What is Generative AI (GAI) & How Does it Differ from Conversational AI (CAI)?

AI-generated content can be found all over the internet, and although it does have some ethical issues and grey areas, it’s difficult to ignore the major benefits it can have on business productivity and efficiency, especially when it comes to customer service. Creating different types of content in just a few seconds can free up your agents to deal with bigger issues, alongside simplifying a variety of processes within your contact centre.

So how does it compare to conversational AI and how does it work? Our guide below will go through everything you need to know to help you understand where this technology could fit into your business operations.

While many are aware of what generative AI is and the types of content that it can produce, the processes involved in generating the content through artificial intelligence are less obvious if you’re unfamiliar with how the technology learns and adapts.

Generative AI uses neural networks to identify patterns and other structures in the data it was trained on. So in a contact centre, this data would include sales, customer information, past customer interactions and anything your team creates reports on. Artificial intelligence can then learn more about your business through the data it has access to, and automatically generate relevant content for FAQs, chatbot responses and more. For example, a text generator will train on large datasets of text content and can use natural language processing (NLP) to interpret it.

Generative AI has a variety of useful features that make it a worthwhile addition to many types of businesses. Below, we’ll explain some of the main features and how they work.

• Content is generated without direct copying: Generative AI uses deep learning techniques to understand different language tones, styles and patterns and uses these to create new content that isn’t a direct copy of something already found in the databases.
• Generates a wide range of outputs: Rather than producing one tailored response, generative AI is designed to offer a bigger output of options for the user. For example, when used in chatbots, it might offer relevant guides and blogs to a customer seeking support.
• Offers more creativity: Because the focus of GAI is on creating fresh content, it can be more creative with its output.
• Uses learned patterns and relationships: Content is generated based on what it can learn over time from datasets it has access to, which means that the content will become better over time.

New content generation

One of the main features of GAI is that users can create a huge variety of content, from images to text. This means that users can input detailed or vague prompts to guide the output and make sure it matches what they want. Mass content generation can be used to create automatic response options for customers and agents, alongside being used to automatically generate helpful and relevant support pulled from your databases. This means your customers can receive the support options that the technology thinks would be most helpful, while also freeing up the time for your customer service representatives.

Brainstorming and ideation

Whether you’re using generative AI to help your customer service team or working within the product and service development sector of a business, generative AI can be used to create new ideas and work on improving processes internally. Users can simply input an issue and prompt the artificial intelligence to come up with solutions and improvements which can then be built on within your teams.

Data monitoring

Since generative AI is trained on your data to discover patterns and create predictions, it can assist with customer research and create valuable insights. It can also find out how different datasets might link, such as your sales over time and marketing spending and create relationships between these that might have been missed by a human.

Generative AI can be used for many purposes within a contact centre environment. From generating call scripts to creating reports, below we’ll go through some of the most beneficial uses of the technology for improving contact centre processes.

Generating scripts & responses

Generative AI can be linked to your business data to create effective customer service scripts for chatbots to use. GAI will continue to learn from the information being fed through data collected across the business, which allows you to provide personalised support to customers in a way that works for them. In turn, this reduces your reliance on physical agents to handle all customer communications.

Tracking and categorising customer trends

Thanks to the data monitoring features and consistent learning, generative AI can pinpoint multiple trends in customer behaviours and provide actionable insights. This allows your team to meet customer needs in more ways. For example, Netflix has used GAI to improve their personalised recommendations service using databases on customer behaviours and preferences. This means that they can easily find TV shows and films that they’ll love without needing to manually search for them.

Create targeted marketing campaigns

Generative AI can be used to produce creative PR and marketing campaigns based on what has been learned by the technology. For example, UNILAD has published a blog which includes AI-generated images of what humans will look like 1000 years into the future. These campaigns and posts can create a buzz around your brand because the content produced is new and creative. However, it can also be used more subtly to find out which marketing techniques your customers have responded best to in the past and create new campaigns that will drive business.

When it comes to using the technologies, the most obvious difference is that CAI is mainly used to provide human-like conversation, while GAI generates content in multiple formats. The different types of AI also learn differently since CAI is trained on large datasets with human input and conversations, and GAI trains using patterns it finds across various datasets.

We hope this guide helps you gain a better understanding of where and how generative and conversational AI can be used in contact centres to improve customer service.