Rethinking Knowledge Creation in the Age of Digital Innovation: insights from IT Sector
Recent literature highlights that generative artificial intelligence (GenAI) is expected to play a central role in knowledge-intensive work, particularly in sectors strongly exposed to technological innovation. Beyond facilitating existing knowledge management (KM) practices, GenAI appears to introduce a fundamentally new epistemic dimension that challenges traditional models of organizational knowledge creation, most notably Nonaka’s SECI model. While the SECI framework has long explained knowledge creation through the dynamic interaction between tacit and explicit human knowledge, it does not explicitly account for knowledge processes emerging from human-AI interaction. Drawing on these premises, the aim of this research is to investigate how GenAI alters knowledge creation dynamics and to identify new knowledge conversion mechanisms emerging from human-machine interaction. To achieve this, the study adopts an exploratory single case study within the IT sector, an environment characterized by high innovation intensity and rapid technological change. The selected case organization was chosen based on its positioning along three key dimensions known to influence technology adoption: orientation to innovation, technological culture and organizational complexity. Data were collected over a period of one year and five months through thirty semi-structured interviews with employees across R&D, operations and senior management. Data were triangulated by direct observation and analysis of project documentation. The qualitative data were analysed using content analysis supported by inductive coding techniques inspired by grounded theory. This process led to the construction of an inductive coding tree capturing core themes and their relationships. Findings reveal that GenAI generates knowledge dynamics that cannot be fully mapped onto the traditional model. In particular, new knowledge conversion modes emerge between human knowledge, machine-generated data and artificial knowledge. These include transformations from human tacit and explicit knowledge into data, from data into artificial knowledge and from artificial knowledge back into human explicit knowledge. Alongside these conversion modes, several conceptual domains were identified. Based on these results, the study proposes a novel conceptual framework that integrates the SECI model with a machine dimension, offering a more comprehensive representation of contemporary knowledge creation in organizations which integrate GenAI into their processes. The framework highlights the importance of strategic integration of GenAI into KM processes and the value of human-in-the-loop validation. This research contributes to KM theory by extending classical models to account for artificial knowledge generation and provides a foundation for future empirical studies on human-machine knowledge ecosystems.