Artificial Intelligence in Records Management: Standardization Needs in Developing Countries: Case Study of Zimbabwe

This conceptual research paper explored the standardization needs of Artificial Intelligence (AI) in records management within developing countries, with a particular focus on Zimbabwe. The study is grounded in the Technology–Organization–Environment (TOE) framework developed by Tornatzky and Fleischer (1990), which explains how technological, organizational, and environmental factors influence the adoption and implementation of innovations. Guided by this theoretical lens, the paper employed a qualitative, documentary review methodology to analyze existing literature, international standards (such as ISO 15489-1:2016 and ISO 23081-1:2019), and national policies relevant to AI and records management. The objectives of the study are to: (i) assess the current state of AI application in records management in Zimbabwe, (ii) analyze the adequacy of existing international standards in addressing AI-driven recordkeeping, (iii) identify key areas requiring standardization to enhance interoperability and compliance, and (iv) propose a framework for standardizing AI-based records management systems. Findings revealed that while AI adoption in Zimbabwe’s records management sector is growing, it remains uncoordinated and unstandardized, with significant challenges in metadata consistency, legal compliance, and ethical governance. The study results further indicated that current ISO standards do not fully account for the complexities of AI-powered automation, leading to gaps in data integrity, algorithmic transparency, and interoperability. Drawing from the TOE framework, the paper proposed a context-sensitive standardization framework comprising four components: (i) data preparation and quality management, (ii) AI algorithm transparency and explainability, (iii) performance evaluation and ethical oversight, and (iv) policy alignment with international and national regulatory instruments. The study concluded that standardization is critical to ensuring the authenticity, reliability, and usability of AI-generated records in Zimbabwe and other developing nations. It recommended the development of localized AI standards, capacity building for records professionals, integration of AI governance in policy frameworks, and regional collaboration to harmonize AI-driven records management standards across Africa. The proposed framework provides a pathway toward trustworthy, efficient, and legally compliant records management systems in the era of the Fourth Industrial Revolution.

Artificial Intelligence, Records Management

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