AI-powered agents and robots could spur roughly $2.9 trillion in annual US economic value by 2030, according to McKinsey’s midpoint automation scenario. Realizing that potential will hinge less on breakthrough inventions than on how organizations redesign workflows and how quickly people’s skills adapt, note McKinsey’s Alexis Krivkovic, Anu Madgavkar, Lareina Yee, Sven Smit, and coauthors. To gauge how skills could evolve, the authors created the Skill Change Index, measuring each skill’s exposure to automation across adoption scenarios in the next five years. Impacts vary widely, with people-centric skills on one end facing limited exposure, and routine and manual tasks on the other end of the spectrum. In the midpoint scenario, roughly one-quarter to one-third of work hours tied could be automated, while in a faster-adoption scenario, the most affected skills could reach 60 percent.
Image description:
A line-and-dot chart shows the Skill Change Index (0–100) for workplace skills, indicating exposure to automation. The x-axis ranks skills by percentile from lowest to highest exposure; the y-axis shows index value, with higher values meaning more exposure to automation. Two curves are shown: an early adoption scenario (higher curve) and a midpoint scenario (lower curve). In the early scenario, index values rise from about 43% at the 25th percentile to 59% at the 75th percentile; in the midpoint scenario, values rise from about 23% to 33%. Individual dots represent top skills, color-coded by skill category. Examples of lower-exposure skills include a good driving record, leadership, coaching, and negotiation. Midrange skills include communication, customer relations, management, writing, and problem-solving. Higher-exposure skills include detail orientation, quality assurance, inventory management, invoicing, and SQL programming.
Note: This image description was completed with the assistance of Writer, a gen AI tool.
Source: Lightcast; US Bureau of Labor Statistics; McKinsey Global Institute analysis
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