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Customer service chatbots may soon become smarter, more conversational, and more helpful.

This article is part of a new series on the potential of artificial intelligence to solve everyday problems.

Some household item is acting up, and you require assistance. Or perhaps you have a query about your travel arrangements or insurance coverage. When you visit the company’s website, a digital imp appears in a small text window. “How can I assist you?” ” it inquires. Or you call a customer service number and a cheerful robot asks the same question.

You go ahead and type or tell the chatbot what you want. Its canned responses are inaccurate. It doesn’t fully comprehend your situation. You give up in despair after several erroneous linguistic volleys.

That experience is so common that customer service professionals have dubbed it “the spiral of misery.”

But there is good news. Customer service chatbots are becoming less robotic. And they are on a path to improve significantly over the next several years, according to researchers, industry executives and analysts, pulled along by advances in artificial intelligence. They will become more intelligent, more conversational, more humanlike and, most important, more helpful.

“Even now, there are times you sort of can’t tell it’s not a human,” said Bern Elliot, an analyst at Gartner, a technology research firm. “It’s not as good as you’d like, but it is moving in that direction. And innovation is occurring at a rapid pace.”

A.I. is used in research projects. Natural language processing has produced amazing feats of understanding and producing language. A.I. Software can create computer programs, write stories and poems, answer trivia questions, and translate dozens of languages. These projects typically have nearly unlimited computing power and access to vast amounts of readily available data on the internet.

Consumer digital assistant software, such as Apple’s Siri and Amazon’s Alexa, also searches the internet for answers.

However, for the majority of businesses, everything is more constrained. Their customer information, which is required to answer questions, is not available on the internet but is kept in corporate data centers. They have less data than the internet behemoths, and it has accumulated over time, stored in various formats and locations. (A.I. algorithms struggle when there isn’t enough data.) It’s more of a geological dig than an internet search.

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.

Suppliers provide software tools, which businesses can then customize and train on their own data.

According to Gartner, the business market for virtual assistants, also known as chatbots, will grow 15% this year to more than $7 billion. Some of these bots are intended to help employees, but the majority are for customer service.

No company’s journey to chatbot technology has been more humbling and instructive than IBM’s. Following its Watson supercomputer’s victory over human champions on the television game show “Jeopardy! “About a decade ago, IBM began experimenting with applying Watson’s natural language processing to other fields.” Cancer diagnosis and treatment were early priorities, and IBM dubbed 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.”

Early chatbots were programmed with a predetermined set of questions and answers. But that led to dead ends if the software did not understand the questions. Today, Dr. Soffer said, much of the recent innovation lies in “teaching the system to understand and tease out a person’s intent.”

Creating software that can determine the essence of a person’s inquiry is a central challenge. “You assume there are only so many ways a person can say something, but you learn that is not really true,” said Bob Beatty, chief experience officer for G.M. Financial.

Initially, the financial services arm of General Motors had a rudimentary chatbot that simply delivered canned answers to a set list of questions. But it began working with IBM in 2019 to develop an interactive chatbot. G.M. Financial had a two-year plan to develop and roll out its chatbot, powered by Watson Assistant.

The coronavirus pandemic lockdowns in March 2020 meant a surprise acceleration of that timetable. Mr. Beatty sent home the 700 or so agents who worked at the company’s call centers in Arlington, Texas and Chandler, Ariz. While rushing to equip the call center agents for remote work, G.M. Financial, with emails and a notice on its website, steered customers toward its nascent chatbot rather than the phone.

The chatbot struggled at first. But the G.M. Financial developers and IBM engineers programmed in the ability to answer more and more inquiries — no matter how they were phrased — like, “What is my payoff amount?” or “Did you receive my March payment?”

Even simple questions require personalized answers that the software has to look up in a company database, though. At the start, the chatbot called Nanci (its name is within the word “financial”) was resolving less than 10 percent of customer inquiries. But within two months, the success rate rose to 50 percent — and is now at 60 percent, according to G.M. Financial.

So far, Nanci has been a text-only chatbot, but the company is adding a voice version. And it is working with IBM to automate more complex tasks like changing payment and due dates.

The main purpose of the chatbot technology, Mr. Beatty said, is to improve the customer experience and nurture brand loyalty for its parent company, General Motors. But the average call-center inquiry lasts six minutes and costs $16, according to industry estimates. At G.M. Financial, many customer questions are now answered by the chatbot. In January, Mr. Beatty estimated, the company saved a total of $935,000.

So far, call-center staff has not been trimmed. The technology, Mr. Beatty said, will allow agents to spend more time on difficult problems — for example, speaking to a customer who has lost a job and needs to extend a car lease or loan.

“That’s something a trained, empathetic team member can do in a way A.I. cannot,” he said.

For most businesses, a hurdle to progress with A.I. is not having enough training data. Modern A.I. software requires vast amounts of data to pore through to improve its accuracy — to learn, in its way. Some new A.I. technology may be able to overcome that obstacle by automatically generating more training data or to learn from lesser amounts of data.

Anthem, a major health insurer covering more than 45 million people, has no shortage of data, and it also has a technology staff of a few thousand including data scientists, A.I. experts and applications developers. IBM’s Watson Assistant is one of many tools Anthem uses.

Anthem shows what is happening now with A.I.-fueled chatbots — but also what might be possible in a few years. Its current technology, including its mobile app, is called Sydney and is 90 percent accurate in answering questions about co-payments (“I’m getting a knee replacement. How much does my insurance cover?”) and medications (“Does my prescription have any drug-drug interactions?”), according to the company.

But the long-term goal, said Rajeev Ronanki, president of digital platforms at Anthem, is to use A.I. to sift through all its claims and clinical data to deliver personalized health advice. And other data: Sydney can even upload fitness tracker information.

There are, for example, more than 380 care and treatment options for people with diabetes, Mr. Ronanki said. What are the diet, exercise and medication regimens that have produced the best results for similar patients — by age, gender, other conditions and medical history?

That information could be delivered as treatment guidelines to a physician and as health advice to an individual through an increasingly intelligent and conversational chatbot.

A.I., Mr. Ronanki said, can “help us move from reactive sick care to proactive, predictive and personalized health care.”

And a solution, perhaps, to the spiral of misery.

Source : https://www.nytimes.com/2022/03/03/technology/ai-chatbot.html

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