Robotics & Automation News

Market trends and business perspectives

Americans split on impact of automation in the workplace

Automation in the workplace is a polarizing issue for Americans, according to the results of a new American Staffing Association Workforce Monitor survey conducted online by Harris Poll.

About equal percentages of respondents say that automation – for example, robots or artificial intelligence – will be a good or a bad thing for the future world of work.

Specifically, 34 per cent of Americans say automation will be a positive development for the workforce in the next 10 years or more—compared with 31% who say it will be negative. A plurality (35 per cent) are neutral on the matter or just don’t know. 

However, more than four in five Americans think that increased automation will revolutionize work (83 per cent)—and that this transformation is inevitable (82 per cent).

A substantial majority think that automation will fundamentally change the quantity (79 per cent) and types (68 per cent) of jobs available in the US. Seven in 10 (72 per cent) say its increased use will lead to higher unemployment.

But most Americans are in denial that automation will ever affect their work life. Nearly three quarters (73 per cent) do not believe that their work can be easily replaced by robots or artificial intelligence, and 85 per cent agree that the human factor outweighs any benefits from mechanizing their job.

Nine in 10 (90 per cent) say that there are some tasks that automation will never be able to take over from humans.

Richard Wahlquist, ASA president and chief executive officer, says: “Automation is revolutionizing the who, what, where, and how people will work in the future.

“The ASA Workforce Monitor found that nearly nine out of 10 (87 per cent) Americans believe that to succeed in this new world of work, additional training will be needed.”

Harris Poll conducted the survey online within the US on behalf of ASA March 7-9, 2017, among a total of 2,133 US adults age 18 and older.

Results were weighted on age, education, race/ethnicity, household income, and geographic region where necessary to bring them into line with their actual proportions in the US population.