Managers have been disproportionate casualties of the rolling waves of post-COVID-19 tech layoffs that started in late 2022. Popularized by large companies such as Meta, Google, and Amazon, phrases like “flattening the org” and “reducing bureaucracy” are now synonymous with thinning the management layers that ballooned during the 2021–2022 hiring sprees. Retrospectively, such flattening can seem prescient given that AI models can now automate schedules, draft performance reviews, coordinate communication across teams, and aid in the prioritization and decision support typical of management. Pushed to the experimental extreme, this can now mean 50 ICs reporting into one supervisor. The logic here is simple and stark: since AI can, or will soon, be able to handle a lot of what managers used to do, fewer managers are necessary. Instead, decision making can be distributed within teams as individual contributors become more adept at orchestrating and supervising agentic workflows with increasingly refined judgment and decreased reliance on managerial oversight. Everyone, in effect, is a manager now.
The problem with this narrative is that organizations are reducing managers at precisely the time they are becoming increasingly important to realizing their AI investments. Several sources of recent data back this up. A main conclusion from Microsoft’s 2026 Work Trend Index Annual Report is that “organizational factors—culture, manager support, talent practices—account for twice the reported AI impact of individual effort alone.” Once leadership sets AI strategy and incentives, “…it’s managers who operationalize it, and the data shows the impact of their ability to do so.” Specifically,
…when managers actively modeled AI use, employees reported a 17-point lift in reported AI value, a 22-point lift in critical thinking about their AI use, and a 30-point lift in trust in agentic AI. When managers created psychological safety around experimentation, employees reported up to 20 points higher AI readiness and value—and were 1.4x more likely to be high-frequency users of agentic AI.
The impact of managers is even greater on more advanced AI users, what Microsoft calls “Frontier Professionals” (16% of those surveyed, users who “use agents for multi-step workflows and building multi-agent systems”). This group is more likely to report that their manager uses AI (85% vs. 64%), establishes quality standards for AI work (83% vs. 57%), encourages experimentation (84% vs. 61%), and rewards work redesign regardless of outcome (26% vs. 11%). The report notes that “in many cases, employees are moving faster than the organization around them.” Microsoft calls this the “Transformation Paradox.” According to the Microsoft data, managers are the layer that helps resolve it. They translate organizational strategy into team practices that let individual work with AI produce value.
Of course, once AI adoption is the norm and managers no longer need to manage that change, one could argue that many aspects of the role remain susceptible to automation and the role will contract. We don’t know how this will play out yet, but if management roles were already contracting we would expect to see early signs, and the data shows the opposite. LeadDev’s Engineering Leadership Report 2026 surveyed 600 engineering leaders, 55% of whom are engineering managers or managers of managers. The report notes that “AI is simultaneously expanding what leaders can do technically and what is expected of them organizationally, without reducing the demands on their time in either dimension.” Not only are managers becoming more hands-on technically,
- 63% of engineering leaders say their scope and area of responsibility increased over the past 12 months.
- 60% saw increased communication with team members, customers, and stakeholders.
- 22% have more teams reporting to them.
- 29% have more direct reports.
- Architectural decisions and technical strategy saw the most respondents citing increased time dedicated to it.
One way to interpret these figures is to say that more teams and more reports show flattening working as planned from a business perspective. Another reading—not mutually exclusive—is that the role is in transition and most organizations have not fully wrestled with what that involves: managers doing their old work at greater scale, and the new work of making AI a core team practice. Either way, that’s not contraction. Contraction would mean the scope of the role itself is shrinking as AI and ICs absorb more of the work. More teams and more reports is what flattening produces, not evidence the role is going away.
To be clear, none of this means organizations should stop scrutinizing reporting structures and removing genuinely unhelpful layers of bureaucracy that stifle decision making. But it does mean asking a harder question before the next round of cuts: are you reducing management based on what managers used to do or based on the critical work they are doing now or will need to do next?
The “what they used to do” answer treats managers like overhead. The emerging evidence suggests that managers are currently playing the role of infrastructure, the critical layer that translates AI investment into actual value at the team level. Flattening on the assumption that AI will facilitate its own adoption or that value will emerge from unguided individual effort is making a productivity bet that the data doesn’t support.