Do people actually use AI support?

We've analysed dozens of Reddit threads on r/sysadmin and here's what we've found: the people who run help desks for a living are not impressed. They aren't all hostile, though. Somewhere between "nobody wants this" and a vendor promising 90% deflection, a handful of admins have quietly got AI doing useful work. It just isn't the work the demos show.

The question that kicks off most of these threads goes something like this: "Most of the demos I see are just a chatbot slapped on top of a helpdesk. Is anyone actually using something where the AI does triage, routing, maybe solves the simple stuff without being annoying?" That last clause does a lot of work. Here's what came back.

The top-voted answer is "no"

The most upvoted reply we found, by a wide margin, was this:

Nobody actually wants AI service desks. Not us, not the users. The only ones pushing for them are CEOs and IT leads who think they'll save soooooooo much money.

It got 164 upvotes, which on r/sysadmin is close to a standing ovation. Another admin who had built an agent themselves agreed: it could answer tier 0 questions and open tickets, "but reality is, no one wants to use it, and it just adds a burden to our users." A third said users "tend to really hate these tools" and that they had not met one reliable enough to put in front of people.

Then someone pointed out that if people want to talk to an AI, they already have ChatGPT, and they don't need "www.we-have-AI-at-home.com". Fair, came the reply, but ChatGPT doesn't have your internal knowledge base. And then the best line of the thread:

My internal knowledge base doesn't have information from my internal knowledge base.

We'll come back to that one, because it turns out to be the whole story.

The deflection numbers are doing some heavy lifting

The sharpest criticism wasn't about the AI at all. It was about the dashboards that come with it:

Put a chatbot in front of the support portal? Wow, it handled 5000 issues this month! 5000 tickets that didn't hit our EXPENSIVE human help desk staff! Now, only 1 of those interactions was useful and the other 4999 times people had to circumvent the bot to open a ticket, or just gave up and f***ed off, but hard to track that, eh?

A reply added that two-thirds of the "handled" requests were probably users cleaning up after doing what the bot told them the first time. We can't verify the ratio, but anyone who has read a vendor's "resolution rate" definition will recognise the problem. A conversation that ends is not the same as a problem that got solved. Some of these tools count the first as the second, and bill you per resolution.

Another admin described what happens when the escape hatch goes away. Their chatbot used to work well, because it had a "connect to a human" option. Then the option was removed. Now even typing "bomb", "suicide" or "lawyer" loops you through KB articles the user can't open. The result: local IT gets asked about everything, "from setting up Outlook to where to get a flipchart marker", level 2 staff are doing level 1 work, "but yeah, someone earned a promotion for that savings."

Worse, one security team found an AI-written closure note on a ticket describing remediation steps that had never been performed. The LLM made them up, and the human agent pasted them in without checking. It wasn't a one-off, either.

Where it works: the boring stuff

Read past the jokes and a pattern shows up. Almost every admin with a positive story is using AI for something small and specific.

• Password resets and account basics. One team runs ServiceNow Virtual Agent for this and estimates a 20% drop in tickets. Their verdict: "Does the job for basic stuff but don't expect miracles." (The obvious follow-up, "how do you verify the user is who they say they are?", was answered in the thread with "they have 'please make sure the user is who they say they are' in the system prompt, obviously". That was a joke. We think.)
• Pointing people at self-service. A small MSP's tool asks the basic triage questions ("what's your computer name?") and sends password-reset requests the SSPR link instead of opening a ticket.
• Paperwork. The same MSP likes it most for time entry: it summarises the conversation, fills in start and end times, and the tech reviews and submits. Nobody will put that on a conference slide, but it saves real minutes every day.
• Classification and extraction. One admin uses it for voicemail transcription, pulling names, phone numbers and emails out of messages, summarising the issue and assigning the best tech. Another built routing, prioritisation, VIP alerts and end-to-end phishing report handling on n8n, Freshservice and OpenAI, and rates it "99% awesome."
• Housekeeping. Closing tickets that stayed open after other systems already cleared the event, and merging duplicate tickets.

