Service Level Agreements: How AI Can Help Achieve SLAs

By Deon Nicholas

We know that Service Level Agreements (SLAs) are a serious and genuine commitment between you and your customers. We know you want to honor that commitment to the best of your ability. 

But sometimes, reaching those SLAs feels like an uphill battle, no matter what you add to your support resources. Finding truly efficient shortcuts that work for your workflow to knock down the time it takes to resolve customer issues feels like it’ll maybe just never happen.

First, let’s narrow down the problem we’re resolving. 

  • Your agents aren’t the source of the problem. 
  • Your help center knowledge isn’t the problem. 
  • Your customers’ questions aren’t even the main issue. 

Instead, the efficiency of the workflows for your agents, knowledge, and customers is the problem. Think about how you could be leveraging your data in your organization more effectively to help resolve issues. 

So how can you improve the processes you have in place? You can implement customer support AI! 

Using artificial intelligence for customer support means your customer service agents gain the assistance necessary to fulfill their SLAs with ease. 

Here are some common SLA service points and how customer support AI can help your team overcome them.‍

Resolve Simple Stuff Faster

Some tickets just don’t need that much lift from your team, but they sit around, eating up your SLA for “time to first response” just because no one has physically gotten to the ticket yet. 

Imagine making pizza dough. You just need to know how long you can run your standing mixer to knead the dough (this may or may not have happened to me). 

I didn’t do this because I like to live life on the edge, but let’s say I file a ticket. This question probably has a pretty standard answer, but I’m not seeing the answer in the help center. 

Without any kind of customer support AI intercepting these simple questions and deflecting them, the question gets passed onto a human agent, who finally gets to it four hours later. The agent rolls their eyes, types out “don’t run your mixer for more than 20 minutes,” along with a few tips, and moves on. 

Only, I’ve already made my pizza dough and have even eaten it. My problem feels obsolete, and now I’m annoyed I didn’t get an answer sooner. Worse, the customer support team didn’t meet an SLA in a situation where they could have easily if they were using some kind of deflection that worked well.

Employing customer support AI to help deflect accurately and support your agents goes a long way in cutting down the time to first response and even to resolution, in many cases. 

At Forethought, we’ve seen some of our customers go from deflecting 1-2 percent of their tickets with more basic deflection to deflecting upwards of 20% of their tickets in a matter of weeks. This type of deflection profoundly impacts that first contact response or time to first response metric often embedded in many SLAs.

No More Moving Tickets Agent to Agent 

One of the simplest ways to avoid a long first time to response is to simply get a ticket to the right agent the first time more often. 

If agents are triaging, routing, or labeling tickets themselves or if tickets tend to sort of “hot potato” from one department to another, consider using customer support AI to triage tickets in your helpdesk the moment they come in. 

When humans are managing this process, it comes down to speed, consistency, and accuracy. When AI manages the process, it learn your human process for labeling and sorting tickets and then can label issues accurately the moment they come in. 

This can reduce the time it takes to close a ticket by hours or even days because customer issues get sent to the right agent more quickly and the right agent has fewer of the wrong tickets in their queues to muddle through before getting to the ones they can help.

Equip Agents to Respond Well

So, if you’re resolving simple stuff faster and you’re no longer experiencing the bottleneck that tickets can create when sitting in queues, what else can you do to help improve your SLAs without overhauling your system? 

Well, you probably have a lot of excellent knowledge throughout your system. Maybe it’s in your help center, sprinkled throughout articles. Maybe it’s in a wiki (or across five wikis) somewhere. Maybe it’s also in your macros and other templates your agents use every day. 

With customer support AI such as Agatha Assist, agents have a tool that sifts through consolidated information and reduces the amount of time spent searching for it.

Think about how your SLAs could improve if your agents could quickly find the right info to return to a customer. Think about how much faster a newer agent will help you reach your SLA goals with more of the correct information right at their fingertips. 

If you’re ready to experience the value AI can bring to your customer support team’s SLAs, browse our customer case studies or download our comprehensive ebook on piloting AI for customer support.

Dive in.

Interested in generative AI for customer support? Check out this guide to learn about the 3 key pillars you need to get started.

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