Artificial Intelligence and Workforce Preparedness in India: A Human-Capital and Sociotechnical Perspective on Labour-Force Adaptation

Abstract

Artificial intelligence (AI) is increasingly changing the organisation of work, the composition of occupational tasks and the skills required to remain employable. For India, these changes are particularly important because the country combines a large working-age population with substantial differences in education, occupational exposure, digital capability and access to training. Consequently, the employment consequences of AI are likely to depend not only on the extent of technological adoption but also on the preparedness of workers and institutions to adapt. This study examines the preparedness of the Indian labour force to adapt to AI-driven employment changes, corresponding specifically to the fourth objective of a broader empirical investigation of AI and employment in India. Drawing on human capital theory, the technology–organisation–environment perspective and a sociotechnical view of AI-enabled work, the study conceptualises workforce preparedness as a multidimensional capability involving AI-related skills, organisational training, continuous learning, skill-gap management and institutional support. Primary data were collected from 600 respondents working across manufacturing, IT services, HR and finance, healthcare, and sales and marketing. Reliability analysis, principal component analysis, Pearson correlation, regression analysis, independent-samples t-tests and one-way ANOVA were employed. The workforce preparedness scale demonstrated acceptable internal consistency (Cronbach's α = .775). The overall workforce-preparedness mean was 3.807 on a five-point scale. The factor analysis showed strong sampling adequacy (KMO = .957; Bartlett's test p < .001), while four components jointly explained 53.52% of the variance. Workforce preparedness was strongly associated with AI adoption (r = .817, p < .001) and AI economic impact (r = .810, p < .001). The reported regression model yielded R = .784, R² = .615 and F(1,598) = 954.800, p < .001, with an unstandardised coefficient of .814. Significant differences were also identified across gender, age, job level and years of experience, whereas differences across sectors in workforce preparedness did not reach the conventional 5% significance level. The findings indicate that Indian workers perceive themselves as moderately well prepared for AI-driven changes, but preparedness remains uneven across demographic and career groups. The study contributes to the emerging AI-workforce literature by arguing that readiness should be understood not as a static individual attribute but as a jointly produced capability arising from worker skills, organisational support, institutional learning systems and the wider technological environment. The results suggest that India's AI transition will require continuous learning, workplace reskilling, practical AI literacy, industry–academia collaboration and inclusive policy support.