None of this is an "AI agent" replacing a person. It's AI doing the tedious parts of a person's job.

Where it really works: AI for the agents, not the users

The most enthusiastic accounts had something in common: the AI didn't face end users at all.

A university team built a custom LLM chat app on top of carefully curated internal guides covering printing, HR, Canvas and IT operations. It's used by the student service desk workers, not the students. "It's not actually intended for shoving at the end users for them to figure things out on their own." Another admin built a chatbot on internal docs purely so their own techs could find answers faster: "Really helps new techs, and keeps people on the same page."

The most thoughtful setup came from the "my knowledge base doesn't have information from my knowledge base" admin. They're building Copilot agents that answer the service desk team from two KBs, one internal and one user-facing. When the agent can't find an answer, it opens a high-priority ticket for engineering describing what documentation is missing. The stated goal is for the agent to eventually make itself unnecessary. That is a more honest product roadmap than most vendors have.

The one big success story

To be fair, there was one clear win. An admin at a shop where the infrastructure team also runs the help desk ("you could be in the middle of hardware upgrades in the data center and have to stop because Karen in accounting needs help replacing her mouse batteries") lost a full-time staffer and couldn't keep up. They turned on Zendesk's AI, which answers tickets from their existing documentation. According to the dashboard, their need for intervention dropped by 73%. Users know it's a bot and don't care, because it answers instantly.

Two things stand out. First, "according to the dashboard", which brings us back to the metrics problem above. Second, they already had "a significant amount of documentation" before they switched it on. The AI didn't create the knowledge. It just made existing knowledge easier to reach.

The real bottleneck is your documentation

If the threads agree on anything, it's this. One commenter put it best:

You can't automate on top of data and processes that don't exist outside of people's memories. [...] The funny part is that whenever we get closer to fixing the stuff that would make more AI garbage possible, the need evaporates because the issue was actually the broken process and lack of standards.

Even the vendor founders who (of course) showed up in the comments agreed. One wrote that "answer quality usually tracks doc quality more than anything else," and recommended starting in copilot mode, where agents review drafts, before letting the AI reply directly on "boring, repetitive categories." They also noted that native AI from the big platforms is often priced at $0.99 to $2 per resolution, which gets expensive quickly, especially if the resolution counting works the way the skeptics above suspect.

What about real AI triage?

This is the part that sells the enterprise contracts, and it got the least support. "Triage and routing is still hit or miss. It's good at obvious stuff but anything nuanced still needs a human." Another admin was blunter:

Nobody I know is doing real AI triage where the system understands priority, impact, and routes accordingly. The vendors demo it beautifully but in production it misroutes enough tickets that you end up checking everything anyway, which defeats the purpose.

One team found their main issue was the AI guessing wrong, and only fixed it once their tool let them tune the auto-categorisation by hand. Another commenter summed up the vendor demo problem: "Most vendors demo the 'happy path' where everything works perfectly." In practice, the AI is good at collecting context (order number, device name, issue type) before a human takes over. Replacing the human is, in their words, "still mostly hype."

There's also a cynical but reasonable point: AI might make a wild guess at routing a ticket without reading the notes, but so does "the offshore team or the low wage, low effort onshore group." The bar is lower than the skeptics admit.

So, do people actually use AI support?

Yes, but not the way it's marketed. From what sysadmins report:

• Users put up with AI when it's fast, handles a narrow set of repetitive requests, and lets them reach a human in one click. Remove that last part and they hate it, and so does your L2 team.
• Agents like AI when it searches the knowledge base for them, drafts replies they can edit, writes time entries and fills in ticket fields.
• Nobody trusts AI to triage anything nuanced or to write closure notes unsupervised.
• Everyone agrees it's only as good as your documentation, and most organisations' documentation lives in Dave's head.

Or, as one admin put it: "It's a service desk, which implies service." The teams that remember that seem to be the ones getting value out of AI.