Every conversation about using generative AI tools in customer service seems to start with the customer-facing side: increasing deflection rates through chatbots. Draft replies faster. Heck, the AI can do it all — you’ll never need to touch a ticket again!
While the customer-facing side of support can be a good place for AI (if done well), it also involves a lot more setup and a lot more trust. But customer support teams do a lot more than just answer customer questions.
There’s weekly reports, deep dives on why certain ticket types keep coming up, quarterly planning, deck making for meetings, and understanding what causes churn.
None of these things touch customers, but all of them take up time that’s better spent with those customers.
This article will go into detail on a few of the ways we see support leaders using AI to help speed up their internal processes, leaving everyone with more time to spend on customers.
The Basics of How AI Works and What It Does Well
Let’s first focus on what’s actually happening when you use an AI, because it matters for everything else I’ll talk about, and there seems to be a lot of misunderstanding about what an AI can and can’t do.
An AI model like ChatGPT, Claude, or Gemini is built by feeding it enormous amounts of data and having it learn the patterns of that data.
So LLMs (large language models) use human writing as data points, and search for the patterns in that writing: what words tend to follow other words, how an argument is typically structured, what a good answer to a question tends to look like.
When you prompt it, an LLM answers you by predicting, piece by piece, what should come next based on those learned patterns.
That’s it. There’s nothing in there forming an original thought, it’s just highly complex math finding the most statistically likely next word, over and over, at an enormous scale.
Which is exactly why those of us working with them every day can see AI writing from a mile away.
Good writing usually means something specific, like a fresh angle, distinct voice, a point nobody else has made in quite the same way. Good writing isn’t algorithmic.
Humans naturally vary between short and long sentences for the purposes of setting pace and building emotion. They use em dashes when appropriate (not just to replace commas), and they certainly don’t use the words “quietly” and “gap” every third sentence.
Ahem. But to get back on track…
Coding and data analysis are a bit of a different story, because they have a more clearly correct answer. Code either works or it doesn’t. And a number either matches its data or it doesn’t.
When the task has clear rules and a checkable outcome, predicting the statistically likely next step is a lot easier, because correct code and correct analysis follow patterns just as consistently as LLM’s use clichés in writing (that analogy is for all you writers out there who also get… ragey against the machine. Hush. I’m funny.)
So now that we’ve covered how AIs work, it helps create ideas of what you can do with them that have nothing to do with touching customers.
Build a stack: MCPs and Skills
Historically, if you wanted, say, Zendesk to talk to one of your other tools, you needed there to be an integration, or you needed to have a developer write custom code connecting the APIs for each tool so they could talk to one another.
Every pairing needed its own custom-built connection, a square peg shaped for exactly the same size square hole and nothing else.
MCP (Model Context Protocol) in AI changes that. Instead of building a one-off connection for every pairing, someone builds a single MCP server for a given tool, in effect squaring off that tool’s hole so anything using the standard MCP shape can plug straight in.
Yes, someone has to build that MCP server, but for tools used widely, like Zendesk, that work is often already done. Like Swifteq’s MCP server for Zendesk. More on that shortly.
Once that MCP server exists you can simply connect your Claude account to have a door into that system.
That’s step one: a door into your data. Step two is telling the AI what to do once it’s through that door, in a way you don’t have to repeat every single time.
Most people’s first instinct is to just ask for what they want, in the moment, every time. “Write me this week’s report.” Except next week you ask again, slightly differently, because you’re a human and humans do things slightly differently.
But precisely because of those differences, the report comes back structured a little differently too. Multiply that by every recurring report, every deep-dive, every deck, and you’re spending as much time re-explaining the task as you saved by not doing it by hand.
A Skill fixes that. It’s a saved, specific set of instructions like what data to pull, how to structure it, and what’s worth flagging.
The AI follows those instructions the same way every time you point it at that task. You write it once, the way you’d write an SOP for a new hire, except this “new hire” actually reads it and follows it exactly, every single time.
That first version won’t be perfect, and that’s fine. You run it, notice the tone’s off or it’s flagging things you don’t care about, and adjust the instructions.
Do that for your weekly summary report, and it stops being hours of pulling numbers on Friday afternoons and instead is a push of a button that runs first thing Monday morning for the most accurate data.
If you’re running Zendesk, you don’t have to build this yourself
Everything above assumes someone on your team is willing to wire up the connector from scratch. Not every team has that person, and you don’t need one to get started.
If Zendesk is your platform, Swifteq’s MCP Server does the door-opening part for you: it connects Claude, ChatGPT, or Cursor directly to your Zendesk ticket data for exactly this kind of reporting and analysis. No custom code, no developer sprint.
It’s built to fetch data when it’s asked for rather than store it, which keeps it in line with the governance line below instead of working against it.
It’s not the only way to solve this piece, but it’s a fast way to get the hardest part solved so you can spend your time on the Skills instead.
7 Ways Support Operations Teams Are Using AI for Non-customer-facing Work
With the mechanics in place, here’s where they actually pay off. A lot of what eats a support leader’s week is exactly the kind of task that rewards consistency over creativity.
Nobody is looking for you to have a unique angle in a weekly report, they’re merely looking for consistency across your reporting.
Here are the ones worth building first:
The weekly summary report
Right now, this is probably someone pulling volume, first response time, resolution time, and tag counts by hand, then updating a spreadsheet and writing into a slide deck what changed since the week before and what it might indicate.
