Weekly Report – February 1, 2016
The “gig economy,” the “on-demand economy,” the “sharing economy,”— whatever term we use, there’s a lot of talk about rapid technology-enabled changes in the nature of work.
The fact is we are living in an increasingly connected world, with new tools and opportunities, commerce transacted on digital platforms and many of us dependent on the labor of a fragmented and transitory workforce. The growth of these new-model companies present both challenges and opportunities for many workers and policymakers tasked with shaping economic development strategies to generate economic growth and the quality jobs that come with it.
Last December 10, the U.S. Department of Labor hosted a forum on the “Future of Work” to identify the “remarkable technological, economic and cultural changes” of recent years and their “profound impact throughout the economy and the workforce”.
According to labor market expert Michael Bernick, since 1979, similar forums on the future of work have been held on a regular basis. They have been hosted by the state or federal governments or private foundations and have spoken in similar elevated terms about remarkable changes and profound impacts.
In nearly all cases over the years, the projections about the future of work have turned out to be wrong. Technology has destroyed jobs, as projected, but other jobs have been created, and in very few cases have the jobs created been envisioned in the years before. In the 1980s, none of the conferences and books on the future of work envisioned the jobs created with the internet or the internet economy.
Further, the projections in the 1980s and 1990s about sectors such as manufacturing or retail trade disappearing have proved to be wrong, as have projections about the disappearance of low-tech jobs such as office clerks or home health aides or food preparation workers. In many ways, what is striking, since 1979, has been the stability in terms of sectors and occupations.
The chart below, developed by Bernick and economist Richard Holden, shows the number of jobs by sector in 2000 and the numbers projected by the Bureau of Labor Statistics (BLS) for 2010. What stands out is the continued diversity among these 17 industry sectors, including manufacturing and retail trade.
Similarly BLS occupational projections shows the continued diversity of occupations.
More recent research by McKinsey’s Michael Chui, James Manyika, and Mehdi Miremadi found that as the automation of physical and knowledge work advances, many jobs will be redefined rather than eliminated—at least in the short term.
Their results to date suggest, first and foremost, that a focus on occupations is misleading.
Very few occupations will be automated in their entirety in the near or medium term. Rather, certain activities are more likely to be automated, requiring entire business processes to be transformed, and jobs performed by people to be redefined, much like the bank teller’s job was redefined with the advent of ATMs.
More specifically, their research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies (for more, explore our interactive examining the potential for US jobs to be automated.
In the United States, these activities represent about $2 trillion in annual wages. 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.
The organizational and leadership implications are enormous: leaders from the C-suite to the front line will need to redefine jobs and processes so that their organizations can take advantage of the automation potential that is distributed across them. And the opportunities extend far beyond labor savings.
When they modeled the potential of automation to transform business processes across several industries, they found that the benefits (ranging from increased output to higher quality and improved reliability, as well as the potential to perform some tasks at superhuman levels) typically are between three and ten times the cost. The magnitude of those benefits suggests that the ability to staff, manage, and lead increasingly automated organizations will become an important competitive differentiator.
Below are four interim findings elaborating on the core insight that the road ahead is less about automating individual jobs wholesale, than it is about automating the activities within occupations and redefining roles and processes.
The automation of activities
These preliminary findings are based on data for the US labor market. They structured their analysis around roughly 2,000 individual work activities, and assessed the requirements for each of these activities against 18 different capabilities that potentially could be automated.
Those capabilities range from fine motor skills and navigating in the physical world, to sensing human emotion and producing natural language. We then assessed the “automatability” of those capabilities through the use of current, leading-edge technology, adjusting the level of capability required for occupations where work occurs in unpredictable settings.
The bottom line is that 45 percent of work activities could be automated using already demonstrated technology. If the technologies that process and “understand” natural language were to reach the median level of human performance, an additional 13 percent of work activities in the US economy could be automated.
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. For instance, Narrative Science’s artificial-intelligence system, Quill, analyzes raw data and generates natural language, writing reports in seconds that readers would assume were written by a human author.
