Challenge
Every phone call, email, chat, and ticket submission was routed to a single queue for Allevia’s four Tier 1 technicians and three leaders to triage by hand. With 5,000 to 7,000 tickets a month and 50 to 60 calls a day, the team regularly carried a backlog of 20-plus untriaged tickets, and some tickets sat for 24 to 48 hours, a long time by Allevia’s own standards.
Solution
CW AI Agents™ were deployed within ConnectWise PSA™ workflows on the help desk board, where most of its tickets arrive. The agents summarize and prioritize each ticket, set the type, subtype, and item, and dispatch the work to a technician based on who has solved the issue before and who is available.
Results
Agents now touch 100% of help desk board tickets, roughly 2,000 per month. The standing triage backlog has cleared. Tickets are dispatched within 24 hours instead of sitting for two days, and the phone answer rate has risen from 75-80% to 85-90%. Work is also distributed more evenly, and SLA discipline has spread to other divisions.
From manual triage to agentic dispatch
Allevia Technology calls its help desk “client support,” a deliberate choice for a managed service provider (MSP) that puts people first. The division runs 11-strong: four Tier 1 technicians handle inbound calls and new tickets, four Tier 2 technicians handle longer-term solution work, and three leaders oversee the operation. Behind them, roughly 35 technicians company-wide work service tickets.
Before CW AI Agents, all of that volume funneled through a single manual step. Every phone call, submission, chat, and email arrived in one queue. A Tier 1 technician or a leader read each item, assessed priority, and decided where it should go. Allevia has never had a dispatcher and has never assigned tickets round robin; leaders balanced a ringing phone against whichever ticket was closest to breaching an internal SLA, in real time, all day.
With that many tickets and only four people triaging, we would get backlogged pretty regularly. We often had 20 tickets waiting in our new queue that needed to be triaged and worked, on top of 50 to 60 incoming calls a day.
When someone was out sick or on vacation, or when an outage affected every client at once, the team had to pull technicians from other work to help. Tickets could sit for 24 to 48 hours. Allevia does not contract SLAs with its clients; the standards are internal, and under those standards, two days was unacceptable.
CW AI Agents in the flow of the work
Allevia pointed CW AI Agents at the help desk board, where most client support requests arrive. The agents now touch every ticket on that board, roughly 2,000 of the up to 7,000 tickets the company sees each month. They write the summary, set the priority, and fill in type, subtype, and item: the same judgment work a Tier 1 technician used to do by hand, performed in the moments after a ticket lands.
Dispatch was the bigger change. Instead of a leader deciding who takes the next ticket, the agents route it to a technician who has previously solved that issue, has a strong success rate with it, and has open availability on the calendar.
“Dispatch has really helped us eliminate that 20-plus-ticket backlog. Now [CW] AI Agents can see who has solved that issue before, who has a high success rate, and who is available soonest, then send the ticket to the right person right away.” - Hayley Martin, Client Support Division Leader, Allevia Technology
The waiting period between a ticket arriving and a human owning it has largely disappeared. Most tickets are now scheduled within 24 hours of creation, usually sooner: a significant reduction from the two-day worst case.
The clearest outcome: more clients reach a human
Allevia prioritizes phone calls because it prioritizes people. As Martin explained, a client who calls wants to reach a person, so a missed call carries more weight than a slow email response. The team’s answer rate was the constraint: when leaders and Tier 1 technicians were consumed by triage and dispatch, calls went to voicemail.
We were normally answering about 75% to 80% of our phone calls. Since implementing AI Agents, we’ve gotten that up to between 85% and 90%. Because we’re no longer balancing that ticket backlog while also answering phones, we’re able to pick up more calls. That’s been our biggest success.
Customers feel that directly. Fewer of them leave voicemails about waiting on hold because someone picks up. The time the agents give back does not disappear into more ticket work; it goes into conversations.
More consistent work distribution, guided by evidence
Manual assignments also required leaders to manage a natural service-desk pattern: people tend to gravitate toward the tickets and clients they know best. Allevia had already pushed hard for a “take the next ticket, not the one you want” rule, but enforcing that standard still depended on someone watching the queue.
