According to the U.S. Census Bureau, the percentage of U.S. occupations classified as “routine” (manufacturing, transportation, maintenance) has dropped over the past 40 years from 60% to 40%, while those classified as “non-routine” ( those who constantly solve unexpected and different problems) rose at an equal rate from 40% to 60% over the same timeframe. The resulting line graph marks a ragged X—an apt metaphor for the manufacturing era of the 19th and 20th centuries.
While much ink has been spent analyzing the downturn of routine occupations, less attention goes to what’s driving the non-routine ones, or to the impact of the trend on business and society. Research firm Gartner did so last week at the firm’s European Symposium ITxpo in Barcelona, Spain.
Gartner defined the key elements of non-routine processes as discover, create, innovate, lead, relate, and team. In short, non-routine occupations belong to knowledge workers who drive constant advancements at all levels of business, science, and technology.
Technology in the Driver’s Seat
And while technology has been the beneficiary of knowledge workers over the past 60 years, it’s now in the driver’s seat, simplifying and enabling non-routine processes for non-routine occupations. Gartner cited the Apple Knowledge Navigator video from 1987 as depicting a future where intelligent agents and artificial intelligence would be able to assist knowledge workers to accomplish tremendous feats via online searching and collaborating that were only concepts at the time. That future is rapidly approaching.
Gartner’s presentation charted the rapid rise of intelligent machines (also called smart machines), from robots to analytical systems and personal assistants, that are realizing these predictions from 26 years ago. Intelligent machines are those that assist, advise, extend, observe, and help knowledge workers perform non-routine work.
Intelligent machines result from a confluence of evolving technologies, including exponentially faster and more efficient hardware and processing capabilities; deep learning algorithms; deep neural networks; increases in network scale to interconnected nodes at 10 to the 11th power; and massive explosions of content, commonly known as Big Data.
Movers, Sages, and Doers
Gartner categorizes these intelligent machines into three categories: movers, sages, and doers.
Movers – In the first category, there are simple, yet self-learning robots that pick and deliver stock for packaging at warehouses, without human intervention. Other movers Gartner cited include driverless cars (now legal for experimentation in three US States), mega-sized earth movers that operate without human intervention, and L3 (Legged Squad Support System) robots that act as tremendously strong, durable, and agile pack mules for infantry forces in all terrain.
Sages – Gartner defined the sages as informational-based helpers that include personal assistants and smart advisors, such as medical systems that are allowing researchers and physicians around the world to effectively cope with rare diseases by collaborating on diagnostics and sharing best practices for treatments.
Doers – Doers are machine-focused helpers that include self-learning personal assistant robots, and networks of industrial machines such as those providing real-time analytics and pattern matching across industrial power grids to predict when components are likely to fail and replace them before massive outages can occur.
Gartner sees the rise of intelligent machines around us as an entirely new way of people and technology collaborating to be more productive than ever before, and as delivering strategic competitive advantages to those who adopt them.
Social, Mobile, Information, and Cloud
Moreover, behind these intelligent machines are foundational technologies that map to the Gartner Nexus of Forces (Social, Mobile, Information, and Cloud). Those technologies and the synergies from combining them are what all businesses must embrace to stay competitive. They include systems for truly collaborative social computing, plus those that deliver results in any format to any device, apply predictive analytics to massive amounts of data in real time, and integrate existing IT systems without bias toward a single vendor.
While the evolution of intelligent machines will reduce the need for routine occupations—with a profound impact on employment, people, and careers—Gartner does not see the process leading to a dystopian future where only elite knowledge workers will thrive with the support of robots and intelligent machines.
Rather, with proper ethical and governmental guidance, they see intelligent software systems and machines transforming what and how we accomplish the tasks that run business and societies for the betterment of all.
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