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Is AI enough? How it takes more than deploying generative AI to stay on the cutting edge

by Elizabeth Tobey Imagine a world where a customer is searching for a solution to a question they have about your product. They skip the search engine – who wants to wade through answers to find the one that’s right and up-to-date? – and head directly to your website. But instead of queuing for an agent on chat, or scanning through an FAQ, the connection of choice is the little box in the lower right corner that, historically, has been a rigid and limited chatbot. Except now we’ve created a conduit between brand and customer that feels like the best-trained human a company could hope for.

Generative AI, and the data that underpins and informs its output, is the technology that will fully realize this dream – and will make it accurate and effective, every time.

So how did we get here – and what needs to happen next in order to make this a reality for your business?

Artificial Intelligence is not a new technology: first introduced in the 1960s, we have been using, refining, and evolving AI capabilities and use cases for well over half a century now. The past decade, on the other hand, has allowed us to rapidly accelerate the sophistication and believability of AI in both visual and written circumstances. Enter: Generative AI.

Generative AI first hit the mainstream in the mid-2010s as we saw a rapid proliferation of nearly-authentic deep fakes: visual mediums that have been enhanced or changed by AI to present falsified, or altered, results from the original artifact. This could mean swapping your face for an actor, or changing the words a speaker is uttering in a video with the same believable tone and cadence as an original, authentic speech.

While we often consider the novel or nefarious applications of these earlier examples of generative AI, this technology has unlocked a world of possibilities – and use cases – to improve the world and the lives we lead. Every industry – from entertainment to healthcare – has benefitted from this technology.

The meteoric rise of ChatGPT in our collective day-to-day lives is a continuation of this technological advancement: like deep fakes and past text-based generative AI examples (some of which come from earlier iterations of GPT technology), this technology is a Natural Language Processing tool (NLP) driven by AI, drawing on Large Language Models (LLMs) to create unique human-like conversations. The core difference between ChatGPT (and technologies like it) and chatbots of past decades is that the output from ChatGPT is new: the technology is trained on billions of data points to produce something that is coherent and concise, but not always correct and optimal for the use case.

When it comes to customer experience interactions, however, coherent and concise is only our starting point: generative AI needs to be trustworthy and correct the first time and every time a consumer or employee interacts with it in order to be sufficient. To do this, we need to pair LLMs with a robust dataset that can train and continuously inform that the outputs being produced are accurate. In addition, we need to layer the guidelines of brands – how they speak, and how a brand’s constitution (which guides all business decisions) is parlayed into actionable outputs for the AI in question.

Let’s go back to that customer who is searching for an answer from your brand. Using the capabilities of generative AI, that customer can input a query and the AI will automatically search the entire repository of information a brand holds and synthesize it into a complete, understandable paragraph to deliver exactly the information the customer requires. And it can follow a conversation to provide answers for subsequent questions – complete with links to supplemental materials or connections to other tools in the customer experience journey that will be most helpful for next steps and resolution.

And because this conversational AI experience is informed by the best customer interactions ever recorded and the brand’s specific guidelines, rules, and requirements, both company and customer can trust the answers are correct, represent the brand’s identity, and act in the best interests of the consumer. Limitless possibilities for interactions and information with a specific and structured path to ensure quality, consistency, and accuracy.

We’re seeing legacy technology – AI – partner with new capabilities – machine learning, natural language processing, data lakes, and more – to become something new and exponentially more powerful than previous use cases. 2023 is undoubtably the year where generative AI is not just a household topic, but a foundational part of every industry’s innovation roadmap. Those who accelerate ahead of the competition will do so by looking first at data to inform how AI interfaces across every customer interaction, thereby changing fundamentally every interface between a user and relevant data.

Learn more about how AI technology will transform CX in this LinkedIn Live discussion.

About the Author Elizabeth Tobey Elizabeth is the Head of Marketing for NICE's Digital Solutions group. Previously, she held leadership roles spanning marketing, communications, community, and CX departments at video game, social media, and cloud platform technology companies in both the B2C and B2B spaces. Over the course of her career, she has focused on identifying new and effective ways to create meaningful dialogue between a brand and its customers, and on developing products and programs that are built on relationships, outcomes, and impact.

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