How do support agents use AI in their own work?
Support agents use AI for the reading and writing around a ticket, not for the decision on the ticket. The useful tasks are the ones where an agent can check the output quickly against something they already have: the ticket thread, the help centre article, the refund policy or the order record.
The list below is ordered roughly from lowest to highest risk. A ticket summary that is slightly wrong costs the agent a minute. A reply that promises the wrong refund costs the company money and the customer's trust. Start at the top of the list and move down only when the team has a checking habit that works.
Summarise a long thread
Turn a 14-message ticket into the customer's actual request, what has already been tried and what is still open, so the next agent does not ask the same questions again.
Write an internal handoff or escalation note
Draft the note for a team lead or second-line team: the problem, the evidence, the policy that applies and the decision being asked for.
Adjust tone
Make a correct but curt reply warmer, or a rambling reply shorter, while keeping every fact and commitment the same.
Draft a reply from supplied policy
Paste the relevant policy text and the customer's message, ask for a draft that only uses that policy, then check each sentence against it.
Find the right help article
Ask which of several pasted articles answers the question. The agent still reads the article before linking it.
Draft a knowledge base update
Turn a set of resolved tickets about the same problem into a first draft of a help article for the content owner to review.
Is this the same as buying a customer service chatbot?
No. Agent-assist use, where a person reads and sends every reply, is a skills question. A chatbot or AI agent that answers customers directly is a software purchase, usually from a helpdesk vendor, and it raises questions about accuracy, escalation paths, disclosure and who is accountable for what it says. Those questions belong to whoever owns the support platform and the policies, not to an individual agent.
Disclosure is one difference worth knowing. The EU AI Act, in Article 50, requires providers to design AI systems that interact directly with people so that those people are informed they are interacting with an AI system, unless that is obvious from the context. The published text sets 2 August 2026 for that obligation to apply. Rules elsewhere differ. This is general information, not legal advice; check the current position with a qualified adviser before deploying anything customer-facing.
When a customer asks 'am I talking to a bot?', the honest answer depends on the set-up. If an agent drafted the reply with AI and then reviewed and sent it, a person is answering. If a system replies automatically, say so. Teams that use agent assist should agree the wording in advance rather than leave each agent to improvise.
What does a checked AI reply look like?
A checked reply uses only facts the agent can point to, and it hands anything outside policy to the person who can decide it. Here is a fictional example.
Fernhill Home is a fictional online homeware shop. Its supplied policy says: damaged items must be reported with a photo within 7 days of delivery; refunds go to the original payment method within 5 working days of approval; exceptions outside the 7 days need a team lead's approval. A customer writes, 9 days after delivery, that a mug set arrived with two mugs broken. They are angry, want a full refund and ask for compensation for the hassle.
The agent's prompt: 'You are drafting a reply for a support agent to review. Use only the policy below and the customer's message. Do not offer anything the policy does not allow. If the request falls outside policy, say what happens next instead of deciding it. Keep it under 120 words, warm and plain.' The policy text and the message follow.
A weak first draft, of the kind an assistant can produce when the instruction is loose: 'We are so sorry! We have processed a full refund, which will arrive immediately, and added a 20% discount code to your account for the trouble. This will never happen again.' Every commitment in it is invented. No refund has been processed, nothing arrives immediately, there is no discount code in the policy, and no one can promise the last sentence.
The reviewed reply: 'I am sorry two of the mugs arrived broken. Could you reply with a photo of the damaged mugs and the box? Your message reached us 9 days after delivery, which is outside our usual 7-day window, so I have asked my team lead to review it as an exception. I will update you once they have decided. If the refund is approved, it goes back to your original payment method within 5 working days.' The agent then writes the escalation note, also drafted with AI and checked, so the team lead has the dates and the photo request in one place.
| Sentence in the weak draft | Check against the policy | What the agent did |
|---|---|---|
| Full refund processed | No refund has been approved; the claim is outside 7 days | Replaced with an escalation to the team lead |
| Arrives immediately | Policy says 5 working days after approval | Stated the real timing, conditionally |
| 20% discount code | No compensation exists in the policy | Removed; compensation is the team lead's call |
| Will never happen again | Not a promise anyone can keep | Removed |
Who is responsible for what an AI-drafted reply says?
