Open Access

Artificial Intelligence Adoption and Workforce Reskilling in Indian Manufacturing SMEs: Determinants of Scaled Adoption and Financial Performance Outcomes

Volume 3, Issue 7

  • Author(s)Rohan V. Deshpande
  • AffiliationDepartment of Management Studies, VJTI, Mumbai
  • Page No.59-64
  • Volume, Issue & YearVolume 3, Issue 7, July 2026
  • Published On2026/07/07
  • JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
  • ISSN No.3048-9350
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Abstract

Small and medium enterprises (SMEs) constitute approximately 30% of India's manufacturing gross value added and over 110 million jobs, yet face persistent disadvantages in adopting artificial intelligence (AI) relative to large manufacturers, including capital constraints, skilled talent shortages, and limited digital infrastructure. While prior research has documented aggregate AI adoption trends, the firm-level determinants of progression from pilot-stage experimentation to scaled adoption, and the role of workforce reskilling investment in enabling this progression, remain underexamined in the Indian manufacturing SME context specifically.
This study surveys 412 manufacturing SMEs across six sectors (automotive components, textiles, pharmaceutical formulation, electronics assembly, industrial machinery, and food processing) in Maharashtra, Tamil Nadu, and Gujarat, combining a structured adoption-maturity survey with logistic regression analysis of scaled-adoption determinants and a six-month longitudinal workforce competency assessment across 860 production and quality workers participating in firm-sponsored AI reskilling programmes. Financial performance outcomes (revenue growth, EBITDA margin) were compared across adoption-maturity cohorts using firm-level financial statement data.
Scaled AI adoption rose sharply with firm size, from 10% among micro enterprises to 59% among large SMEs (250-499 employees). Reskilling spend as a share of payroll was the strongest predictor of scaled-adoption success in the logistic regression model (standardised β = 0.41), ahead of leadership digital literacy (β = 0.34) and prior ERP/MES system maturity (β = 0.29). Workforce competency scores rose substantially across all assessed skill categories following six-month reskilling participation, with AI tool operation competency showing the largest gain (1.8 to 5.4 on a 10-point scale). Firms with scaled AI adoption for two or more years reported 13.1% YoY revenue growth and 3.8 percentage point EBITDA margin improvement, compared with 4.2% revenue growth and 0.3 percentage point margin improvement among non-adopting firms. Skilled talent shortage was the most frequently cited adoption barrier, reported by 71% of surveyed firms.

Keywords: artificial intelligence adoption, workforce reskilling, manufacturing SMEs, India, digital transformation, productivity, EBITDA margin, technology adoption, logistic regression

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