Generative AI in Contact Centers: A Complete Guide

If you’re involved in any form of contact center management, you know that information is incredibly valuable. From call logs and transcripts to customer records; creating, maintaining, and updating that information is essential if you want to improve efficiency and productivity. 

Related content:

• Contact Center AI: Complete Guide
• Best Use Cases for AI in Contact Centers
• Contact Center vs Call Center: What's the difference?
• Omnichannel vs Multichannel in Contact Centers: What's the difference?

So, why aren’t your agents creating perfect call logs and always making sure customer records are as up-to-date as possible? And shouldn’t you, as a manager, be creating detailed content to support agents during calls? 

The reason is simple. These tasks, no matter how vital they might be, are dull.

Fortunately for you, there’s a new type of technology available that can help tackle these laborious processes. Generative AI, with its ability to create context-driven information in seconds, can transform how your contact center treats the creation, management, and deployment of information and content. 

Though the world is still in a relatively early stage of AI development, the capabilities promised by Generative AI can make your contact center’s operations more efficient and your agents' lives easier right now. 

Let’s be clear: Public-facing interfaces like ChatGPT have no real correlation with how AI is used in a contact center. Generative AI is better considered a resource, something you can apply to solve specific problems with information within your organization. 

“AI is more like Oil than God. It’s an economically useful commodity that can be scaled and refined to act as a multiplier on everything we do. Gasoline didn’t replace the horse and buggy; it let buggies carry humans further, faster.” - Packy McCormick

Just as the quote above illustrates, AI is a solution that supports your agents to better serve customers and improve productivity. Generative AI is only one side of a potential contact center AI system (Conversational AI is the other), but its role is so important that we’ve created this standalone guide. 

If you’re interested in how Generative AI can work within your existing contact center to drive change and improve efficiency, keep reading to learn more. 

What Is Generative AI in Contact Centers?

If you’re new to the topic, AI can be confusing. In fact, even if you know a thing or two about Large Language Models (LLMs), ChatGPT, and AI in general, it’s still sometimes hard to understand what the specific functions of modern AI can offer. 

Don’t worry – we’re here to make you feel like an expert in no time. 

Generative AI describes a computer technology that can read and generate natural language. It can both discriminate and generate, meaning it can understand text, speech, imagery, and other forms of media to interpret intent, context, and sentiment. As the name suggests, Generative AI can generate content in an array of different formats based on user prompts or automated criteria. 

In a contact center, Generative AI supports agents and customers with many of the more laborious or mundane tasks that involve text or speech. When combined with Conversational AI, the two act as a layer that sits atop your existing processes and listens, reads, and learns from all language-based activities. 

Generative AI generates replies, responses, resources, and other information in seconds – simplifying all manner of tasks such as basic ID&V, creating call transcripts, or supporting agents with live contextual information during calls. 

So that’s a long way around of saying that Generative AI, as its name suggests, is a tool for generating information and content in natural human language. 

That’s the what, but what doesn’t really explain why it’s valuable to your contact center…so let’s take a closer look at the how.

How Does Generative AI in Contact Centers Work?

Before we go any further, we’re not suggesting you use Generative AI on its own in your contact center. Instead, you need a wider AI solution that incorporates Generative AI with other tech such as Conversational AI – like we do with our Cognigy.AI platform. 

Where Conversational AI is responsible for customer-facing interactions, Generative AI creates unique context-driven responses and other types of information, which can be utilized in all manner of ways – from summarizing calls to generating guides and information used to support agents during a call. 

A common form of Generative AI utilizes  ‘Large Language Learning Models’ (LLMs), which allow the AI to generate accurate content based on real human language and speech patterns. 

So that’s the how – but we recognize that it might still be a little bit confusing. To help shed more light, let’s explore how Generative AI works for your two most important considerations – your agents/team and your customers/users.

How Generative AI works agent-side

• The AI monitors agent conversations on voice and digital channels generating automatic transcripts and call logs. 
• Generative AI can interpret customer requests and create context-relevant information to aid the agent during a call. 
• The AI can interpret customer sentiment for each call and perform instant sentiment analysis to give agents deeper insight into customer satisfaction. 
• Contact centers can vastly improve chatbots and other mundane support processes by allowing Gen AI to create more natural and satisfactory responses. 
• Generative AI can improve live translation for agents during calls, allowing for multilingual contact centers without the inflated costs of human translation. 

