Everything so far has covered how Zendesk AI is supposed to work. Now let’s talk about what actually happens when users or teams put it to the test. From theory to practical actualapplication

Overhyped results by Zendesk?

According to Zendesk, When it works, its AI speeds up your ticket queue and takes the edge off your first-response times. It makes your support system feel a little tighter, a little faster, and a little easier to manage.

But it is easy to overhype. AI will not understand sarcasm. It will not always tag things correctly. It might suggest help articles that have nothing to do with the question. You will need to watch for these misses and adjust over time.

The tools themselves aren’t broken, but they’re only as good as the workflows and content underneath them. If your macros are out of date or your help center is a mess, AI will just push the wrong answers faster.

That’s why early-stage results can be misleading. It looks like automation is working because replies are going out faster. But if those replies are off-topic or agents are fixing them afterward, you’re not saving anything. 

What good results actually look like

When Zendesk AI is set up right and paired with clean content and solid workflows, it can take real pressure off your team.

Here’s what good results look like:

• Agents are handling fewer tickets manually
• Time to first response goes down, especially on common issues
• Resolution times on FAQs or order updates drop noticeably
• Suggested replies match the customer’s intent and get used without heavy editing
• Customers stop reopening tickets just to get clarification

If you’re seeing those signs, AI is doing its job. Not perfectly, not magically just in a way that actually helps. If you’re not seeing those things, chances are AI is either sitting idle or silently creating extra work

Chapter 5.1: Use cases by ticket type

Based on what support teams are actually seeing in production, here’s where Zendesk AI tends to work best:

Refund requests
AI can guide customers through the refund process by confirming order numbers, linking to policy articles, and collecting the right details upfront. It won’t issue the refund itself, but it clears a path so the agent doesn’t start from zero.

FAQs and how-to tickets
These are the easy wins. Questions like “How do I change my password?” or “Where can I view my past orders?” are perfect for automation. AI can send a macro, suggest a help article, or auto-close the ticket depending on how it’s configured.

Order status and shipping updates
When connected to your backend (like Shopify), AI agents can pull real-time shipping info and respond instantly. That saves agents from digging through orders just to tell a customer their package is in transit.

Routing based on topic, tone, or language
With triage enabled, AI can detect intent, urgency, and even emotional tone to sort tickets more accurately. That means fewer misrouted tickets, faster escalations, and less bouncing between queues.

These are the high-frequency, low-effort tickets that slow teams down. Zendesk AI won’t solve the complex stuff, but it can get the repetitive work out of the way.

Chapter 5.2: What a working integration looks like (Shopify example) 

One of the clearest use cases where Zendesk AI actually performs well is with Shopify.

Ecommerce brands using Zendesk with Shopify see quick wins because the volume is high and the questions tend to be repetitive. It is the kind of environment where automation can make a noticeable difference fast.

Here is what that looks like in practice:

AI agents can respond to order tracking and shipping questions by pulling live data from Shopify. Customers get answers immediately without waiting for an agent to log in and check manually.

Refund or return requests are partially automated. The AI collects the necessary info, confirms the order, and even links to return policies. In most cases, the agent just needs to review and click approve.

Copilot helps with edge cases by drafting replies to situations like a missing package or incorrect item. Agents get a solid starting point and make small edits instead of writing from scratch.

Meanwhile, human agents spend less time answering routine tickets and more time dealing with complaints, logistics issues, or escalations.

It is not set-it-and-forget-it. You still need clean Shopify data and a clear refund process. But once the integration is solid, AI becomes an actual time-saver instead of just another button in the dashboard.

Before and after AI in support workflows

Before AI	After AI
Agents tag, route, and respond to everything	AI handles common tickets automatically
Same FAQs get answered over and over	Macros and replies are suggested in real time
Tickets pile up in one general queue	Triage routes tickets to the right teams faster
Macros and help docs are underused	Copilot makes them visible and easy to use
Response times stay slow, even on simple issues	First response time drops without adding headcount
Agents feel stuck in repetitive work	Agents focus on escalations and edge cases

Chapter 5.3: What real teams are saying  

You’ve seen the features, the use cases, and the workflows. Now here’s what actually matters. These are real reviews from users who have put Zendesk AI into live support environments.

The feedback comes from public sources like Capterra and SoftwareReviews. Some of it is positive. Some of it is critical. All of it reflects what teams are really experiencing when they rely on Zendesk AI in day-to-day support.

User feedback of Zendesk AI

“It’s pretty bad honestly. We tried for 30 days, and the intent model is not built for all businesses. And it’s expensive. We are looking checking now ultimate or Jochem.ai. The only thing what worked well was the transcription feature for Zendesk talk. That should be a default feature in Zendesk professional.”
— Reddit User
Source: Reddit

“Zendesk seems to be in the same boat, acquiring companies and bolting AI where it can. But I am struggling to get up to speed with it and make it useful. But when I look at some other alternatives to ZD they have much more seamlessly integrated with AI and made it all part of the interface.”
— Reddit User
Source: Reddit

Here is what that looks like in practice:

AI agents can respond to order tracking and shipping questions by pulling live data from Shopify. Customers get answers immediately without waiting for an agent to log in and check manually.

Refund or return requests are partially automated. The AI collects the necessary info, confirms the order, and even links to return policies. In most cases, the agent just needs to review and click approve.

Copilot helps with edge cases by drafting replies to situations like a missing package or incorrect item. Agents get a solid starting point and make small edits instead of writing from scratch.

Meanwhile, human agents spend less time answering routine tickets and more time dealing with complaints, logistics issues, or escalations.

It is not set-it-and-forget-it. You still need clean Shopify data and a clear refund process. But once the integration is solid, AI becomes an actual time-saver instead of just another button in the dashboard.

Before and after AI in support workflows

Before AI	After AI
Agents tag, route, and respond to everything	AI handles common tickets automatically
Same FAQs get answered over and over	Macros and replies are suggested in real time
Tickets pile up in one general queue	Triage routes tickets to the right teams faster
Macros and help docs are underused	Copilot makes them visible and easy to use
Response times stay slow, even on simple issues	First response time drops without adding headcount
Agents feel stuck in repetitive work	Agents focus on escalations and edge cases

Chapter 5.3: What real teams are saying  

You’ve seen the features, the use cases, and the workflows. Now here’s what actually matters. These are real reviews from users who have put Zendesk AI into live support environments.

The feedback comes from public sources like Capterra and SoftwareReviews. Some of it is positive. Some of it is critical. All of it reflects what teams are really experiencing when they rely on Zendesk AI in day-to-day support.

User feedback of Zendesk AI

“It’s pretty bad honestly. We tried for 30 days, and the intent model is not built for all businesses. And it’s expensive. We are looking checking now ultimate or Jochem.ai. The only thing what worked well was the transcription feature for Zendesk talk. That should be a default feature in Zendesk professional.”
— Reddit User
Source: Reddit

“Zendesk seems to be in the same boat, acquiring companies and bolting AI where it can. But I am struggling to get up to speed with it and make it useful. But when I look at some other alternatives to ZD they have much more seamlessly integrated with AI and made it all part of the interface.”
— Reddit User
Source: Reddit

“Some users find the advanced features complex and note that the platform may not be ideal for small businesses due to higher costs and steep learning curves.”
— Desku Blog
Source: Desku

“Zendesk, despite receiving the highest number of reviews at 422, has the lowest average rating of 1.58 and a significantly higher number of negative reviews (354).”
— Competitors App
Source: Competitors App