What's the Right AI Tool For the Job?

Time is the most precious resource you have when running a support team: every minute your team spends on a call, email, ticket or live chat is extremely costly. (The average cost of a live interaction is more than $7 for a B2C company and more than $13 for a B2B company.)

But customer preferences for support are evolving, and it opens up new opportunities for support leaders to make these interactions more cost-effective. Our research shows that most people prefer to self-serve rather than talk to a human – and that the only thing customers really care about is solving their problem. If the quality of the interaction is high, they don't care what the medium is.

If you've seen the news lately, it won't surprise you to see AI touted as a solution. But it's one thing to be aware of AI – and quite another to implement it. And what's the right tool for the job when it comes to customer support?

Here are three concrete examples of how AI tools can help you speed up resolution, reduce costs and make your customers happy.

Three AI examples for support: finding the right tool for the job

Scenario: A customer of a pharmacy needs to move their prescription from one location to another, has questions about prescription status or needs to request a new repeat from their doctor.

Solution: With recent advances in LLMs, a chatbot can understand their intentions using natural language, collect any necessary inputs (like preferred location and prescription name) and automatically perform these steps without involving a live team member.

Since chatbots can now deliver an experience that meets CX standards, support leaders across industries are finding new opportunities to leverage them. Every support team should consider where chatbots can alleviate their demand for support calls, support emails or live agent chat (especially at a time when 90% of consumers expect immediate responses, i.e. within 10 minutes or less, when they have a customer service question).

Scenario: A customer starts to submit a ticket for a very specific product problem (i.e. 'why is my TV blinking?').

Solution: Using an in-app case deflection search tool (such as Yext's search integration with Zendesk), a set topic can trigger a search in the side panel – so that the customer is encouraged to find their own solution before submitting a ticket.

The benefits of AI-led search are two-fold: first, as mentioned, research shows that most people would rather find information themselves with great search than have to talk to a person. And on the business side, you save money by not having to manually answer every question.

Second, you can implement AI-led search on your website or via support portal – or even integrate it into ticketing systems. That way, as people type their ticket information, relevant content is automatically surfaced in that portal to deflect the number of cases opened.

Scenario: An agent is on the phone and needs to answer a customer's nuanced question – fast.

Solution: With AI-led search for support agents, an agent can simply search, 'how to respond when a customer wants to cancel their membership', and receive the right answer – no matter where that information lives. AI-led internal search allows agents to quickly search for answers across all kinds of verticals of information (from company policies, to product details, to methods of troubleshooting for common problems.)

As you can see, AI can do a lot more than generate memes: when you leverage the right solution for the right situation, AI can be a huge blessing to the support industry.