In most large enterprises, employees are constantly navigating stalled systems, broken workflows, and hidden inefficiencies. Atera’s recent research shows 88% of employees lose time every day to these frustrations, often far more than leadership realizes.
This gap quietly erodes productivity and limits the impact of AI and automation. The problem isn’t the AI itself. It’s that it is being layered onto workflows that are already fragmented and only partially visible. Until organizations surface and address this hidden friction, AI will underdeliver.
At its core, the principle is simple: remove the friction, and people can work. But to remove friction, leaders must tackle the visibility problem. They often lack a complete view of where work slows, why it stalls, and how often employees quietly absorb the cost rather than report it. Closing that gap requires a different operating lens: leaders must observe where work slows, listen to employees about recurring bottlenecks, and analyze patterns beyond traditional metrics. Only then can friction be addressed at its source before AI is introduced on top of it.
The Friction Tax
Employees across the enterprise are losing meaningful time to issues that have nothing to do with the complexity of their roles.
The scale of this problem is far larger than most organizations realize. More than a third of employees lose over 20 minutes a day to stalled systems, and many estimate these disruptions cost their organization at least $100 per week. That translates into millions in lost productivity. And beyond the time lost, 61% of employees say they have delayed or even avoided important work entirely because the process to complete it was too broken.
This isn’t limited to frontline employees. Nearly half (47%) of leaders report losing three or more hours each week to similar inefficiencies. What starts as small interruptions compounds into a steady drag on execution, slowing teams down and limiting overall business momentum.
The human impact is clear: people are avoiding tasks, delaying decisions, and cutting corners just to keep work moving. At the same time, meta-work, like chasing approvals, reformatting documents, or switching tools, consumes a growing portion of the day.
Friction is widespread, costly, and affecting every level of the organization, yet much of it goes unnoticed as over one in three (37%) of employees say they sometimes choose to not even open tickets. Leaders should identify and quantify where work gets stuck before adding technology, so they can fix the friction that really slows people down. To do this, they need to address the gap between what employees experience and what leadership actually sees.
And this isn’t just occasional. It’s built into how work gets done. The problem is that most leadership systems are designed to track outcomes, not the breakdowns that slow people down along the way.
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The Visibility Gap
Many leaders rely on tickets, dashboards, and performance metrics to understand how work flows. But these systems only capture what is reported, not what is experienced.
On the ground, the reality looks different. Often, the process is too slow or too complex, so they work around it instead. Over time, these workarounds accumulate into a hidden layer of operational drag that never enters official reporting.
When issues aren’t reported, they aren’t measured. And if they aren’t measured, they aren’t prioritized. This creates a structural blind spot in how organizations allocate attention and resources. It also explains why 72% of employees feel leadership overestimates their capacity. The daily inefficiencies shaping their work are simply not reflected in leadership data.
For CIOs, this creates a deeper challenge. AI and automation initiatives are often built on the same incomplete signals. If the underlying data excludes unreported friction, then systems designed to optimize work will inevitably optimize only part of it. This is one reason many AI initiatives struggle to deliver expected ROI. They are layered on top of workflows that haven’t been fully understood.
The starting point, therefore, is not more technology. It is expanding visibility into how work actually happens.
Spotting the friction
Expanding the CIO’s line of sight requires moving beyond system outputs and into lived workflow reality. Until leaders can see the full picture, it’s difficult to fix what’s actually holding teams back, and optimization efforts remain partial.
The first step is direct observation. Walk through workflows with teams and identify where execution slows in real time. Ask employees where they repeatedly get stuck, where approvals stall, or where they rely on informal workarounds to keep moving. These moments often reveal more than any dashboard ever could.
Next, broaden the data lens. Tickets and dashboards only reflect surfaced issues. To understand true friction, leaders must also look for what never gets reported like repeated manual steps, skipped processes, or informal “shadow” workflows that have become normalized. Even short observation windows can expose the scale of friction when viewed through this lens.
Closing the friction gap
Once friction is visible, it must be quantified. Estimating time lost and operational cost reframes inefficiency from anecdotal frustration into measurable business impact. This creates the foundation for prioritization and ensures that improvement efforts target the highest-value constraints first.
From there, organizations can focus on the bottlenecks that matter most, which are the ones that consume the most time, create the most frustration, or most directly impact business outcomes. Workflows can then be redesigned, approvals simplified, and repetitive tasks reduced. Importantly, sharing these improvements with employees builds trust and reinforces that their lived experience is shaping change.
A natural extension of this work is moving toward more autonomous IT models. Not as a replacement for human oversight, but as a way to reduce recurring, predictable disruptions at the source. When routine issues are resolved automatically, teams regain time for higher-value work, and leaders gain a clearer signal of where deeper friction still exists.
Bridging the AI readiness gap
Even when workflows improve, organizations often discover an uneven readiness for AI adoption. Many leaders may be prepared to automate decisions, while employees may hesitate to trust AI with routine tasks if underlying processes are inconsistent or not fully transparent in practice.
The way forward is incremental. Start with low-stakes applications of AI that remove friction rather than introduce change for its own sake. Standardize reporting, streamline approvals, or handle repetitive data tasks. These early wins build confidence and demonstrate that AI is a support layer, not a disruption layer.
Open communication and transparency are also a must. Employees need to understand what is being streamlined, why it matters, and how it improves their work experience. When that clarity exists, adoption becomes less about enforcement and more about alignment.
Removing friction to let people work
Enterprises need AI that adapts to the end user, not the other way around. Yet employees spend hours navigating stalled systems, chasing approvals, or managing workarounds just to get basic work done. The real cost isn’t only time. It’s focus, creativity, and the ability to contribute at a higher level.
At scale, these disruptions compound into a persistent drag on performance. But they are not inevitable. Organizations that take the time to surface friction, quantify it, and address it systematically can fundamentally change how work flows.
When that happens, AI stops being an overlay on broken systems and becomes an amplifier of well-functioning ones. Technology then supports how people actually work, rather than compensating for what holds them back. When friction is gone, people are able to do their best work.
About Atera
Atera is a leading the future of IT with the world’s first patented Autonomous IT platform powered by a digital fleet of self-learning AI agents that transform how IT is done.
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