WORKFORCE TRANSFORMATION IN MANUFACTURING INDUSTRIES: THE ROLE OF ARTIFICIAL INTELLIGENCE, AUTOMATION, RESKILLING, UPSKILLING AND SOFT SKILLS

The employment of new technologies like Artificial Intelligence (AI) along with robotics, intelligent automation and digital manufacturing are changing the nature of work and workforce capabilities in manufacturing industries. While previous studies have put a lot of focus on technological adoption and on Industry 4.0, comparatively less empirical attention has been paid to the organizational and human-capability mechanisms through which technological transformation leads to the transformation of the workforce.…

By the Truvace deskOctober 21, 20262 min read1 sourcePeer-reviewedTRV-2026-2066· cite & verify

WORKFORCE TRANSFORMATION IN MANUFACTURING INDUSTRIES: THE ROLE OF ARTIFICIAL INTELLIGENCE, AUTOMATION, RESKILLING, UPSKILLING AND SOFT SKILLS — "Minimum Wage Event with US Secretary Perez at Boloco" by MDGovpics is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

In brief

Researchers surveyed 550 people in the manufacturing sector and analyzed the responses with PLS-SEM. They tested how AI adoption, automation intensity, reskilling, upskilling and soft skills relate to workforce transformation, defined as job redesign, changing competency needs, human-technology collaboration and movement to higher-value work.

The results suggest that transformation in factories depends on human capability development as well as on technology itself. The evidence is associational and survey-based, so it describes relationships among reported measures and does not show which workers gain or lose from AI deployment.

Main points

The sample was 550 respondents from the manufacturing sector, analyzed with PLS-SEM.
AI adoption (β = 0.272) and automation intensity (β = 0.220) were positively associated with workforce transformation.
Upskilling (β = 0.311) and reskilling (β = 0.289) showed the strongest positive links.
Soft skills had a smaller but significant interaction effect (β = 0.060, p = 0.004).
The structural model explained a large share of variance in workforce transformation (R2 = 0.845).

The gain

Among surveyed manufacturing workers, AI adoption, reskilling and upskilling were each positively associated with workforce transformation, including job redesign and a shift to higher-value work.

The rundown

The study starts from a gap it identifies in earlier work: prior research focused on technology adoption and Industry 4.0, with less empirical attention to the organizational and human-capability mechanisms behind workforce change.

It builds an integrated framework grounded in socio-technical systems theory, the resource-based view and the automation-augmentation perspective. Workforce transformation is treated as a multifaceted process rather than simple job replacement.

The authors conclude that manufacturing workforce transformation is not only about technology but also about developing and integrating human capabilities for human-technology collaboration. They position the framework as a contribution toward Industry 5.0.