Time-to-resolution is a key metric in customer service, which is why there is a lot of interest in improving chatbots. But if a chat interface is a pi

Improved chatbot customer experience: sleep() is all you need

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2024-07-02 06:30:03

Time-to-resolution is a key metric in customer service, which is why there is a lot of interest in improving chatbots. But if a chat interface is a piece of string, it has two ends, and only one end is a computer. Until we get to the point where we have AI talking to AI and we’ve completely removed the human, one end of that experience is a human being. That means to optimize time to resolution, the human and the machine have to be in harmony.

Both ends of the chat experience have a common incentive: they both want to minimize time-to-resolution. But this incentive alignment is imperfect because there is another incentive: cost minimization. As the human on the other end of the line, I know I am 100% committed to reducing time-to-resolution because my cost is my time, but the cynic in me knows that the business I’m speaking to is also laser focussed on reducing labour cost, and that is not necessarily perfectly aligned to time-to-resolution - we’ve all been on hold.

That’s why we speak to an AI-powered chatbot at the start of the interaction. The business hopes I won’t need a human. How do I know I’m speaking to AI anyway, aren’t LLMs good enough to pass a customer-service limited variation of a Turing test? Does the model just need more fine-tuning on QA pairs?