As AI becomes an everyday tool, middle managers find themselves at the intersection of stable operations and rapid change. That is where much of the transformation will be decided and where conditions are often weakest.
The role of the middle manager has always carried an inherent tension: ensuring reliable delivery of what is known while enabling development of what is new. AI makes this tension more acute, more visible, and harder to ignore, says Carl Heath.
– In practice, middle managers are expected to deliver as usual while simultaneously guiding their teams through a technological shift filled with uncertainty. Many lack both the language and the tools to do so.
Psychological safety and strong support is a must
A critical capability is leading through uncertainty. Being able to say, “I don’t know exactly how this will work, but we will test and learn together.” While simple in theory, this requires psychological safety and strong support from above.
Research on middle managers in transformation highlights their role as emotional balancers. They are often responsible for maintaining engagement without minimizing anxiety, and for creating meaning in change that is not always voluntary. When the change is driven by a technology many feel insecure about, this need intensifies.
– AI competence at the middle‑management level is rarely about technical depth. It is about judgment: understanding what AI is good at, where it falls short, and when the output is “good enough” to be used. It also involves protecting time for reflection and learning.
Here, a systemic problem appears. Organizations still overwhelmingly measure and reward stable operations. In such environments, even the most change‑oriented manager will prioritize what is measured. Not what leadership says is important.
– AI transformations will not fail in strategy rooms. They will fail in everyday operations if middle managers are not given time, mandate, and legitimate space to lead them.
Three questions for the executive team:
- What tradeoffs do our middle managers face between delivery and AI change?
- Do they have enough time, mandate, and psychological safety to lead AI adoption?
- Do our metrics reward stability over learning and experimentation with AI?
Carl Heath is a senior researcher at RISE and a doctoral researcher at the University of Gothenburg, focusing on leadership, AI and digital resilience. His work explores how organisations can navigate technological change while strengthening human judgement, trust and agency. He approaches AI not as a technical issue, but as a leadership and organisational challenge rooted in learning and everyday practice.