DEX Research & Data

Research commissioned by Nexthink and conducted by Vanson Bourne found that nearly half of all employee technology problems never surface as help desk tickets. Employees absorb friction silently: they work around broken tools, accept degraded performance, and stop expecting technology to work reliably. The result is a systematic undercount of the actual experience burden employees carry.

This finding has direct implications for how IT organizations interpret their ticket metrics. Low ticket volume is not the same as a healthy experience. It may indicate an environment where employees have learned not to report problems: a significantly worse outcome.

Daily technology friction is not an edge case: it is a widespread, chronic condition across enterprise workforces. Most of it never generates a ticket. It accumulates as background noise: the slow application, the unreliable VPN, the authentication failure that resolves after three attempts. Each incident is small enough to absorb. Collectively, they represent significant lost productivity and growing employee frustration.

Organizations that have deployed proactive endpoint telemetry and experience measurement experience significantly fewer IT incidents per employee than organizations operating reactively. The relationship is causal, not merely correlational: proactive detection allows issues to be resolved before they cascade into broader incidents. Organizations that invest in the data infrastructure to see their experience can act before small problems become large ones.

Across enterprise workforces, employees lose an average of 28 minutes per week, nearly 24 hours per year,to technology issues that interfere with their work. At 5,000 employees, that is over 140,000 hours of productivity lost annually to problems that, in many cases, could be detected and resolved proactively. At 50,000 employees, the number approaches 1.4 million hours per year.

These are conservative estimates that capture only the time employees spend dealing with technology problems directly. They do not capture the cognitive overhead of switching between workarounds, the frustration that affects sustained concentration, or the compounding effect on deadline-sensitive work.

When employee time lost to technology friction is translated into salary cost, the business impact becomes visible in financial terms that CFOs and business leaders can act on. At a median knowledge worker salary, 28 minutes per week of lost productivity translates to approximately $10,900 per employee per year in foregone output. For a 10,000-person enterprise, this represents over $100 million in annual productivity at risk: from issues that a mature DEX program is designed to detect and eliminate.

This calculation excludes the cost of help desk time, remediation labor, device replacement, and employee attrition related to technology frustration.

Nearly half of the enterprise workforce believes their technology is actively working against them, not for them. This sentiment is not a perception problem, it is a signal. It indicates that technology deployments are not meeting the needs of the people they were designed to serve. When employees feel less productive because of technology, they disengage from IT-led initiatives, reduce their willingness to report problems, and become resistant to new tool adoption,including AI.

More than half of IT leaders acknowledge that they do not have the data needed to understand what employees are experiencing with workplace technology. This is not a technology gap: most of these organizations have endpoint management tools in place. It is a data model gap: the tools they have are optimized for infrastructure monitoring, not experience measurement. The data exists at the device level; what is missing is the analytical model to translate it into experience insights.

AI tools require compute resources, memory, and network bandwidth that many enterprise endpoints were not configured to deliver. Without endpoint visibility, IT organizations cannot identify which devices are ready for AI workloads, which employees will have a degraded experience from day one, and which deployments are at risk of underperformance. DEX measurement is not a prerequisite for buying an AI tool: it is a prerequisite for deploying one effectively.

Organizations with mature endpoint visibility and structured employee sentiment programs achieve significantly higher AI tool adoption rates than those without. The mechanism is straightforward: mature DEX organizations can identify adoption barriers early (device performance issues, network constraints, application conflicts), resolve them proactively, and communicate the availability and benefits of AI tools to employees in a targeted, credible way. Employees in high-DEX-maturity environments have higher trust in IT and higher receptiveness to new tool rollouts.