VB Takeaways: The Truth About Generative AI For Customer Service


In this section of Institute for Experiential AI Executive Director Usama Fayyad’s takeaways from conversations with executives and leaders at VentureBeat Transform, Usama shares his thoughts on the last of three roundtable discussions, in which attendees discussed the potential and challenges of using generative AI for customer service.


Generative AI for customer service

This roundtable explored ways to leverage LLMs for Customer Service. It’s a topic that gets a lot of attention. The discussion we had attempted to sort out misunderstandings in this area. For instance, some attendees thought this is an easier, more well-defined application area — which it may appear to be on the surface — but it’s actually much more complex than it appears. Others think the area is “safe” in that the data from a call center belongs to the company. In reality, there may be issues with personally identifiable information and other privacy considerations. There’s also a lack of clear ownership because many call centers are outsourced and accessing all of the data is a challenge. We explore leveraging AI to improve customer service in the insurance industry in our on-demand webinar, which you can watch here

Some startups claim they have a new generation of more robust and reliable chatbots that leverage generative AI. However, in my many years of exposure to this field, I have yet to see anything of the sort. I remember being on the advisory board of Abe.AI. It was one of the many startups in the space trying to build chatbots using robust conversational AI with the ability to seamlessly hand off escalations to (what we call tier2 and tier3) human customer service representatives and managers. This handoff was always challenging. I am eager to see if generative AI will change this, but I remain skeptical until I see real evidence of large-scale deployment. At least one startup in the room claimed they are there already. In the back of my mind I worry about the opaque functions of LLM models and their black box nature making it even more difficult to understand the outcomes and recommendations coming from models. 

I am eager to continue my conversations at future events, such as our annual business leaders conference, which will teach attendees how to lead with AI responsibly.

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