AI adoption in Indian banks: Investigating the public-private divide

Abstract

Artificial Intelligence (AI) has increasingly become central to innovation and efficiency within modern banking systems. Yet, despite similar regulatory environments, public and private sector banks display notable differences in how AI technologies are adopted and deployed. This study examines these differences by comparing strategic priorities, organizational readiness, regulatory constraints, and economic considerations shaping AI adoption across both sectors. Using a mixed-methods approach, the research draws on survey data from 12 Indian banks (six Public sector banks and six Private sector banks) over a 24‑month period. Using a mixed‑methods design, we conducted (a) a structured survey (N = 360 senior managers) to compute an AI Adoption Index (AAI), (b) a longitudinal assessment of AI‑driven performance metrics (fraud‑detection accuracy, loan‑processing time, and customer‑service response rate), and (c) a controlled implementation experiment of a chatbot‑based customer‑service prototype in a matched pair of banks. The analysis focuses on AI applications in credit assessment, fraud detection, customer service automation, risk management, and operational processes. The findings indicate that private sector banks generally exhibit higher AI maturity, driven by flexible capital allocation, competitive pressure, and customer-centric business models. In contrast, public sector banks face persistent challenges related to legacy systems, bureaucratic decision-making, and constrained talent acquisition, although recent government-led digital initiatives have begun to mitigate some of these barriers. Results reveal that PrSBs exhibit a 48 % higher AAI (mean = 0.68, SD = 0.07) than PSBs (mean = 0.46, SD = 0.09; p < 0.001). The findings suggest that institutional factors, such as strategic autonomy and capital flexibility, strongly moderate AI uptake and its operational benefits. Policy implications for the Reserve Bank of India (RBI) and recommendations for bridging the AI gap are discussed. The paper concludes with policy-oriented recommendations aimed at strengthening responsible and inclusive AI adoption within public financial institutions, with implications for financial stability and economic inclusion.