“Companies are rushing to find ways to implement or incorporate generative AI into their businesses to drive efficiencies,” said Will Fritscher, deputy chief client officer at TP. “But instead of looking at AI as a way to cut costs, we should actually look at it in terms of improving the customer experience and increasing value.”
Achieving this requires solving two intertwined challenges: automating routine tasks to power live agents, and ensuring AI output is accurate, reliable, and accurate. And what is the key to both of these goals? Striking the right balance between technological innovation and human judgment.
A key role in customer support
The potential impact of Generative AI on customer support is two-fold. Customers benefit from faster and more consistent service for simple requests.
They also show great human attention to complex and emotionally charged situations. For employees, eliminating repetitive tasks increases job satisfaction and reduces burnout. You can also use this technology to streamline customer support workflows and improve service quality in a variety of ways, including:
Automated routine inquiries: AI systems handle simple customer requests like resetting passwords or checking account balances.
Real-time assistance: During interactions, AI pulls in context-relevant resources, suggests responses, and guides real agents to solutions faster.
Fritcher points out that TP relies on many of these features in its customer support solutions. For example, AI-powered coaching combines AI-driven metrics with human expertise to provide feedback on 100% of customer interactions instead of the traditional 2%.
4% were monitored by pre-generation AI.
Call summaries: AI automatically documents customer interactions, saving live agents valuable time that can be reinvested in customer care.
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This content was created by Insights, the custom content division of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
