A Governance-Centered Framework for AI-Based Knowledge Systems Supporting Organizational Resilience and Process Continuity

Abstract

Digital transformation research has extensively examined service digitization, platform governance, and algorithmic regulation. However, comparatively limited attention has been devoted to the architectural design of AI-enabled institutional memory systems that underpin organisational resilience and process continuity. Across both public administrations and large private-sector organisations, demographic transitions, workforce mobility, and escalating system complexity increase the risks of knowledge erosion and operational fragility.

This article develops a governance-centred four-layer architectural framework for AI-based knowledge systems designed to strengthen institutional memory across digitally intensive sectors. Grounded in the knowledge-based view of the firm, organisational learning theory, and contemporary AI governance scholarship, the framework integrates: (1) data governance foundations ensuring structural integrity and cybersecurity; (2) intelligence processing capabilities leveraging AI technologies; (3) structured human oversight mechanisms preserving expert validation and accountability; and (4) governance and compliance protocols embedding transparency, auditability, and regulatory alignment.

The study advances digital government research by reconceptualizing artificial intelligence not as an isolated automation tool, but as an embedded institutional capability that enhances adaptive capacity, accountability, and process stability. An illustrative cross-sector scenario demonstrates how AI-enabled knowledge architectures reduce onboarding time, improve risk anticipation, and preserve tacit expertise while maintaining transparency and regulatory alignment.

By linking AI system engineering to governance-centred design principles, the proposed framework contributes a transferable model for strengthening organisational resilience in both public and private institutional contexts. Future empirical research should test the framework's operational impact across sector-specific implementations.