The transition from manual to service-oriented and information-based work, driven by technological advancements, has reshaped the modern economy, demanding more analytical and cognitive skills. This change challenges traditional management strategies, as knowledge work’s intangibility requires approaches that are the opposite of those that successfully manage manual work. While early artificial intelligence (AI) applications streamlined manual tasks, applying AI to knowledge work revealed complexities in less structured environments. As AI capabilities improve, there is the potential to enhance knowledge-based work by enhancing collective intelligence (CI). At the intersection of management literature and intelligence research are opportunities for AI to improve the three essential functions underlying intelligence in any system—memory, attention, and reasoning. AI augments these functions in human systems, thereby opening the possibility of elevating CI in workplaces. Because of the most pressing research gaps, future exploration is needed in order to understand AI’s role in fostering a collaborative, efficient, and equitable workplace in ways that balance technology optimization with human-centric considerations.
Article
Artificial Intelligence and People at Work
Anita Williams Woolley
Article
Coordinating Knowledge: A New Lens to Understanding the Role of Technology in Episodic Coordination
Elena Karahanna and Jennifer Claggett
Previous research in coordination lacked a practical explication of the metaknowledge used to enact coordination, which is particularly problematic as more coordination processes become (or attempt to become) digitized. One can better understand this meta knowledge by focusing on the coordination episode. The authors of this article define coordinating knowledge as knowledge that facilitates the exchange of information between two or more actors in order to achieve a shared goal by guiding (a) the timing, (b) the selection of actors, (c) the content, and (d) the method of the exchange. By integrating four bodies of literature (structured mechanisms, domain expertise, team familiarity, and transactive memory systems) that provide important insights into coordination the authors anatomize the framework into 14 specific types of coordinating knowledge that can impact how a coordination episode is enacted and its outcomes. Specifically, coordinating knowledge about triggers refers to knowledge indicating a need to initiate a coordination episode and may take the form of time-scheduled triggers, event-sequence triggers, and emergent triggers. Coordinating knowledge about actors refers to knowledge that helps select with whom to coordinate and may take the form of role, assignment, or individual knowledge about actors. Coordinating knowledge about content refers to knowledge that either helps select or present content shared during the coordination episode and may take the form of predetermined content selection or presentation, emergent content selection, recipient-tailored content selection, and shared understanding. Finally, coordinating knowledge about method refers to knowledge that helps select the appropriate medium of communication for a coordination episode and may take the form of predetermined method selection, media-fit method selection, or recipient-tailored method selection. Coordinating knowledge is conceptualized as a profile construct with meaningful combinations of coordinating knowledge that can be used to address different coordination dependencies and other contingencies. This conceptual framework affords a new understanding of how coordination is enacted and opens avenues to future research to explore how the presence and utilization of specific types of coordinating knowledge are likely to impact coordination performance. By explicating and elaborating upon coordinating knowledge, scholars and practitioners will be better positioned to design information systems to aid in the exchange of information by embedding different types of coordinating knowledge. Thus, the coordinating knowledge lens will be useful in understanding the evolving role of technology in coordination processes.
