High-Skilled White-Collar Work? Machines Can Do That, Too!
High-Skilled White-Collar Work? Machines Can Do That, Too
By Noam Scheiber July 7, 2018
One of the best-selling T-shirts for the Indian
e-commerce site Myntra is an olive, blue and yellow colorblocked design. It was
conceived not by a human but by a computer algorithm — or rather two
algorithms.
The first algorithm generated random images that it tried
to pass off as clothing. The second had to distinguish between those images and
clothes in Myntra’s inventory. Through a long game of one-upmanship, the first
algorithm got better at producing images that resembled clothing, and the second
got better at determining whether they were like — but not identical to —
actual products.
This back and forth, an example of artificial
intelligence at work, created designs whose sales are now “growing at 100
percent,” said Ananth Narayanan, the company’s chief executive. “It’s working.”
Clothing design is only the leading edge of the way
algorithms are transforming the fashion and retail industries. Companies now
routinely use artificial intelligence to decide which clothes to stock and what
to recommend to customers.
And fashion, which has long shed blue-collar jobs in the
United States, is in turn a leading example of how artificial intelligence is
affecting a range of white-collar work as well. That’s especially true of jobs
that place a premium on spotting patterns, from picking stocks to diagnosing
cancer.
A popular T-shirt sold on the Indian e-commerce site
Myntra was conceived by two algorithms. One generated random images; the other
identified those that resembled existing designs without duplicating them.
“A much broader set of tasks will be automated or
augmented by machines over the coming years,” Erik Brynjolfsson, an economist
at the Massachusetts Institute of Technology, and Tom Mitchell, a Carnegie
Mellon computer scientist, wrote in the journal Science last year. They argued
that most of the jobs affected would become partly automated rather than
disappear altogether.
The fashion industry illustrates how machines can intrude
even on workers known more for their creativity than for cold empirical
judgments. Among those directly affected will be the buyers and merchandise
planners who decide which dresses, tops and pants should populate their stores’
inventory.
A key part of a buyer’s job is to anticipate what
customers will want using a well-honed sense of where fashion trends are
headed. “Based on the fact that you sold 500 pairs of platform shoes last
month, maybe you could sell 1,000 next month,” said Kristina Shiroka, who spent
several years as a buyer for the Outnet, an online retailer. “But people might
be over it by then, so you cut the buy.”
Merchandise planners then use the buyer’s input to figure
out what mix of clothing — say, how many sandals, pumps and flats — will help
the company reach its sales goals.
In the small but growing precincts of the industry where
high-powered algorithms roam free, however, it is the machine — and not the
buyer’s gut — that often anticipates what customers will want.
That’s the case at Stitch Fix, an online styling service
that sends customers boxes of clothing whose contents they can keep or return,
and maintains detailed profiles of customers to personalize their shipments.
Stitch Fix relies heavily on algorithms to guide its
buying decisions — in fact, its business probably could not exist without them.
Those algorithms project how many clients will be in a given situation, or
“state,” several months into the future (like expanding their wardrobe after,
say, starting a new job), and what volume of clothes people tend to buy in each
situation. The algorithms also know which styles people with different profiles
tend to favor — say, a petite nurse with children who lives in Texas.
Myntra, the Indian online retailer, arms its buyers with
algorithms that calculate the probability that an item will sell well based on
how clothes with similar attributes — sleeves, colors, fabric — have sold in
the past. (The buyers are free to ignore the projection.)
All of this has clouded the future of buyers and
merchandise planners, high-status workers whose annual earnings can exceed
$100,000.
At more conventional retailers, a team of buyers and
support workers is assigned to each type of clothing (like designer,
contemporary or casual) or each apparel category, like dresses or tops. Some
retailers have separate teams for knit tops and woven tops. A parallel
merchandise-planning group could employ nearly as many people.
