When to Use AI in Support (and When Not To)

There’s no shortage of opinions about AI in customer support. Some claim it will replace agents entirely, while others argue it’s too risky to trust. 

AI is powerful, but it works best when used intentionally. 

Some tasks are perfect for automation, some clearly need human judgment, and many live somewhere in between.

Where AI shines

Repetitive tickets with predictable structure

Some ticket types align well with AI not because of the topic, but because of their consistency. When a resolution relies on structured data, stable policies, and well-defined context sources such as order systems, KB articles, or account information, AI can reliably map the customer’s issue to the right answer.

When inputs are consistent, outputs tend to be consistent too.

This is where AI meaningfully reduces workload by drafting accurate replies and powering one-touch or zero-touch workflows that clear out routine tickets.

Drafting replies agents don’t need to handwrite

Many tickets aren’t complex, they’re just time-consuming to type.

AI can produce a clean first version, and the agent only adds what’s unique to that customer. Instead of starting from scratch, they refine what’s already there.

Not every ticket has a fully documented solution, though. Sometimes the agent knows the answer but AI doesn’t have enough context to draft it on its own. Text expansion solves this: the agent writes the essential details, and AI turns those into a complete, polished message. Humans supply the judgment, AI supplies the time savings.

Surfacing information instantly

AI is excellent at identifying:

• the relevant help center article
  
  
  

• key details from long conversations
  
  
  

• important policy points
  
  
  

It draws from the same places agents do: help center content, past tickets, internal notes, and structured systems like order or subscription tools. When these sources line up and tell a consistent story, AI retrieval becomes reliable and feels almost intuitive. When they're fragmented or outdated, the cracks show.

This is why content quality matters so much: the clearer and better structured your sources are, the smarter your AI behaves.

Where AI shouldn’t take the lead

Emotionally sensitive or high-stakes conversations

Escalations, sensitive reports, and outage communication benefit from human care and nuance.

Situations that require judgment or interpretation

Some issues depend on context that isn't captured in systems or documentation yet. Edge cases, unusual account states, and scenarios that require a bit of human reasoning fall into this category. With the right context sources, AI can absolutely help here, but only once that information is available and reliable.

Until then, the agent leads and AI supports.

When your knowledge base is unclear

If your content is outdated or contradictory, AI will reflect that inconsistency.

This usually signals that the content needs refining before being used as a foundation for automation.

Where AI and humans work well together

Most support conversations fall between routine and complex.

AI helps with:

• structure
  
  
  

• tone
  
  
  

• context gathering
  
  
  

• summarization
  
  
  

Humans bring:

• nuance
  
  
  

• exception paths
  
  
  

• decisions that require empathy or discretion
  
  
  

This blended approach isn’t a fallback, it’s often the optimal way to handle support.

A simple decision framework

If it’s predictable, automate it.

If it’s repetitive, assist it.

If it’s sensitive, use a human.

If it’s unclear, improve the content.

The bottom line

AI isn’t here to replace agents. It’s here to remove the repetitive work that slows them down.

Use AI where it excels, use humans where they matter, and invest in the content and systems that make both more effective.

If you're exploring how this balance could work in your Zendesk setup, it's something we help teams think through every day.