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MIT News: Alumni profile

Can AI provide better customer service?

Liz Tsai ’11, SM ’13

January 4, 2024

Customer service experiences can really stick with you—a positive interaction can inspire brand loyalty, and a negative one can prompt a complete boycott. But encounters with automated customer interfaces, which often rely on limited phone menus or inept chatbots, rarely generate rave reviews.

So Liz Tsai ’11, SM ’13, came up with an alternative. In 2016, she cofounded HiOperator as CEO to develop a customer service system with a combination of back-end automation and generative AI (read: trained computer models that can generate high-quality answers to written questions).  

Taking on big challenges is nothing new for Tsai, who was just 15 when she arrived at MIT. And while she always loved math and science, at the Institute she discovered even more interests. “Part of what was magical about MIT was that it really does encourage you to explore a lot of things,” she recalls. In fact, she double majored (in mechanical engineering and materials science and engineering), double minored (in political science and biological engineering), and earned a master’s in the MIT Media Lab. An early career in commodities trading took her to Switzerland and Singapore. Next, she worked at a fintech startup, and though the company wasn’t successful, she discovered that the entrepreneurial mindset suited her. 

As she spoke to company leaders, the need for better customer service—a crucial function with high labor costs—came up again and again. And with an industry turnover rate of up to 100% annually, it’s rare to have a staff full of well-trained, well-informed representatives. 

While other customer software tools use AI to help make human agents more productive in certain areas, Tsai says that HiOperator’s HiQ platform is the first AI-based system that automates almost everything an agent does, including resetting passwords, submitting refunds, and processing returns/replacements. It uses generative AI to write customer-facing email, chat, and text messages, providing support that, according to Tsai, feels like “talking to a really sharp human.” 

Tsai says companies that previously relied on humans alone to handle customer service queries see customer satisfaction scores go up when they employ the system. But she insists that it won’t eliminate human customer service agents. Instead, it could free them to help troubleshoot complex requests, like health and safety concerns for a food company.

This kind of automation “is not about replacing human labor outright,” she says, “but rather about increasing service industry output while providing more interesting and fulfilling jobs.”

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