How Amazon Automated Work and Put Its People to Better Use
At an automation conference in late 2018, a high-ranking banking official looked up from his buffet plate and stated his objective without hesitation: I’m here, he told me, to eliminate full-time employees. I was at the conference because after spending months researching how Amazon automates work at its headquarters, I was eager to learn how other firms thought about this powerful technology. After one short interaction, it was clear that some have it completely wrong.
For the past decade, Amazon has been pushing to automate office work under a program now known as Hands off the Wheel. The purpose was not to eliminate jobs but to automate tasks so that the company could reassign people to build new products — to do more with the people on staff, rather than doing the same with fewer people. The strategy appears to have paid off: At a time when it’s possible to start new businesses faster and cheaper than ever before, Hands off the Wheel has kept Amazon operating nimbly, propelled it ahead of its competitors, and shown that automating in order to fire can mean missing big opportunities. As companies look at how to integrate increasingly powerful AI capabilities into their businesses, they’d do well to consider this example.
The animating idea behind Hands off the Wheel originated at Amazon’s South Lake Union office towers, where the company began automating work in the mid-2010s under an initiative some called Project Yoda. At the time, employees in Amazon’s retail management division spent their days making deals and working out product promotions as well as determining what items to stock in its warehouses, in what quantities, and for what price. But with two decades’ worth of retail data at its disposal, Amazon’s leadership decided to use “the force” (machine learning) to handle the formulaic processes involved in keeping warehouses stocked. “When you have actions that can be predicted over and over again, you don’t need people doing them,” Neil Ackerman, an ex-Amazon general manager, told me.
The project began in 2012, when Amazon hired Ralf Herbrich as its director of machine learning and made the automation effort one of his launch projects. Getting the software to be good at inventory management and pricing predictions took years, Herbrich told me, because his team had to account for low-volume product orders that befuddled its data-hungry machine-learning algorithms. By 2015, the team’s machine-learning predictions were good enough that Amazon’s leadership placed them in employees’ software tools, turning them into a kind of copilot for human workers. But at that point the humans could override the suggestions, and many did, setting back progress.
Eventually, though, automation took hold. “It took a few years to slowly roll it out, because there was training to be done,” Herbrich said. If the system couldn’t make its own decisions, he explained, it couldn’t learn. Leadership required employees to automate a large number of tasks, though that varied across divisions. “In 2016, my goals for Hands off the Wheel were 80% of all my activity,” one ex-employee told me.” By 2018 Hands off the Wheel was part of business as usual. Having delivered on his project, Herbrich left the company in 2020.
The transition to Hands off the Wheel wasn’t easy. The retail division employees were despondent at first, recognizing that their jobs were transforming. “It was a total change,” the former employee mentioned above said. “Something that you were incentivized to do, now you’re being disincentivized to do.” Yet in time, many saw the logic. “When we heard that ordering was going to be automated by algorithms, on the one hand, it’s like, ‘OK, what’s happening to my job?’” another former employee, Elaine Kwon, told me. “On the other hand, you’re also not surprised. You’re like, ‘OK, as a business this makes sense.’”
Although some companies might have seen an opportunity to reduce head count, Amazon assigned the employees new work. The company’s retail division workers largely moved into product and program manager jobs — fast-growing roles within Amazon that typically belong to professional inventors. Product managers oversee new product development, while program managers oversee groups of projects. “People who were doing these mundane repeated tasks are now being freed up to do tasks that are about invention,” Jeff Wilke, Amazon’s departing CEO of Worldwide Consumer, told me. “The things that are harder for machines to do.”
Had Amazon eliminated those jobs, it would have made its flagship business more profitable but most likely would have caused itself to miss its next new businesses. Instead of automating to milk a single asset, it set out to build new ones. Consider Amazon Go, the company’s checkout-free convenience store. Go was founded, in part, by Dilip Kumar, an executive once in charge of the company’s pricing and promotions operations. While Kumar spent two years acting as a technical adviser to CEO Jeff Bezos, Amazon’s machine learning engineers began automating work in his old division, so he took a new lead role in a project aimed at eliminating the most annoying part of shopping in real life: checking out. Kumar helped dream up Go, which is now a pillar of Amazon’s broader strategy.
If Amazon is any indication, businesses that reassign employees after automating their work will thrive. Those that don’t risk falling behind. In shaky economic times, the need for cost-cutting could make it tempting to replace people with machines, but I’ll offer a word of warning: Think twice before doing that. It’s a message I wish I had shared with the banker.