Meet the People Who Train the Robots (to Do Their Own Jobs)

At an employee meeting late last year, the agents debated what it meant to be human, and what a human travel agent could do that a machine couldn’t. While Harrison could comb through dozens of hotel options in a blink, it couldn’t match the expertise of, for example, a human agent with years of experience booking family vacations to Disney World. The human can be more nimble — knowing, for instance, to advise a family that hopes to score an unobstructed photo with the children in front of the Cinderella Castle that they should book a breakfast reservation inside the park, before the gates open.

Ms. Neasham, 30, saw it as a race: Can human agents find new ways to be valuable as quickly as the A.I. improves at handling parts of their job? “It made me feel competitive, that I need to keep up and stay ahead of the A.I.,” Ms. Neasham said. On the other hand, she said, using Harrison to do some things “frees me up to do something creative.”

Ms. Neasham is no ordinary travel agent. When she left the Army after serving as a captain in Iraq and Afghanistan, she wanted to work at a start-up. She joined Lola as one of its first travel agents. Knowing that part of her job was to be a role model, basically, for Harrison, she felt a responsibility for Harrison to become a useful tool.

Founded in 2015 by Paul English, who also started the travel-search site Kayak, Lola was conceived as part automated chat service and part recommendation engine. Underlying it all was a type of artificial intelligence technology called machine learning.

Lola was set up so that agents like Ms. Neasham didn’t interact with the A.I. much, but it was watching and learning from every customer interaction. Over time, Lola discovered that Harrison wasn’t quite ready to take over communication with customers, but it had a knack for making lightning-fast hotel recommendations.

At first, Harrison would recommend hotels based on obvious customer preferences, like brands associated with loyalty programs. But then it started to find preferences that even the customers didn’t realize that they had. Some people, for example, preferred a hotel on the corner of a street versus midblock.

And in a coming software change, Lola will ask lifestyle questions like “Do you use Snapchat?” to glean clues about hotel preferences. Snapchat users tend to be younger and may prefer modern but inexpensive hotels over more established brands like the Ritz-Carlton.

While Harrison may make the reservations, the human agents support customers during the trip. Once the room is booked, the humans, for example, can call the hotel to try to get room upgrades or recommend how to get the most out of a vacation.

“That’s something A.I. can’t do,” Ms. Neasham said.

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Hiroko Masuike/The New York Times

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