There is no shortage of angst when it comes to the impact of AI on jobs. For example, a survey by Pew Research Internet finds Americans are roughly twice as likely to express worry (72%) than enthusiasm (33%) about a future in which robots and computers are capable of doing many jobs that are currently done by humans.
However, at least one set of experts believes jobs will be shredded, but not eliminated. Instead of worrying about job losses, executives should be helping to reduce jobs in which AI and machine learning take over boring tasks, while humans spend more time with higher-level tasks.
That's the word from Erik Brynjolfsson and Daniel Rock, with MIT, and Tom Mitchell of Carnegie Mellon University, who points out that the impact of machine learning, the self-programming, self-adjusting core of AI, on jobs. is iffy. "ML will affect very different parts of the workforce than earlier waves of automation," they state in a recent paper. Instead, automation will occur on a task-by-task basis.
"Tasks within jobs typically show considerable variability in 'suitability for machine learning' while few -- if any -- jobs can be fully automated using machine learning," they continue. "Machine learning technology can transform many jobs in the economy, but full automation will be less significant than the re-engineering of processes and the reorganization of tasks."
What jobs are most likely to see tasks handled by AI or machine learning? Oddly enough, funeral directors rank high on the automatable list. Here are the roles Brynjolfsson and his co-authors identify as top candidates for machine learning:
Morticians, undertakers, and funeral directors
And the jobs least likely to be shredded by AI/machine learning:
Public address system and other announcers
Plasterers and stucco masons
Brynjolfsson and his colleagues say we're having the wrong debate when it comes to AI: instead of pondering how jobs will be wiped out, people need to focus on "the redesign of jobs and re-engineering of business processes." While AI and machine learning will be everywhere, the suitability for machine learning of work tasks varies greatly." The high and low suitability-for-machine-learning tasks within a job can be separated and re-bundled."
Dr. Irving Wladawsky-Berger, former IBM mover and shaker and now one of the most informed observers of the digital economy, provided perspective on the Brynjolfsson report, noting that some of the job activities "are more susceptible to automation, while others require judgment, social skills, and other hard-to-automate human capabilities. But just because some of the activities in a job have been automated, does not imply that the whole job has disappeared." To the contrary, he continues, "automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as enabling them to focus on those aspects of the job that most need their attention."
Bracing for job shredding calls for rethinking the various tasks for which employees assume responsibilities. Brynjolfsson and his co-authors warn, however, that arbitrarily bundling machine-learning-enabled and non-ML tasks into a single job is counterproductive. "Bundling suitability-for-machine-learning and non-SML tasks prevents specialization and locks up potential productivity gains."
Ultimately, the key to success in this emerging environment is to be able to marshal and capitalize on AI capabilities to deliver more value and service to customers. Employees can play a vital role in identifying opportunities, training models and algorithms, and taking a leadership role in determining if the systems are delivering business value in an ethical way.
Jobs will be enriched and elevated by AI and machine learning, but the best jobs will be those created to employ AI that links customers to the services and products they need.