Information Governance as a Dynamic Capability for Data-Driven AI Innovation: A Systematic Literature Review
TL;DR
This study addresses the gap between abstract ethical principles and practical, distributed demands of modern AI governance through a PRISMA 2020 guided systematic review of 78 peer-reviewed studies published between 2020 and 2024, aiming to synthesise a cohesive architecture for modern AI governance.
Abstract
Artificial intelligence (AI)-enabled systems, such as large language models (LLMs) and federated learning, are fundamentally transforming organisational workflows into distributed, model-centric ecosystems. While these advancements drive innovation, they simultaneously heighten information governance (IG) challenges regarding data privacy, algorithmic accountability, and systemic transparency. Although various ethical frameworks have been proposed to address these concerns, their practical operationalisation remains fragmented across different sectors. This study addresses this gap through a PRISMA 2020 guided systematic review of 78 peer-reviewed studies published between 2020 and 2024, aiming to synthesise a cohesive architecture for modern AI governance. The review identifies five core IG functions essential for maintaining integrity: accountability, data quality, privacy-by-design, compliance, and risk management. These functions are not merely theoretical; they are enabled by six distinct technological clusters, including privacy-preserving federated learning, machine-learning operations (MLOps), and AI–blockchain traceability. These tools allow organisations to move beyond manual oversight towards automated, scalable governance. Furthermore, the research highlights that human governance must be distributed across ethical oversight and technical validation. This necessitates a multidimensional competency architecture that spans legal awareness, analytical proficiency, and socio-technical skills. Ultimately, IG is evolving from a reactive, post hoc compliance exercise into a proactive, lifecycle-oriented socio-technical capability. By integrating advanced technological clusters with human-centred agency, organisations can bridge the gap between abstract ethical principles and the practical, distributed demands of modern AI governance. This holistic shift ensures the creation of robust, transparent, and secure information ecosystems that are resilient to the complexities of the digital age.