Not because middle managers are obsolete. Because most organizations stopped asking what middle management was actually for somewhere around the last decade. AI is about to make that neglect impossible to ignore.

Earlier in my management career, an executive told me something that I remember despite the stark connotations: “The hardest role for me to staff is someone who can keep the front lines happy while taking a shellacking from senior leadership.”

Few roles are as clearly defined as those at the top and bottom. Executives set the vision. Individual contributors execute. It’s the layer in between — directors and managers — that decides how efficiently and effectively the vision is implemented.

That layer has always had a thankless job. Middle managers are sandwiched, by design, between managing a team below them and managing executives above them. Every meeting is a translation exercise. Every decision lands them in the awkward position of carrying someone else’s water — either delivering news from above to a team that didn’t ask for it, or surfacing pushback from below to executives who don’t want to hear it.

Done well, the work is largely invisible. Done poorly, the work of middle management adds bureaucracy (more meetings), redundancy (relaying information without adding value), and slows velocity.

The Myth of Boundaryless Leadership

Which is partly why, in the last decade, we saw an enthusiastic corporate experiment to do away with the layer entirely.

The pitch was seductive. Eliminate middle management, the thinking went, and you’d get faster decisions, leaner orgs, and — the cherry on top — executives who were “boundaryless.” A boundaryless executive would be close to the ground, in touch with the work, free of the bureaucratic intermediaries who slowed things down. Companies announced flatter structures with the kind of fanfare usually reserved for product launches.

In practice, the experiment produced a different result. Executives who had built their careers in a structured org didn’t suddenly become great hands-on coaches. Instead, two patterns emerged. Some executives became micro-managers — pulled into the weeds of decisions they had no business making, slowing the very velocity they were trying to unlock. Others were reduced to the equivalent of bird poopers: dropping in unannounced on a team’s work, leaving a strong opinion behind, and flying off to the next thing without context, follow-through, or accountability for the wreckage.

A handful of middle managers survived this era by adapting in a different (albeit also suboptimal) way. They stepped back and let their reports interact directly with executives. They positioned themselves as facilitators rather than gatekeepers. This worked, sort of. It preserved velocity and made the executives feel close to the work.

But it also reduced the manager’s role to information pusher and feedback enforcer: relaying messages, chasing action items, making sure the executive’s latest input got incorporated. Useful in the short term. But parroting executive talking points and relaying already-made decisions is not what strengthens organizations over time. It’s what hollows them out.

And this is where AI re-enters the story. Because if you’ve defined middle management as the layer that produces the artifacts of coordination (the status decks, the synthesis docs, the action-item trackers) then yes, agents are coming for that work, and they should. An individual contributor with the right tooling can generate most of what used to require a manager’s calendar. The artifacts were never really the job, though. They were scaffolding around the job.

The mistake, I think, was treating middle management as a layer to be optimized rather than a role to be defined. A layer can be thinned or flattened. A role has to be done by someone, and if you don’t define it, the work it was supposed to cover doesn’t disappear — it just gets done poorly, by the wrong people, at the wrong altitude.

Middle Managers as Accountability Agents

So what is the role, when it’s done well?

The version I’ve come to believe in — and advocated for at Intuit — is the manager as accountability agent. Worth being precise here: individual contributors are already expected to own their decisions. That’s table stakes. The accountability I’m describing operates one level up. It’s the manager’s accountability for the guidance, scope, and risk tolerance they set for the team — and it’s the part most organizations under-define.

So the accountable manager’s version isn’t “Fred shipped it because the CEO said so.” It’s “the team shipped it because I backed the call based on our roadmap and customer needs — and if the customers hate it, that’s on me as much as on Fred.”

That shift — from relay station to accountable owner — changes everything downstream. The accountable manager by definition must engage with the executive direction rather than just transmit it. She has to push back when strategy doesn’t fit the team’s context, or adapt it when it almost does. She has to absorb pressure from above so the team can focus, and provide cover when the team takes a risk. And she has to design the operating mechanisms that let the work actually flow rather than just appear to.

