Will there be enough work in the future? What’s the likely impact of our continuing technology advances on jobs? How will they impact productivity? These are all-important questions to reflect on as our increasingly smart technologies are now being applied to activities that not long ago were viewed as the exclusive domain of humans.
While no one really knows the answer to these questions, most studies of the subject have concluded that relatively few occupations, 10% or less, will be entirely automated and disappear over the next 10 – 15 years. Instead, a growing percentage of occupations will significantly change as technologies automate the more routines tasks within those occupations. People will still be involved, but their jobs will be transformed by the advanced tools they now have to master. Moreover, a growing technology-based economy will likely create all kinds of new occupations, which will more than offset declines in occupations displaced by automation- as has been the case over the past couple of centuries.
One would also expect that technology advances will increase the productivity of the workers involved in these new or transformed occupations. But, if we look at the past 10-15 for guidance, we’ll find that productivity growth has significantly declined over this timeframe, notwithstanding huge technology advances like smartphones, cloud computing, big data and artificial intelligence. Economist have proposed competing explanations for the declining productivity growth, but have so far failed to reach consensus. Understanding this productivity puzzle may well hold the key to future productivity improvements and long-term economic growth.
The most satisfying explanation I’ve seen was given in a recent paper by MIT’s Erik Brynjolfsson and Daniel Rock, and University of Chicago’s Chad Syverson. After considering four potential explanations, the authors concluded that there’s actually no productivity paradox. Given the proper context, there are no inherent inconsistencies between having both transformative technological advances and lagging productivity.
Over the past two centuries there’s generally been a significant time lag between the broad acceptance of new technology-based paradigms and the ensuing economic transformation and institutional recomposition. Even after reaching a tipping point of market acceptance, it takes considerable time – often decades – for the new technologies and business models to be widely embraced by companies and industries across the economy, and only then will their benefits follow – including productivity growth. The paper argues that we’re precisely in such an in-between period.
A similar argument was articulated in The Technology-Augmented Employee, a Forrester research report published several weeks ago by analyst J. P. Gownder. “Despite billions of dollars invested in technology, growth in employee productivity has slowed since 2004,” writes Gownder. “Even though global technology spending will for the first time pass $3 trillion globally in 2018, this productivity paradox should concern CIOs and other decision-makers: For all these investments, shouldn’t we expect a return in the form of more effective employees?”
The answer, he argues, is that most of those technology investments have not made their way down to employees. His report examined data on how organizations use technology to augment employee performance, and found that many companies don’t provide sufficient tools to do so.
For example, beyond widely used core devices like PCs, tablets and smartphones, few employees use more advanced devices like wearables or purpose-specific mobile devices. Beyond major applications like e-mail and calendars, many employees aren’t using apps as part of their work. Same is true with user interfaces. While mature user interfaces – e.g., keyboards, mice, touchscreens- are widely used, few employees are using more advanced input technologies, like voice recognition, to interact with adaptive, contextually sensitive AI applications. Nor are they working side by side with robots and virtual assistants to augment their human capabilities. Few employers provide these more advanced tools and, as a result, their employees’ technologies are mostly limited to the tried-and-true basics.
To overcome the current gaps in employee productivity, Gownder recommends that companies consider how a given technology will help an employee better solve problems across three key dimensions: decision context, execution support, and human-managed machines.
Continue reading the blog on Medium, here.
This blog first appeared May 21, here.