This article originally appeared in The Wall Street Journal
Uber Technologies Inc. and other pioneers of the so-called gig economy became some of the world’s most valuable private companies by using apps and algorithms to hand out tasks to an army of self-employed workers.
Now, established companies like Royal Dutch Shell PLC and General Electric Co. are adopting elements of that model for the full-time workforce.
Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others.
When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise—and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects. The program tracks and evaluates their activity so it can refine the next round of matches.
Shell said it began testing the system earlier this year and in January will roll out the “Shell Opportunity Hub” across its business-to-business marketing arm, which has 8,000 employees.
“We’re looking at how we can efficiently access and use the diverse talent we already have in Shell,” said Caroline Missen, the Shell executive who oversaw the pilot.
These management tools are part of a broader shift to apply artificial intelligence to hiring and other human-resources work.
The overall human resources and workforce management software market has grown 23% in the past two years to reach $11.5 billion this year and is projected to grow another 25% by 2020, according to research firm Gartner.
There is evidence computers may be better suited to some managerial tasks than people are. Humans are susceptible to cognitive traps like confirmation bias. People using intuition tend to make poor decisions but rate their performance more highly, according to a 2015 University of New England analysis of psychological studies. And in an increasingly quantitative business world, managers are asked to deliver more data-driven decisions—precisely the sort at which machines excel.
“What managers do mostly is identify potential, build teams, assign tasks, measure performance and provide feedback. Generally speaking, humans aren’t very good at these tasks,” said Tomas Chamorro-Premuzic, a professor of business psychology at University College London. “Someday, we might not need managers anymore.”
Other researchers suggest AI, too, can fall prey to traps when making decisions. AI systems are often trained to make decisions by finding similarities to historical data. But that can make them bad at predicting rare events, such as when employees would excel at a task they haven’t encountered before, said Michael Veale, a researcher in responsible machine learning at University College London. “What makes a great salesman this year might not make a great salesman next year,” Mr. Veale said.
Companies that make and use workforce-management software acknowledge these concerns but say machines are no substitute for human judgment and ability to manage interpersonal relations. Instead, they say their software speeds up administrative work and uses data to help human managers improve decisions they previously made only by drawing upon gut instinct and experience.
“Our goal here is to optimize managers’ time,” said Bill Bartow, vice president of global product management at Kronos Inc, which earlier this month announced software that evaluates vacation requests without human intervention and assigns tasks based on a mix of worker preferences and qualifications. Early clients include specialty retailer Brookstone and the University of Colorado Boulder.
Several startups and established firms offer tools to automate and optimize the allocation of work shifts and assignments, enabling one person to manage many more workers than before.
Insiris Ltd., a U.K.-based maker of workforce-management software, uses machine learning to draw up assignments for 100 river pilots at a major European port, taking into account dozens of variables like each ship’s draught size and each pilot’s past performance.
“If you’re a human allocating work, a computer’s going to be much more efficient at that,” said Matthew Summers, Insiris’s co-founder and managing director.
Others firms focus on more-complex forms of management. Chicago-based Nexus A.I. uses its algorithms to search employees’ profiles and backgrounds to determine which ones would work best together on particular projects. It also performs automated performance reviews.
B12, a website-design startup based in New York, built a system it calls Orchestra to assemble and manage the workflow of “flash teams” of both full-time and freelance workers. B12 uses Orchestra when it is building websites and offers it to other companies at no charge.
Orchestra makes use of an automated system called StaffBot that assigns project roles to the most qualified people with time to spare, said Adam Marcus, B12’s co-founder and chief technology officer. Another system uses predictive analysis to determine whose work would benefit most from an expert review—and structures the workflow accordingly. “We can catch twice as many errors,” Mr. Marcus said.
Catalant opened five years ago as a marketplace where companies can search for outside consultants whose experience and skills match the task at hand, attracting clients like Procter & Gamble Co., Pfizer Inc. and GE.
More recently, GE and other clients suggested Catalant use the software to sift through existing employees. A new version of the tool now collects and scans employees’ work histories, along with other information they and their managers type in, to match them with internal projects.
GE, known for parachuting employees from division to division to spread expertise, began experimenting with Catalant to find internal projects for the entrepreneur-in-residence program at GE Ventures, which brings in entrepreneurs to help scout investments. GE plans to roll out a broader pilot across multiple divisions in 2018, a spokeswoman said
Sue Siegel, GE’s chief innovation officer, said she wouldn’t rule out one day working for a machine.
“If the robot has personality and a sense of humor and can understand the human condition,” she said, “hey, who knows?”
Appeared in the December 11, 2017, print edition as ‘Algorithms Move Into Management.’