The company is, and in practice the agent who sends it. A customer cannot tell which sentence came from a model, and they are entitled to rely on what the company tells them.
A widely cited Canadian small-claims decision illustrates the point from the chatbot side. In Moffatt v Air Canada (2024 BCCRT 149, decided 14 February 2024), the airline's website chatbot told a customer he could claim a bereavement fare after travelling, which conflicted with the airline's own policy page. The British Columbia Civil Resolution Tribunal rejected the argument that the airline was not liable for its chatbot, held that it remained responsible for all the information on its website, and found negligent misrepresentation. That case concerned an automated chatbot, not an agent's draft, and it is one tribunal decision in one jurisdiction. The practical lesson for agents is simpler: if it goes out under the company's name, treat it as the company's statement.
That is why the checking step is not optional. Confident, fluent text is exactly where errors are easiest to miss. The NIST Generative AI Profile calls this risk confabulation: confidently stated but erroneous or false content that can mislead the reader.
Want to see how this connects to a role?
Explore the planned team trial ↗What customer information should stay out of an AI tool?
Anything your company has not approved for that specific tool. As a working default, keep out payment card numbers, passwords and security answers, health or other sensitive details, and full customer records that are not needed for the task. Use the company's approved workspace, not a personal account.
Most agent-assist tasks do not need the customer's identity at all. 'Customer reports two broken mugs, 9 days after delivery' is enough for a draft; the name and order number can go in after the AI step. Where personal data is processed, the organisation still has data protection duties; the UK ICO's guidance on AI and data protection covers transparency, accuracy and fairness for exactly this kind of use.
Will AI replace customer service agents?
Nobody can answer that reliably for your team, and we will not pretend to. What is clear is how the work changes when AI is used well: routine drafting gets faster, so a larger share of an agent's time goes to the tickets that need judgement, such as exceptions, complaints, upset customers and problems the policy does not cover.
The skills that matter more, not less, are knowing the policy well enough to spot when a draft breaks it, writing an escalation that a team lead can decide in one read, and recognising when a customer needs a person rather than a faster template. Those are skills that can be practised.
How should a support team practise using AI?
Practise on fictional tickets first, with a rubric the whole team can see. A short, repeatable routine works better than a one-off workshop.
Pick three ticket types
Choose common, low-risk categories such as delivery delays, damaged items and password resets. Avoid complaints with legal threats or vulnerable customers for practice.
Write fictional versions
Create a policy extract and a customer message for each, including one that falls just outside policy, because that is where invented promises appear.
Agree the rubric
Score the reply on four points: every fact traceable to the policy, no invented commitment, correct escalation, and tone that fits the customer's message.
Compare drafts
Have two or three agents run the same ticket and compare what they removed. The differences are more instructive than the drafts.
Move to real tickets slowly
Use AI on real tickets only for the categories the team has practised, inside the approved tool, with the same rubric in mind.
When is AI, or this guide, the wrong answer?
Some tickets should be handled without an AI draft at all: bereavement, safety issues, formal complaints, legal threats, data subject requests and anything involving a vulnerable customer. The time saved is small and the cost of a wrong tone or a wrong commitment is high.
This guide also does not help you choose, configure or evaluate chatbot software, write bot flows or set up a helpdesk's built-in AI features. Your helpdesk vendor's documentation and support team are the right place for that. Tosheo.ai is being built to train the people: a planned support role path with short lessons and graded practice tasks like the one above. It does not deploy or manage customer-facing AI.
Sources and scope
UK Information Commissioner's Office, Guidance on AI and data protection, updated 15 March 2023 ↗
NIST AI 600-1, Generative Artificial Intelligence Profile, July 2024 ↗