Drawing on literature related to the resource-based view (Barney, 2001) and the knowledge-based view in particular (Eisenhardt et al., 2000), knowledge has been highlighted as a key resource for business success since the 1990s (Grant, 1996). Digital technologies and solutions are increasingly challenging the concept and use of knowledge in organizations (Edwards & Lönnqvist, 2023; Iaia et al., 2023).
While digitalization is pervading every aspect of people’s lives, artificial intelligence (AI) stands as a transformative force shaping industries and societies. In an AI-driven world, knowledge is no longer confined to human intellect alone but extends to algorithms and machine learning models. These technologies assimilate vast amounts of data to generate insights and actions, challenging usual notions of knowledge and its management (De Bem Machado et al., 2022; Ferraris et al., 2019). While traditional methods of creating, sharing, and storing knowledge involved primarily face-to-face interactions, books, and physical documents, the digital revolution and the advent of AI have revolutionized these knowledge management (KM) processes.
The perception of knowledge is also challenged due to the integration of AI into an increasing number of business processes and workflows. For example, knowledge identification and gathering have evolved to include leveraging AI tools to extract, analyse, and interpret information efficiently (Ferraris et al., 2019). Knowledge documentation and storage, on the other hand, could be of less importance, as the introduction of ChatGPT in November 2022 can not only be used as a tool for generating information and knowledge, but can also act as an external knowledge retention base to reduce or even completely avoid costly measures in the companies in this regard. Consequently, the question also arises as to whether knowledge is power will continue to apply or how this will be shown in concrete terms in the future.
As we delve into knowledge risk management (Durst & Zieba, 2019; Durst et al., 2019), approaching an AI-driven world offers several negative and positive risks. Starting with the potential downsides (the negative risks), the vulnerability of data privacy and security can be named, AI-driven organizations and these organizations´ KM relies heavily on extensive data processing and storage, making data privacy and protection a critical concern. A further risk that cannot be overlooked is represented by the potential for bias and unfairness in AI algorithms. In addition, the increasing reliance on AI in knowledge processes raises concerns about the loss of human expertise and creativity. Over-reliance on AI may lead to a depreciation of critical human skills and knowledge, disrupting the delicate balance between technology and human intellect. Misinterpretation of knowledge is an additional risk associated with AI adoption. AI algorithms, though advanced and continuously developing, are not immune to errors. Inaccurate analysis or misinterpretation of knowledge can result in flawed insights and flawed decision-making, potentially leading an organization down an erroneous path.
On the other hand, an AI-driven world brings multitude opportunities for businesses, the smaller ones in particular. AI has the potential to revolutionize KM, drastically improving efficiency and productivity. This can in turn significantly impact an organization’s competitive advantage and overall success. Another promising opportunity lies in better ways of predicting, analysing and mitigating (knowledge) risks. AI can forecast material risks by analysing patterns in real-time. This proactive approach allows organizations of different kinds to identify and implement preventive measures and mitigate risks before they escalate. In this vein, a critical consideration is how counterproductive behaviours may evolve or be addressed in this digital era. AI can be utilized to detect knowledge sabotage, hiding or hoarding occurrences and mitigate a knowledge sharing environment (Connelly et al., 2012; Perotti et al., 2022). In this environment, however, it must also be asked whether knowledge hiding and hoarding is still meaningful at all, since a larger number of people now have access to knowledge.
Therefore, the organizers of this track encourage academics and practitioners to share their findings and insights on how to advance the knowledge (risk) management literature in the context of an AI-driven world.
Some of the topic areas to be discussed in the track include, but are not limited to:

• How does AI impact individuals’ trust in AI-generated knowledge compared to human-generated knowledge?
• What strategies can businesses employ to effectively integrate AI-generated knowledge into their existing knowledge management systems?
• How does the accessibility and abundance of AI-driven knowledge influence knowledge-seeking behaviours among individuals and organizations?
• What ethical considerations are involved in the management and sharing of knowledge derived from AI algorithms?
• How can organizations balance the benefits of AI-driven knowledge with the potential loss of tacit knowledge and human intuition?
• In what ways does AI-driven knowledge influence decision-making processes at individual and organizational levels?
• How can AI be leveraged to identify and mitigate counterproductive knowledge behaviours within an organization?
• What measures can be implemented to ensure the ethical and responsible use of AI-generated knowledge to prevent misinformation and bias?
• How does the integration of AI in knowledge management impact the role and skills of knowledge managers and professionals?
• How can AI-powered knowledge management enhance innovation and creativity within organizations?
• How is the role of power changing in an AI-driven world?
• Is AI-enabled KM a possible solution for skill shortage?
• What are the new risks related to knowledge in an AI-driven world and how to manage them?

Susanne Durst, Reykjavik University, Iceland
Francesco Antonio Perotti, University of Agder, Norway/University of Turin, Italy
Alberto Ferraris, University of Turin, Italy