AI-Driven Monitoring and Evaluation: The Future of Transparent and Accountable Governance in Public Project Implementation

Artificial intelligence (AI) is emerging as a disruptive technology in public governance, particularly in the form of monitoring and evaluation (M&E) systems that are required for transparent and accountable implementation of public projects. Focusing on AI's potential to improve efficiency and reduce corruption, as well as its role in fostering institutional integrity, the paper explores how it can be employed to bolster government, specifically through its incorporation into M&E processes. The objectives were the analysis of AI in promoting innovation and transparency in M and E systems; the assessment of the institutional and infrastructural conditions related to AI; and the impact of AI-enabled M and E systems on reduction of corruption. The study drew on the systems theory of technology, human resource, and institutional structures and used a mixed-method approach involving quantitative survey and qualitative interviews. Results indicated that the use of AI positively affected governance by automating the data collection process, providing the ability to track projects in real-time and identifying inefficiencies. Nevertheless, only a few challenges were noted in developing countries specifically and referred to as infrastructural incompatibilities, bandwidth limitations, and ethics, contrary to developed countries where a vast range of negative experiences were noted. But these objections did not nullify the positive impact of AI-powered systems, they only limited it. While there is limited empirical research on the topic, the study confirmed global trends in the use of AI in governance as well as the unique challenges of developing countries like Kenya. The paper concluded that AI holds significant potential for supporting transparent government, but it should not be implemented without critical thought being given to infrastructure development, capacity, and ethical controls. Among these recommendations are concrete actions for the integration of AI into existing governance processes, as well as policy recommendations for investments in infrastructure and training for AI. Future work should focus on, for example, AI ethics, blockchain technologies for M&E systems, and global benchmarking to enhance the scalability and ethics of public sector governance using AI.

Artificial intelligence, Monitoring and evaluation, Governance

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