The most persuasive case for AI in IT support is not a futuristic demonstration. It is an ordinary incident resolved nearly five hours sooner. SolarWinds analysed information from more than 2,000 IT systems and 60,000 data points collected between August 2024 and July 2025 to measure whether AI assistance changes day-to-day performance.
The tools studied perform familiar support work: suggesting answers to tickets, finding relevant knowledge articles, and summarising issues. Average resolution time fell from 27.42 hours before AI adoption to 22.55 hours afterwards—a 17.8 percent improvement and 4.87 hours saved per incident.
That reduction can free people for complex problems and preventative work. It can also produce a substantial financial effect, although the strongest adopters show that software alone does not create the result.
Five Hours Per Incident Adds Up
SolarWinds illustrates the scale with a medium-sized IT team handling 5,000 incidents annually. Saving 4.87 hours on each case would return 24,350 working hours. At an assumed help-desk cost of $28 per hour, the value exceeds $680,000.
The comparison between organisations is even wider. AI users resolved tickets in an average of 22.55 hours, while non-users took 32.46 hours. That 30.5 percent gap represents nearly ten hours per issue, time during which an employee may be blocked and an operational problem remains active.
These calculations are not a guaranteed business case for every company. Incident complexity, staffing costs, existing automation, and ticket quality differ. They do provide a practical formula: establish the organisation’s current resolution time and volume, then measure whether the deployed system changes them.
The Best Adopters Changed Their Workflow
The ten strongest AI adopters reduced average resolution time from approximately 51 hours to 23—more than half. Their shared characteristic was not access to secret technology. They incorporated AI into daily support rather than isolating it in a side experiment.
Teams with mature self-service portals, knowledge bases, and automation were better prepared to benefit. AI can surface a useful article only if useful articles exist. It can recommend an action more reliably when processes and escalation rules are clear. Weak foundations limit what even a capable assistant can improve.
This explains why adoption requires broader operational change. Support staff need to know where AI enters the workflow, when to accept a suggestion, and how to correct poor output. The organisation must maintain the information feeding the system and learn from recurring incidents rather than treating every ticket as an isolated event.
Faster Support Creates Space for Proactive IT
The strategic benefit appears after repetitive work consumes less time. IT teams can investigate patterns, address underlying causes, improve employee tools, and prevent incidents before they generate tickets. The function shifts from reacting to requests toward improving how the business operates.
Leaders should begin with a baseline, not a purchase. Resolution time, annual incident volume, knowledge-base quality, and existing automation reveal both the potential saving and the work required. Multiplying annual incidents by the reported 4.87-hour average offers a starting estimate, but an internal pilot should establish the real result.
AI also remains a tool rather than a miracle. Suggestions need oversight, summaries need accuracy, and automation must not hide a serious issue behind a faster closure statistic. Performance measures should include quality and recurrence alongside speed.
SolarWinds’ study supplies something enterprise AI discussions often lack: evidence tied to an everyday operating metric. The findings suggest meaningful gains are available, but the “Top 10” adopters make the condition equally clear. AI becomes valuable when it is embedded in a prepared support organisation, used consistently, and turned into capacity for better work rather than merely another feature switched on.
