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Assessing AI and Technology Readiness in Indian Banking: Workforce Adaptability, Organisational Transformation, and Strategic Implementation

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  • Neha Chhabra Roy

    (Narsee Monjee Institute of Management Studies)

Abstract

Global financial services are steadily being transformed by artificial intelligence (AI), but adoption remains uneven. Indian banking institutions are evaluated at two stages to determine their AI readiness. The Extended Technology Adoption Model (ETAM) is first applied to identify behavioural, organisational, and infrastructure drivers of adoption. A Random Forest (RF) classification model is employed in the second stage to group banks into high, medium, and low readiness segments. A structured survey of senior managers in Indian commercial banks was conducted, along with secondary research. According to the results, large banks demonstrate strong technological preparedness, but smaller and mid-sized banks still face challenges due to limited skills, infrastructure gaps, and regulatory uncertainty. The strongest predictors of readiness are organisational support, IT infrastructure, and regulatory clarity. ETAM’s theoretical scope is expanded by embedding infrastructural and legal considerations. Hybrid approaches that combine behavioural models with machine learning can provide a more comprehensive assessment of readiness. In practice, the framework provides banks and regulators with a diagnostic tool for targeting AI adoption strategies.

Suggested Citation

  • Neha Chhabra Roy, 2026. "Assessing AI and Technology Readiness in Indian Banking: Workforce Adaptability, Organisational Transformation, and Strategic Implementation," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-19314-8_10
    DOI: 10.1007/978-3-032-19314-8_10
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