Is AI slowing down the human handoff?
Resolution times are at a six-year high, yet tickets are taking longer to reach a human. Here’s why the gaps happen.
Scan Reddit forums like r/sysadmin and r/ITManagers and you’ll find IT service desk administrators venting anonymously about AI fumbling ticket handoffs to human agents: high-priority tickets shunted to the back of long queues, tickets ping-ponging between the wrong teams, fixes delayed by days.
The data tells a more complicated story. In Freshworks' analysis of more than 174 million IT tickets from 2025, the median organization’s average resolution time improved to 21.63 hours, down from 21.96 the year before and its best level in six years. But the median organization’s average time to assign a ticket to an agent or group rose to 16.36 hours from 15.80, about 34 minutes.
What’s going on? AI is changing what gets handed off. As it clears routine tickets, the ones that reach people are harder, and whether they arrive with context or without it increasingly decides whether employees experience AI as help or as a hurdle. “Every handoff is a place where context is lost and work waits,” says Freshworks CIO Ashwin Ballal.
Employees feel that wait. According to HappySignals’ Global IT Experience Benchmark Report 2026, employees estimated they lost 2 hours 7 minutes of work time on an incident fixed at first contact. After one reassignment, the estimate rose to 3 hours 36 minutes; after two, 5 hours 47 minutes. Still, not every handoff is a mistake, HappySignals notes. One reassignment can get a ticket to the right team faster, and the goal is not zero handoffs but fewer that “add no value.”
Every handoff is a place where context is lost and work waits.
Ashwin Ballal
Chief Information Officer, Freshworks
Where the handoff doesn’t connect
Most organizations start by deploying AI agents to take on Tier One work: password resets, software access requests and other routine fixes, Ballal notes. As those tickets disappear, what’s left for people is harder.
The trouble starts with Tier Two and Three tickets, where issues get more complex and context more nuanced. AI triage can misread the priority or nature of an issue, treating an urgent incident as a routine one, says Stephen Mann, principal analyst and content director at ITSM.tool. “The AI is not at fault; it just hasn’t been trained properly,” says Mann. “Most of it comes down to process design.”
When a ticket does finally reach the right analyst, it can arrive cold, without a usable account of the issue or the solutions already tried. Too often, the problem isn’t the lack of an AI summary but an unruly one, says Isaac Sacolick, president of StarCIO and a former CIO. A recap of every interaction can run 20 pages and bury what really matters, including what fixes have already been tried. “The AI needs to be trained to be very explicit about the types of information a service desk person needs,” he says.
Dashboards can obscure this. Mann warns that a service desk whose ticket volume falls from 3,000 a month to 1,500 after deploying AI might look like a success story. “Is the AI resolving those 1,500 tickets or are people just not coming to you for support anymore and turning to Claude or ChatGPT to get what they need?” he asks. According to Buchanan Technologies’ 2026 IT Experience Gap Report, 72% of workers have used their own AI tool to solve a technical issue instead of going through IT.
Read also: How to manage the AI-to-human handoff
What good looks like
Mann and Sacolick both say IT teams should measure success not on resolution rates and ticket deflection alone but on the experience of the worker who needs help. “The fix is designing your technology-enabled processes around the people that benefit from them, not the IT desk that operates them,” says Mann.
At Katz Media Group, an iHeartMedia company, CTO Robert Lyons says the company now measures outcomes, not just IT activity. Each AI project gets a KPI up front, such as ticket deflection, paired with a counter-metric: employee satisfaction, which Katz aims to keep at 75% or higher. It also tracks ticket touches. “Ideally, it’s first touch, but depending upon the complexity of the request, sometimes it can go two to three,” Lyons says.
Summaries help, too. When a complex case is “bouncing team to team to team,” Lyons says, an AI summary that spares the final team from reading “hundreds of notes” is “game changing.” Sacolick says a good one covers who the person is, what their job is, what they were doing, and how critical it is, in under a minute of reading.
So is AI slowing down the human handoff? In some ways. But AI is changing what gets handed off, and a handoff that arrives without context, without a way to push back, and without anyone counting it will cost employees time whether or not AI is the cause. The question for IT leaders is less whether handoffs get slower than whether they’re designed to keep pace.
