The fear of job loss due to automation is no longer relegated to only physical-labor manufacturing jobs and relatively simple transaction-based, customer-service workers (i.e., bank tellers, grocery store clerks, and travel agents). Companies are increasingly adopting sophisticated “cognitive” technologies across a new swath of knowledge-worker jobs in fields such as finance, health care, and insurance.
However, figuring out what this increased adoption rate really means seems elusive. Special reports and articles from respected economists and IT professionals present divergent and ambiguous views concerning whether or not automation will displace millions of knowledge workers.
In a recent New York Times Magazine article, “The Robots Are Coming for Wall Street,” executives from Goldman Sachs and Barclays bemoaned and praised a growing trend of financial analysts becoming displaced by smarter, big data-oriented software. On the other hand, a recent Huffington Post piece, “We Might Be All Wrong About Robots Taking Our Jobs,”, highlighted the optimistic views of MIT Professors Erik Brynjolfsson and Andrew McAfee, co-authors of The Second Machine Age, saying that the growth of smart machines and automation catalyzes the emergence of new and exciting knowledge worker jobs.
Another view put forth inside a March Pew Research Report, “Public Predictions for the Future of Workforce Automation,” noted that “two-thirds of Americans expect that robots and computers will do much of the work currently done by humans in 50 years, but most workers expect that their own jobs will exist in their current forms in five decades.”
“Labor economists look at dislocation on a very large scale, and there are some economists who say that everything works out in the long run, so don’t worry. Then there are others who say that this time it is different, and we should be very worried,” says Julia Kirby, co-author with Thomas H. Davenport, of Only Humans Need Apply: Winners and Losers in the Age of Smart Machines, to be published in May by Harper Business.
“None of these people give the individual any kind of sense that they can be doing something to shape their own fate,” Kirby adds. “That is the kind of vacuum we saw in the whole discourse about this.”
If history is our guide, job displacement from smart machines jobs simply means that knowledge workers must learn how to adapt, similar to how civilization successfully transitioned from an agricultural to an industrial society. They must learn how to supplement and enhance their skills. In short, knowledge workers need to become augmenters, a scenario in which “humans and computers combine their strengths to achieve more outcomes than either could alone.” That is the key message that comes out of Davenport’s and Kirby’s upcoming book.
“We decided that the most responsible thing to do was to focus on what individuals can do to increase the likelihood that they will keep a job,” Davenport says, adding that “nobody really knows exactly how many jobs are going to be lost through automation. We think it is probably smaller than what many people are saying.”
Still, “there are very real downsides to the new wave of smart machines,” the authors note. For example, Allstate was an early adopter of automation for insurance underwriting tasks in the mid-1990s. During that process, two-thirds of 1,000 Allstate knowledge workers were able to move upstream to higher-skilled jobs in portfolio management, enterprise risk management, and agent relationships. The remaining one-third did not have the right skills to move upstream and ultimately lost their jobs.
While the Allstate example is a sobering representation of an industry-wide, knowledge-worker, job-loss scenario, there is an upside potential concerning the adoption of such advancing technology. It’s the promise of augmentation, Davenport and Kirby argue. Augmentation can be driven through five key strategies knowledge workers should pursue to add value to machines and have machines add value to them:
- Stepping up
- Stepping aside
- Stepping in
- Stepping narrowly
- Stepping forward
Individuals who take on these strategies must be willing “to burn the midnight oil to improve their own skills, and either make friends with smart machines or find a way to do things they cannot do. Complacency is not an option. But despondency isn’t required either.”
People who step up make high-level decisions. They are senior executives who decide where cognitive technologies need to be utilized, and how new systems fit into the business organization overall. They are the few at the top of the augmentation pyramid, the authors write. “They are deciding what smart people do, what smart machines do, and how they work together.”
Those who step aside understand how to let machines do the work that machines are best at, such as computational tasks, while “simultaneously choosing to base your own livelihood on forms of value that machines just cannot deliver.” Stepping aside means focusing on how to enhance our so-called “multiple intelligences.” This is the space where “logical computation cannot provide optimized answers,” such as practicing emotional intelligence or even utilizing our ability to solve problems through irrational means.
Stepping in means knowing how to make machines productive. These are the people who know how to connect technical and business environments. “Their role involves both identifying situations for which the machine isn’t well suited, and helping it to deliver even greater productivity advances over time,” the authors note.
Those who step narrowly are the ones who “hyperspecialize.” They take the time to follow their passions into work that cannot be automated. They have a clear sense of direction and take the necessary steps to become experts in their field. It means “pushing ever deeper into a subject, with all the force of past achievement helping you, and learning the next thing about it through the kind of focused consideration and experimentation that machines can’t manage,” Davenport and Kirby explain.
People who step forward are technically proficient and entrepreneurial individuals who actually build the smart machines. People who step forward may have the brightest future in the age of automation. They are programmers, data scientists, researchers, product managers, marketers, consultants, and more. They are “particularly adept at learning new skills and updating their resumes to reflect their new skills,” Davenport and Kirby suggest. “Their reward will be a valuable one: Working in an exciting industry, and drawing a good paycheck from it over many years.”