How AI adoption is moving IT operations from reactive to proactive
Ops leaders want faster fixes without adding headcount. We've squeezed what we can from automation and self-service. Now AI is on the desk-leaders just want proof it works, not promises.
A recent study from SolarWinds looked at data from 2,000+ IT systems and 60,000 data points across Aug 2024-Jul 2025. They measured practical AI features: auto-suggested ticket responses, smarter article recommendations, and instant ticket summaries. The results show real efficiency gains-not theory.
What the data says
Mean time to resolve dropped from 27.42 hours to 22.55 hours after AI adoption. That's a 17.8% improvement-about 4.87 hours saved per incident.
For a mid-sized help desk handling 5,000 incidents a year, that's 24,350 hours back. At $28 per hour, you're looking at more than $680,000 in value you can reinvest in higher-impact work.
The efficiency gap is widening
Teams using AI close tickets in 22.55 hours on average. Those without AI average 32.46 hours.
That's a 30.5% gap-nearly 10 hours per incident. Multiply that across a year and the opportunity cost gets hard to ignore.
Why the winners win
The best results didn't come from fancy tools. They came from making AI part of daily work.
The "Top 10 AI adopters" cut resolution times from ~51 hours to ~23 hours. Their edge: they embedded AI into workflows, paired it with strong processes, and doubled down on self-service and automation. Culture and execution did the heavy lifting.
A simple playbook for Operations leaders
- Baseline first: Know your current MTTR. If you're around 32.46 hours, you have room to move.
- Embed, don't experiment: Put AI into triage, assignment, and resolution-not just a pilot on the side.
- Tune the foundations: Clean up your knowledge base. Tighten routing and automation rules. AI amplifies what already works.
- Instrument outcomes: Track MTTR, first-contact resolution, ticket deflection, reopen rate, and backlog burn.
- Coach the team: Set clear usage guidelines for AI suggestions and summaries. Reward adoption tied to outcome metrics.
- Change how work flows: Shorter feedback loops, faster KB updates, and continuous rule tuning beat one-time "deployments."
Quick ROI math you can share
Annual hours saved = incidents per year × 4.87 hours.
Dollars saved (or reinvested) = annual hours saved × hourly fully loaded cost. Simple, transparent, and easy to defend in a budget meeting.
Practical starting points
- Ticket triage: Use AI to summarize, tag, and route with confidence thresholds you control.
- Agent assist: Auto-suggest responses and surface the best KB article in-line.
- Knowledge upgrades: Close the loop-convert solved tickets into KB articles, then let AI recommend them next time.
- Two-week pilot: Pick one high-volume queue, set baselines, deploy assistive AI, measure, and scale.
From reactive to proactive
Once you cut MTTR and reduce noise, your team can pivot to pattern-spotting and prevention. Think problem management, recurring-issue eradication, and cleaner change windows.
That's the shift: less firefighting, more stability, and a service desk that contributes to business outcomes-not just ticket closure.
If you want help upskilling your team
Explore focused training and tools that map AI skills to job roles here: Complete AI Training: Courses by Job.
The gap between adopters and laggards is growing. Set your baseline, embed AI into the work, and let the numbers guide your next move.
Your membership also unlocks:
