Source page: McKinsey & Company
Commentary
Skills reset for the AI age
Artificial Intelligence | Future of Work | Workforce
March 3, 2026 – 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, Michael Chui, 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.
To read the report, see “Agents, robots, and us: Skill partnerships in the age of AI,” November 25, 2025.
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Visual form
Line Chart and Scatter Plot: Skill Change Index percentile curves with individual workplace-skill markers.
Layout / body structure
The chart ranks skills from lower to higher automation exposure along the horizontal percentile axis. Two lines show the midpoint and early-adoption scenarios, while colored dots identify example skills and their categories along the same exposure scale.
What is being compared
It compares automation exposure across workplace skills and contrasts a midpoint adoption scenario with a faster early-adoption scenario.
Measurement system
The vertical scale is the Skill Change Index from 0 to 100, with higher values indicating greater exposure to automation. The horizontal scale ranks skills by percentile from least exposed to most exposed.
Visible structure inside the graphic
Both scenario lines slope upward from lower-exposure skills to higher-exposure skills. People-centric skills such as leadership, coaching, and negotiation sit toward the lower-exposure end, while routine or technical task skills such as invoicing and SQL programming sit toward the higher-exposure end.
Main takeaway from the visual
The chart shows that AI-driven skill change is uneven. Human-facing judgment and relationship skills are less exposed, while routine, detail-heavy, and automatable task skills face much higher potential change.
Key standout values or extremes
In the early-adoption scenario, the index rises from about 43 at the 25th percentile to about 59 at the 75th percentile. In the midpoint scenario, it rises from about 23 to about 33 over the same percentile range, while the most affected skills could approach 60 percent exposure.
Controls / sequence, when applicable
This is a static line-and-dot skill-exposure chart; there are no in-chart controls to operate.
Companion media, when applicable
There is no separate companion audio or video; the Skill Change Index line-and-dot chart is the full visual on this page.