Generative AI and the Contact Center: Embracing the Next AI Revolution to Augment CX
Merely two years ago, customers would cringe at the sound of an automated voice. The instant they realized they were speaking with a virtual assistant, they’d shout “Speak with an agent!” Troubleshooting with a chatbot is still a test of patience as one suffers through a scripted choose-your-own-adventure of one-liners. Moreover, the company leads the dance in these endless back-and-forth interactions, taking the caller down the path they want to go rather than the other way around.
Preferably, through generative artificial intelligence (like ChatGPT), contact centers can engage with their customers in a much more interactive and lifelike manner. If companies integrate generative AI correctly into their contact centers, they’ll meaningfully improve the customer experience (CX), boosting customer satisfaction, loyalty, and revenue.
How Generative AI Will Enhance CX
One of the main goals of generative AI in the contact center is to facilitate dynamic, conversational, self-service interactions. Having robust self-service options benefits the contact center and the customers; the company doesn’t have to force its live agent to handle repetitive calls while the caller can resolve queries quickly and painlessly. Indeed, there is no good reason interactions like checking an account balance shouldn’t be fully automated. With generative AI, contact centers can expand how many tedious use cases can become self-service, taking a load off their overworked agents.
Likewise, generative AI can answer a wider range of questions than chatbots. Recall how many modern chatbot models promote structured decision trees with limited adaptability. However, with a contact center using generative AI, customers can ask, for example, their cable provider during a power outage when service will return. In this scenario, the bot will operate outside its traditional call tree (which might primarily be billing services) and explain that service will get restored at 6 p.m. Additionally, generative AI can learn over time as it gleans new information from interactions. Essentially, it will constantly refine its responses, learning from customer feedback in real time to make its answers more helpful and relevant, improving CX significantly.
Properly Integrating Generative AI into the Contact Center
While businesses should be eager to integrate generative AI into their contact centers, they must be selective with which use cases they choose to automate. Simple and common requests that come into a contact center, like checking points or resetting passwords, should be automated. Nevertheless, there are instances where an agent is preferable to a chatbot. In many call centers, companies will have specified customers or interactions that always go to a live agent, either to maximize cross-selling or upselling opportunities or to prevent large or important customers from getting offended since they had to speak with a bot (no matter how sophisticated) and not a person.
Depending on the time of year, a contact center might want as many calls as possible to go to live agents, allowing them to experience critical face-to-face interactions and foster better working relationships with their clients. At other times of the year, thousands of calls could come into the contact center, especially for card activations or renewals. In these cases, self-service is more practical, effective, and beneficial for CX. The need for self-service also fluctuates based on the business and industry.
Likewise, the decision to implement generative AI depends on the complexity or simplicity of the use cases in question. Although, as the technology evolves, generative AI will be sufficient for those interactions requiring a broader understanding of customer data. Take retail, for example, where a customer calls about the availability of an item and then places an order. Through generative AI, the bot could seamlessly ask if the customer would be interested in purchasing another similar item.
Recommendations for Enterprise with Multi-Vendor and Multi-Contact Center Environments
Another consideration with generative AI integration, particularly for larger enterprises, is that the more contact centers one has, the more challenging it becomes to maintain consistent CX. Indeed, implementing automation and self-service capabilities into a single-threaded environment (single contact center) is much more straightforward than a multi-vendor or multi-contact center environment.
In these more complex environments, companies usually cannot see across the chasm from one of their contact centers to the other. Plus, they lack orchestration between unlike vendors and outsourced contact centers. Data sharing is also difficult, forcing businesses to pull reports individually from each center. Therefore, these larger enterprises should leverage a vendor agonistic platform that can sit in front of all their contact centers, helping automate calls and perform smart routing and load balancing.
Companies with multiple vendors and call centers can also support CX by layering intelligence over their platforms with generative AI. While still conceptual, generative AI could facilitate the orchestration between these distinct contact centers, as it consumes the data from all of them. Imagine a customer calling a contact center in Ohio and being unable to resolve the issue at hand. A generative AI-enabled platform could look across the business' call centers and recognize that the one in New Jersey would be an ideal place to transfer the caller.
Throwing Down the Generative AI Gauntlet
With generative AI poised to upgrade contact centers, it's no wonder many are referring to ChatGPT as the start of the AI revolution. And now that ChatGPT has thrown the proverbial gauntlet down, a host of large corporations will answer the challenge. Google, Amazon, IBM, Microsoft, and Nvidia won’t let ChatGPT run away with the generative AI market. The race to create the next best platform will see these competitors leapfrog one another. In the meantime, businesses can prepare for the next breakthrough by identifying needs within their contact centers and determining which interactions should be automated.
Brian Gilman is chief marketing officer at IntelePeer. Prior to IntelePeer, Gilman was vice president of product, solutions, and integrated marketing at Vonage, and he has served in key leadership roles with top telecom, contact center, and collaboration platform providers such as Avaya, Dimension Data, Polycom, and Vidyo. Gilman has produced multiple telecom research reports cited by the U.S. Internet Council and Business 2.0, among others.
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