Generative AI and CX: How Enterprise can Effectively Integrate the Two

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As the fastest-growing consumer application in history, many wondered if ChatGPT’s ability to quickly and accurately deliver curated information would render Google Search obsolete. While people now have a more measured outlook on the technology, it is undeniable that ChatGPT, and, more broadly, generative AI, will transform AI and the customer experience (CX). In particular, businesses can leverage generative AI to replace legacy chatbot technologies, empowering them to drive conversational communications, enriching CX and improving customer satisfaction.

Why Generative Will Replace Chatbots and Improve CX

While the capabilities of chatbots have become significantly better over the past several years, they are still limited compared to generative AI. Chatbots provide preprogrammed answers, resulting in static models of structured decision trees. Traditional chatbot environments also aim to progress a customer interaction so that a live agent has enough information to engage with that customer. However, these time-consuming interactions are not particularly effective. Either the customer will eventually ask a specific question requiring skills-based routing to billing, sales or support, or the chatbot will go off-topic or reach a figurative dead end, frustrating the customer.

The potential of generative AI in a CX context pushes the envelope of what a customer can ask and expect to receive in real-time. Generative AI provides detailed conversational context, infusing sophistication and completeness into customer interactions unavailable in any chatbot today. Indeed, a customer service solution powered by a conversational AI platform like ChatGPT can create far more dynamic and informative interactions that don’t make the customer feel like they are struggling through an endless choose-your-own-adventure game.

Likewise, because generative AI “learns” or improves as it gathers more information, CX will get better over time. At first, ChatGPT produced responses that were broad or even focused on the wrong things. But, as it collected information and data, the answers became more accurate, precise and relevant. The ability of generative AI to learn and answer questions accordingly will be invaluable in an enterprise environment. Ultimately, its ability to evolve and right-size questions will enhance success, completion and conversion rates.

Best Practices When Integrating Generative AI, Including Pitfalls to Avoid

Although generative AI is a powerful tool, its effectiveness is dependent on its deployment within the proper context. For example, if a non-technical customer asks a question, and the bot answers with a highly technical response, the CX will not align with the questioner’s expectations or needs. Therefore, it is paramount that brands identify use cases for generative AI and establish parameters and customer profiles, including clearly defined goals and measurable outcomes.

Companies can fine-tune models with curated data to ensure relevant results that boost CX for organization-specific use cases. Enterprises should also strive to verify results and site sources or returned data to limit data bias or toxicity. Moreover, the excitement of ChatGPT shouldn’t blind businesses to the reality that it may not always be the perfect fit for every possible customer inquiry, even within the same thread or journey. Sometimes, another AI engine might be more suitable.

There are also several pitfalls enterprises should be aware of when deploying generative AI – namely, that while this technology is incredibly innovative, it can still make mistakes. In fact, generative AI will sometimes create content that might not necessarily be factual though plausible. Regardless, such occurrences could degrade CX, so brands should employ sufficient governors and guardrails where necessary. Also, businesses shouldn’t overestimate generative AI’s capabilities, as it is only as effective as the data at its disposal. People need specific answers to resolve their challenges, and data is essential to keeping these answers as relevant and fitting as possible.

Other concerns revolve around privacy and security, especially for industries like healthcare, financial services, etc. Highly complex interactions will require regulations that specify which answers should be given securely to the correct customer profiles.

Don’t Deploy for the Sake of Deploying

Businesses that carefully and intentionally integrate generative AI across their different customer touchpoints will reap numerous benefits. Nevertheless, it’s crucial not to get so swept up in the hype that one forgets that most business use cases for generative AI are still getting fleshed out. Everyone is in the process of trying to figure out how to wrap their arms around ChatGPT. Finding vendors with the expertise to help facilitate deployment is equally important. Above all, it’s absolutely vital that enterprises not deploy generative AI just for the sake of deploying but to elevate CX.

Brian Gilman
Brian Gilman is Chief Marketing Officer for IntelePeer. Bringing more than two decades of experience to his position as CMO, Brian is a transformational marketing leader with an impressive record of success in developing and implementing strategic B2B marketing plans and is responsible for the creation of global thematic and vertical campaigns that span across all IntelePeer products, services and solutions. Prior to IntelePeer, Brian was vice president of product, solutions and integrated marketing at Vonage.

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