Challenges in AI-driven Knowledge Management

Abstract

While artificial intelligence (AI) has introduced unprecedented opportunities in knowledge management (KM), its integration into organisational knowledge ecosystems presents significant challenges. Chief among these are issues of data quality, algorithmic bias, privacy, and security. AI-driven KM systems rely heavily on large datasets, yet incomplete, inconsistent, or biased inputs can compromise the validity of generated insights. Furthermore, the opacity of machine-learning algorithms raises concerns about transparency, accountability, and trust, particularly when decisions affect strategic choices or employee well-being. Ethical dilemmas also emerge, as AI-enabled knowledge systems may inadvertently reinforce social inequalities or reduce human agency in decision-making. In addition, cybersecurity threats and vulnerabilities in data governance expose organisations to risks of knowledge leakage, intellectual property theft, and reputational damage. Organisational challenges compound these issues, as resistance to AI adoption, lack of digital literacy, and inadequate change management limit the effective use of AI-driven KM. Moreover, regulatory uncertainty and fragmented governance frameworks create further ambiguity for businesses operating in global contexts. This chapter critically examines these challenges, drawing on case studies from sectors such as finance, healthcare, and energy, to illustrate the implications of ethical, technological, and organisational risks. It argues that overcoming these challenges requires not only robust governance and ethical frameworks but also a balanced approach that safeguards human oversight while leveraging AI's transformative potential.

Ähnliche Arbeiten