Four fundamentals of workplace automation
As the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated-at least in the short term. What will be the impact of automation efforts like these, multiplied many times across different sectors of the economy? Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life? Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric? Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated. When we modeled the potential of automation to transform business processes across several industries, we found that the benefits typically are between three and ten times the cost. The magnitude of automation potential reflects the speed with which advances in artificial intelligence and its variants, such as machine learning, are challenging our assumptions about what is automatable. It’s no longer the case that only routine, codifiable activities are candidates for automation and that activities requiring “Tacit” knowledge or experience that is difficult to translate into task specifications are immune to automation. In many cases, automation technology can already match, or even exceed, the median level of human performance required. Amazon’s fleet of Kiva robots is equipped with automation technologies that plan, navigate, and coordinate among individual robots to fulfill warehouse orders roughly four times faster than the company’s previous system. Sales organizations could use automation to generate leads and identify more likely opportunities for cross-selling and upselling, increasing the time frontline salespeople have for interacting with customers and improving the quality of offers. Conventional wisdom suggests that low-skill, low-wage activities on the front line are the ones most susceptible to automation. These interim findings, emphasizing the clarity brought by looking at automation through the lens of work activities as opposed to jobs, are in no way intended to diminish the pressing challenges and risks that must be understood and managed. Nor do we yet have a definitive perspective on the likely pace of transformation brought by workplace automation. Critical factors include the speed with which automation technologies are developed, adopted, and adapted, as well as the speed with which organization leaders grapple with the tricky business of redefining processes and roles. All this points to new top-management imperatives: keep an eye on the speed and direction of automation, for starters, and then determine where, when, and how much to invest in automation. Making such determinations will require executives to build their understanding of the economics of automation, the trade-offs between augmenting versus replacing different types of activities with intelligent machines, and the implications for human skill development in their organizations.
The Real Story of Automation Beginning with One Simple Chart
There’s a chart I came across earlier this year, and not only does it tell an extremely important story about automation, but it also tells a story about the state of the automation discussion itself. The application of new technologies to oil drilling means that of the 440,000 jobs lost in the global downturn, as many as 220,000 of those jobs may never come back. Don’t worry, right? Because everyone unemployed by machines will find better jobs elsewhere that pay even more Well, about that, that’s not at all what the history of automation in the computer age over the past 40 years shows. Medium-skill manufacturing/office jobs have been disappearing, and in response, the unemployed have found new employment in new low-skill service jobs. As an added bonus, the jobs that are being automated are more productive jobs than most of the jobs being newly created. A landmark 2017 study even looked at the impact of just industrial robots on jobs from 1993 to 2007 and found that every new robot replaced around 5.6 workers, and every additional robot per 1,000 workers reduced the percentage of the total population employed by 0.34% and also reduced wages by 0.5%. During that 14-year period of time, the number of industrial robots quadrupled and between 360,000 and 670,000 jobs were erased. As the authors noted, “Interestingly, and perhaps surprisingly, we do not find positive and offsetting employment gains in any occupation or education groups.” In other words, the jobs were not replaced with new jobs. Charting the Course of HistoryOne of the most telling statistics I’ve come across in regards to the automation discussion is how almost everyone in the US knows we’ve lost manufacturing jobs over the past three decades. What few people know however is that at the same time the total number of jobs has decreased, total manufacturing output has increased. Most people don’t know that, or blame things like immigrants or offshoring for job losses, even though offshoring is only possible due to technology improvements and only accounts for 13% of manufacturing job loss. According to a Daily Yonder analysis, 80% of jobs created in 2016 were in the 51 metro areas of a million people or more. The Ignored Math of Increasingly Productive New BusinessesAnother thing to recognize is that as technology enables businesses to hire fewer workers, that means to obtain “Full employment”, where everyone who wants a job has one, the economy requires that everyone work shorter work weeks, or else an ever growing number of businesses is needed in order to employ the same amount of people. Hundreds of thousands of jobs were just lost due to oil rig automation and no one batted an eye. Yes, Amazon is creating lots of new jobs too, but for every job it creates, it has eliminated two or more by eliminating its far less efficient brick and mortar competition. Unless you’re talking about net job creation, and the details of those jobs created, you’re dishonestly talking about employment.