7 Generative AI Uses Cases for Contact Centers

The feature improves the platform in two significant ways: it answers more queries and detects when the customer’s intent changes.

First, the solution parses through business-specific resources – including the company website, product manuals, and knowledge articles.

Then, it spotlights relevant insights and presents them to the customer, allowing the conversational AI to resolve queries the business hasn’t specifically built a bot to answer.

Second, it detects when a customer’s intent changes midway through an interaction, pulling the conversation back on track and enhancing containment rates.

6. InMoment Draws Insights from Customer Feedback

InMoment became the first voice of the customer (VoC) vendor to launch a GPT-powered solution with its Smart Summary Generator.

The tool gathers disparate sources of customer feedback and strips away trends, turning them into structured and concise text.

Such sources include contact center transcripts, online reviews, and surveys.

According to InMoment, it takes only minutes to generate a trends overview. As such, contact centers can use the Smart Summary Generator to keep tabs on emerging issues.

7. NICE Alerts Supervisors to Agent Issues

NICE Enlighten is a collection of AI models embedded within the NICE CXone solution, aiming to deliver insights to contact center managers.

Now, NICE has released Enlighten Actions, which harnesses generative AI to turn insight into action.

For instance, Enlighten may detect when customer satisfaction is low in real-time. By alerting the supervisor, they can step in to save the experience.

It may also alert the supervisor when an agent exhibits a particular behavior and report complaints as they occur. The latter use case can aid contact center reporting efforts.

What Else Is Possible?

These are only some of the many use cases contact center vendors have launched in recent months.

Indeed, EvaluAgent has released a tool to enhance its auto-QA solution. Meanwhile, Calabrio is collaborating with OpenAI to augment its WFM offerings.

Yet, many more capabilities will soon come to the fore, as GPT also has significant potential to bolster custom data charting, trending, and routing use cases.

Moreover, expect it to change the low-code tools that many contact centers use to build solutions for self-service, proactive outreach campaigns, and bots.

As evident in the Google example, the ability to use natural language – instead of static drag-and-drop elements – may shake up the field.

Finally, the recent launch of the next iteration of ChatGPT – GPT-4 – brings even more possibilities.

Indeed, it comes with three exciting new features. These include the ability to mimic a user's writing style, analyze and classify images, and handle larger text inputs.

Nevertheless, for now, expect all use cases to keep a human-in-the-loop.

After all, despite all the promise of the large language models that power generative AI applications, they are prone to mistakes.

As such, they are not yet ready to be let loose on customers without someone in the middle.