Want to see how we do chatbot analytics? Join us for a product demo webinar.


As artificial intelligence grows, evolves, and refines its implementations, there are sure to be hiccups along the path of innovation. Some consumer facing AI-based products are so efficient and seamless that it skews our expectations for what AI and machine learning can accomplish elsewhere. That said, hastily built AI solutions which are not trained properly, and then left to interact with users without any supervision, can put a damper on what the public at large expects from a smart solution.

It’s no surprise that as AI’s potential captures headlines, in turn questions about what exactly we can hope to achieve with it come to light. Here are a few recent stories touching on the expectations we have for artificial intelligence in customer service, and the greater consumer space.


‘Virtual moron’ Telstra Chatbot Codi Slammed As Useless
Mail Online

Australian telecommunications company Telstra found itself in hot water on social media this month, as its customer took to social networks to blast Codi – their new virtual assistant.

As Wysdom CTO Karthik Balakrishnan pointed out last year, failure to train is training to fail. AI supervision is paramount to initial success in cognitive customer care. Letting your customers use the platform before it’s ready can lead to further negative connotations and reluctance to use digital means for support down the road.


Chatbots Aren’t Failing Us, Our Expectations For Them Are

This article flips the script on how we evaluate chatbots and automated assistants. Instead of hoping for a solution that instantly works in every situation like a human agent, we should create and celebrate more purpose-driven solutions that complete a narrow set of tasks in a quicker and more efficient manner.

The article goes on to differentiate bots built for discovery versus those focused on service.


How To Make A.I. That’s Good For People
New York Times

Stanford Artificial Intelligence Lab Director Fei-Fei Li urges readers to consider AI’s effect on society as a whole. She touches on the aforementioned need for human training and supervision, and not solely from AI researchers, but subject matter experts that can inform the platform beyond the basics.

“Making A.I. more sensitive to the full scope of human thought is no simple task. The solutions are likely to require insights derived from fields beyond computer science, which means programmers will have to learn to collaborate more often with experts in other domains.”

Subscribe to our newsletter

Get inspired

Read more insights on the latest in virtual agent analytics and performance management

Subscribe to our newsletter

We use cookies to ensure that we give you the best experience on our website. By clicking “I Accept” or if you continue to this site, we will assume that you consent to the use of cookies unless you have disabled them.