Public-sector digital transformation in the age of generative AI
Digital transformation (DT) remains central to information systems and public administration scholarship, particularly amid the rapid emergence of generative artificial intelligence (GenAI). This study presents a systematic literature review of 125 peer-reviewed articles published between 2021 and 2026 to synthesise contemporary public-sector DT dynamics. Following the PRISMA 2020 reporting standard and thematic synthesis, the review maps conceptualisations of DT, identifies key organisational, technological and environmental drivers, and examines their implications for public value. The findings indicate that DT is predominantly conceptualised as a sociotechnical and public-value-oriented process shaped primarily by leadership, organisational culture and strategic alignment rather than technological investment alone. Organisational and managerial factors emerge as the most consistent predictors of transformation outcomes across diverse institutional contexts, while technological and environmental conditions influence the pace, direction and unevenness of implementation. Despite the rapid diffusion of AI and GenAI in government practice, only a limited proportion of the literature substantively engages with AI, and fewer studies address GenAI, large language models or foundation models, revealing a widening gap between technological developments and scholarly inquiry. Building on the established organisational-technological-environmental framework, the review identifies four additional explanatory dimensions: citizen co-production, digitally induced administrative burden, street-level administrative reconfiguration, and multidimensional public-value evaluation. The study concludes by identifying priorities for future research on GenAI governance, accountability and equity, while offering practical implications for public managers and policymakers pursuing AI-enabled public-sector transformation.