talks about hours saved, faster lead times, and how the need for expert resources appears to be decreasing.

If you talk with an expert working in the same organisation, the picture may look different. They tell you they tried using AI for a report that usually takes them a couple of days to write. The machine produced a draft in under fifteen minutes. “And it was actually quite usable,” they say, with a slightly awkward laugh. Then they add, half-jokingly: “So what am I going to do next year?”

These two conversations take place in the same organisation, but they rarely meet. Leadership talks about AI in terms of productivity. The individual asks what it means for their professional identity and their value in the organisation. In the space between them lies an interesting leadership opportunity that often goes unnoticed.

Adopting AI is a change of identity

We tend to treat AI as a technology project: choose the tool, train the users, measure the uptake. This is necessary, but it overlooks something essential. For an expert, their own expertise is not just a tool. It is part of who they are.

Experts have often built their sense of self on what they are good at. They write clearly, put together an analysis, find a solution to a difficult problem. When a machine produces something similar in an instant, more changes than just the job description or the way of working. Many find themselves reflecting on what is actually valuable in their own work. For many, that is a more uncomfortable question than it first sounds.

According to self-determination theory, which is central to understanding human agency, intrinsic motivation is built on three basic needs: autonomy, competence, and relatedness. AI can touch every one of them. For example, it can strengthen the sense of competence when routine tasks shift to the machine and time is freed up for more demanding work. Or it can erode that sense, if a person begins to feel they are merely a checker of the machine’s output.

Quiet resistance ahead

If leadership does not help people with questions about their own value and identity, the consequence rarely shows up as open resistance. Open pushback would actually be the easier situation, because it can be discussed. More commonly, enthusiasm gradually fades. People stop feeling that their own expertise, judgement, and willingness to take responsibility matter.

In an earlier blog post, we reflected on how leadership strengthens or erodes agency. This is the same phenomenon: when people do not feel that their contribution matters, they withdraw. AI is currently perhaps the single most powerful factor testing identity and the experience of agency in organisations.

The value of expertise does not disappear, it changes form

A credible view, however, is that human value at work is by no means disappearing. Its focus is, however, clearly shifting. In the AI era, expertise can be seen shifting in at least four directions:

Formulating questions. AI answers what it is asked. Recognising the right question requires experience, an understanding of context, and situational awareness. A machine does not easily grasp, for example, what was left unsaid in a bid negotiation.

Judgement. A machine generates options faster than anyone can read them. But which of them suits this particular organisation, this moment, and these people? Judgement is a skill that develops only through doing and making mistakes. That is also why it is difficult to replace.

Interpretation. The same answer can mean different things in different contexts. An expert’s value increasingly lies in being able to say: “This is true, but in our situation it means something else.”

Taking responsibility. AI does not bear responsibility for the consequences. Someone is needed to stand behind the decision, justify it to the client, and correct any mistakes. Responsibility is in no way smaller in the AI era than before.

This shift in how value is experienced does not, however, happen by itself. If you have built your professional identity on producing answers, it takes a sensitive ability to recognise the new ways in which your value shows up. Many remain stuck in the old, struggling with the loss of their expertise.

What is required of the leader?

The leader’s task is not to reassure people that AI will hardly change their work. It genuinely will. To respond to real concerns about value and identity, the leader can do at least three important things during the transition:

1. Redefine what expertise means

In leadership, it is worth emphasising judgement, experience-based situational awareness, and the importance of interpretation as growing areas of expertise. The new value of expertise can be made visible in everyday work in many ways. What is discussed in meetings, and how? What is rewarded, or who is thanked, when a project succeeds?

New, insightful questions and perspectives that arise from seeing your own organisation’s reality clearly are more important than ever. They are the top performances of the AI era, and that is worth saying out loud.

2. Give people a role in adoption

In many organisations, tools and ways of using them are chosen somewhere else, and experts are left to adapt. This set-up can accelerate the erosion of agency. We have written about imposed change from the leader’s perspective, and the same applies to others as well. Change that is served up ready-made is hard to feel ownership of.

I would rather try involving people in planning how AI is used in their own work. Which tasks will shift to the machine? Where will human judgement always be needed? Where will we direct the time that is freed up? In doing so, leadership signals that experts know their own work best. When they get to help define the new shape of their work, concern about their own identity and value turns into a desire to influence their role and their work going forward.

This is not a soft add-on alongside the actual adoption. It is a prerequisite for successful adoption.

3. Strengthen the sense of competence

It is important to make room for experts’ concerns and to give them the experience of being heard. Identity, however, does not change through conversation alone. People need experiences that show them they are still capable and valuable.

The leader can, in particular, help identify the expertise that remains even as tasks change. These are often the meta-skills of expertise that are not tied to any single task. For example, behind writing a report may lie the ability to identify the essential findings, understand the business, assess the reliability of information, and draw conclusions. These do not disappear even if the way the report is produced changes. They are precisely the skills that help ensure the information produced by AI truly serves the organisation’s needs.

A question worth asking

AI will change expert work regardless of how its adoption is led. What does depend on leadership, however, is how people experience their own place afterwards: does it shrink to that of the machine’s sidekick, or does it grow into that of someone who exercises judgement, interprets, and carries responsibility?

At the core is a choice: does your organisation talk about the changing meaning of work, or about AI merely as a tool? If the answer is the latter, the conversation about the former is probably already taking place. Not as part of leadership, however, but in the corridors, in everyday encounters, and in each person’s own mind.

What kinds of conversations has AI sparked in your workplace? Tell us, or get in touch if you’d like to explore how to lead AI-driven change in a way that strengthens people’s sense of meaning, identity, and agency.

This blog was written by Antti-Juhani Wihuri, a leadership and leadership team development expert. A-J is particularly energised by leadership team engagements where the aim is to elevate the team's interaction, dynamics, and collaboration to a new level in support of achieving strategic goals. At Netco, he is inspired by client-centricity, exceptionally skilled colleagues, and the ability to see beyond the surface.