Technology companies are being pushed to deliver faster outcomes while justifying growing investment in AI, SaaS, and digital infrastructure. But productivity does not improve just because new tools are deployed. It improves when employees can use those tools without the constant drag of slow devices, unstable applications, and fixes that do not fully solve the problem.
That is the productivity tax of digital friction. When repeated failures build across the environment, critical time gets lost in fragments, even when the official view of service health looks stable. Without a clear view of digital employee experience, IT can miss the gap between what systems report and what employees experience day to day.
As investment continues to rise, the pressure grows for teams to show that productivity is rising with it. Gartner's forecast that worldwide AI spending will total $2.52T in 2026 is one signal of how quickly budgets are expanding and how quickly scrutiny follows. When output doesn’t rise with spend, the conversation shifts from ambition to accountability.
Underreporting and workarounds are part of how teams operate
After any IT disruption, the instinct in a high-velocity environment is to get unblocked fast. People find the quickest path back to work, even when that means stepping outside the formal support process. The cost is that IT sees the interruption only after it has already been absorbed by the employee. A slow application or recurring access issue may look like an isolated complaint, when it is really part of a wider pattern affecting more people than the ticket data suggests. Without that connection, the organization keeps solving the visible request while the root issue continues to drain time in the background.
On average, only 43% of IT problems are reported by employees, and only 30% of employees in IT, technology, and telecom say they’re completely satisfied with their digital experience. Once this way of work becomes routine, people start planning around the instability. Tasks get pushed into the “good hours” when systems are known to be more reliable, while upgrades and changes start to carry more risk because nobody wants to deal with the disruption that can follow.
Our analysis across millions of endpoints shows employees experience an average of 14 negative digital experiences per week including crashes, slow load times, and glitches that interrupt work without triggering an outage. That’s enough friction to slow delivery even without understanding the root cause until it’s too late.
Recurring failures create a permanent capacity drain
IT gets stuck because the same failures return faster than the organization can eliminate them. In fact, teams spend 31% of their time fixing recurring issues. When a third of the week is spent on repeat fixes, the work that prevents the next cycle keeps getting pushed out: standardization, hardening, cleanup, and change quality. This limits any organization’s ability to invest in higher-value work that drives innovation and efficiency.
That’s where reliability starts to slip in a way that’s hard to see from the outside. It’s not a single collapse. It’s drift. The bar for “good enough” lowers because there’s no time to do the deeper work, and the organization ends up paying for the same problems twice, employee time lost in the moment, and IT time spent re-solving what should have been closed permanently.
Uptime can look fine while workflows are still slow
Status pages and infrastructure health are useful, but they do not show how work is moving for the people using the environment. Work can become slower and more fragmented long before anything is officially considered down, and teams start adjusting around that friction in small ways that never appear as an outage or a clean support trend.
Digital employee experience (DEX) gives IT a view of technology from the employee’s side of the environment. By connecting signals across devices, applications, networks, and user sessions, it helps explain why work feels slower or less reliable even when traditional monitoring does not show a clear problem.
Application issues need that level of context because they do not affect every employee in the same way. They can vary by location, user group, device state, or working conditions. Application Insights helps teams understand how critical apps perform across those differences, so recurring problems can be traced back to the underlying cause instead of being treated as isolated complaints.
The same visibility gap becomes more expensive during technology rollouts and updates. Leaders report only 55% visibility into rollout success, which means issues can spread across the environment before IT sees where the change is breaking down. When problems appear, support demand grows and teams end up spending the next few weeks stabilizing the environment instead of moving on to higher-value work. That makes it difficult to prove whether new technology investments are improving output or simply adding more operational overhead.
Prevention starts when fixes scale beyond the service desk
Once patterns are visible, the constraint becomes speed and reach. When a known failure mode starts affecting large groups, resolving it one ticket at a time guarantees a long tail. Automation & Orchestration makes prevention possible by applying remediation across affected groups as known patterns appear. Fixes no longer depend on a human touching every device or answering the same request repeatedly.
One large-scale example is Qualcomm, where a VPN problem affecting 90% of employees was addressed with an automated workflow that achieved a 98% success rate, reducing disruption without pushing the same work through tickets. When repeatable problems can be treated as patterns, they stop consuming capacity indefinitely.
The same approach matters just as much for the smaller problems that slowly drain output, because they’re easy to ignore until they spread. Reboot loops are a classic case, and they’re exactly the type of problem automation can remove quickly and at scale before it turns into a wider productivity hit.
Support works best when it stays in the flow of work
Queues and handoffs are expensive when an issue blocks delivery, and support delays often come from the effort required to gather enough context before anyone can act. Frustration starts when the person impacted feels like they’re doing support work, while IT is trying to reconstruct what happened after the fact. This is especially important in technology companies where small support delays can quickly affect release cycles, customer response times, and the team’s ability to keep delivery moving.
Spark fits into this model as an Autonomous IT Agent. It helps resolve common IT problems in the flow of work, using live context to guide employees and execute approved fixes. This means employees spend less time proving what went wrong, while IT spends less time rebuilding the story after the fact.
Across the broader operating model, Nexthink Workspace gives IT teams a central place to decide, design, and automate actions from DEX data, so support can move closer to the employee experience rather than sitting behind another disconnected channel. The value is not just in answering requests faster, but in reducing the amount of support demand that reaches the service desk in the first place.
The bottom line
When delivery teams keep compensating for the environment, the company pays for the same problems twice. First in lost focus and delayed work, then again in IT time spent re-fixing issues that should already have been removed. Breaking that pattern does not require a bigger support queue. It requires visibility into where time is being lost, enough control to eliminate recurring failure modes, and a more consistent way to manage change across the environment.