Intended to clear up misconceptions on the subject of automation, the following report employs government and private data, including from the McKinsey Global Institute, to develop both backward- and forward-looking analyses of the impacts of automation over the years 1980 to 2016 and 2016 to 2030 across some 800 occupations. In doing so, the report assesses past and coming trends as they affect both people and communities and suggests a comprehensive response framework for national and state-local policymakers.
In terms of current trends, the report finds that:
1. Automation and AI will affect tasks in virtually all occupational groups in the future but the effects will be of varied intensity—and drastic for only some. The effects in this sense will be broad but variable:
• Almost no occupation will be unaffected by the adoption of currently available technologies.
• Approximately 25 percent of U.S. employment (36 million jobs in 2016) will face high exposure to automation in the coming decades (with greater than 70 percent of current task content at risk of substitution).
• At the same time, some 36 percent of U.S. employment (52 million jobs in 2016) will experience medium exposure to automation by 2030, while another 39 percent (57 million jobs) will experience low exposure.
2. The impacts of automation and AI in the coming decades will vary especially across occupations, places, and demographic groups. Several patterns are discernable:
• “Routine,” predictable physical and cognitive tasks will be the most vulnerable to automation in the coming years.
• Automation risk varies across U.S. regions, states, and cities, but it will be most disruptive in Heartland states.
• Men, young workers, and underrepresented communities work in more automatable occupations.
3. To manage and make the best of these changes five major agendas require attention on the part of federal, state, local, business, and civic leaders.
• Promote a constant learning mindset
– Invest in reskilling incumbent workers
– Expand accelerated learning and
certi cations
– Make skill development more financially
accessible
– Align and expand traditional education
– Foster uniquely human qualities
• Facilitate smoother adjustment
– Create a Universal Adjustment Benefit to support all displaced workers
– Maximize hiring through a subsidized employment program
• Reduce hardships for workers who are struggling
– Reform and expand income supports for workers in low-paying jobs
– Reduce financial volatility for workers in low-wage jobs
• Mitigate harsh local impacts
If the nation can commit to its people in these ways, an uncertain future full of machines will seem much more tolerable.
Chosen excerpts by Job Market Monitor. Read the whole story at Automation and Artificial Intelligence: How machines are affecting people and places
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