Executive Summary

Replacing people with AI may seem tempting, but its also likely a mistake. Amazons hands off the wheel initiative might be a model for how companies can adopt AI to automate repetitive jobs, but keep employees on the payroll by transferring them to more creative roles where they can add more value to the company. Amazons choice to eliminate jobs but retain the workers and move them into new roles allowed the company to be more nimble and find new ways to stay ahead of competitors.

At an automation conference in late 2018, a high-ranking banking official looked up from his buffet plate and stated his objective without hesitation: Im here, he told me, to eliminate full-time employees. I was at the conference becauseafter spending months researching how Amazon automates workat 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 its 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 shownthat automating in order to fire can mean missing bigopportunities. As companies look at how to integrate increasingly powerful AI capabilities into their businesses, theyd do well to consider this example.

The animating idea behind Hands off the Wheel originated at Amazons 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 Amazons 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, Amazons 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 dont 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 goodat 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 teams machine-learning predictions were good enough that Amazons 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 couldnt make its own decisions, he explained, it couldnt 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 wasnt 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 youre 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, its like, OK, whats happening to my job? another former employee, Elaine Kwon, told me. On the other hand, youre also not surprised. Youre 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 companys retail division workers largely moved into product and program manager jobs fast-growing roles within Amazon that typically belong to professional inventors. Productmanagers 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, Amazons 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 companys checkout-free convenience store. Go was founded, in part, by Dilip Kumar, an executive once in charge of the companys pricing and promotions operations. While Kumar spent two years acting as a technical adviser to CEO Jeff Bezos, Amazons 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 Amazons broader strategy.

If Amazon is any indication, businesses that reassign employees after automating their work will thrive. Those that dont risk falling behind.In shaky economic times, the need for cost-cutting could make it tempting to replace people with machines, but Ill offer a word of warning: Think twice before doing that. Its a message I wish I had shared with the banker.

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How Amazon Automated Work and Put Its People to Better Use - Harvard Business Review