Charbel Khneisser, SVP, Solutions Engineering, Global at Riverbed Technology, argues that enterprise IT must move beyond faster incident resolution and focus instead on preventing disruption. He explains how AI-powered, self-healing systems can correlate data across devices, networks, applications and employee workflows to address problems before productivity is affected.
For forty years, the IT service desk has measured itself on how fast it can fix what just broke. Mean time to resolution (MTTR) became the chief metric every helpdesk was built to chase and every leadership team learned to read. But a faster fix is still resolution only after the fact, and across the Middle East, where enterprises are racing to build some of the world’s most digitally mature workplaces, “after the fact” is no longer acceptable. Regional AI adoption inside enterprises climbed from 62% in 2023 to 84% in 2025, and the appetite is not for smarter alerts, it is for systems that act before an employee ever notices something went wrong. When infrastructure can sense a failure forming and correct it automatically, MTTR stops being a badge of honor and starts looking like a record of every disruption a system failed to prevent. The metric IT has optimized for decades is quietly becoming obsolete.
IT organizations have spent years trying to modernize by optimizing ticket handling. When the industry expanded self-service, introduced chatbots and automated triage, it improved throughput but it shifted the operational burden from the helpdesk to the employee. The ticket may have disappeared from the dashboard, but the friction of identifying and addressing the root cause of a problem persisted.
The digital workforce is most effective when it has uninterrupted access to systems and solutions. That experience requires a proactive, preventative approach rather than relying on a service desk’s ability to quickly react. It’s why the next phase of enterprise IT must be the Era of Zero Disruption.
Zero Tickets is a Burden Shift, not a Solution
The industry has spent years treating lower ticket volume as a sign of progress and efficiency. However, zero tickets does not mean zero disruption. If employees have to stop working, search a portal for a solution or explain the same issue multiple times, the burden of remediating an IT challenge has been redistributed away from the service desk and onto the workforce. There is also a further layer of disruption, when the help desk must re-engage and interrupt employees again to diagnose the issue. The goal is to reduce friction, improve the digital employee experience (DEX) and enhance productivity, yet most support resources only emerge after that friction has reached and disrupted the end user, rather than identifying and resolving problems without needing to involve them at all.
“MTTR measured how effectively we recovered from disruption. The next era of IT will be defined by how rarely employees experience it in the first place.” — Charbel Khneisser, SVP, Solutions Engineering, Global at Riverbed Technology
The stakes are especially high in a region where employees have already made up their minds about AI’s value. Roughly 80% of Middle East workers say AI has improved their productivity, and 87% report it has improved the quality of their work, according to PwC’s 2025 Middle East Workforce Hopes and Fears survey. That confidence is evidence that employees are ready for AI to do more than watch, they expect it to act.
Self-healing systems transform this legacy approach by correlating signals across the full experience. Leveraging cross-domain context, including device health, network conditions, application behavior and user workflow data and back-end performance, IT can turn symptoms into understanding. Distinguishing isolated noise from emerging failure supports prioritized action by determining whether an issue is singular or shared, while autonomous AI catches problems early and fixes them automatically before user experience is impacted at scale.
Self-Healing is a Data Problem, not an AI problem
Most organizations believe the most challenging part of implementing AI is choosing the appropriate model. In practice, the hardest part is providing AI with a dependable operational foundation. If the underlying data is partial or the visibility is fragmented, the output and analysis will be as well. It’s why self-healing systems require more than automation. They need centralized, high-fidelity data that is complete, granular and stable enough to support real-time correlation across the entire digital estate.
The public sector offers an early glimpse of what this looks like at scale. The UAE’s Proactive Government Performance System, launched in 2025, processes more than 150 million data points a month to generate upwards of 50,000 proactive insights a year. This empowers government agencies to intervene before performance gaps become visible failures. Enterprise IT is now facing the same inflection point.
Self-healing systems are also underpinned by experiential context. Metrics alone will not always detail what an employee went through. For example, a system can appear healthy at a dashboard level while a workflow feels broken to the person trying to complete it. The most mature prevention models combine telemetry with workflow and experiential context so IT can not only see that something degraded, but also where it disrupted the employee’s ability to work. This enables AI models to detect patterns sooner, connect signals across domains, identify likely root causes and initiate the right remediation inside defined guardrails.
The Most Important Metric is Uninterrupted Work
Executives should understand the fundamental difference between AI that advises and AI that protects productivity. The cost of digital friction does not always show up as a major outage or a flood of tickets. More often, it appears as the small interruptions that drain time across the enterprise. Think of the login that fails just before a meeting, the workflow that stalls at a critical moment or the collaboration tool that forces a restart right before a customer call. These may seem minor on their own, but at scale, they become a meaningful tax on productivity, confidence and employee satisfaction. Since traditional support models only count what gets reported, not all the work employees absorb, retry or abandon is documented.
This is why the next maturity model for enterprise IT is uninterrupted work. The center of the operating model must move upstream from the helpdesk to systems that sense problems early, correlate across domains, pinpoint root cause and resolve issues before employees lose momentum. Nowhere is the opportunity to lead on this shift clearer than in the Middle East, where national digital agendas are already pushing government and industry toward prediction over reaction. The Era of Zero Disruption is a shift in responsibility away from the employee and back to the system. MTTR measured how effectively we recovered from disruption. The next era of IT will be defined by how rarely employees experience it in the first place.