Instead, create a skill with the following prompt:
Pull total ticket volume, first response time, resolution time, and tag counts from the past 7 days. Compare each number to the previous week and to the same week last month. Write a short summary flagging anything that moved more than 10%, and call out which tags are trending up or down.
That’s it. Plain instructions, written the way you’d explain the task to a new hire, saved so the AI runs them the same way every time instead of you retyping some version of that request every Friday afternoon.
You still decide what counts as worth flagging. The pulling, comparing, and writing up stops being the part of your week that eats a Friday afternoon when you just want to wrap up and go home.
Schedule the Skill to run Monday morning, and you have your summary of the week prior ready for review over a cup of coffee when you first sit down.
A “why do we keep seeing this” deep-dive
Say a tag’s volume spikes. Normally figuring out why means opening a chunk of those tickets and reading them one by one.
Instead, tell your AI to look at every ticket with the relevant tag during the time period with the spike, and ask it to summarize what’s actually being asked across all of them, grouped by underlying cause, and flag anything that lines up with a product change or release date.
You go from “billing tickets are up 30%” to an actual answer: three-quarters of them started after a new payment system was implemented in the app two weeks ago.
Quarterly planning
Three months of ticket tags is real data about where customers are struggling, but it usually just sits in the help desk because nobody has a spare afternoon to pull it together.
Point the AI at the queue and ask it to group every ticket from the quarter by tag, rank by volume and by how long those tickets took to resolve, and note anything trending up or down compared to last quarter.
What comes back isn’t the planning doc itself but it is the input every planning doc needs and rarely gets: an honest, ranked picture of what customers were dealing with. Then take that data into a different Skill and have the AI help compile goals for the quarter.
The monthly deck, without rebuilding it from scratch
Once the weekly numbers and the quarterly tag rankings are already being pulled the same way every time, the monthly deck is mostly a formatting thing.
Point the AI at that same reporting skill, plus whatever CSAT or NPS numbers you’re tracking, and ask it to lay the month out in whatever structure your leadership team expects: this month’s volume and response times against last month, the top few tags and how they moved, anything worth flagging.
You’re not writing the deck from a blank slide every month. You’re reformatting numbers you already trust, because they came from the same source last month too.
Understanding churn instead of guessing at it
Cancellation tickets and exit notes usually get read once, by whoever handled that specific case, and then never looked at again as a set.
Point the AI at every cancellation from the last quarter and ask it to categorize the stated reasons, rank them by frequency, and flag anything trending upward month over month.
You go from “I think it’s pricing” to an actual ranked list with the data to back it up.
Sorting and tagging in real time
Categorizing incoming tickets by type and priority is a high-volume, low-complexity task, exactly where a cheap, fast AI model earns its keep. If you are on Zendesk, use a tool like Ticket Classification to automate tagging.
Set it to run in real time or at regular intervals, so the queue is sorted as tickets flow in and agents open an organized inbox instead of a raw, undifferentiated one at the start of their day.
Draft replies before anyone touches the ticket
Some teams take this a step further with a scheduled agent that runs and drafts first-pass responses to the routine, easily-templated tickets: a password reset, a shipping question, whatever’s predictable and well documented enough to draft with confidence.
But it’s worth being clear about what this is and isn’t: it’s a draft waiting for a person to review and send, not the AI replying on its own. The moment it touches the customer is still a human moment.
Where the Personal Account Stops Being the Right Call
It’s easy to start this kind of work on a personal AI account. It’s fast, probably already logged in, and it works. Right up until real customer data is involved.
You don’t want customer names, ticket contents, account details, and other personal information being pulled into your personal AI.
Personal and consumer-tiered accounts have a training setting tucked into privacy preferences, and it’s easy to end up with it switched on without quite remembering doing it.
Even with that setting off, there’s no admin visibility into what’s connected to that account or what’s being pulled through it. There’s no audit trail, and if someone leaves the team, there is no clean way to cut off their personal login’s access to customer data that is flowing through it.
The team and enterprise plans of these products are specifically designed for commercial use, where customer data is contractually excluded from training.
They come with admin-level controls over what’s connected, logging you can review, and access that gets revoked the moment someone’s off the team.
None of this is a reason to slow down or avoid building the thing. It’s a reason to draw the line early rather than discover it later.
The moment your back-office AI work starts touching real customer data instead of your own notes and drafts, that’s your cue to move it off a personal account and onto something governed. For your own protection as much as the company’s.
Start with One Skill, Today
You don’t need to rebuild your whole back office at once. Pick the one recurring thing that already eats the most hours, probably the weekly report, since it’s the simplest to define.
Open Claude or Codex, point it at last week’s ticket data, and give it plain instructions like the ones above: what to pull, what to compare, what’s worth flagging.
Run it once. Where it’s off, tell it so, the same way you’d correct a new hire. Once it’s doing what you want, ask it to save those instructions as a Skill, so next Monday, you’re one message away from the same report instead of starting from scratch.
Do that once, and you’ll know exactly what to point at next: the deep-dive, the quarterly pull, the deck. Do it, and the four-hour deck stops being the tax you pay for doing the work customers never see.
Written by Anne-Marie Traas
Anne-Marie is a Fractional Head of Customer Success focused on providing an optimal customer experience in every interaction. She specializes in driving process and product improvements, creating thorough and easy-to-understand product documentation, and teaching others how to communicate more effectively through the written word.
