Sep 2026· Engineer Journal of the Institution of Engineers Sri Lanka· 0 citations· 52 references
E-Government and Public Services
TL;DR
The study discovered that challenges to AI adoption include outdated ICT infrastructure, legacy systems, poor data quality, limited specialised talent, organisational resistance, fragmented regulations, socio-ethical concerns, and weak citizen trust.
Abstract
The Sri Lankan public sector has continued to struggle with operational inefficiencies, bureaucracy, low public trust and weak service responsiveness to citizens. This hinders effective governance. These issues necessitate structural transformations rather than haphazard technological upgrades. This study examines how Artificial Intelligence (AI) can enable Governance Process Reengineering (GPR) in Sri Lanka’s government sector. This research utilises a Systematic Literature Review, following the PRISMA 2020 approach, to select literature from 60 peer-reviewed articles across multiple databases, published between 2015 and 2025. The analysis is grounded in the integrated Technology-Organisation-Environment (TOE) framework and Technology Affordance and Constraints (TACT) Theory to provide a multidimensional assessment of AI adoption. The study discovered that challenges to AI adoption include outdated ICT infrastructure, legacy systems, poor data quality, limited specialised talent, organisational resistance, fragmented regulations, socio-ethical concerns, and weak citizen trust. AI has the potential to improve accountability, transparency, and efficiency; enhance service delivery; simplify administrative processes; create public value through redesign; and enhance data-driven decision-making and proactive governance. AI-driven GPR redesigns government processes to enhance public value through accountability, transparency, and public centricity. A proposed 10-year roadmap, tailored to Sri Lanka, adds value by eliminating fragmented and ineffective government structures and translating them into effective governance reforms.
The SLR demonstrates that effective AI and digital platform governance demands a holistic, context-sensitive approach that actively balances efficiency with justice, innovation with accountability, and risk with public value.
The TOE-I structure developed here gives policymakers and public sector managers a shared vocabulary for diagnosing where their own agencies stand and where intervention would matter most, and suggests that AI adoption in government is less a technology problem than an organisational and human one.
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