Editorial
The Gist
- Accelerated training. Generative AI in CX helps new agents practice live scenarios, reducing lead time to proficiency and ensuring consistent customer experiences.
- Immediate answers. AI in CX solutions minimize research time for agents, improving customer satisfaction by providing quick and accurate responses.
- AI integration. Companies deploying generative AI see significant benefits in CX, streamlining operations and enhancing customer interactions.
A little more than a year ago the age of AI kicked into high gear following OpenAI’s release of ChatGPT-4 and the unveiling of its amazing conversational, problem solving, content creation and artistic capabilities. The public was immediately mesmerized by the incredible possibilities for applications in business, healthcare, education, scientific research and other fields.
Meanwhile, the public was equally if not more alarmed by the gravity of possible negative outcomes if the technology were allowed to develop unchecked by guardrails. In fact, it’s fair to say that the majority of the coverage in the press has been about the dangers of AI vs. its benefits, and this has had a dampening effect on business leaders’ willingness to move forward with deployments that tap into its benefits, particularly AI in CX.
That said, there is a lot to be gained by taking advantage of the capabilities made available by AI in CX, and many businesses have looked past the F.U.D. (fear, uncertainty and doubt) and put AI in CX to effective use to improve their CX. Below are some examples of how AI in CX is making a difference now.
Related Article: AI in Customer Experience: The Impact on Customer Journeys
How AI in CX is Making a Difference
1. Accelerating Training for Customer Service Agents
Two of the biggest challenges for contact centers include:
- The lead time it takes for new agents to reach the necessary proficiency to begin taking live contacts with customers
- The disparity between the experience delivered by veteran agents vs. newbies.
Generative AI in CX has been deployed to help with both of those by simulating live call scenarios that agents can use to practice during training.
These AI in CX simulations enable new agents to get acclimated to real-world conditions in an environment where it’s safe to make mistakes and enable them to rapidly run through as many repetitions of specific scenarios as are necessary to gain high levels of proficiency before they transition to taking live calls. This also helps to narrow the gap between new agents and experienced agents, enabling customers to get a more consistent experience regardless of the tenure of the agent handling the contact.
Some well-known brands are already reaping the benefits of deploying this technology, including Sallie Mae and Western Union, who both deployed a solution delivered by Zenarate. On the strength of that solution, Western Union has reduced agent time to proficiency by more than 50% and improved CSAT scores by 33%. Sallie Mae has reduced agent certification time for live calls by more than 75%.
Related Article: AI Customer Experience Ushers in a New Era of Engagement
2. Minimizing (or Eliminating) Lag Time for Agents to Research Answers to Customers’ Inquiries
Another key CX challenge for contact centers is the lead time it takes for agents to research a customer inquiry before they can provide an answer. Agents frequently have to put customers on hold while they research answers to questions, and they often have to dip into multiple systems to get the information needed to give an appropriate response. That process can take several minutes while customers wait in limbo to get a resolution to their inquiry, which is not a great experience for them.
Generative AI in CX has been deployed with great effect to help solve this problem by training LLMs on companies’ internal systems and knowledge bases to retrieve answers for customers’ inquiries in seconds vs. minutes. Datamark, a company that supplies solutions for this use case, estimates that the time savings range from 30 seconds to two minutes and that this has a favorable impact on both the employee and customer experience. End users who have deployed generative AI for this use case include El Paso 311, PDI Technologies (administrator for Shell Fuel Rewards) and L’Oréal Canada.
Related Article: Steering Forward: The Dawn of AI in Customer Experience
3. Offloading Low-Complexity Inquiries to Chatbots or Virtual Assistants
Perhaps one of the areas that has stirred up the most controversy is whether generative AI in CX solutions are poised to replace live contact center agents outright. Concerns range from wholesale job loss in contact centers to potential negative impacts to CX from generative AI applications that can’t deliver experiences on par with live agents. Meanwhile, some companies have taken the plunge and deployed generative AI solutions to handle customers’ inquiries directly and have achieved favorable outcomes.
For example, Toyota’s Destination Assist service enables drivers who have factory installed navigation systems to download directions to their vehicles at the touch of a button. Previously, when Destination Assist was activated, drivers would speak with a live call center agent who would transmit directions back to their cars. Following the rollout of an AI-powered version of Destination Assist in 2023, 95% of requests are now handled by the automated agent, which frees up live agents to handle more complex requests.
Another example is Klarna’s in-house developed AI assistant that handled about two-thirds of its customer service conversations over the course of a year from 2023 to 2024 while covering a wide range of contact types (e.g., refunds, returns, resolution of invoice inaccuracies) and achieving customer satisfaction scores on par with live agents. The AI assistant has helped Klarna reduce repeat inquiries by 25% and cut the time to serve by more than 80%, both of which are indicative of better experiences for customers.
Related Article: 8 Ways AI Can Elevate Your Customer Experience
4. Streamlining the Shopping Experience for Customers
Earlier this year Amazon started rolling out Rufus, its generative AI-powered chatbot that helps shoppers make decisions about which items to buy especially when they don’t know which features are most important to consider when making the purchase decision. For example, you can consult Rufus to help you figure out which big screen TV to buy, what factors to consider when choosing a pair of earphones, or what items to buy to prepare for an upcoming trip.
In response, Rufus can offer recommendations depending on whether you’re focused on price, popularity or particular use cases (e.g., earphones to use for Zoom meetings, listening while working out, playing with a band). Rufus can even suggest and answer follow-up questions to help you narrow down your choice. The chatbot can do this because it is trained on the Amazon product catalog, Amazon customer reviews, Amazon community Q&A responses as well as other data from across the web.
Meanwhile, Sam’s Club has rolled out its AI-powered answer to stationing “receipt checker” persons at the exits to its warehouse club stores. That solution is working so well to reduce the exit bottleneck in 20% of its stores where it’s already deployed that they plan to roll it out to the full network by the year’s end.
Besides those already mentioned, some companies have deployed bespoke generative AI solutions to score cell phone trade-ins, to translate community responses in technology user groups into multiple languages and more.
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Final Thoughts
The bottom line is that if your company is stuck in neutral regarding the path forward for AI in CX deployments, you’re missing out. There’s no shortage of opportunity to benefit from generative AI, and it’s time to commit. If your executive team is not sure where or how to get started with AI, try shifting your vantage point from "Where should we start with AI?" to "Which CX use cases are we already trying to solve will be made easier to tackle by applying generative AI?"
In a nutshell, start with the use case not the technology, and the path forward will become a lot clearer.
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