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The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to derive strategic value from emerging AI systems.

The analytical framework in this paper is based on a learning-centered approach. Specifically, by building on the knowledge-based perspective, this paper elaborates on how AI can contribute to organizational learning to create a competitive advantage in knowledge-intensive contexts.

This paper argues that the power of AI as a strategic resource lies in its self-learning capacities. Such learning capacities are only realized in partnership with humans through mutual learning. This paper formulates the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization.

This is a timely contribution as many organizations are considering adopting AI technologies for strategic purposes. This paper translates the proposed framework into several practical implications for managing and developing AI to meet strategic business goals.

Generational membership is argued to have an impact on how social technologies are used for knowledge sharing and communication in organizational contexts. Previous research has especially underscored the difference between digital natives and digital immigrants in how they make sense of and interact with social technologies for work. The purpose of this paper is to provide a multidimensional perspective and to explore generational differences as well as other factors deriving from both work-related and personal characteristics.

The paper presents a summary of the findings from interviews with 58 consultants from 17 managing consulting firms. Participants were selected based on their knowledge-intensive roles and their willingness to share information about their knowledge practices.

Findings highlight the significance of the organizational rank, knowledge needs, individuals’ enthusiasm for technology use and personality disposition in shaping workers’ attitudes toward social technologies for knowledge practices. This work builds from a social construction of technology perspective to provide a comprehensive insight into the roles played by work and personality-related factors beyond age and generational differences in the use of social technologies in and for work.

This research contributes to the discourse on generational differences and the use of social technologies. It puts this question into a broader context, and highlights other factors that shape this relationship.

Personal knowledge management (KM) lends new emphasis to ways through which individual knowledge workers engage with knowledge in organizational contexts. This paper aims to go beyond an organizational approach to KM to examine key personal KM and knowledge building (KB) practices among adult professionals.

This paper presents a summary of the findings from interviews with 58 consultants from 17 managing consulting firms. Participants were selected based on their knowledge-intensive roles and their willingness to share information about their knowledge practices. Data analysis was inductive and revealed multiple personal KM activities common among research participants, and the way these are supported by informal ties and various technologies.

This work highlights ways in which “shadow information technology” undergirds personal knowledge infrastructures and supports KM and KB practices in the context of management consulting firms. The results uncover how personal knowledge infrastructures emerge from personal KM and KB practices, and the role of informal social networks as well as social media in supporting personal KM and KB.

This study contributes an overall conceptual model of factors that help knowledge workers build a personal knowledge infrastructure. By affording an understanding of socially embedded personal KM activities, this work helps organizations create a balance between KM strategies at the organizational level and personal knowledge goals of individual workers.

Much of the previous research on KM adopts organizational approaches to KM, accentuating how organizations can effectively capture, organize and distribute organizational knowledge (primarily through KM systems).