Despite the hype and massive investments in generative AI, most businesses aren’t yet realizing value from their AI projects. Forrester found that while 80% of businesses recognize AI’s strategic value and expect it to increase in the next 12 months, less than a quarter say they can realize AI’s value today. Gartner reports similar findings with 49% of those surveyed saying that demonstrating AI value is their biggest obstacle.

Experts agree that one of the main reasons businesses aren’t seeing a return on their AI investments is because they’ve rushed into adopting the technology without first defining their use cases.

Because brands are protective of their customer experiences, and they don’t want to jeopardize their hard-earned CX equity, more caution is being given taken with customer experience AI implementations.

When the right use cases are identified upfront, customer experience leaders have the greatest potential to see strong returns on their generative AI investments.

With the correct match between technology and customer experience design, CX leaders can realize more savings than with nearly any other investment. The key is pinpointing exactly where AI in customer service can save money, increase productivity and improve the customer experience.

AI Is Changing ROI Calculations

Traditionally, CX leaders have thought about ROI by asking three questions:

1. What does it cost to serve a customer today?
2. How much less could it cost to serve a customer tomorrow?
3. What is the investment price to get from point A to point B?

In practice, CX leaders then sought to automate high-volume, low-complexity customer interactions while routing the remaining low-volume, high-complexity interactions to live agents. If they got the balance right, CX leaders would see incremental reductions in labor costs.

With generative AI, we can move beyond traditional use cases and allow technology to solve much more complicated problems and even drastically reduce the need for customers to reach out to your brand.

Use Cases for AI That Generate Fast and High ROI

Increasing Digital Channel Containment

AI in customer service, like chatbots, has become very good at handling straightforward or simple questions and now with AI’s natural language understanding capabilities, they’re becoming able to handle even very complex questions.

Architects no longer need to create extremely detailed decision trees reliant on keywords and pre-drafted chatbot responses. Instead, AI can recognize intent and pull in elements of the company’s knowledge base to create personalized responses that directly answer the question asked.

Containing more customer interactions within digital channels is a clear cost saver but only if the customer has an effortless experience within those channels. Let’s not forget that customer satisfaction levels are currently at an all-time low despite digital interactions being at an all-time high.

Technology is advancing quickly, but, more importantly, so is CX strategy. That’s really the crux. Without a solid CX strategy, even the most robust technology will fail to deliver the effortless experiences customers demand. Until very recently, the technology just wasn’t there.

Improving Insight-Driven Deflection

The cheapest customer call is the one that never happens because:

1. Nothing has gone wrong.
2. You’ve already identified a problem and have fixed it or reached out to your customers to inform them of the issue.

You can reduce the number of interactions by identifying issues and mitigating them before they become bigger problems. You can do this through conversational intelligence.

Conversation intelligence is the process of collecting customer feedback and conversation data from all customer support channels and integrating it with first and third party data to drive deeper customer insights using an algorithmic approach along with generative AI. With conversation intelligence, you can create an always-on predictive analytics engine that helps identify key product or service issues before they can damage brand reputation.

When a company experiences an unusual uptick in calls about a specific product or issue they can escalate the issue to product development where the team can immediately start working on a fix. The company can also alert customers who have bought the product and give them proactive information about how they solve or mitigate the problem before they call.

By identifying this issue faster and taking steps to fix the problem, the  company will drastically reduce the numbers of frustrated customers. Even better, through proactive outreach the company will show their customers that they value their time and loyalty.

Effortless Customer Experience is the Goal

Creating an effortless customer experience must be every brand’s goal.

Advanced digital channel containment and conversation intelligence are two effective ways to apply AI in customer service and there are many more use cases. As brands explore where AI can deliver value, I’d urge them to identify the CX problems to be solved before buying or developing the AI solution.

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