Generative AI in Contact Centers: Transforming Customer Service and Streamlining Operations

Learn about the history, benefits, limitations, and effective implementation strategies for leveraging Generative AI.

Last updated on: 
August 10, 2023
Alexander Kvamme
Alexander Kvamme
July 13, 2023
3 minute read

What is Gen AI? 

Generative AI is a type of artificial intelligence system capable of generating text, images, or other media in response to prompts.

Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics.

History of Generative AI: 

Generative AI, particularly in the context of Large Language Models (LLMs), has its roots in the field of artificial intelligence. The idea of LLMs came into prominence with the launch of the Transformer architecture by Google's Brain team in 2017. This groundbreaking innovation changed the way AI systems understand and generate text, significantly improving their effectiveness and accuracy. Crucially for businesses, the Transformer design could be trained more efficiently on modern hardware, accelerating the development of new AI applications.

In 2019, OpenAI released GPT-2, an advanced LLM that underscored the substantial potential of generative AI. GPT-2 used a method called reinforcement learning from human feedback (RLHF), which involved training the AI using input from human evaluators. This approach led to much more nuanced and sophisticated text generation, improving the quality and relevance of the AI's output.

The impact of generative AI on everyday life became more apparent in 2022, with the launch of ChatGPT by OpenAI. ChatGPT provided an interface that allowed users to directly engage with the LLM. This made the benefits of generative AI accessible to a wider audience, opening up new possibilities for uses in customer service, education, and other areas where natural language interaction is valuable.

By 2023, the field saw further advancements when Meta (formerly Facebook) decided to open-source its LLM, LlaMA. Open-sourcing a model of this magnitude was a significant event because it democratized access to advanced AI technology, enabling a broader range of businesses, researchers, and developers to experiment with and build upon the technology. This step is likely to spur further innovation and competition in the field.

The Power of Generative AI for Your Contact Center:

Generative AI represents a significant evolution in artificial intelligence technology, with the potential to unlock transformative solutions in numerous sectors. However, in the midst of a hype cycle, it's crucial to remember that the true value of any technology lies not in the technology itself, but in the solutions it enables. With this in mind, contact centers, which are fundamentally about communication, are poised to be one of the sectors most transformed by Generative AI.

One of the major use cases for Generative AI in contact centers is in understanding language. Transfer learning, a key feature of Generative AI, allows organizations to profoundly deepen their understanding of their customers. This is a significant departure from legacy rules-based machine learning models, which are often rigid and require extensive pre-training on specific tasks. These legacy models were limited in their ability to adapt to new data or tasks without substantial retraining.

With Generative AI, however, we see a departure from these constraints. Traditionally, organizations could manually monitor only about 5% of customer conversations due to resource constraints. With Generative AI, it's possible to automatically monitor and analyze 100% of these interactions. This technology can learn from vast amounts of data and adapt its understanding over time, offering the flexibility and depth of understanding that was previously unattainable. This comprehensive monitoring can help organizations extract valuable insights, understand customer needs and concerns more effectively, and provide personalized service at scale.

Another potential use case lies in the generation of language. Generative AI models can be trained to interact directly with customers, responding to their inquiries and providing assistance. They can also work as virtual assistants to human agents, suggesting responses and guiding conversations. However, due to potential risks such as 'jailbreaking' (where AI models give unexpected responses) and 'hallucination' (where AI models generate information that isn't factually correct), this application might be a little further off from practical deployment (we'll explore this in more detail in the next blog post).Importantly, Generative AI also brings the advantage of accessibility for non-technical personnel. In the past, making adjustments to machine learning applications would require editing code, which often meant relying heavily on IT departments. With Generative AI, changes can be made by editing prompts, enabling non-technical staff in contact centers to interact more directly with the AI, and reducing their dependency on IT.

In summary, the capabilities of Generative AI to understand and generate language could revolutionize contact centers, from enhancing customer understanding to offering new modes of interaction. And while it's important to acknowledge the hype around this technology, the potential solutions it enables are genuinely transformative. 

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