Fewer Managers, More Managing

I've watched supervisors go from spending two hours a day reading data and preparing reports to sometimes spending less than ten minutes checking what the AI produced.

The part that interested me wasn't only the report getting faster. In the firms that used the time well, those people spent more of the day training, developing and motivating their teams, and the company became more connected rather than less.

That's the change I want you to consider before you decide AI means you need fewer managers. Give the tracking work to a system that can do it reliably, then help the manager use the recovered hours for the people work that was getting squeezed out.

A founder I know had a company that had become top-heavy while revenue was healthy. Before changing the reporting lines, I suggested connecting an agent to the tools where the work already lived, Slack, Gmail, the CRM and GitHub, and seeing how much of the tracking it could take over.

That was advice, not a result I'm claiming for his company. But it is a pattern worth looking for in your own firm, you can spend a lot of effort automating client delivery while the management around it still runs on people fetching updates from each other.

I find it useful to separate two parts of the manager's job. Tracking is finding out what happened, assembling reports, chasing a status update, passing information between people who can't see the same picture.

Judgment is deciding what to do with that picture, having a hard conversation, coaching someone through a problem, noticing who is ready for more responsibility. The agent can help prepare that work too, but the person still owns the relationship and the decision.

Those are different demands on the week. If the same manager spends the morning gathering information, the time left for a conversation with someone on the team is whatever survives afterward.

There is plenty of research on the coordination load, although most of it isn't a direct measure of your managers. Asana's work-about-work research, covering more than 10,000 knowledge workers, reported 60% of time spent on work about work, including 352 hours a year talking about work.

That is a broad knowledge-worker finding, not proof that 60% of every manager's job can be automated. I would use it as a reason to inspect the week, not a percentage to put into a staffing plan.

Microsoft's 2025 report on the workday reported 117 emails a day and 153 Teams messages per weekday for the average worker in its data. Its much-repeated interruption-every-two-minutes figure was based on the top 20% of users by incoming ping volume, not every employee.

A manager sitting between several teams can recognize that sort of day. But you still have to see which messages need judgment and which are just requests for information the system could already supply.

A Quantum Workplace summary of Gartner research puts manager responsibilities at 51% more than managers can effectively handle, and says one in five would leave people management if they could. That is a warning about workload, not evidence that the people doing the job aren't capable.

There is also research on work changing when AI arrives. An account of a Harvard Business School study involving 50,032 developers using GitHub Copilot reports project management falling by 10% as a share of activity while coding's share rose.

A smaller share of activity doesn't tell you that ten percent of manager hours became free, or that AI directly performed the missing coordination. It does suggest the mix of work can change, which is what I would investigate rather than assume.

In the service firms I've watched, the report-preparation example is more concrete. The supervisor still receives the information and checks the AI output, but doesn't spend the same two hours reading data and compiling it by hand.

I've also seen systems check for projects with no recent client contact, overdue tasks and consultant interactions that need attention. The system warns the manager so they can address the situation before it becomes a churn problem, rather than asking the manager to discover every warning signal manually.

That does not mean the system prevented every lost client. I'm describing the monitoring and intervention process, and the change in what the manager spends time doing.

The tool has to be connected to the information it needs, and somebody has to write down what it should watch. "Check whether projects are healthy" isn't enough if the firm has never agreed what a warning looks like.

GitLab CEO Bill Staples described the direction in the company's May 2026 Act 2 memo: "If an agent can do it, we automate it, and find things where our judgement or skill is essential."

The same memo says, "Every layer of management increases the number of places where priorities and communication gets filtered." That's GitLab's operating argument, not a reason for you to remove a layer before you know which work it carries.

My own operation is a small-scale example of the same approach. When I described it in August 2026, about 90% of my software and publishing work was automated compared with mid-2025, including feature exploration, bug fixing, log analysis and research.

That is my description of my own work, not a benchmark for how much of your firm can run without people. My agents have dozens of command-line tools for interacting with the systems, and when I catch myself doing a task twice, I look for a script or a skill the agent can use instead.

It leaves me spending more time writing, thinking, studying and working out how systems should operate. A manager's version starts with the repetitive questions and reports in their own week, not with copying my setup.

Now, managers often take longer to adapt than the operational team, and I don't read that as resistance. In the delivery work I see, operational teams tend to engage quickly when they can see the benefit, while supervisors and managers can need dedicated time to learn the new way of working.

The tracking work is familiar and visible, you have a spreadsheet, some numbers and a report to show for the day. The people work is harder to organize, and in many firms nobody has written down how a good one-on-one or a useful feedback conversation should run.

So when tracking shrinks, the manager can be left with hours and no shared process for the part of the job that needs to grow. Leadership needs to help them make that transition, not assume the coaching appears because the report is finished earlier.

The reporting lines can change after that, but I would be careful about what the wider data proves. Gusto's payroll analysis of 8,500 small companies found individual contributors per manager rising from roughly three in 2019 to roughly six in 2024.

It also reported manager hiring falling about 40% to 50% from 2022, compared with roughly 11% for individual contributors, and the share of employees in people-manager roles falling 34%. Those are changes in payroll records, not a causal estimate of how many roles AI removed.

The sector detail matters for this audience too. Professional services, information and finance were among the sectors that flattened least in that analysis, so the headline doubling is not a target I would hand a service-firm owner.

