How to Use Conversational AI to Scale Your Customer Support Team

By Deon Nicholas

Technology introduces a host of benefits for companies. It can improve efficiencies, reduce costs, and streamline processes. When looking at its impact on customer support, businesses want to optimize agent productivity while reducing costs. In addition, they need to do it at scale. Enter conversational AI—it holds the key to modernizing customer support teams and boosting the department’s performance.

What is conversational AI?

Conversational AI describes customer- or employee-facing chatbots that create human conversations with a machine. They aren’t traditional chatbots that only work via a predetermined set of if-then statements and decision trees that focus on keywords to respond.

Conversational AI understands sentiment and intent using natural language processing, natural language understanding, and machine learning. It provides better, more accurate answers that lead to a reduction in customers needing agent interaction. 

Traditional chatbots are much less effective, resolving issues only 28 percent of the time. That’s not a technology that will provide you with answers to scaling. 

How does conversational AI provide more accurate self-service?

Conversational AI understands human language. It reviews the entire query, not just keywords. From there, self-service AI customer support can provide the most accurate answer based on its ingestion of lots of documents and data. It can also classify urgency and spam.

You can also build custom workflows with built-in AI to allow customers to take complex actions without agent intervention. 

Conversational AI supports scaling by providing a self-service option for customers that’s consistent and much more likely to resolve their queries. It can deflect a substantial number of tickets as well. As a result, they don’t clog up agent queues. 

These ticket deflections represent the most common, easily answerable questions. It automates the process while giving customers responses fast. In turn, that allows you to scale your customer support team without putting more on their plate. They can then work on the most complicated customer inquiries.

What’s the impact of conversational AI on agent productivity?

If self-service doesn’t resolve the issue, agent interaction is necessary. With conversational AI, you can improve how they work and respond. 

First, AI routes the inquiry to the agents that can best answer the question based on analyzing and labeling sentiment. For example, if your business has multiple products or services, you may have specialists for each. 

After routing, AI continues to assist agents. The AI engine offers agents:

  • Smart searching and custom filers 
  • More context in tickets by looking at existing macros, past tickets relating to similar questions, and current ticket history
  • Productivity support in the manner of autocomplete, in-line commands, and other shortcuts

With the power of these features, your ability to scale customer support operations focuses on the intelligent use of technology rather than additional headcount. While you will need more agents as you grow, you optimize their performance by providing conversational AI tech. 

What does optimized scaling look like with conversational AI?

Implementing conversational AI into your customer support operations can happen rapidly. It learns from your existing data, so there are no rules to configure. After launching it, what will scaling look like? What metrics are you tracking? 

For scaling to be successful, you want to see better response efficiency, reduced time spent on tickets, and increased ticket deflections. A great example of a company realizing these scale benefits is our case study on Thumbtack. It found that agents received quicker, more accurate assistance. The accuracy in routed tickets was 85 percent, making operations more streamlined, consistent, and faster. 

What is the value of leveraging AI for scale?

Looking at AI’s value to scalability, you can crunch the numbers on technology investment versus hiring more people. However, there’s more to it than that. AI redefines the process of how your agents work, meaning they can be more productive. 

The value is all measurable based on key performance indicators around efficiency. When you have a consistent, repeatable AI-supported workflow, scale is a natural consequence that doesn’t require more money or resources.

Conversational AI streamlines customer support scaling.

The best way to look at any operational challenge is to consider what tools are available to optimize processes. Conversational AI enables that within a customer support team. It reduces ticket volume for agents and supports how they respond to customers.

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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