The male-dominated construction sector and the female-dominated childcare sector are least affected by AI transformation

Infografik of April 16, 2026

The infographic is taken from DIW Weekly Report 14+15, 2026. It shows how strongly different occupational groups could be affected by generative artificial intelligence, combined with information on whether these occupations are female-dominated or male-dominated. Overall layout: The graphic consists of multiple horizontal bars arranged vertically by occupation. The horizontal axis runs from 0 to 100 percent and represents the extent to which generative AI could change occupational profiles. A trend line distinguishes between female-dominated professions on one side and male-dominated professions on the other. Displayed occupations (selection): Occupations shown include Office and Administration, Childcare, Construction, Civil Engineering, Food and Service Engineering, Technology and Mechanics, Care, Retail, IT Infrastructure and Support, Software Development, Banking and Finance, Production and Manufacturing, Marketing, Real Estate Industry, Project Management, and Education. Key visual information: Construction (a male-dominated profession) and Childcare (a female-dominated profession) show the lowest AI transformation potential, with values close to the lower end of the scale. Occupations such as Software Development, Banking and Finance, Marketing, and Project Management show higher AI transformation potential, reaching well above the midpoint of the scale. The chart visually contrasts female-dominated and male-dominated professions but does not show individual numerical labels for each bar. Main takeaway stated in the graphic: The male-dominated construction sector and the female-dominated childcare sector are the least affected by AI transformation. Licensing and data sources: The infographic is licensed under Creative Commons CC BY 4.0. Sources include the Indeed GenAI Skill Transformation Index, the 2022 microcensus, and the authors’ own calculations.

Topics: Digitalization , Education , Gender , Labor and employment

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