TITLE:
From Knowledge Management to Knowledge Intelligence: An AI-Enabled Framework for Business Performance, Decision Support, and Organizational Learning
AUTHORS:
John Antill
KEYWORDS:
Knowledge Management, Artificial Intelligence, Decision Support, Organizational Learning, Knowledge Governance
JOURNAL NAME:
American Journal of Industrial and Business Management,
Vol.16 No.8,
August
21,
2026
ABSTRACT: Artificial intelligence is transforming how organizations manage, retrieve, interpret, and apply knowledge for business performance. While knowledge management has traditionally focused on knowledge capture, repositories, lessons learned, communities of practice, expertise sharing, and organizational learning, many organizations continue to experience fragmented knowledge assets, inconsistent reuse, weak metadata, limited expertise visibility, and poor feedback loops between knowledge creation and business application. These limitations affect proposal development, project execution, onboarding, innovation, operational decision-making, quality management, and organizational responsiveness. This conceptual article proposes an AI-Enabled Knowledge Intelligence Framework for improving business decision support, organizational learning, and knowledge-based performance. Drawing on the knowledge-based view of the firm, intellectual capital theory, dynamic capabilities, organizational learning, business intelligence, management information systems, and knowledge governance, the article positions artificial intelligence as an augmentation layer within a broader business management system. The framework includes six interdependent dimensions: knowledge sources, knowledge structuring, AI augmentation, human validation and sensemaking, knowledge application, and learning feedback. The article argues that AI-enabled knowledge management should not be treated as a technology implementation alone, but as a managerial capability that integrates people, processes, governance, information systems, and performance measurement. Research propositions and managerial implications are provided to support future empirical study and practical adoption in business and industrial management contexts.