The Curious Corporations’ Guide to Generative AI in the Contact Center

On Nov. 30, 2022, OpenAI released an early demo of ChatGPT, and over the next five days, the chatbot gained more than 1 million users.

ChatGPT is a generative artificial intelligence/large language model (LLM) chatbot that produces text responses meant to approximate human-like responses. It's just one example of a new breed of generative AI that is taking the tech industry by storm; its disruptive force has earned it the moniker the "iPhone moment of AI," a tipping point for mass adoption.

Indeed, companies are already building tools for employees and customers leveraging generative AI technology. And many organizations are now embarking on strategies to reimagine customer engagement operations, with generative AI at the core.

According to industry analyst firm IDC, "ChatGPT-like technologies deployed in the contact center have the potential to change the environments that handle large amounts of mostly unstructured information that needs to be retrieved and reconstructed in a usable format. It is no wonder that customer service is one of the first use cases for the potential of ChatGPT, which in turn has opened the door to discussing other LLMs."

In an age of contact center overwhelm where staffing constraints abound, generative AI can be a vital part of the equation to close what Verint calls the engagement capacity gap, the continuing divide between customer expectations and the resources companies have to meet those expectations. But organizations must be sure to walk, not sprint, in their evaluation and adoption of generative AI. While it is a useful appliance, it's not ready to be unleashed in the raw and must be used with the appropriate guardrails in place.&

So, how do chief technology officers,, heads of innovation, and customer experience and contact center professionals cut through the hype to understand how to use generative AI/large language models effectively and responsibly?

There are four pillars that should serve as helpful guideposts in this endeavor:

  1. Security and compliance. It's important to ensure use of the technology is governed and managed from the perspective of legal compliance, data privacy, and the protection of your businesses' sensitive proprietary information. This is necessary now and moving forward as changes take place in the technological and regulatory landscape.
  2. Extensibility. To truly take advantage of generative AI's potential for unlocking actionable insights from ever-growing datasets, it must be open and extensible so that when a new data source comes along, it can easily ingest this data. Orchestration platforms can also ensure extensibility becomes interoperability, allowing contact centers to thread AI across their tech stacks.
  3. Validation. Generative AI prompts and the information returned must be able to be validated so results are reliable and trustworthy. Context, brand voice, and appropriate tone for the use case are all elements needed to be considered for validation.
  4. Scrutability. AI that self-learns and auto-suggests can seem scary, but it is safe when used appropriately. Therefore, generative AI must be able to be used where its models, learnings, outputs, and outcomes can be explained and deciphered. Most organizations should opt for some human-in-the-loop oversight to ensure that the generative AI model is making decisions in a responsible and ethical manner.

We live in a conversational economy, and nowhere is that more evident than across contact center and customer experience strategy, technology, and operations. From business intelligence to asynchronous digital messaging to self-service task completion, conversational front-end user experiences are enabled by conversational AI and now augmented and accelerated by large language models in the form of generative AI. This creates an end-to-end lifecycle of conversational access to analytics for CX leaders, customized workflows for contact center agents, and personalized customer journeys across all touchpoints for customers themselves.

Shifting the Focus from Technology to Business Transformation

The most exciting thing about generative AI is that it is ushering in a whole new world of business transformation as opposed to digital transformation. Organizations can spend less time programming and deploying technology and more time leveraging data to quickly solve business problems.

The demand for technologists will give way to the need for business transformationists. Innovative companies have realized that AI is enabling CX teams to focus on orchestration as opposed to applications. Contact centers are almost overinvested in technology solutions in their stacks. An appropriate needle needs to be threaded across those investments and channels that allows for an AI-driven orchestration of all solutions. Conversational AI systems infused with generative AI support interoperability of disparate contact center technologies; this enables CX pros to start with customers' specific personas or profile-related journeys and the desired outcome in mind and then weave in the technology applications after the workflow or conversation flow is mapped.

In practice, this means instead of a contact center leader wondering how a voice IVR can be aware of what is going on in a live chat within the context of a personalized customer journey, the AI orchestration layer can be used to enable smooth hand-offs across technology stacks. This is thanks to conversational AI's swift recognition of anticipated journeys and easy retrieval and delivery of solutions or pivots via APIs.

Frank Schneider is vice president and artificial intelligence evangelist at Verint.