AI in the Workplace Is Already Here. The First Battleground? Call Centers

In recent years, companies have begun using machine-learning models to scan and analyze conversations between agents and customers.

CHATTANOOGA, Tenn.—Johnathan Bragg has always looked at his job selling home-repair insurance the same way an artist looks at a canvas.
“I got this road map in my head of what it looks like when you’re delivering world-class customer service—what triggers people, what makes people trust you,” Mr. Bragg said. “It’s like when da Vinci was painting.”
Mr. Bragg is one of the top-performing sales agents for HomeServe USA Corp., a home-repair service company that sells plumbing, heating, cooling and electrical repair plans to about five million customers in North America. For 11 of the past 12 months, working from a cavernous call center on the outskirts of town, he has been in the top 10% of its 432 agents, he said, for the simple fact that he listens to what people want.
“I don’t just say stuff and read scripts,” said Mr. Bragg. “I listen to everybody, whoever you are, and I retain what it is that makes that person interested. I can get just about anybody to buy anything.”
Recently, with business growing, HomeServe hired a new agent to assist Mr. Bragg and his co-workers. Named Charlie, she’s an artificial intelligence-powered virtual agent that HomeServe built using a conversational AI platform from Google and other technologies. She answers 11,400 calls a day, routes them to the appropriate departments, processes claims and schedules repair appointments. She whispers in agents’ ears whether a customer is eligible for certain coverage plans and types on agents’ screens why the customer is calling.
“I tell agents to think of Charlie as a personal assistant,” said Jessica Cloud, vice president of automation and innovation.
Charlie isn’t universally liked inside the Chattanooga call center. She can be controlling, including requiring agents to say specific words when they talk to customers, and penalizing them if they don’t. She sometimes routes callers to the wrong department. “We’re taking up a collection to get Charlie a hearing aid,” said Mr. Bragg’s colleague Robert Caldwell, another top-selling agent, sitting in a cubicle nearby.
Sometimes she suggests unwelcome ideas for what agents should say next. Charlie recently told Mr. Bragg a caller wanted to enroll in a repair plan. She didn’t understand that the man’s water pipe had burst, that he was waiting for a repair and that he was livid. When Mr. Bragg picked up the call and repeated what Charlie told him to say—“I see you’re trying to enroll”—the man exploded in rage.

‘I don’t just say stuff and read scripts,’ said HomeServe customer-service agent Johnathan Bragg, seen here at the company’s Chattanooga, Tenn., offices.
From management, Charlie is getting rave reviews for her efficiency and is about to get a promotion. Soon, she’ll start telling agents specifically what they should say and do next. She’ll also start grading the humans on their performance.
“She’s supposed to make the job easier, not just make us do what she said,” said Mr. Bragg. He worries Charlie makes too many mistakes. “I’m a top performer. She’s not my supervisor.”
‘A massive restructuring’
A new generation of artificial intelligence is rolling out across American workplaces and it is prompting a power struggle between humans and machines.
Recent advances in technologies such as ChatGPT, natural-language processing and biometrics, along with the availability of huge amounts of data to train algorithms, has accelerated efforts to automate some jobs entirely, from pilots and welders to cashiers and food servers. McKinsey & Co. estimates that 25% of work activities in the U.S. across all occupations could be automated by 2030.
Today, however, AI’s biggest impact comes from changing the jobs rather than replacing them. “I don’t see a job apocalypse being imminent. I do see a massive restructuring and reorganization—and job quality is an issue,” said Erik Brynjolfsson, director of the Stanford Digital Economy Lab. McKinsey estimates 60% of the 800 occupations listed by the Bureau of Labor Statistics could see a third of their activities automated over the coming decades.
For workers, the technology promises to eliminate the drudgery of dull, repetitive tasks such as data processing and password resets, while synthesizing huge amounts of information that can be accessed instantly.
But when AI handles the simple stuff, say labor experts, academics and workers, humans are often left with more complex, intense workloads. When algorithms like Charlie’s assume more human decision-making, workers with advanced skills and years of experience can find their roles diminished. And when AI is used to score human behaviors and emotions, employees say the technology isn’t reliable and is vulnerable to bias.
One of the most fertile testing grounds is the call center, or as labor experts call it, the “factory of the information economy,” and HomeServe is among the early adopters. Across the industry, workers are measured on dozens of tasks from “average handle time” to “first call resolution” and worker burnout rates are high. In a 2022 survey, 65% of call-center agents anticipated leaving their jobs in the following two years, according to market research firm Customer Management Practice, which polled 1,000 workers between April and June last year.

