Für diese Tätigkeit liegen 5 Quellenbelege vor. Keiner untersucht diese Tätigkeit unmittelbar; die Aussagen stammen aus breiterem Kontext.
Das Fertigen und Montieren bleibt körperliche Präzisionsarbeit — was Robotik in der Serie übernimmt, ist die eigentliche offene Frage.
Bauteile zu fertigen und zu montieren heißt, mit Geschick und Präzision am Werkstück zu arbeiten — und genau das kann die heutige KI nicht ausführen: Sprach- und Bildsysteme haben keinen Zugriff auf die physische Handhabung, weshalb Fachleute diese Aufgabe durchweg niedrig bewerten und keine der geprüften manuellen Fähigkeiten derzeit als ersetzbar gilt. Es gibt sogar Hinweise, dass die Nachfrage nach Produktions- und körperlichen Fähigkeiten in KI-nahen Betrieben eher leicht steigt. Offen bleibt allein, wie sich die Serienmontage aufteilt, wenn Robotik repetitive Füge- und Zuführschritte übernimmt und der menschliche Anteil auf Varianten, Nacharbeit und Prüfung wandert — das ist eine begründete Vermutung, kein Befund.
Diese allgemeine Einordnung ist keine Messung in jedem einzelnen Beruf.
Eine fehlende Bewertung bedeutet nicht, dass die Tätigkeit nicht betroffen ist.
Occupations where most tasks remain relatively unaffected by GenAI, with low task variability and a stable low mean exposure score. | Occupations with low GenAI exposure, where some tasks show moderate automation potential, but overall occupational exposure remains limited. | Not Exposed 7212 Welders and Flame Cutters 0.13 0.05 | Not Exposed 7214 Structural Metal Preparers and Erectors 0.11 0.02 | Not Exposed 7211 Metal Moulders and Coremakers 0.13 0.01 | Minimal Exposure 8211 Mechanical Machinery Assemblers 0.27 0.17 | Minimal Exposure 8212 Electrical and Electronic Equipment Assemblers 0.28 0.19
Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140) · 2025 · ILO WP 140, Table 5, PDF-S. 41 (Kategorie-Definitionen); Table A1, PDF-S. 57 und PDF-S. 63
The task involves hands-on physical work requiring skill and precision, such as welding using various machines, which is not currently automatable by generative AI technologies like ChatGPT. These technologies excel in tasks involving data processing, language understanding, and information generation but do not possess capabilities for physical task execution, hence aligning closer with the revised score.
Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140) · 2025 · ILO WP 140, Table 4 „Sample of adjustments and justifications between the survey and expert scores", PDF-S. 34 (gedruckt S. 31)
In occupations dominated by manual and physical tasks, such as Craft and Related Trades Workers, Plant and Machine Operators, and Farmers, Gardeners, Foresters, and Fishers, experts consistently assigned lower scores compared to the survey respondents, reflecting the practical limitations of automating tasks that require physical manipulation or manual dexterity. | the limitations of generative AI in handling physical and manual components | leading to significantly reduced adjusted scores compared to the survey.
Generative AI and Jobs: A Refined Global Index of Occupational Exposure (ILO Working Paper 140) · 2025 · ILO WP 140, Abschnitt 4 „Expert validation survey", PDF-S. 32 (gedruckt S. 29); ergänzend PDF-S. 34
Zero of the more than 2,800 skills assessed were determined to exhibit „very high capacity" to be replaced by the current generation of GenAI tools, with the majority of examined skills (69%) determined to have either „very low capacity" or „low capacity" to be substituted, indicating that GenAI currently remains limited in performing tasks that require physical execution, nuanced judgment or hands-on application. | Skills rooted in human interaction – including empathy and active listening, and sensory processing abilities – and manual dexterity, endurance and precision, currently show no substitution potential due to their physical and deeply human components. These findings underscore the practical limitations of current GenAI models, which lack the physicality to perform tasks that require hands-on interaction
The Future of Jobs Report 2025 · 2025 · WEF Future of Jobs Report 2025, Kap. 3, PDF-S. 44 (gedruckt S. 43)
The report also provides some evidence that AI adoption may increase the demand for some blue-collar skills, possibly through a productivity effect that results in higher demand and spills over to other workers at the establishment. For example, the report finds that higher establishment AI exposure is associated with increased establishment demand for skills related to production and technology, and physical skills. | Demand increased slightly for some skill groupings, such as production and technology and physical skills. These skill groupings are found disproportionately in low-exposure occupations and include skills such as maintenance, repair, forklift driving and more fundamental skills such as dexterity, heavy lifting and range of motion. This provides some suggestive evidence of the types of skills that may see increased demand due to complementarities with AI in production. However, these increasing skill demands were quite modest with substantial cross-country heterogeneity.
Artificial Intelligence and the Changing Demand for Skills in the Labour Market (OECD AI Papers No. 14) · 2024 · OECD AI Papers No. 14, Executive Summary, PDF-S. 9; Präzisierung PDF-S. 13
Diese Lösungen unterstützen die Tätigkeit in einem passenden fachlichen Kontext. Das ist keine Aussage über ihre Eignung oder gemessene Wirkung in jedem zugeordneten Beruf.
Zitierhinweis: SapientShift, Tätigkeit T-004 „Bauteile und Konstruktionen fertigen und montieren“, https://sapientshift.com/taetigkeiten/bauteile-und-konstruktionen-fertigen-und-montieren. Methodik und Grenzen.