Other numbers tell you the pressure on management roles is wider than one dataset. Deloitte reported middle-management job postings down 42% between spring 2022 and the end of 2024, and in October 2024 Gartner predicted that 20% of organizations would use AI to flatten through 2026, eliminating more than half of current middle-management positions in those organizations.

One is a posting trend and the other is a forecast. Neither tells you that half of your own management work has disappeared.

The big-company announcements are louder. Amazon set a target to increase its ratio of individual contributors to managers by 15%, Google reported cutting 35% of managers of small teams, and Coinbase described replacing pure managers with player-coaches, people who manage and also do delivery work.

Those are different decisions in different businesses, not one proven AI playbook. The quieter pattern I've seen in service firms is a manager leaving and the seat not being refilled because the agent has taken enough of the work for the remaining responsibilities to fit elsewhere.

A supervisor gets promoted and takes on what remains. I've often seen younger, more AI-fluent supervisors do that, but age isn't the test for who can handle the job, the skills and judgment are.

That can remove a management layer without a layoff. It doesn't mean headcount is mathematically unchanged when somebody leaves, it means the firm doesn't have to begin by dismissing the people who are already there.

And once the tracking is handled, the constraint can move to skills. Who can run the system, check its output, manage the exceptions and help the rest of the team improve?

This is where I think owners can miss the better use of the time. They see fewer hours required for reporting and start counting a salary saving, while the firm still needs more attention on developing people.

In the firms where I watched that two-hours-to-ten-minutes change, some managers used the recovered time for training and motivation, and I saw teams operate in a more connected way. Other owners have told me similar stories, less fetching data and copying it around, more attention to the work that helps the business.

That is my observation, not a measured effect on morale across all firms. Some teams don't make that change, and the time gets used up without a clear benefit.

The management research explains why I wouldn't treat people time as the leftover. Gallup estimates that managers account for at least 70% of the variation in employee engagement scores across business units.

That doesn't mean managers cause 70% of every person's feelings. It means the way teams are managed is strongly tied to differences in their engagement, and taking management away without replacing its functions is a serious decision.

In its 2026 State of the Global Workplace reporting, Gallup looked at employees in US organizations investing in AI. Those who strongly agreed that their manager actively supported their team's AI use were 8.7 times as likely to strongly agree that AI had transformed how work got done.

That is an association in reported experience, not a promised return from giving a manager an agent.

The useful move is the same one in Delegate the Inputs, Own the Outputs. Let the system assemble the information, keep a person responsible for the decisions about people, and give that person enough time to do the work.

On the Delivery Model Ladder, that can support the move from Stage 2 toward Stage 3. The operating structure changes because the work changed, not because you renamed the roles.

There are a few ways to get this wrong, and the first is assuming the saved hours will find their own use. BCG's June 2026 survey found 42% of frontline regular AI users reported saving at least a workday a week, while 66% received limited or no guidance on using the saved time.

That isn't a manager-only sample, but the planning problem applies. If you want the hours to go into mentoring, decide whose mentoring, when it happens and who checks that it happened, rather than leaving it as a good intention.

I would use the system to help track the agreed work too, with the same clear boundaries you use for other management information. You are checking whether the plan happened, not treating every unfilled minute as misconduct, and the point is to keep the extra time from disappearing.

Another failure is removing the management layer while leaving all its human work unassigned. Korn Ferry's workforce survey found 41% of employees said their organization had cut management layers, and 37% said a lack of managers left them feeling directionless.

There is an older warning in Zappos's move to holacracy, where about 18% of the workforce had left by January 2016. That departure figure included people taking offered severance, so it isn't a clean measure of people leaving because a flatter structure failed.

Still, deleting a line on the chart doesn't delete the coaching, escalation and development that used to travel through it. Somebody needs to carry that work after the change, and they need hours for it.

You can also overload managers by doing the reverse, adding responsibility for AI without taking anything away. Harvard Business Review's June 2026 article on overloaded middle managers describes consulting-firm managers expected to validate AI output, coach the team and maintain quality on top of the existing job.

Don't give someone all of that and call the software a time saving before the tracking duty has actually gone. Make sure the agent does the work reliably, then redesign what the manager is responsible for.

And write down the judgment part too. What does a good one-on-one cover, how is feedback given, what should a manager notice about a person, and when should that lead to a conversation?

People learn with other people. The contact, encouragement and correction are part of how someone grows into the next role, and an org chart that loses that work hasn't become efficient just because it has fewer boxes.

Gartner's flattening forecast carries that warning itself, mentoring pathways can break and juniors can lose development opportunities. If the old structure taught people partly through proximity, you need to provide that learning deliberately when the structure changes.

Use the recovered hours to prepare the team for an AI-native firm, including running agents, validating output and understanding the delivery system. Those skills don't arrive with the license either.

There is a size limit to the org-chart argument. If you still carry all the coordination yourself and nobody has a dedicated management role, an agent can save you time, but there may be no management layer to remove.

And even in a larger firm, the remaining people work may justify another hire. The decision should follow that work, not a ratio somebody else reported.

Before you post the next manager or coordinator vacancy, list the reports compiled by hand and the status questions repeated through the week. That is the beginning of an agent's job description, and you need to test which parts it can actually take.

Then write the role that remains, including the time for coaching and development, and decide whether somebody can grow into it or whether it needs a new person. Give the hours a job before you take a seat off the chart.