How Gen AI works for customers

• Generative AI is an invisible capability that analyzes all customer input and creates responses designed to resolve queries, speed up mundane processes, or accelerate call priority. 
• By understanding customer language, the AI also empowers customers to quickly progress through self-service processes by following context-driven prompts.
• The AI can identify when a customer’s requirements need to be escalated to an agent, helping to improve satisfactory outcomes and reduce waiting times. 
• Generative AI can also present call context and summaries to agents at the start of a call, meaning customers don’t need to repeat themselves and can solve their problems more effectively. 

Generative AI’s main role is to generate language used to respond to customers – but your human agents already do that – so why bother investing in AI? 

To answer that question, we need to look at the benefits to see why, once you’ve tried Generative AI, it’s tough to imagine how you ever used any other approach… 

Benefits of Generative AI in Contact Centers

Make no mistake, choosing to use AI in your contact center is an investment and a commitment. As with any business investment, you need to weigh up the benefits against the costs and see if it's worth it for you. 

Specifically looking at the Generative AI side of things, the benefits on offer for your organization are pretty compelling…

Saves Time

One of the clearest, direct advantages of Generative AI in a contact center is the time-saving associated with call logs and other laborious text-driven tasks. Rather than forcing an agent to create a summary after each call, the AI will automatically create one in your predefined format. Call logs are only one such example – any text/voice-based task can leverage Generative AI to cut tasks from minutes to seconds. 

Provides Next-Gen Agent Assist

Agents have nothing to fear from AI – it’s there to help them. Generative A in combination with Conversational AI can provide cutting-edge agent support by understanding the context of a customer’s call or query. It can then automatically display the relevant supporting information, such as a customer’s return receipt or order information, all without the agent having to take any manual actions. 

Elevates voice and chat experiences

Generative AI helps support smoother interactions, whether through text chat or voice, by building more natural responses to make customers feel more engaged. 

Accelerates bot building

Chatbots existed long before AI, but the capabilities Generative AI brings to bot building are hard to ignore. Gone are the days of manually plotting out every process to create user flows. Generative AI can model processes in seconds and generate a variety of responses and text prompts to engage users. 

Improves Satisfactory Outcomes

By cutting down on laborious tasks such as creating call summaries, improving agent support to minimize friction during calls, and supporting better customer engagement by voice or text, Generative AI helps improve overall results for customers and agents alike. Less time wasted, more queries resolved, and far happier customers. 

Types of Generative AI in Contact Centers: Use Cases 

There’s no better way to understand how a technology works than to see it in action. Use cases help demonstrate how you might use Generative AI (in conjunction with Conversational AI) to revolutionize tasks like call logs or sentiment analysis. 

The best way to get full value from Generative AI is to develop realistic use cases based on the specific processes and problems that you encounter in your contact center. Here are some common examples to help you get started: 

Self-service 

One of the most compelling areas to use Generative AI is in improving customer self-service. Generative AI can use your existing call logs for example.  or customer data and then used to build more efficient ChatBots and Virtual Agents, which allow customers to accomplish basic tasks without the need for agent intervention. 

If you’re tired of fielding calls for simple things like a password change, Generative AI can provide enormous benefits that free up agents to focus on more complex problems that are integral to improving vital metrics like CSAT scores. 

Live Agent Support

Give your agents better support via AI-driven Agent Copilot. With an AI built using Conversational AI and Generative AI, the digital agent can understand customer needs and then use Generative AI to produce relevant solutions or replies – which it can then present to agents without them having to perform manual research. 

There are lots of ways an AI Agent helps support human employees. Other examples include a Generative AI taking a call transcript and then creating a summary that captures your core KPIs or other parameters, which removes the manual burden for every agent in your center and frees up huge amounts of time so they can move on to new calls. 

Translation

Contact centers often struggle with natural language translations, as the cost of real human translation or multilingual agents can be expensive. Instead of relying on basic digital translations, which often result in errors, use Generative AI to support agents in real time with natural responses created in the customer’s language. 

Upselling

If your center aims to upsell customers, Generative AI can help improve successful sales by equipping agents with a customer’s existing information, and current status and then creating specific upsell opportunities. For example, Generative AI would verify a mobile phone customer calling to request more data, display their information and current plan to the agent, and then present upgrade offers and plans the agent can select. 