But herein lies the catch that most experiments in the “flat org” movement missed: this version of middle management only works if executives above let it work. An accountability agent who pushes back has to be allowed to push back. A manager who provides cover for their team needs to have standing in the executive’s eyes to absorb the heat. A director who adapts strategy needs an executive who treats the adaptation as judgment, not insubordination.

Which is to say: executives get the middle management they deserve (in other words, are willing to tolerate). Is the executive cohort willing to be managed by accountable agents, in the specific sense of being challenged, slowed down, and occasionally told no?

That’s the part of the deal that the boundaryless-executive era forgot. You can’t have managers who exercise judgment if the people above them have made it clear that judgment isn’t welcome.

If you’re an executive, the question isn’t how flat you can make your company to take advantage of AI. That’s the version that has already been answered by experiments in the 2020s. The real question is two-part. First: which middle managers act as accountability agents in my organization? Second, and harder: have I been creating the conditions where accountability-agent work is actually possible, or have I been rewarding the information-pushers because they’re easier to manage?

If you can answer both, you have a real advantage. If you can’t, your competitors will answer it for you.

Close Tack is a publication on product, AI, and organizational management for people who lead teams and care about how the work actually gets done.

These limits are real (yet the gap is shrinking every month that goes by). What doesn’t get enough attention is how well AI agents DO remember - and what this means for organizations.

I set up an AI agent this week to parse news events for me. (When I speak of agents, I use Gemini, Notebook, Perplexity, Claude and their derivatives for various tasks). Setting it up (perhaps you could call this “introducing myself” to my agent) took some time. Out of the box, of course, it didn’t know anything about me - my preferences, styles, or existing background.

By the time I finished briefing it, the agent had absorbed roughly nine pages of instructions: what topics to scan for, how to double check its work, what angles I find interesting, when to push back on me.

The magic here is that my agent will pull all of that context, intact, every time I ask it to. At the present moment, the models may lead it to be right roughly 90% of the time - but in my experience that is a far higher percentage of success than would be attainable with a human assistant.

Even the most assiduous human assistant would not be able to reliably reference such in-depth background content every time, nor so quickly.

That’s not a failure of humans - that is how human memory works. We retain what knowledge we attend to often, and let the rest drift. That is to say, selective attention is our human superpower. Memory, and recall, is AI’s superpower.

When assessing a team structure, leaders are accustomed to assessing (whether by force of habit or intent) their individual employee’s superpowers. Likewise, we need to recognize where our digital agents shine to unlock the greatest improvements in velocity and innovation.

Memory is the treasury and guardian of all things

Cicero - Roman orator, philosopher, and statesman - lived by his training to deliver hours-long speeches without notes. For Cicero, memory wasn’t a parlor trick. It was the operating system of his profession and, by extension, of Roman governance itself.

An AI agent is, in the most literal sense, a treasury and a guardian. It holds what you give it, and it returns it intact, safeguarding it against the selective drift of human attention.

Which brings us back to team structure.

When I managed product teams, I spent a lot of time thinking about which humans to put in which roles: who was best at managing relationships with difficult stakeholders, who was best at driving execution in a critical domain, who had the passion and drive to innovate in a greenfield area.

The addition of AI agents to this equation does not simply give every team member a productivity boost. It adds a new kind of team member - one whose superpower is to use memory and near-perfect recall, and whose weakness is the judgment and selective attention that your human teammates bring. The two are not redundant. They are complements.

The leaders who will get the most out of the AI shift are the ones who stop treating agents as faster versions of their existing tools, and start treating them as a different role on the team. That positioning changes what you hand to the agent, what you keep with the humans, and how you structure the handoffs between them.

The failures of AI agents are real. But fixating on their shortcomings is a bit like dismissing a new hire because they cannot do what your most experienced person does.

The more useful question, for a leader, is the one you would ask about a new team member: what is this person uniquely good at, and what should I have them do?

Where are you finding digital agents genuinely complement the humans on your team?