Customer support and ticket management are among them.
High volumes, repetitive requests, unpredictable peaks, and response expectations increasingly close to 24/7. In many companies, this results in a chronic bottleneck: backlogs build up, turnaround times lengthen, and pressure on people becomes the norm.
The case described by Var Group in the study Accelerating innovation with AI in Switzerland, produced for Google and digitalswitzerland, starts from a scenario that is all too familiar for many companies: a service organization overwhelmed by repetitive first-level tickets that clog daily operations and—without truly continuous coverage—cause the backlog to rebuild every night, with immediate impact on response times and customer satisfaction. Meanwhile, internally, the pressure doesn’t remain a peak but becomes the norm, eventually turning into chronic stress and burnout.
The answer is automation integrated into the existing systems.
The key takeaway from the Var Group case is the approach: AI isn’t added as an optional layer, but embedded at the heart of day-to-day work—where decisions are made about what’s urgent, what can be closed immediately, and what requires skilled intervention.
The solution is an Intelligent Ticketing Automation system, integrated with ERP and CRM, that reads and classifies every request to identify intent and sentiment. The most recurring tickets are handled autonomously through database checks, while complex ones are summarized and routed to the most suitable operator, with the essential information already at hand.
In essence, AI takes over the parts that often consume time without creating value—from initial triage and data retrieval to applying standard rules and preparing the context. This way, people step in when it truly matters, not simply when the system is overloaded.
The numbers that reshape operational efficiency
The report leaves little room for interpretation. Automation is able to handle around 60% of incoming volume autonomously, while the backlog is cleared within a few weeks. In parallel, support becomes truly continuous, with immediate 24/7 responses, and the cost per ticket drops by more than 40%.
These numbers matter because they describe a shift in the operating model, not a minor tweak. AI absorbs the most repetitive and standardizable work, frees operators from low-value requests, and reduces the friction that slows support teams down every day.
An approach like this tends to scale better than others because it acts on a high-frequency process with clear rules and measurable recurring costs, making the impact visible and manageable over time. In this scenario, the value isn’t replacing people, but removing noise—the work that doesn’t require specialist expertise yet consumes hours and attention. When that noise decreases, both service quality and quality of work improve.
A simple lesson: the most useful AI is the kind that becomes invisible.
The Var Group case highlights an idea that goes beyond ticketing. AI becomes truly transformative when it stops being a special project and becomes an invisible part of everyday work—not because you notice it, but because you feel the absence of what used to block everything: backlogs and waiting times, unnecessary escalations, and constant stress.
