middle managers – Tech firms are flattening org charts and turning managers into AI adoption drivers, raising questions about what’s left of middle management.
AI is making one group feel especially exposed: middle managers. The irony, for anyone in that role, is that they’re often expected to push colleagues toward the very AI systems that are being used to cut headcount and reshape teams.
Companies across the tech sector are flattening their organizational structures to boost efficiency. and the result is a visible shift away from “pure” management roles.. Coinbase has been reported as eliminating “pure managers” as part of AI-linked job cuts. while Block has moved to describe the same concept using a different label: “player-coaches.” Meta and Snap have also made related announcements. signaling that the trend is spreading beyond a single company or playbook.
At the center of this change is a new tension for managers who remain.. Even as organizations reduce traditional management layers. many managers are still expected to lead day-to-day AI adoption—monitoring how employees use AI tools. building dashboards. flagging teams or individuals who appear to be using the technology less than expected. and urging colleagues who are lagging behind.
The mechanics can feel less like coaching and more like enforcement. particularly when managers are asked to track usage and measure engagement.. In this environment. managers can end up functioning as a kind of operational front line for AI rollout. whether or not that matches how they were originally hired or trained to manage.
Meanwhile, several large employers are also reported to be tracking and incentivizing employee use of AI tools.. Disney. JPMorgan. and KPMG are among the companies referenced in this reporting. underscoring that the phenomenon is not limited to Silicon Valley and that AI adoption is increasingly treated like a performance metric.
This raises a larger organizational question that goes beyond staffing levels: what is the right role for a manager in the AI era?. For years. business-school frameworks have positioned managers as the bridge between executives who set strategy and employees who carry it out—motivating teams. clarifying priorities. and getting the best performance from workers.
But AI is changing the balance of that equation.. Some managerial tasks that used to depend heavily on human supervision and routine oversight are increasingly being automated.. At the same time. many remaining managers are being asked to oversee AI agents and to manage larger spans of responsibility than before. meaning they are juggling more direct reports while also handling new operational expectations tied to AI systems.
The deeper issue. as the debate builds. is whether flattening management structures ultimately improves organizational effectiveness—or whether the pendulum has swung too far.. If managers are simultaneously being reduced and asked to absorb more responsibility. the burden may shift toward roles that are hard to sustain. especially when those roles require constant follow-through on technology adoption.
There’s also a cultural risk in turning AI usage into a scoreboard.. Even if participation is voluntary in principle. dashboards and incentives can create pressure that changes how employees think about their work and the tools they’re asked to adopt.. In practice. that can make collaboration feel more transactional. even inside organizations that once relied on informal learning and peer support.
A practical challenge sits underneath the entire trend: when AI becomes part of the workflow. managers often have to translate fast-moving technical capability into daily work habits.. That translation typically includes training. setting expectations. and helping teams adapt—yet the same organizations that are reshaping management structures may also be making it harder for managers to spend time on that kind of human-centered support.
For readers weighing where this goes next. the core question is whether organizations will define management differently in response to AI—focusing more on judgment. problem-solving. and team development—or whether they will treat managers mainly as drivers of adoption metrics.. Either way, the role is being rewritten, and for many middle managers, the change is happening quickly.
If you’d like to share your perspective on what the middle-manager job should look like in the AI era, the piece invited readers to send their thoughts by email.
AI management middle managers org chart flattening AI adoption metrics player-coaches workforce restructuring