Artificial Intelligence And E-Governance Transformation: An Assessment Of Institutional Preparedness In Nigerian Ministries, Departments And Agencies (MDAS)

Abstract

The convergence of Artificial Intelligence (AI) and electronic governance (e-governance) has emerged as a defining paradigm in the organization of public administration globally. In Nigeria, the Federal Government has articulated ambitious aspirations to deploy AI and associated technologies across Ministries, Departments and Agencies (MDAs); yet systematic assessments of institutional preparedness remain scarce. This paper examines the readiness of Nigerian MDAs to adopt and integrate AI-driven egovernance frameworks, drawing on the Technology-Organization Environment (TOE) framework as the primary theoretical lens. Through a critical synthesis of extant literature, policy documents, and official government reports, the paper identifies significant gaps in digital infrastructure, human capital, regulatory coherence, and organizational culture that constrain the effective integration of AI in Nigeria’s public sector. The study maps prevailing policy initiatives including NITDA’s Strategic Roadmap and Action Plan 2.0 (SRAP 2023–2027), the National Digital Economy and EGovernance Act 2024, and the Nigeria Data Protection Act 2023 against the requirements for genuine AI-readiness. The paper argues that institutional preparedness is not merely a technological problem but a governance challenge requiring coordinated reforms in leadership commitment, data governance, capacity development, and inter-agency collaboration. A conceptual framework—the MDA-AI Readiness Assessment Model (MARAM) is proposed, anchored on four interdependent pillars: technological infrastructure readiness, human capital and organizational readiness, regulatory and policy readiness, and governance and accountability readiness. The paper recommends that Nigerian MDAs adopt the MARAM framework as a diagnostic tool for phased AI integration, prioritizing investment in digital infrastructure and human capital while strengthening regulatory coherence across existing policy instruments. It further recommends that agency leadership institutionalize data governance structures and interagency collaboration mechanisms to sustain AI-readiness beyond initial adoption. Future empirical research should validate the MARAM framework across diverse MDA contexts.