Researchers also need to consider the potential impacts of automation. (Credit: Cornell)

The consequences of workplace automation will likely impact just about every aspect of our lives, and scholars and policymakers need to start thinking about it far more broadly if they want to have a say in what the future looks like, according to a new study.

According to the study, past examples of new technology suggest it will take longer than companies predict for workplaces to become fully transformed by artificial intelligence (AI), and some jobs might not be as easily replaceable as economists believe. This means researchers have more time to gain a deeper understanding of how workplace automation will affect society, in order to have more say in how it unfolds.

Fully understanding workplace automation, the researchers said, requires an interdisciplinary approach that considers everything from the power dynamics within tech companies to the design of our societal institutions. The researchers identified four factors scholars should study in order to assess AI’s future impact: variation; power; ideology; and institutions.

Considering variety among jobs is important, because not all jobs — even in the same fields — are identical. Researchers generally use U.S. Department of Labor databases to predict how automation might affect certain job categories, but most studies don’t consider differences in implementation, skills, tasks, and work practices across organizations or locations.

Because designers and engineers don’t function independently, power is another crucial factor, the paper said. Which AI technologies are pursued and how aggressively they’re implemented depends on the dynamics within companies, as well as the priorities of the government entities that might fund or regulate those companies.

Researchers also need to consider the potential impacts of automation — and the widespread unemployment it will likely bring — on our institutions. Researchers and policymakers also need to weigh the societal benefits of work, in order to make informed decisions about which jobs are worth saving.