Until recently, becoming a team lead usually meant being given responsibility for other people.
Now you can become a team lead without having a single person reporting to you.
You might have twenty AI agents instead.
The question is no longer just whether you can do the work.
It’s whether you can organize, direct and assure the work of others.
From Doing Work to Directing Work
Consider a software developer.
Traditionally, the developer writes code, investigates problems, implements changes and tests the results.
With AI agents, the developer might delegate these activities.
One agent investigates a defect. Another proposes a solution. A third implements it. A fourth develops tests.
Meanwhile, the developer coordinates their activities, evaluates their results and decides what happens next.
The work hasn’t disappeared.
But the developer’s role has changed.
Instead of performing every activity personally, the developer is increasingly responsible for organizing work performed by others.
And that requires a somewhat different set of skills.
The New Team
Managing a team involves more than assigning tasks.
Someone must establish objectives, explain expectations, distribute responsibilities, coordinate dependencies and evaluate results.
These responsibilities don’t disappear because the team members happen to be AI agents.
In some respects, they become more important.
An AI agent may produce a convincing result without having understood the objective.
It may make assumptions that were never agreed upon, overlook relevant constraints or optimize something that wasn’t particularly important.
And unlike a human colleague, it may not recognize when it should challenge the assignment.
Giving an agent a task is therefore not necessarily the same as ensuring that the task is understood.
The person directing the work must provide sufficient context, establish boundaries and determine how the result will be evaluated.
That sounds remarkably like management.
Delegating Work, Not Accountability
There is another interesting consequence.
When you perform work yourself, you develop an understanding of it through the process of doing it.
You encounter difficulties, make decisions, discover assumptions and gradually build confidence in the result.
When an agent performs the work, much of that experience is no longer yours.
You receive the result without necessarily having experienced the reasoning and decisions that produced it.
This creates a challenge.
How do you know whether the work is good?
You might review the result yourself. You might ask another agent to evaluate it. You might establish automated checks or require evidence supporting particular conclusions.
But none of these mechanisms removes the need for judgment.
An agent reviewing another agent’s work may simply reproduce the same misunderstanding.
And a result that satisfies every predefined check may still fail to achieve its intended purpose.
The responsibility for deciding whether the work is acceptable remains with the person directing it.
We can delegate execution much more easily than we can delegate accountability.
The Importance of Knowing What Good Looks Like
This raises a question about professional competence.
If an AI agent can perform an activity better or faster than you can, do you still need to understand that activity?
Perhaps not to the same depth.
But you do need sufficient understanding to establish meaningful expectations and evaluate the outcome.
A team lead doesn’t necessarily need to be the best programmer, architect or tester on the team.
But they need to understand enough about the work to organize it, recognize problems and make informed decisions.
The same applies when the team consists of AI agents.
In fact, the ability to define what good looks like may become more important than the ability to produce it personally.
This doesn’t eliminate professional expertise.
It changes where that expertise creates value.
Everyone Becomes a Team Lead
We often discuss AI in terms of productivity.
How much faster can someone write code, produce documents, analyze information or prepare presentations?
But perhaps productivity is only part of the story.
AI agents allow individuals to coordinate work that previously required several people.
A consultant might direct agents conducting research, analyzing evidence, developing recommendations and preparing deliverables.
A tester might coordinate agents generating test ideas, executing checks, investigating failures and documenting findings.
A manager might use agents to collect information, monitor developments and prepare decisions.
In each case, the individual becomes responsible for a small system of delegated work.
And the effectiveness of that system depends increasingly on their ability to direct and assure it.
Perhaps we should therefore think differently about AI competence.
Learning to use an AI tool is useful.
Learning to manage work performed by AI agents may be considerably more important.
Because when everyone can have a team, everyone needs to learn something about leading one.
The future professional may not be the person who can do the most work, but the person who can make the most work happen — without losing control of its quality.
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