I’m wandering through the halls of the Venetian at AWS re:Invent, and I’ve got this nagging thought about contact centers. We’ve always called them cost centers, haven't we? Places where we send money to disappear, or at least to be spent apologizing to people for late deliveries.
But sitting in session BIZ219 at this year’s AWS re:Invent with Ayesha Borker, Principal Solution Architect and Jack Hutton, Principal Product Manager within the Amazon Connect at AWS, I began to wonder if we’ve got it all wrong.
They’re pitching Amazon Connect not just as a way to fix problems but as a growth engine. It’s a bit of a pivot. We are moving from the era of “How quickly can I get you off the phone?” to “What else can I help you buy?” And it’s powered by AI.
Importing 25 Years of Retail Wisdom
The most compelling part of the pitch wasn't actually the new features. It was the history lesson.
Ayesha Borker reminded the room that Amazon has been rehearsing for this moment since 1998. That was when they launched their first recommendation engine—the "customers who bought this also bought that" feature that we now take for granted.
For 25 years, the Amazon Marketplace has been a massive laboratory for understanding human intent. They have learned how to predict what you want before you know you want it. They have learned how to reduce churn by showing you the right alternative when your size is out of stock.
Now, they are simply lifting that entire infrastructure—the algorithms, the predictive attributes, the "item-to-item" filtering—and dropping it directly into Amazon Connect.
It solves the classic problem Ayesha highlighted:
"I've got the data, but I don't know what to do with it."
By baking models like "Frequently Paired," "Trending Now," and "Recommended for You" into the contact center, they are giving agents the same superpowers that power the Amazon homepage.
The "Stella" Effect
In the session, they showed a demo involving an AI stylist named "Stella." A customer, Maya, was looking for a dress for a wedding in Napa. Stella didn't just search for "dress." She checked the context. She realized it was an outdoor wedding. She suggested a lighter fabric.
When Maya picked the dress, the "Frequently Paired" algorithm immediately popped up with a matching necklace. It was smooth.