Reporting

Generative AI, when combined with a Speech-to-Text (STT) service, can take transcriptions of customer calls and then analyze sentiment or look for other patterns. This can be used to power up your reporting and get a deeper insight into both agent performance and customer satisfaction metrics. You can even use Generative AI to take these summaries and data entries and then create data-driven, user-friendly reports/presentations. 

Best Practices for Generative AI in Contact Centers

Got a use case in mind? Ready to start exploring AI for your business? 

Before you do, it’s worth running through some best practices that will help you get the best possible results from your AI implementation and minimize mistakes. 

Here are the things you need to know before you dive in. 

Know Its Limitations

Generative AI is only one type of AI technology and it can’t stand on its own. You can’t just sign up for a popular Generative AI tool like ChatGPT and make your agents use it to solve problems. 

Generative AI needs to be part of a wider platform to function effectively and help your agents and customers. It must also be developed and deployed around your specific team’s processes and challenges.

Cognigy.AI, for example, develops dedicated AI agents powered by Generative AI and Conversational AI that are built for your specific team’s needs. Without this tailored approach, Generative AI is just too limited for the dynamic challenges posed by your contact center. 

Avoid AI Mishaps

Whenever you adopt new technology in a business, risk is involved. Generative AI is no exception and has some unique risks you need to be aware of. 

Some Generative AI tools that have no protection or filter can be prone to misuse. Users can force malicious responses by gaming the prompt system. Others can experience ‘Hallucinations’, where the AI generates nonsense or false responses despite believing itself to have fulfilled the prompt. 

In both of these cases, using a platform like Cognigy.AI mitigates the risk. Cognigy remains the customer-facing interaction layer, handles the information (including things like PII which are regulated) and then selectively uses LLMs to complement and improve processes where it adds value.

Thus, no personal data ever goes to the LLMs and the prompts behind the scenes have been carefully engineered and thoroughly tested by developers in advance before going into production. 

Create Real-World Use Cases

Before you can implement Generative AI, you need a defined use case within your contact center. As with any investment, you need to make sure the benefits outweigh the costs, and so you need real-world processes that can be enhanced through Generative AI.

Map out your core processes and, using the information you’ve learned in this article, consider how Generative AI might be used to improve them. Only then can you approach an AI technology partner with your example use cases, which will be used to help frame the most appropriate solution. 

Understand Your Privacy Requirements

Data privacy is a big deal and will only increase in importance as more and more of the world’s information and interactions become digitized. Public-facing tools like ChatGPT don’t offer the right level of security to protect sensitive user information – so you need to take stock of what data you process and ask your AI technology partner how they can help protect it. 

Implement Generative AI in Your Contact Center Using Cognigy

If you want to grow your contact center, you need to focus on improving efficiency. Less time wasted and faster call resolutions mean more satisfied agents and happier customers – for whom speed is the most important component in customer experience. 

Generative AI offers a transformative way to improve efficiency and speed up satisfactory outcomes. But it’s only one piece of the puzzle. 

If you want to explore the benefits a complete AI system can have on your business,  try Cognigy.AI. Our platform combines Generative AI and Conversational AI to deliver bespoke AI Agents that solve some of the most time-consuming or inefficient tasks in your center. 

Book a demo today to see how we can help. 

Frequently Asked Questions

Will generative AI replace customer service?

No – Generative AI is not a replacement for human agents. Instead, it acts as a way to accelerate better outcomes by empowering customers to complete self-service processes and supporting agents during calls. 

Can Generative AI Answer Phone Calls?

On its own, Generative AI has no telephony architecture so it can’t answer calls. It can, however, be used as a tool during calls, to listen to live conversations and generate text-based responses which are then translated into voice replies via text-to-speech. 

What Is the Difference Between Generative AI and Conversational AI?

If Generative AI is the back-end, non-customer-facing tool that generates language, Conversational AI is the customer-facing interface that interprets speech and intent to direct the Generative AI. See our article on Contact Center AI to learn more about the differences between each AI and how they can work in tandem with Cognigy.AI. 

What Is the Difference Between Generative AI and Chatbots?

Chatbots have predated AI for many years, with early versions using pre-mapped processes and responses to direct users to complete basic actions. Modern chatbots can use Generative AI to improve the quality of their responses, but usually only when combined with Conversational AI to better interpret customer requests.