Defining Knowledge in the Age of Machines

Abstract

The rapid evolution of artificial intelligence (AI) has transformed how organisations define, structure, and apply knowledge, challenging traditional frameworks in knowledge management (KM). Classical distinctions such as tacit and explicit knowledge, along with established models like the SECI process and the DIKW hierarchy, remain useful but are increasingly reconfigured by machine-driven cognition. AI technologies such as machine learning (ML), natural language processing (NLP), and knowledge graphs now extend the boundaries of knowledge systems, enabling the capture of unstructured data, simulation of experiential learning, and predictive decision-making at scale. Microsoft's Project Cortex illustrates this transformation by automating knowledge extraction, categorisation, and contextualisation across diverse repositories, dramatically reducing search time, fostering collaboration, and surfacing expertise dynamically within workflows. Similar applications in healthcare, finance, and pharmaceuticals highlight how AI accelerates innovation, enhances organisational agility, and bridges gaps between tacit and explicit knowledge. By situating AI within the evolution of knowledge, the chapter offers a conceptual bridge between traditional KM paradigms and emerging machine-mediated knowledge systems that redefine organisational learning, resilience, and strategic advantage in the digital era.

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