Common Pitfalls Implementing AI in the Contact Center

Artificial intelligence (AI) is an exciting development for the contact center industry (like pretty much all other industries), with the potential to revolutionize the way we interact with customers and manage operations. However, there are several common pitfalls that organizations should be aware of when implementing AI in their contact center.

The first, of which, is understanding that when it comes to AI, it’s not one-size-fits-all. I’ve heard multiple stories where the AI element of a contact center’s technology stack is often either completely on or completely off, which can be frustrating for experienced agents and costly for the organization. 

This one-size-fits-all approach to AI deployment often slows agents down or fails to provide the answers they need. A better solution is to employ technology that is tailored to individual agents’ needs and preferences, allowing them to work more efficiently and effectively.

The second pitfall I’ve seen is the lack of connectivity between AI models and an organisation’s back-end systems and processes. Without context, AI lacks the ability to provide meaningful guidance to agents. Which again, becomes a frustration. However, organizations must also consider the security implications of allowing AI technology access to all their systems and data.

What we’ve seen recently with the use of Large Language Models (LLM), such as Chat GPT in particular, is businesses failing to grasp the security implications of feeding confidential data into a public cloud-hosted tool. Whether it’s company IP or customer PI, deep consideration must be given to what is fed through these AI technologies.

The third pitfall is the lack of appropriate use case consideration for AI. Some customer use cases require AI, while others do not. Organizations that fail to carefully consider the most appropriate use cases for AI implementation risk hurting their bottom line.

While the implementation of AI in the contact center industry has the potential to improve customer service and operational efficiency, organizations must carefully consider the potential pitfalls before deploying the technology. 

By avoiding the common pitfalls of a one-size-fits-all approach to AI deployment, ensuring connectivity with back-end systems, and considering the most appropriate use cases for AI, organizations can ensure that they are using AI in a responsible and effective way, ultimately leading to better customer experiences and improved business outcomes.