Use AI to flatten middle management this year?
AI-driven management flattening risks degrading mentorship and product quality, even as only 17% of companies use AI productivity gains to cut headcount, creating organizational congestion if traditional review cycles remain.
The question
Big tech is publicly using AI as the rationale to flatten management layers. Do we follow — increasing span of control, cutting one layer, redeploying managers as ICs — or hold the org chart and absorb productivity gains elsewhere?
Counsel's position
Selectively flatten management layers in areas where AI demonstrably augments individual contributor autonomy, while preserving essential leadership and investing in new manager development.
Verdict
The verdict: Selectively flatten management layers in areas where AI demonstrably augments individual contributor autonomy, while preserving essential leadership and investing in new manager development.
How the criteria decide
2 of 3 criteria resolved on cited evidence. 1 had none either way.
| Criterion | Favours | Evidence |
|---|---|---|
| AI productivity gains and management-layer trade-offs | Flatten management layers | Individual AI productivity gains create congestion in traditional decision pipelines An individual developer who is 50% more productive with AI tools but must submit to traditional review cycles will find themselves in a queue. AI exposes and eliminates fabricated inefficiencies managed by mid-level managers The most valuable information that we can bring into the enterprise will improve margins by revealing the massive waste caused by Slurm. AI-driven management flattening risks degrading mentorship and product quality If managers are expected to either be writing a lot more code or have a lot more reports, what I see happening is more asynchronous, agent-driven management |
| Span-of-control + org-shape decisions | Flatten management layers | Block restructured 40% of its workforce around small AI-augmented squads In early 2026, they restructured more than 40% of the company and rebuilt around small squads of one to six people working alongside AI agents. |
| Reversibility and reputational risk in AI-driven org changes | No evidence either way |
AI-driven management flattening risks degrading mentorship and product quality
Given your decision on whether to increase span of control, note that replacing human management with AI agents can create severe operational risks.
Individual AI productivity gains create congestion in traditional decision pipelines
If you hold the org chart, recognize that faster individual output will bottleneck at existing managerial review layers.
Block restructured 40% of its workforce around small AI-augmented squads
As you consider cutting layers, observe how Block broke the headcount-equals-output paradigm by integrating internal AI tools.
AI exposes and eliminates fabricated inefficiencies managed by mid-level managers
When deciding whether to cut management layers, consider that AI's transparency naturally targets roles built around maintaining unnecessary processes.
Only 17% of companies use AI productivity gains to cut headcount
Before following the narrative of AI-driven layoffs, note that the majority of enterprises are absorbing productivity gains to reinvent their workforce.
Read another verdict
- Which process should we point AI at first?
- Put one person in charge of AI — or is a Head of AI premature for us?
- Buy a tool for this process, or build around our own knowledge?
- Centralize AI strategy under CEO or distribute ownership?
- Adopt new AI ROI tools or refine existing methods?
- Invest in pre-build costing or post-deployment ROI tracking?
- Our documents are a mess. Clean them up before AI, or after?
- How do we measure the return on an AI workflow — and what baseline is honest?