Is Generative AI the Next CX Frontier? Three Considerations

In recent months, generative artificial intelligence (AI) has become a mainstream topic, thanks to the popularity of ChatGPT, a publicly available natural language processing (NLP) tool driven by AI that allows for human-like conversations with a chatbot.

Generative AI is a form of machine learning that can generate new content, such as audio, text or images. ChatGPT is a type of generative AI that uses a Large Language Model (LLM) and has a wide range of applications — from answering questions on countless topics to providing advice on computer programming. The list goes on.

Launched in November, ChatGPT quickly turned into an internet sensation. By January, it was recording so much traffic — 13 million unique visitors per day — that OpenAI, the company behind it, decided to launch a premium version for those who want uninterrupted access. And last week, Google announced it’s testing a similar chatbot, Bard, which will be available in the coming weeks.

Like everyone in the industry, Genesys is excited about the promise these technologies can bring to business and the way we deliver on customer experiences. We have tremendous expertise and experience around AI and NLP, and already use these technologies across our product portfolio. The biggest benefit we’ve seen in applying generative AI to customer experience problems is in time savings, especially for routine and repetitive tasks.


“We have tremendous expertise and experience around AI and NLP, and already use these technologies across our product portfolio.”


We offer language generation within today, helping generate email for leads and prospects based on existing information. By tracking previous interactions, it can integrate variations into the language it auto-generates, which can lead to better conversion, retention and results.

We’re beta-testing generative AI on the Genesys Cloud™ platform for various use cases, including new agent-assist capabilities, such as summarization, which provides automatically generated summaries for agents to accelerate their work during wrap-up time following a voice call or a digital interaction. We’re actively working on capabilities in this area to add to our portfolio in the coming months, including smart replies, auto-tagging charts and articles, and machine translation, among others.

It’s important to remember that any output from conversational or generative AI technologies depends on the data used to train them. And while these new technologies can be transformational, we’re taking a deliberate and conscious approach to make sure you can reap the benefits without endangering the precious customer experiences you power through our platform every day.

Here are three considerations we’re applying at Genesys to make this technology safer for your business.

1. Understand What the Engine Is Doing

We aim to create transparency as we continue to build features powered by AI, so you know what the AI is doing. With generative AI, you need to be able to understand what inputs the models are using and audit the output. As we apply these capabilities to our products, we’ll endeavor to provide AI model explainability, so you can remain in control.

Generative AI needs to be trained to absorb and consume the information that resides across a company’s systems — not in a public domain — as well as any relevant data sets the company might use and have access to. We’ve already taken this testing approach with powerful features like Genesys Predictive Routing. By having transparency in the AI, you can determine how and why the product arrived at its decision.

2. Keep Humans in the Loop

As we build features that incorporate generative AI, we’ll allow for human approval of content. For instance, in agent-assist applications, generative AI can save time generating content that would take time to summarize, type and correct. The user should be able to review and approve that content before making it part of the customer interaction record.

Taking this approach — where humans can review and ensure that the content generated fits brand voice, meets legal and compliance requirements, and is accurate — helps safeguard the use of this exciting new technology.

3. Maintain AI Ethics Practices

Along with its many advantages, AI technologies can also produce challenges around appropriate use, abuse prevention and the introduction of bias. At Genesys, we’ve established a set of AI ethics guidelines to govern the way we build our AI products. And we remain open to input to enhance and extend this framework.

Balancing value creation with empathy, incorporating privacy design principles, understanding and reducing bias, and nurturing transparency are some guidelines to consider as generative AI usage becomes more common.

AI’s Place in CX Transformation

As new AI technologies continue to emerge, they have the potential to transform the way we work — provided we take the necessary steps to ensure a customer’s security and safety. I foresee many more exciting applications to come in the next few years that can augment and improve the way we deliver customer experiences.