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.
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 email 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.
Decision context: providing information to help employees act. Making informed, timely decisions is hard. Even when data is available, other obstacles often prevent individuals from extracting actionable insights form the data, or even deciding which data sets to analyze. Smart tools that understands the context of the decisions, the processes involved and the best data to access, can offer invaluable assistance to the employee.
Execution support: machines taking on part of the workload. Beyond assisting the employee, machines can handle part of the actual workload. Tools like spreadsheet applications have long been doing so. A new generation of AI applications can take execution support to the next level. The report cites Eva as an example of such a tool. Eva listens to the participants in a meeting, records and transcribes their conversations, builds work clouds to summarize the meeting, extracts the follow-up actions and sends them to the appropriate employees.
Human-managed machines: machines taking tasks off human employees’ plates. In the most advanced stage, the machine takes over a major part of the workload while being overseen and complemented by the humans they work with. Robotics Process Automation (RPA) bots, for example, are increasingly able to take on repetitive, predictable actions previous conducted by humans, enabling the humans to oversee the bots, improve their process, and handle exceptions and escalations.
To overcome the gap between the promise of the technology-augment worker and the current reality, the report recommends focusing on the business problem being addressed rather than on the technologies used.
First, determine what business problems you can solve with technology augmentation. Next, test the proposed solution in an innovation lab or other piloting environment, iterating and learning at every step until you can make a go or no-go decision. And, when ready, integrate the robotic coworkers into the team so they can augment the work of the team’s human members.
“As the future of work spans from lightly technology-augmented employees all the way up to full job replacement by robots, the workforce will increasingly become a hybrid of humans and machines,” writes Gownder in conclusion. “Human-machine teaming… will grow into a key workforce technology discipline.” Companies should keep in mind that:
- The division of labor will become more important than ever before. Humans need to upskill their strategic roles while computers take on tactical support. “By focusing on complementary but different tasks, human-machine teams can be far more effective than either group alone.
- RQ (robotics quotient) will become crucial to workforce effectiveness. RQ is a measure of the skills set that human workers have when working with augmenting technologies. “Companies with high RQ among their staffs will create more top-line revenue and profits than those with lower RQ ratings.”
- Wearable devices will combine with smart software and smartphones to create an all-body network. “Combining real-time inputs from multiple devices with smart software will allow workers in roles as diverse as field service technicians, salespeople, and even classroom teachers to augment their performances by leaps and bounds.”
- Humans will teach machines, implicitly as well as explicitly. “Machine learning adds to this capability, allowing software to watch and improve. Deep learning will take this to another level entirely as AIs take unstructured data and examples and simulate human mastery.”