Automation, data, and us

The robotics company iRobot first released the automated vacuum-cleaning robot, Roomba, in 2002. Two decades ago, that was the cutting edge of automation-based technology. Today, it permeates every aspect of our lives. While we may not have flying cars quite yet, technologies that were considered the stuff of science fiction a generation ago are now taken for granted. But how does this change affect us? What are we to do when machines do everything?

What To Do When Machines Do Everything is an excellent book from 2017 on the impact of automation, A.I., and big data on everyday life and the workplace. It was written by Malcolm Frank, Paul Roehrig, and Ben Pring of Fortune 500 technology and consultancy firm Cognizant. It brings together extensive documentation of the current impact of these all-important modern technologies as well as solutions for adapting them into your business model.

The authors are far from the first to compare the current trends in automation to a new industrial revolution. This is a nearly universal trope in writing on this topic. On a similar note, they also consider the ways in which massive technological change will impact the job market. And while there is plenty of doom and gloom out there regarding an automation apocalypse wiping out huge swathes of the job market, none of this is to be had in this book.

The authors compare data today to the coal and steel of prior industrial revolutions. Just as the steam engine fueled radical new technologies in the 19th century, so today date is leveraged by companies to create ever smarter and more efficient products. On a similar note, 19th century factories put many people in more artisanal and agricultural trades out of business. But they also created whole new jobs, to say nothing of vastly improving the standard of living overall.

That is the view the authors take of the present automation and data revolutions. Yes, some people will lose out due to automation. But heretofore unknown careers and opportunities will be created. Data analytics, for instance, is becoming an increasingly essential job. Companies harvest tons of data, and it is important to have people who can understand the underlying trends.

This is just one example. The authors, writing in 2017, predicted that automation will cost the U.S. economy 19 million jobs by 2027. But they will also create 21 million jobs. There is a sense of inexorability to it all. Robotics and machine learning will continue to advance and transform industries. All available studies suggest that there’s no way around this. So you have to know how to adapt to the changes.

One thing that the authors focus in on is that via artificial intelligence, a lot of white collar jobs are also becoming obsolete. It’s not just factory workers that are at risk of being replaced by machines. Just think of how many low-level accounting jobs don’t exist anymore thanks to Turbo Tax.

In terms of business application, the authors have a lot of solid advice about how to leverage data and automation in the realm of back-office work. This can help you stay competitive by focusing on more important tasks, while the robots handle the boring, clerical work.

Much ink has been spilled on this topic over the last decade or so, and What To Do When Machines Do Everything? compiles a lot of the very best of that research into one place. This makes it feel relevant even four years later. So even though automation has become a hot topic of discussion, the book has some actually shocking revelations about the extent to which certain tasks and occupations are already being automized.

Take journalism, for instance. In 2017, the Associated Press alone published approximately 20,000 articles online that had been written by algorithms. These algorithms will only improve as time goes on, getting better and better at mimicking human writing and doubtless phasing out numerous human reporters. It’s safe to say, we’ve come a long way since the first Roomba.

What To Do When Machines Do Everything? is available for purchase here.

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