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. IBM’s Watson can suggest available treatments for specific ailments, drawing on the body of medical research for those diseases.
The redefinition of jobs and business processes
According to their analysis, fewer than 5 percent of occupations can be entirely automated using current technology. However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated. In other words, automation is likely to change the vast majority of occupations—at least to some degree—which will necessitate significant job redefinition and a transformation of business processes.
Mortgage-loan officers, for instance, will spend much less time inspecting and processing rote paperwork and more time reviewing exceptions, which will allow them to process more loans and spend more time advising clients. Similarly, in a world where the diagnosis of many health issues could be effectively automated, an emergency room could combine triage and diagnosis and leave doctors to focus on the most acute or unusual cases while improving accuracy for the most common issues.
As roles and processes get redefined, the economic benefits of automation will extend far beyond labor savings. Particularly in the highest-paid occupations, machines can augment human capabilities to a high degree, and amplify the value of expertise by increasing an individual’s work capacity and freeing the employee to focus on work of higher value.
Lawyers are already using text-mining techniques to read through the thousands of documents collected during discovery, and to identify the most relevant ones for deeper review by legal staff. Similarly, 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.
The impact on high-wage occupations
Conventional wisdom suggests that low-skill, low-wage activities on the front line are the ones most susceptible to automation. They’re now able to scrutinize this view using the comprehensive database of occupations we created as part of this research effort. It encompasses not only occupations, work activities, capabilities, and their automatability, but also the wages paid for each occupation.
Their work to date suggests that a significant percentage of the activities performed by even those in the highest-paid occupations (for example, financial planners, physicians, and senior executives) can be automated by adapting current technology. For example, we estimate that activities consuming more than 20 percent of a CEO’s working time could be automated using current technologies. These include analyzing reports and data to inform operational decisions, preparing staff assignments, and reviewing status reports. Conversely, there are many lower-wage occupations such as home health aides, landscapers, and maintenance workers, where only a very small percentage of activities could be automated with technology available today.
The future of creativity and meaning
Capabilities such as creativity and sensing emotions are core to the human experience and also difficult to automate. The amount of time that workers spend on activities requiring these capabilities, though, appears to be surprisingly low. Just 4 percent of the work activities across the US economy require creativity at a median human level of performance. Similarly, only 29 percent of work activities require a median human level of performance in sensing emotion.
While these findings might be lamented as reflecting the impoverished nature of our work lives, they also suggest the potential to generate a greater amount of meaningful work. This could occur as automation replaces more routine or repetitive tasks, allowing employees to focus more on tasks that utilize creativity and emotion.
Financial advisors, for example, might spend less time analyzing clients’ financial situations, and more time understanding their needs and explaining creative options. Interior designers could spend less time taking measurements, developing illustrations, and ordering materials, and more time developing innovative design concepts based on clients’ desires.
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. Clearly, organizations and governments will need new ways of mitigating the human costs, including job losses and economic inequality, associated with the dislocation that takes place as companies separate activities that can be automated from the individuals who currently perform them.
Other concerns center on privacy, as automation increases the amount of data collected and dispersed. The quality and safety risks arising from automated processes and offerings also are largely undefined, while the legal and regulatory implications could be enormous. To take one case: who is responsible if a driverless school bus has an accident?
Nor do they 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. These factors may play out differently across industries.
Those where automation is mostly software based can expect to capture value much faster and at a far lower cost. (The financial-services sector, where technology can readily manage straight-through transactions and trade processing, is a prime example.) On the other hand, businesses that are capital or hardware intensive, or constrained by heavy safety regulation, will likely see longer lags between initial investment and eventual benefits, and their pace of automation may be slower as a result.
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 degree to which executives embrace these priorities will influence not only the pace of change within their companies, but also to what extent those organizations sharpen or lose their competitive edge.
So, we need to be wary of too grand claims about how technology and robots will be eliminating us all; or how there will not be enough jobs to go around. The past four decades suggest that we do not see clearly more than a short time out in the labor market.