AI-Driven Transformation in the Indian IT Services Sector: A Comparative Study of Pre- and Post-COVID-19 Eras
Author Name: Aathira Nandakumar
Abstract
This study examines the AI-driven structural transformation of the Indian Information Technology (IT) services sector through a comparative analysis of the pre-COVID-19 era (up to FY 2019–20) and the post-COVID-19 era (FY 2020–21 to FY 2024–25). India’s IT-BPM sector, which expanded from US$ 118 billion in FY2015 to an estimated US$ 283 billion in FY2025 -with exports contributing US$ 224 billion -represents one of the world’s most strategically significant technology services ecosystems. The central inquiry of this research is concerned with how the COVID-19 pandemic functioned not merely as a disruption but as a decisive structural inflection point that compressed and accelerated an AI transformation which, in the pre-pandemic period, had been advancing incrementally.
Prior to the pandemic, AI adoption in Indian IT was characterised by pilot-stage experimentation, narrow deployment of Robotic Process Automation (RPA) and basic machine learning within BPO and analytics sub-sectors, and limited strategic alignment -with fewer than 15% of Indian enterprises having embedded AI into their broader corporate strategy. Despite India scoring 3.09 times the global average in AI skills penetration between 2015 and 2021, the sector’s competitive advantage remained rooted in the labour arbitrage model, and AI remained an “adjacent” capability rather than a core architectural principle. Existing literature, while rich in studies of digital transformation (Vial, 2019; Soto-Acosta, 2020) and the structural evolution of Indian IT (Arora & Gambardella, 2004; Dossani & Kenney, 2007), has not produced a consolidated, comparative, secondary-data-driven examination of AI-driven transformation across the pre- and post-COVID eras in the Indian IT services context. This study addresses that gap. The study employs a descriptive and exploratory research design grounded in a qualitative-dominant, secondary data methodology. Drawing on NASSCOM Strategic Reviews (2021–2025), corporate annual reports of TCS, Infosys, Wipro, HCL Technologies, and Tech Mahindra, McKinsey Global Institute publications, Gartner and IDC industry reports, and peer-reviewed academic literature, the research analyses longitudinal trends across seven key dimensions: revenue and export performance, AI adoption trajectories, workforce composition and employment patterns, talent strategy evolution, business model transformation, competitive dynamics, and the policy and institutional environment. Four hypotheses are examined through systematic triangulation of secondary evidence -assessing whether COVID-19 accelerated AI adoption (H1), whether the post-COVID revenue trajectory is qualitatively different (H2), whether AI has caused structural employment shifts (H3), and whether post-COVID business model transformation is primarily AI-driven (H4).
