Only Humans Need Apply: Winners and Losers in the Age of Smart Machines

Under pressure to remove alleged human bias from its “Trending Topics” section, in August Facebook fired the editors who were selecting and writing headlines for the stories, explaining that this “will make the product more automated.” The results of trusting algorithms more than humans have continued to make headlines ever since with the Trending “product” promoting a fake news story about Fox News’ Meghan Kelly, a conspiracy article claiming the 9/11 twin towers collapsed because of “controlled demolition,” and Apple’s Tim Cook announcing that Siri will physically come out of the phone and do all the household chores (a story from an Indian satirical website, Faking News, that was Trending’s top story on the day of the iPhone 7 launch event), to mention just a few of the more embarrassing machine failures.

Silicon Valley has never displayed much love for fallible humans, but has shown a lot of confidence in the continuous improvement and now, self-improvement, of machines. Do humans still have an important role to play in our automated lives which are increasingly controlled by sophisticated algorithms and seemingly smarter machines?


In Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, knowledge work and analytics expert Tom Davenport and Julia Kirby, a contributing editor for the Harvard Business Review, offer optimistic, upbeat and practical answers to this much-debated question. “The upside potential of the advancing technology is the promise of augmentation—in which humans and computers combine their strengths to achieve more favorable outcomes than either could do alone,” they write.

There is not much difference, contend Davenport and Kirby, between technologies of automation and technologies of augmentation. The difference lies in the goals and attitudes behind the application of these technologies. Automation is unidirectional and focuses “primarily or exclusively on cost reduction” via the elimination of human labor. In contrast, “augmentation approaches tend to be more likely to achieve value and innovation” and they are bidirectional, making “humans more capable of what they are good at” and “machines even better at what they do.”

It is a shortsighted (and short-term) strategy for companies to favor automation over augmentation: “If the goal is to provide truly exceptional or differentiated products and services at scale, only an augmentation arrangement can accomplish that,” write Davenport and Kirby. They advocate a “workplace that combine sophisticated machines and humans in partnerships of mutual augmentation” and mutual benefit.

Competitive considerations apply not only to companies in the race against the machine, but also to their employees. The book addresses primarily the plight of knowledge workers who thought they would escape the fate of factory workers but are now increasingly automated out of a job. “The advice on avoiding that fate,” say Davenport and Kirby, “has been noticeably thin. For the most part, the experts boil it down to a single, daunting task: Keep getting smarter. We are going to argue that there are other strategies, all of them featuring augmentation of human work by machines.”

The authors describe in detail—with vivid and engaging examples—five “options for augmentation:” Stepping Up or moving a level above the machines and making high level decisions about augmentation; Stepping Aside or choosing to pursue a job that computers are not good at, such as selling or motivating; Stepping In or monitoring and improving the computer’s automated decisions; Stepping Narrowly or finding a specialty area in a specific profession that wouldn’t be economical to automate; and Stepping Forward or becoming involved in creating the very technology that supports intelligent decisions.

These strategies will work for knowledge workers (or all workers) who are “willing to work to add value to machines, and who are willing to have machines add value to them.” They will also work for organizations that understand that “no matter how smart these machines get, there is still some potential value from human augmentation.”

What a refreshing perspective in these times of machine-worship, where Silicon Valley’s automation addiction has spread far and wide. Mark Fields, the chief executive of Ford Motor Company, recently promised completely self-driving cars by about 2025, displaying a very Silicon Valley (and silly) attitude by saying “a driver is not going to be required.”

Fields and the many other executives of established companies racing against the disruptive Silicon Valley machine should read Only Humans Need Apply where Davenport and Kirby warn that companies investing in self-driving cars “could find that they have put a lot of energy into developing vehicles that drive themselves but are stuck with regulations that require an alert driver with hands on the steering wheel and feet on the pedals. If that happens, perhaps a company whose strategy all along has given careful thought to how to redeploy the human attention that is freed up by the technology—will win big.”

Just because you can automate, doesn’t mean you should. This is the important lesson of this contrarian, timely, and well-argued book. Augmentation, say Davenport and Kirby, is something “societies should encourage in ways big and small.” Hear! Hear!

Originally published on