AI-Driven Knowledge Management and Decision-Making Quality in Medium-Sized Technology Companies: A Critical Integrative Review
TL;DR
It is argued that generative AI shifts the central knowledge management challenge from retrieval to trustworthiness, and identifies provenance, validation, governance and human review as core practices for trustworthy AI-supported decision-making.
Abstract
This paper presents a critical integrative review of how artificial intelligence-driven knowledge management may support decision-making quality in medium-sized technology companies. It argues that generative AI shifts the central knowledge management challenge from retrieval to trustworthiness. While generative AI improves access to dispersed organisational knowledge, its outputs may lack traceable sources, contain confident errors, or blur the boundary between reliable knowledge and probabilistic text. Drawing on classical knowledge management theory, recent AI and generative AI research, decision-making literature and regional implementation evidence, the paper develops a conceptual framework in which knowledge management practices mediate the relationship between AI-driven knowledge management and decision-making quality. Perceived challenges such as poor data quality, weak governance, limited skills and overreliance on AI may weaken this relationship. The paper identifies provenance, validation, governance and human review as core practices for trustworthy AI-supported decision-making.