Ending the Chatbot’s ‘Spiral of Misery’

Tackling that challenge has become an emerging and increasingly crowded market, called conversational A.I. Big Tech companies like Microsoft, Amazon, Google and Oracle have offerings, as do smaller companies and start-ups including Kore.ai, Omilia, Rasa, Senseforth.ai, Verint and Yellow.ai.

The suppliers provide software tools that companies then customize and train on their own data.

This year, the business market for virtual assistants — a.k.a. chatbots — will grow 15 percent to more than $7 billion, according to a Gartner prediction. Some of those bots are designed to assist employees, but most are for customer service.

No company has made a more humbling and instructive journey to its chatbot technology than IBM. After its Watson supercomputer triumphed over human champions in the TV game show “Jeopardy!” about a decade ago, IBM set about applying Watson’s natural language processing to other fields. An early focus was the diagnosis and treatment of cancer, and IBM called health care its “moonshot.”

In January, after struggling for years, IBM announced it was selling off its Watson Health business to a private equity firm. A few days later, Gartner rated IBM’s Watson Assistant a “leader” in conversational A.I. for business. Watson has gone from cancer moonshots to customer service chatbots.

Today Watson Assistant is a success story for IBM among its remaining A.I. products, which include software for exploring data and automating business tasks. Watson Assistant has evolved over years, being steadily refined and improved. IBM fairly quickly learned that a rigid question-and-answer approach, though ideal for a game show, was too limited and inflexible in customer service settings.

“The real world opened our eyes,” said Aya Soffer, a vice president for A.I. technologies at IBM Research.

The starting point for improvement, Dr. Soffer said, has been a deeper understanding of what happens in call-centers, working with other companies to mine and analyze many thousands of calls between customers and human agents. In dialogues, for example, tracking which questions and which follow-ups led to resolving a customer’s problem, she said, and what were the telltale signals of “conversations that went bad.”

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