For the better part of the last decade, enterprise IT has been playing defense.
Tickets pile up. Performance issues surface. Users complain. IT responds. The cycle repeats. It is not that teams lack visibility anymore. That problem was largely solved with the rise of Digital Employee Experience (DEX) platforms. It is that visibility has not translated into control.
That gap is now being closed.
When ControlUp crossed the $100 million ARR threshold, it was not just a growth milestone. It was a signal that the market is moving past monitoring and into something more consequential: autonomy.
As CEO Jed Ayres put it, “Reaching $100 million ARR is more than a growth milestone. It’s proof that we’re leading a generational shift in IT.”
The shift is subtle in language but massive in implication. We are moving from knowing what is wrong to fixing it automatically and, increasingly, preventing it altogether.
That is a very different operating model.
The first wave of DEX gave IT teams something they had never really had before: real-time insight into how systems actually perform in the hands of users. That alone reshaped how organizations think about productivity and support. But it also exposed a new bottleneck. Seeing problems faster does not mean solving them faster, especially in environments that now span physical devices, virtual desktops, SaaS apps, and multiple cloud platforms simultaneously.
Complexity has outpaced human response.
The natural reaction has been to layer in more tools. More dashboards. More alerts. More automation scripts. But that approach is hitting diminishing returns. Each new tool adds another silo, another data stream, another place where context gets lost.
What is emerging instead is consolidation, not just of tooling, but of decision-making.
This is where the idea of Autonomous Endpoint Management starts to take hold. It is not just about unifying visibility. It is about linking that visibility directly to action. Systems detect issues, determine root causes, and execute remediation without waiting for a human to intervene.
Ayres frames it simply: “We’re bringing together those insights with AI-driven automation to deliver proactive, self-healing IT operations.”
That is not incremental progress. That is a shift in how IT operates.
The catalyst behind this shift is a new layer of intelligence often described as agentic AI. Unlike traditional automation, which relies on predefined rules, these systems can interpret signals across environments and make decisions in context. That means anomalies do not just trigger alerts. They trigger outcomes.
And those outcomes are already happening at scale.
ControlUp reports more than 14 million automated remediations executed every week. That number matters less for its size than for what it represents. Autonomy is no longer theoretical. It is operational.
There is a deeper implication here. Once systems begin resolving issues on their own, the role of IT starts to change. Less time is spent chasing incidents. More time is spent shaping the environment. The focus shifts from reaction to optimization.
This is not just about efficiency. It is about redefining what IT is responsible for.
As Ayres has emphasized in broader discussions around AI, this moment is fundamentally different from previous technology waves. “We’ve been through the cycles before, but this one is different. Bigger. Faster.”
That difference is showing up first at the enterprise level. These are the organizations with the most endpoints, the most complexity, and the least tolerance for inefficiency. When more than half of revenue in this category is coming from enterprise-scale deployments, it is a strong indicator that autonomy is not a niche capability. It is becoming a requirement.
Hybrid work has only accelerated the need. Distributed users, inconsistent networks, and a growing mix of managed and unmanaged devices have made traditional support models increasingly fragile. The idea that IT can manually keep pace with that level of variability is no longer realistic.
Autonomous systems offer a way out, not by removing IT from the equation, but by elevating it.
In an autonomous model, IT does not disappear. It moves up the stack. Instead of resolving the same recurring issues, teams focus on policy, performance, and experience. They define how systems should behave, and the systems handle the rest.
It is a cleaner division of labor and one that aligns with how every other part of the enterprise is evolving.
The long-term trajectory is clear. IT environments will become continuously monitored, continuously analyzed, and continuously optimized. Problems will be resolved before users notice them. Performance will be maintained without constant intervention. The digital workspace, in many ways, will run itself.
Ayres captures the ambition behind that shift in simple terms: the goal is to create a world “where technology works for people, not against them.”
That may sound ambitious, but the building blocks are already in place.
If the last decade was about gaining visibility, this one will be about relinquishing control, strategically, selectively, and with better outcomes as a result.
Autonomy is not a distant goal. It is already reshaping how IT gets done.