Workers at HomeServe’s Chattanooga call center, where Charlie, and AI-powered bot, is taking on an increasing number of tasks.
Proponents say AI promises to fix much of this by handling monotonous tasks and the stress of decision making. In recent years, companies have begun using machine-learning models to scan and analyze conversations between agents and customers. Conversation analytics quickly identify the words and sentiments customers are expressing to find patterns. The technology can detect how each agent is performing and recommends what the human should say and do next.
New AI technology “helps to take decision-making responsibility away from the agent, so they can act,” said Brittany Bell, customer-success manager at Cresta, a conversation-analytics startup with customers including American Express Co., Cox Communications, Inc. and Signet Jewelers Ltd.’s Blue Nile, during a recent presentation.
When humans turn over decision making to a machine, they no longer use their own knowledge and experience—just ask taxi drivers whose street knowledge has been superseded by Google Maps. In her research about call-center automation, Virginia Doellgast, professor of comparative employment relations at Cornell University, has found that humans who are tightly monitored by an algorithm, forced to follow a script or have little control over how they work are more likely to get burned out and find it harder to solve customer problems.
Adds Julian McCarty, the CEO of conversation-analytics company MosaicVoice: “There’s a balance between empowering an agent and telling them what to say.”
Companies including Comcast Corp., Charter Communications Inc.’s Spectrum and Cox Communications are even further along than HomeServe. They are using conversational AI to detect and measure more subjective human emotions and behavior through a technique called sentiment analysis, a tool that decides if conversations are positive, negative or neutral. Some models evaluate words and context to score conversations, and others include voice pitch, tone and cadence. Comcast analyzes most conversations between customers and agents and scores employees on behaviors such as being “warm and friendly,” and “make it effortless.”
In interviews across a range of companies, call-center agents say they value AI’s ability to access information quickly to help them make decisions. Many object if they are forced to use AI-generated recommendations or say scripted words against their own judgment. Several said they are uncomfortable relying on automated performance reviews using technology that uses subjective measures like sentiment.
“It’s very hard for a robot with no emotions to truly judge how a call is going,” said Lise Hildebrand Stern, who left her job at Spectrum last year after nine months because of the impersonal nature of the AI performance scoring and the stress she said it caused. “My metrics suffered because this system was unable to judge me based on my attitude, unlike a human being would be able to do.”
‘Hi, I’m Charlie’
When HomeServe decided to introduce Charlie, company executives wanted to make sure employees viewed her as a partner.
“I think when people start thinking about artificial intelligence, a lot of folks say, ‘I’m going to be out of a job.’ It was important for our center to know this is not to replace their job, but to augment their job,” said Ms. Cloud, the HomeServe vice president.
To humanize Charlie, the creative team developed an avatar that felt representative of their employees. She’s a 42-year-old biracial brunette from Ohio who likes jazz and has two children. (They chose a Midwestern background because she has no accent, and jazz because someone might listen to it in their neighborhood, Ms. Cloud said.) Management asked agents to suggest gender-neutral names for the robot. Charlie won out over Devon, MacKenzie and Jesse. Sarah—an acronym for “self-assisted robotic agent for HomeServe”—was rejected as too impersonal.
Charlie started out with simple tasks such as greeting callers, saying, “Hi, I’m Charlie, your digital assistant,” and asking basic questions, such as, “Please tell me why you are calling today.” After learning to route callers to the proper department, she was able to reduce average call-handle times by 36 seconds, or more than 10%, Ms. Cloud said.
Charlie is a quick study. By late fall, she was trained to handle a water-leak claim (“Is this a major leak?”), while using empathy (“I’m sorry to hear about your leak”) and determine the urgency of the issue (“Are you able to shut off the water yourself?”) She then booked a contractor to come out for the repair. From start to finish, Charlie’s processing time took less than two minutes compared with a human, who averages eight. She now handles 15% of claims volume and is expected to handle 20% by next year. Chief Transformation Officer Kim Ratcliffe said she hopes Charlie can take over 40% of calls eventually.
“When Charlie gets involved, time resolution is faster for the customer,” said HomeServe USA Chief Executive Officer Tom Rusin. During a major December storm, she helped 10,000 customers, equivalent to 12% of the total affected, to book claims and schedule repairs without talking to an agent. At this rate, she will pay for herself within 18 months of purchase. “It’s taking out hundreds of thousands of minutes from our calls a year,” said Mr. Rusin. “And a minute’s expensive.”
There are growing pains as Charlie gets trained, Mr. Rusin said. “In the beginning, you have to relearn what your agents have been doing for years and teach it to the computer.” At the U.K. office of HomeServe, Hana, the British version of Charlie, routinely failed to route calls to the water line repair department until programmers realized she was mistaking the word “leak” for “lake” because of British accents. Once a data scientist spotted the mistake, the fix was easy. Mr. Rusin is confident Charlie’s early miscues will get worked out.
“It takes a lot of time at the beginning, then I think growth will come exponentially from there,” Mr. Rusin said.
Stress rises
John Maynard Keynes, the noted economist, predicted that technology would eliminate the monotonous nature of work, freeing up humans to toil less and enjoy life more. What companies didn’t anticipate was that the initial chitchat in a routine call can give workers a break and be a pleasant way for people to connect. Once it is gone, the work that remains is complex, intense and often stressful.
At HomeServe, the company has seen higher call volume. Its agents also are handling more complicated calls. “The agent gets the calls that Charlie can’t figure out,” said Catlin Duvall, manager of HomeServe’s repair department. “That’s a larger percentage of our calls. Now when you pick up the phone they have three problems instead of one. It’s better for the customer. It can be more stressful on agents.”

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