Unfreezing Middle Management to Drive Transformation
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Generative AI, enterprise AI transformation, and operating model changes are dominating boardroom conversations, yet the real adoption remains uneven. From my vantage point, the most important takeaway is this: AI will reshape nearly every business function, but progress will stall unless organizations address a critical internal barrier, the “frozen middle”.
At Everest Group, we see AI as a horizontal force that will fundamentally alter how enterprises operate. This is not limited to isolated use cases or productivity tools. It spans supply chains, HR, software development life cycles, infrastructure management, and even highly specialized domains such as claims processing.
In other words, AI is not just another layer of technology. It is a catalyst for rethinking how work gets done.
However, the full value of AI does not come from simply adding tools into the existing stack. As I have noted previously, the real unlock comes when organizations change how they operate, not just what technology they deploy.
This distinction is where many enterprises begin to struggle.
One of the most striking dynamics in AI adoption today is the uneven distribution of belief in its potential from within organizations.
At the board and CEO level, conviction is high. Leaders broadly agree that AI will be transformative and are pushing for rapid progress. This aligns with the broader narrative we see across industries, where executives are making bold statements about AI’s strategic importance.
However, as you move down the organization, that conviction weakens. By the time you reach mid-level management, you often encounter skepticism and resistance. This is what I refer to as the “frozen middle.”
It is tempting to view this resistance as a failure of leadership or imagination, but that would be a mistake.
The skepticism within the “frozen middle” is not irrational. In fact, it is grounded in experience.
Mid-level managers have lived through multiple waves of technological change, from ERP implementations to digital transformation programs. In many cases, these initiatives promised significant gains but delivered more modest results. Change was difficult, disruptions were real, and the outcomes often fell short of expectations.
That’s why, when AI is presented as another transformative wave, their response is to have measured caution.
These employees are not questioning whether AI can work. They are questioning whether it will deliver on its promises in their specific operational context, and what it will take to get there. This is a crucial distinction; it explains why simply increasing top-down pressure rarely resolves the issue.
Historically, large organizations have managed this uncertainty through a well-established strategy. They wait.
More specifically, they observe early adopters, often competitors or industry peers, and learn from their successes and failures. They then move as fast followers, adopting proven approaches while avoiding early missteps. This behavior is not a sign of inertia, but is instead a rational risk management strategy.
However, AI is disrupting this playbook. Given the strong conviction at the top and the competitive pressure to demonstrate progress, many organizations feel they no longer have the luxury of waiting. They are being pushed to act before clear success patterns have emerged.
This creates a tension: the organization is expected to move quickly, but the people responsible for execution do not yet have the evidence they need to move with confidence.
Many organizations have responded by investing in future-state visioning. They build scenarios, define target operating models, and articulate how AI will reshape their business. This is necessary, but it is not sufficient.
A future vision tells you where you want to go. It does not tell you how others are navigating the journey, what trade-offs they are making, or where the real challenges lie.
For the frozen middle, this gap is critical. They are less concerned with abstract end states and more focused on practical execution.
They want to know:
Without these answers, hesitation persists.
If there is one lever that consistently helps organizations move forward, it is this: transparency into peer activity and decision-making. When mid-level leaders can see how comparable organizations are approaching AI, including the missteps, compromises, and incremental progress, their confidence increases. The journey becomes more tangible and less theoretical.
This is particularly important because AI adoption is not a binary success or failure. It is an iterative journey. Early efforts may not deliver full value, and that is acceptable, as long as organizations learn and adapt. Understanding that peers are navigating similar uncertainties helps normalize the experience and reduces perceived risk.
Ultimately, the challenge is not just technological. It is organizational.
Top-down pressure to adopt AI must be matched with bottom-up confidence in how to execute. Without that alignment, initiatives will stall, regardless of how compelling the technology may be.
Leaders should focus on three priorities:
AI will undoubtedly transform enterprises. The question is not whether it will happen, but how quickly and effectively organizations can navigate the transition.
The frozen middle is not an obstacle to be overcome. It is a signal that organizations need better alignment between vision and execution. Those that succeed will not be the ones that push hardest from the top, but the ones that bring the entire organization, especially the middle, along for the journey.
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