What Is AI in the Contact Center? Your Complete Guide

The hottest topic in service today is generative AI, especially AI in the contact center. The contact center is where customers can turn to for questions or help – by chat, email, or self-service portal so they can take matters into their own hands.

And AI is poised to improve efficiency and productivity within the contact center. According to our research, 95% of decision makers at organizations with AI report cost and time savings, and 92% say generative AI helps them deliver better customer service. Every day, I speak with service leaders who are excited about the potential for generative contact center AI.

Yet, only 24% are actually using any form of contact center AI. What’s in the way? 66% say that their employees don’t have the right skills to successfully put generative AI to use. So let’s look at what AI in the contact center is, its benefits, and four ways you can use contact center AI, along with example use cases and tips to get started.

Table of Contents

• What is AI in the contact center?
• What are the benefits of AI in the contact center?
• Generate service responses to customers
• Generate case summaries
• Generate knowledge articles
• Generate answers
• Get started with AI in the contact center

First the basics: an AI-driven contact center uses technology to automate and enhance different components of customer service and support. These contact centers use AI to handle customer inquiries, manage interactions, and improve overall productivity and efficiency.

With AI, you can turn traditional contact centers into seamless, customized experiences for every customer. AI can give customers quicker and more accurate responses, whether they’re messaging a chatbot or skimming a knowledge article that has personally been recommended to them.

Contact centers play a major role in customer experience. Often, it’s the first place a customer goes to seek support. And if the contact center is difficult to use, impersonal, or slow, that’ll directly – and negatively – impact the customer’s experience.

One benefit of AI in the contact center is that it can be used to create a more personalized service interaction. For example, AI can populate important customer information for agents – like name, location, preferred language – so they have everything they need to give customers the customized service they expect.

AI can also improve efficiency and productivity, leading to an overall great experience and increase the chances of customer loyalty to your brand. It can also reduce operational costs by taking over your agents’ manual processes and tasks, so they can focus on more complex cases.

Here are 4 ways you can use AI in the contact center now.

Your contact center provides multiple ways for customers to contact your business — from phone to email to chat to SMS. While many customers still use the phone, 57% now prefer to use digital channels. Your agents staffing these digital channels need to give accurate and relevant information, reply in a timely manner, and resolve the customer’s issue quickly.

So how can generative AI help? The large language models powering generative AI can automatically generate a human-like reply to any question. When grounded in your customer data and knowledge base, you can personalize these generated replies, making them more trustworthy. Agents can review the suggestions from the model and easily send. For agents working on several cases all at once, AI in the contact center can be a real timesaver.

Let’s look at an example for a fictional internet company we’ll call Nation-Wide Web.

Jane is a Nation-Wide Web customer and notices an unusual charge to her bill. Jane opens up a chat message on the company’s website and is soon connected to an agent, Katie.

Katie has a few messaging windows open from customers, one of them being Jane. Jane shares her concerns about her bill. Apparently, Jane went over her data package for the month. The contact center’s AI tool uses Jane’s question and the context of her account status to generate a personalized message that explains this charge in an empathetic tone, but also that it’s within company policy to waive the fee given the circumstances.

Katie reviews the message and confirms the policy, then sends the message and removes the charge from Jane’s account. Jane is happy she got a quick and easy solution and Katie can focus her attention on customers with more complex issues .

Tip: Taking the time to review any customer communication for accuracy and tone helps to avoid misunderstandings.

To provide your customer with a great experience, you need accurate data to track and optimize your business’ service interactions. This makes the wrap-up summary your agents do after a case is closed one of the most crucial pieces of service data your business can collect.

The challenge? This is a time-consuming task that keeps your agents from helping other customers.

But contact center AI can take the most complex email and chat conversations and generate a proposed wrap-up summary. Your agent just needs to review these summaries before they’re saved to the case log. This saves agents a ton of time and effort on data entry.

Let’s go back to our Nation-Wide Web example.

If you remember, Katie’s AI tool generated a response to Jane and all Katie had to do was review the message, press send, and waive the fee from Jane’s account. In the meantime, the AI is using the data from the message thread and the actions that Katie took in Jane’s account to generate a case summary.

After the conversation with Jane is complete, Katie can read over this proposed summary, adjust some details, and save it to the case record. Reducing after call work helps Katie move on to help other customers faster.

Tip: Create a template for your case summaries so that your contact center AI tool can easily pull conversation data into the CRM without missing important details.

Salesforce research shows that 61% of customers prefer self-service tools for simple service issues. However, to do that, a business needs a large knowledge base that customers can search through to find a solution.

Service agents are often tasked with publishing knowledge articles after resolving a case. But it takes time for agents to manually create, review, and publish an article, which keeps them from helping customers in need.

Contact center AI can automatically generate a knowledge base article after a support case is closed by pulling from case notes, message history, and data from other service tools. From there, your agent just needs to review the article to ensure accuracy and add it to the queue for approval. This takes the pressure off agents to write articles from scratch.

Going back to our Nation-Wide Web example, Austin has slow internet and calls to troubleshoot. He’s connected to Tawni who asks for his router and modem details. Tawni runs through a few common scenarios based on similar cases, but none work for Austin’s setup.

Tawni decides to try something new. She asks Austin to do a full-system reboot through the Nation-Wide Web mobile app. After this is over, Austin’s internet speeds are back to normal and the case is closed. Tawni logs all of this information into the company’s service console, including his router and modem setup and how she solved this issue with a reboot.

Because this was a unique case, the contact center’s AI tool uses the details of the Tawni’s conversation with Austin and the context of Austin’s issue to generate a new knowledge base article. Tawni adds some extra detail and pushes it into the approval queue.

Tip: Include as much detail as possible in your knowledge base articles so customers have all the information they need to solve their problems.

When your agents are in the middle of a service interaction, they don’t have time to read pages of documentation or every detail of a knowledge base article. But, they still need to find the right information to solve your customer’s query.

The same is true for self-service. Reading article after article to find the information you need is not a good customer experience.

Generative AI can help agents and customers get the answers they need faster and easier. Rather than getting a list of pages that may (or may not) have the answer, AI can pull the relevant details from a knowledge article and answer a question directly as plain text.

For our final example, we’ll go back to our Nation-Wide Web customer, Austin.

A few months after his interaction with Tawni, his internet is slow again. He remembers they used the mobile app to fix the issue last time, but now he’s locked out of the mobile app. But instead of calling for assistance, he takes a look at the company’s Help Center. Austin uses the search function to ask the following question: “How do I fix a slow internet connection when I’m locked out of my mobile app?”

Before, Austin would have first needed to find the article on resetting his password and then find the article on using the app to perform a full-system reboot. Now, the contact center’s AI tool generates a personalized response to Austin’s question, pulling together information from multiple articles. “First, click here to request a new password to your mobile app. Once you are logged in, here’s how to use the app to perform a full-system reboot…”

Austin solved his issue without interacting with an agent and still got a personalized experience. If an agent was the one who needed to find specific information within the knowledge hub, they’d have this same experience.

Tip: Making your self-service content easy to find and navigate builds customer trust.