What Is GPT and How Will It Be Used for Customer Service?

This post was co-authored by Ian Beaver and Scott Lindsay.

It’s quite likely that you’ve heard about GPT (generative pre-trained transformer) based artificial intelligence and have seen OpenAI’s family of these models—ChatGPT—in the news lately.

You’ve read how generative models (GPT-3, ChatGPT, BlenderBot, LAMDA, etc.) can emulate human conversation and even mimic the writing of long-deceased historical figures. Or how teachers are worried this technology will be used to plagiarize research papers. There’s even talk that GPT-based programs could someday detect Alzheimer’s.

This is an exciting evolution of conversational AI, to be sure. But when it comes to conversational AI for business, companies are left wondering how GPT models will be used within their operations.

More specifically, how will it work with customer service operations, call centers, self-service, and other customer engagement needs?

ChatGPT and Your Business

At Verint, we know that the race toward fully realized conversational AI can’t just be about speaking the language and understanding all things in general terms. Rather, the conversational AI model needs to know and understand how businesses operate, the terms they use, the context in which they use them, and how those terms drive decisions through an organization’s ecosystems of business applications.

And while ChatGPT is advanced and promising in so many ways, it’s not a plug-and-play solution for customer service quite yet.

Let’s think about ChatGPT as a new employee in your contact center. If you ask that employee on day one to summarize call interactions, they’ll be able to generate sentences and explanations that are generally accurate but may seem suspect or incomplete to the seasoned experts on your team.

But ask that same individual to do the job two months later when they’re starting to understand how the business operates and what customers want, and their summaries will be more actionable, categorical, and specific to the terms that the client uses to run its business.

The problem, again, is that left to handle complex business interactions, ChatGPT can’t immediately deliver accurate information specific to your business.

While its responses may sound impressive, it has not been trained on your business and cannot answer questions directed to your products or services—such as if a warranty claim can be accepted or if a customer’s insurance policy will cover their latest claim.

Worse, it may provide factual inaccuracies, which can lead a customer to, say, schedule an appointment at the wrong medical clinic, or deposit money to the wrong account.

It goes without saying that neither your business’ reputation nor its bottom line can survive sending out bad information—and know that customers won’t be forgiving just because that bad information came from a bot rather than a live agent.

In today’s world, it’s one and the same.

Verint and GPT Models

As a leader in conversational AI technology, Verint is very much aware of GPT-based models. We’ve evaluated the results of several generative language model technologies for multiple use cases within our realm of expertise.

In fact, Verint Da Vinci AI—the power behind many of our automated customer engagement solutions—is built to swiftly incorporate any evolving or revolutionary commercially available technology into the Verint Customer Engagement Platform.

It’s important to note, however, that GPT models can’t evolve without experts like we have at Verint injecting proficiency and specific knowledge into them. We understand the business ecosystems that surround the contact center and how decisions are made and driven forward through those systems.

Verint Da Vinci focuses on creating a business translation layer so that your bots can speak in terms specific to your business and brand. Here are a few of the tools that make that happen:

• Intent Classifier: Predict the speaker’s intention for an utterance, incorporating specific knowledge of your business terminology
• Interaction Analytics: Identify the purpose, disposition, resolution, and actions related to a specific interaction
• Automated Quality Management: Use AI to automate a quality management audit of an interaction and provide a comparative score for agent improvements
• Outcome Prediction: Use call data to predict KPIs such as satisfaction (CSAT), likelihood-to-recommend (LTR) and Net Promoter Score (NPS), churn, or customer effort scores.

Embracing AI Evolution

Verint embraces the evolution of conversational AI technology—and we’re excited to be part of that evolution ourselves.

We know that GPT technologies and other early emergers, such as neural symbolic models, have enormous potential in helping us provide applications that address what we call Engagement Capacity Gap—the continuing divide between customer expectations and the resources your company has to meet those expectations.

Want to learn more about how Verint Da Vinci AI and Verint Conversational AI are driving excellent customer engagement around the globe? We’d be happy to show you more.