“One benefit of [CW] AI Agents handling dispatch is that work gets spread more evenly. People do not get to just pick the easier tickets or the clients they already know best.” - Hayley Martin, Client Support Division Leader, Allevia Technology
Putting team knowledge within reach for newer technicians
Two of Allevia’s Tier 1 technicians have been on the team for more than a year; two are still in their first 90 days. Every client environment is different, and the difference between a fast resolution and an escalation often comes down to knowing that this exact problem has been solved before. The CW AI Agents chat lets a new technician find that history without having to ask a senior colleague.
Martin’s favorite use is for concrete tasks: paste a screenshot of an error from a client machine into the chat, ask for past tickets with the same error, and get the last three back with their resolution notes attached.
“When I paste in a screenshot of an error, [CW} AI Agents can find the last three tickets with that same issue. I can use those resolution notes instead of starting from scratch.” - Hayley Martin, Client Support Division Leader, Allevia Technology
For newer technicians, that shortcut can stand in for the experience they have not yet had time to accumulate. Allevia has seen it help them become productive faster: an observation from the floor rather than a measured figure, and one the team plans to quantify as its data matures.
Surfacing context that a busy technician might miss
The agents also pull relevant context forward the moment a ticket opens: what else is open for this client and who the requester actually is. That second point matters more than it sounds.
When you add a contact to a ticket, you do not always notice their title. Now, [CW] AI Agents can surface that context, like whether the requester is the CFO. It gives us a better sense of the person and the history behind the ticket, which humanizes the interaction.
A second-order effect: SLA discipline across the company
For a year or two, client support had been Allevia’s strictest division in setting priorities and response times. Other teams, including cybersecurity, accounting, and managed services, did not need to follow the same intake pattern as consistently because tickets rarely reached them directly. Agentic dispatch changed that: work now routes to the team that is the right owner, arriving with a priority already set.
“Now, when another team sees a ticket dispatched to them, and [CW] AI Agents have prioritized it as high, they know they need to respond within a certain time frame. It has helped enforce a behavior we had been softly encouraging for a while.” - Hayley Martin, Client Support Division Leader, Allevia Technology
The result is a shared operational language that defines what an SLA is, why triage matters, and how to prioritize a heavier ticket load. Consistency that began in one division is now spreading across the company.
Still tuning, and honest about it
Martin is candid that the journey has included many learnings. Chat adoption has been strongest among Tier 1 technicians. Answer quality has depended on how well the team’s documentation is structured for agents to read. Allevia is actively refining its documentation strategy to close that gap.
Dispatching across the company also revealed a scheduling trade-off: work that once stayed on the help desk and was picked up quickly is now sometimes scheduled differently by different teams. Martin is clear-eyed about where that belongs.
“That’s an operational problem, not a [CW] AI Agents problem. The dispatching, prioritization, and core agent workflows are working the way they should. Now we’re adapting our own systems around them.” - Hayley Martin, Client Support Division Leader, Allevia Technology
That is the shape of a real agentic rollout: the automated work lands, and the organization catches up. For Allevia, the next chapter is measurement: building the resolution-time and escalation baselines that will turn today’s clear operational wins into hard numbers and extending the agents beyond the help desk board.
Key takeaways
- Anchor agentic automation in a high-volume workflow: Allevia applied ConnectWise AI Agents to the help desk board first, where the volume was high enough for triage and dispatch automation to change daily operations.
- Keep automation tied to human service: By reducing manual triage and dispatch pressure, Allevia created more room for its team to answer customer calls live.
- Let routing use evidence, not memory: Dispatch based on prior resolution success, issue fit, and availability helped distribute work more consistently across technicians.
- Treat adoption and documentation as rollout work: The clearest assistive-chat gains came for newer Tier 1 technicians using past-ticket context; broader adoption still depends on trust and better documentation structure.
- Separate operational wins from proof still in progress: Allevia has strong directional results, but resolution time, escalation rate, and time-to-productivity still need follow-up measurement before they become hard proof.
