Challenge
High ticket volume, repeat issues, and documentation sprawl meant too much context lived with technicians instead of in the workflow. Assessment, triage, and routing depended on manual effort. Context was routinely lost during handoff, leading to avoidable escalations and downstream rework. These challenges scaled with demand.
Solution
Starport deployed ConnectWise AI Agents™ inside its ConnectWise PSA™ workflows in two distinct layers. Agentic automation handles agreement checks, company and contact matching, urgency and priority, board and type routing, summary lines, dispatch, and phone transcripts before a technician opens the ticket. CW AI Assistant™ sits at the point of work, with a defined trigger: a 30-second check before handoff.
Results
Within the service desk pilot group, average resolution time fell 40% (20 hours to 12). Tier 1 escalations fell from 35% to 25%, average time per ticket fell 25%, and daily tickets resolved rose 20%. In a snapshot of 4K tickets, seven live agents also processed 12,000+ change actions and reclaimed an estimated 34 technician hours. CW AI Assistant usage grew more than 5x as adoption spread from one enthusiast to the full pilot team.
Starport Managed Service’s service desk was absorbing high ticket volume, manual triage, and context lost at handoff. The team deployed ConnectWise AI Agents™ in two layers. Agentic automation handles assessment and routing before first human touch. ConnectWise AI Assistant™ supports technicians at the point of work. After a structured adoption push, the pilot group cut average resolution time 40%, reduced Tier 1 escalations from 35% to 25%, and resolved 20% more tickets per day.
From technician memory to standardized workflow
Starport had the problem many growing service desks eventually hit: the knowledge that made the team good at its job was not always in the workflow. It was often in the technicians. Ticket volume was high, repeat issues were common, and documentation was spread across enough places that finding the right answer became its own task. Every handoff created a chance to lose context, and lost context later showed up as rework.
The manual work sat at the front of the process. Someone had to assess the ticket, match it to the right company and agreement, and set urgency. Someone also had to choose a board and type, write a usable summary, and get the ticket to the right person. None of that was technical work, but all of it took technician time. As demand grew, the obvious lever was to hire more people. There was also a quieter cost: technicians spending time on tedious, repeatable tasks.
ConnectWise AI Agents helped us move AI from experimentation into measurable operational impact. By embedding AI into triage, assessment, and technician workflows, we've improved consistency, reduced manual effort, and created more capacity across the service desk.
Agents handle pre-work before first human touch
Starport separated its AI story into layers. The first layer is agentic automation inside the ticket flow, before a technician is involved. Seven dedicated agents handle agreement checks, contact and company matching, impact and urgency scoring, board and type/subtype routing, summary lines, dispatch, merging, and phone transcript handling.
The objective was standardized triage with minimal manual post-handling to help technicians spend more time on technical work and less on administrative classification. A snapshot covering roughly 4,000 tickets selected for AI assessment, those agents processed 12,000+ change actions. Starport estimates that work reclaimed about 34 hours of technician time, based on an average of 30 seconds saved per ticket. The value concentrated where the volume was highest: repeat triage activities.
The second-order benefit were structural. Classification, routing, and prioritization became consistent rather than resource-dependent. That gave Starport a cleaner base of service data and a more scalable workflow as volume grows.
The value of ConnectWise AI Agents is that they take repetitive work out of the service workflow so technicians can spend more time solving client issues. That helps us deliver faster, more consistent service without adding more manual effort to the team.
The harder problem was adoption
The second layer, CW AI Assistant at the point of work, did not land the same way. In the early reporting period, only handful of users were active. They generated 2,000+ queries between them, and a majority of activity came from a single user.
There was no natural trigger point for using CW AI Assistant, so nothing prompted it. The payoff was not visible enough to justify changing behavior. Because enthusiasm sat with one person, there was no peer pressure and no shared vocabulary around it.
A champion, a trigger, and a 30-second habit
Starport identified a pilot group, named an AI champion inside it, and built a formal adoption. Most importantly, the team tied CW AI Assistant to one clear workflow trigger: check before handoff. A 30-second minimum check was light enough to repeat on every ticket. It gave technicians a concrete moment where the capability supported.
Around that trigger, the internal champion ran demonstrations and peer coaching, making the value visible rather than asserted. Leadership replaced general encouragement with practical examples and positive reinforcement. The team also made a point of celebrating wins publicly to help transform culture.
Three months later, the pilot group's CW AI Assistant usage had grown from 2,000 to 10,000 recorded queries, a 5x increase. Active participation doubled, covering 100% of the pilot group. Additionally, the top user's share of activity fell to a third and usage had spread across the team.
The operational proof
The behavior change showed up in the service desk KPIs. Within the pilot group, average time per ticket dropped 25%, from 0.8 hours to 0.6. Average resolution time dropped 40%, from 20 hours to 12. Daily tickets resolved per technician rose 20%, from five to six.
The most economically significant movement was in escalation. The Tier 1 escalation rate fell from 35% to 25%, roughly a 28% reduction. Technicians had the right context and prior resolutions in front of them at the moment of work. More work could finish at Tier 1 instead of moving up a tier, creating direct leverage on the cost of service delivery.
AI adoption is not the win on its own. The real value is improving how operations run with it. ConnectWise AI Agents are helping us make AI practical inside the daily service desk workflow.
What Starport is still tuning
Starport is candid that the work is not finished. Adoption has to stay broad rather than collapsing back onto one or two heavy users. Trust, accuracy, and feedback loops need active management. Thin ticket notes can also limit what the AI can do. Some automation remains in shadow mode and has not yet moved into full live impact. The insights and reporting layer is still, in the team's words, an untapped goldmine.
There was also an internal selling job. Executives wanted ROI, leverage, and sustainability. Service leaders wanted quality, role evolution, and process fit. Engineers wanted to know whether AI would actually help them or slow them down. Starport's view is that real adoption happens only when each group hears the value case in its own language.
Toward Predictive IT
The cleaner data coming out of standardized triage is what makes the next step possible. Better enrichment produces better records. Better records make CW AI Agent answers sharper. Sharper answers help lower escalation, and better-documented resolutions feed back into the data. Starport's attention is now on the insights layer, where that compounding data becomes a way to see problems before clients report them.
ConnectWise AI Agents are helping us build toward a more proactive service model. By using AI to improve triage, surface insights, and identify service trends, we are creating a stronger foundation for Predictive IT.
Key Takeaways
- Start with a clear workflow moment: AI adoption becomes easier when teams know exactly when and how to use it in their day-to-day process.
- Separate automation from assistance: Automating pre-work and supporting technician decision-making are different use cases, with different value stories and adoption paths.
- Adoption needs structure: Champions, peer coaching, practical examples, and visible wins help move AI from available tool to daily habit.
- Measure whether usage is spreading: Query volume matters, but broad participation is a stronger signal that AI is becoming part of the team’s workflow.
- Connect adoption to operational outcomes: The strongest AI stories tie behavior change to service desk metrics, such as resolution time, escalation rate, and tickets resolved.
- Cleaner triage creates better data: Standardized assessment, routing, and enrichment improve the records that future reporting, insights, and Predictive IT depend on.
- Tailor the value case by audience: Executives, service leaders, and engineers each need to understand AI’s value in terms that match their priorities.
