Tag: artificial-intelligence

In the last post, I explored a question that is easy to miss as organizations adopt AI/SI:

What happens when the work we automate was also the work that helped people learn?

There is no simple answer.

Some work should disappear. Some tasks consume time without creating much value for the person doing them or the organization receiving them. But other work carries something beneath the surface. It teaches people how to notice, how to question, how to make a decision when the information is incomplete.

The challenge is learning to tell the difference between busy work and valuable work.

Three Kinds of Work

As leaders rethink roles, it may help to see work in three broad categories.

The first is work AI/SI can perform completely.

These may be repetitive tasks where automation genuinely gives people more time for meaningful contribution. Letting go of this work can create welcome space.

The second is work AI/SI can support, while a person retains responsibility for judgment.

Automation might organize the information, identify patterns, or offer possible interpretations. The person still has to test the output, understand the context, and decide what to do.

This work can become more developmental, not less, if people are taught to examine rather than simply accept what the technology produces.

The third is work that may need to remain deliberately human.

Not because the ghost in the machine is incapable of contributing, but because the experience itself helps someone grow: a challenging conversation; a stakeholder interview; the first draft of an important recommendation; a review of the resumes and applications that made it past the digital gatekeeper.

Couple any of these with the opportunity to sit beside an experienced leader and hear why one possibility was chosen over another, and these experiences develop the judgment that future leaders will need.

Delegation Is Also Development

When someone enters a new leadership role, there is often pressure to improve efficiency quickly.

The leader inherits deadlines, expectations, and a team whose capabilities may not yet be fully visible. AI/SI can offer immediate ways to move faster, but a new leader is also shaping how the team will learn.

Every decision about who attends a meeting, prepares a recommendation, speaks with a stakeholder, or evaluates a computer-generated answer becomes part of the team’s development system.

Delegation is not only about distributing the work. It is also about distributing the opportunities to grow.

That does not mean giving people responsibilities without support. Productive development usually includes some combination of challenge, observation, feedback, reflection, and another opportunity to try.

Automation can participate in that process. It can help someone explore alternatives, prepare for a conversation, or identify gaps in their reasoning. What it cannot do on its own is decide which experiences a person needs in order to become ready for what comes next.

That remains a leadership responsibility.

Preserving the Path to Leadership

The organizations that navigate this well may not be the ones that automate the most. They may be the ones that understand what their work has been teaching people and replace those lessons intentionally when the work changes.

That begins with a quieter kind of attention and a few important questions:

Which assignments help people develop judgment?

Where do employees learn the unwritten context behind important decisions?

Who is being invited into conversations that stretch their perspective?

Where might efficiency be removing the experiences that prepared someone else to lead?

AI/SI will continue to change what people do. The deeper leadership question is whether we will remain thoughtful about who they are becoming while they do it.

If you are stepping into a new leadership role or reconsidering how your team develops people as work changes, coaching can provide the space to examine those decisions more closely.

If you would like to explore what that support could look like, I invite you to contact me for a conversation.

Not long ago, some of the least glamorous work in an organization served an important purpose: it was the work that got you started.

A new employee prepared the first draft of a slide deck. A junior team member gathered the information, checked the numbers, or sat quietly in a meeting taking notes. The work was rarely exciting. It was not always efficient.

But it was where people learned.

They began to notice which questions mattered. They saw how experienced leaders evaluated competing ideas. They learned the history behind decisions and the difference between an answer that looked right and one that made sense in context.

Today, much of that work can be completed by AI in seconds. That can feel like progress. In many ways, it is. But something else may be disappearing with every task delegated to the ghost in the machine.

The Experience Hidden Inside the Work

Gartner recently warned that AI could eliminate many of the traditional entry-level responsibilities that helped prepare people for leadership. A Gartner survey of 1,303 senior leaders found that:

• 18% of marketing leaders had eliminated roles because of automation
• 16% had created new roles
• Nearly one-third had redesigned roles

The concern is not simply that certain jobs may disappear. It is that the experiences inside those jobs may disappear with them.

When someone prepares an analysis, they learn how the pieces fit together. When they attend a difficult meeting, they see how influence actually works. When they produce an imperfect first draft, they receive feedback that helps them recognize what good judgment looks like.

These moments build more than technical skill. They build context, discernment, and confidence. Those qualities are difficult to acquire from a course. They grow slowly through exposure, responsibility, mistakes, observation, and conversation.

In other words, they grow from experience, and the opportunity to experience may be in jeopardy.

Efficiency Has a Longer Shadow

A leader looking at a task may reasonably ask:

“Can AI do this faster?”

That is an important question, but it may no longer be enough.

There is another question beside it:

“What was this work teaching the person who used to do it?”

A task can appear routine while also carrying significant developmental value. Remove enough of those tasks, and an organization may become more efficient today while quietly weakening its leadership pipeline for tomorrow.

People may advance without having seen how decisions are formed. They may know how to produce an answer without understanding how to evaluate it. They may enter management without the organizational knowledge that once accumulated through years of ordinary work.

The gap may not be visible immediately. It may only become apparent when the organization needs someone ready to step forward.

A New Responsibility for Leaders

This creates a different kind of leadership challenge.

The goal is not to preserve unnecessary work simply because it is familiar. Nor is it to resist technology that can free people from repetitive tasks. The opportunity is to become more intentional about where development will happen.

If AI takes away one path for learning, another path has to be created. That might mean inviting a less-experienced employee into a strategic conversation. It might mean asking someone to evaluate an AI-generated recommendation instead of merely accepting it. It might mean giving people responsibility earlier, along with the guidance and room to make mistakes that responsibility requires.

The future of leadership development may depend less on formal programs and more on the choices leaders make about everyday work. I’d go so far as to say that this is has always been true. Every assignment communicates something about who is trusted to think, contribute, and grow. As AI changes what work requires, it may be worth pausing before removing a task entirely. Today’s leaders have to ask themselves if the way it used to work is worth preserving, at least in some form, so tomorrow’s leaders have the opportunity to build the critical skills to make themselves successful.

Unless, of course, the goal is to let AI take the lead.

What might we be automating besides the work?