Buyers say this specialization helps them intuitively
understand trends in styles and colors. “You’re so immersed in it, you almost
get a feeling,” said Helena Levin, a longtime buyer at retailers like Charlotte
Russe and ModCloth.
Ms. Levin cited mint-green dresses, a top seller earlier
this decade. “One day it just died,” she said. “It stopped. ‘O.K., everything
mint, get out.’ Right after, it looked old. You could feel it.”
But retailers adept at using algorithms and big data tend
to employ fewer buyers and assign each a wider range of categories, partly
because they rely less on intuition.
At Le Tote, an online rental and retail service for
women’s clothing that does hundreds of millions of dollars in business each
year, a six-person team handles buying for all branded apparel — dresses, tops,
pants, jackets.
Brett Northart, a co-founder, said the company’s
algorithms could identify what to add to its stock based on how many customers
placed the items on their digital wish lists, along with factors like online
ratings and recent purchases.
Bombfell, a box service similar to Stitch Fix catering
only to men, relies on a single employee, Nathan Cates, to buy all of its tops
and accessories.
The company has built algorithmic tools and a vast
repository of data to help Mr. Cates, who said he could more accurately project
demand for clothing than a buyer at a traditional operation.
“We know exactly who our customers are,” he said. “We
know exactly where they live, what their jobs are, what their sizing is.”
For now, at least, only a human can do parts of his job.
Mr. Cates is obsessive about touching the fabric before purchasing an item and
almost always tries it on first.
“If this is a light color, are we going to see your
nipples?” he explained. (The verdict on a mint T-shirt he donned at the
company’s headquarters in New York? “A little nipply.”)
There are other checks on automation. Negotiations with
suppliers typically require a human touch. Even if an algorithm can help buyers
make decisions more quickly and accurately, there are limits to the number of
supplier relationships they can juggle.
Arti Zeighami, who oversees advanced analytics and
artificial intelligence for the H & M group, which uses artificial
intelligence to guide supply-chain decisions, said the company was “enhancing
and empowering” human buyers and planners, not replacing them. But he conceded
it was hard to predict the effect on employment in five to 10 years.
Experts say some of these jobs will be automated away.
The Bureau of Labor Statistics expects employment of wholesale and retail
buyers to contract by 2 percent over a decade, versus a 7 percent increase for
all occupations. Some of this is because of the automation of less
sophisticated tasks, like cataloging inventory, and buying for less
stylistically demanding retailers (say, auto parts).
There is at least one area of the industry where the
machines are creating jobs rather than eliminating them, however. Bombfell, Stitch
Fix and many competitors in the box-fashion niche employ a growing army of
human stylists who receive recommendations from algorithms about clothes that
might work for a customer, but decide for themselves what to send.
“If they’re not overly enthusiastic upfront when I ask
how do you feel about it, I’m making a note of it,” said Jade Carmosino, a
sales manager and stylist at Trunk Club, a Stitch Fix competitor owned by
Nordstrom.
In this, stylists appear to reflect a broader trend in
industries where artificial intelligence is automating white-collar jobs: the
hiring of more humans to stand between machines and customers.
For example, Chida Khatua, the chief executive of EquBot,
which helped create an exchange-traded fund that is actively managed by
artificial intelligence, predicted that the asset-management industry would
hire more financial advisers even as investing became largely automated.
The downside is that work as a stylist or financial
adviser will probably pay less than the lost jobs of buyers and stock pickers.
The good news, said Daron Acemoglu, an economist at M.I.T. who studies
automation, is that these jobs may still pay substantially more than many
positions available to low and middle-skilled workers in recent decades.
And these jobs may be hard to automate in the end.
“If I’m the customer explaining what I want, humans need
to be involved,” Mr. Khatua said. “Sometimes I don’t know what I really want.”
Follow Noam Scheiber on Twitter: @noamscheiber.
A version of this article appears in print on July 7,
2018, on Page BU1 of the New York edition with the headline: A.I. Comes Into
Fashion.
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