AI and Creative Problem-Solving in Expert Work: A Qualitative Study of Management Consulting
Vesslin, Tea (2026)
2026
Kauppatieteiden maisteriohjelma - Master's Programme in Business Studies
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
Hyväksymispäivämäärä
2026-05-29
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202605296560
https://urn.fi/URN:NBN:fi:tuni-202605296560
Tiivistelmä
The growing adoption of generative AI has raised discussion on how AI influences the ways in which organizational problems are interpreted and solved in knowledge work. These developments are especially relevant in management consulting, where consulting work is strongly connected to the interpretation of ambiguous organizational problems and the collaborative development of solutions. Despite the growing discussion surrounding AI in organizations, less attention has been given to how AI influences creativity and problem-solving in management consulting. This study examines the phenomenon from a perspective of problem-solving, as management consulting work often involves the interpretation of ambiguous organizational situations and the development of novel and context-specific solutions.
The theoretical framework builds upon organizational research on AI and expert work, creativity and problem-solving, and management consulting research. Building on these perspectives, the study conceptualizes expertise as relational and context-dependent, emerging through ongoing interaction, interpretation, and collaboration within organizational problem-solving processes. Through this framework, AI is approached as part of collaborative and socially situated problem-solving processes.
The study adopts a qualitative research approach. The empirical data consists of semi-structured interviews conducted with management consultants working in different consulting organizations and roles. The data was analyzed through an inductive qualitative analysis process inspired by grounded theory principles and Gioia methodology.
The findings suggest that AI is increasingly embedded into consulting work as part of collaborative and iterative problem-solving processes. AI was perceived as particularly useful in exploratory search, ideation, and the development of alternative perspectives during ambiguous consulting projects. At the same time, the findings indicate that consulting expertise remains strongly dependent on contextual interpretation, organizational understanding, the legitimization of solutions, client interaction, and collaboration. Consulting problems were described as socially embedded and evolving processes that require ongoing negotiation, reframing, and contextual judgment together with organizational actors.
The findings further suggest that AI transforms the distribution of cognitive labor within consulting work. As routine tasks become automated, consultants’ roles appear to shift toward orchestration, coordination, critical evaluation, and sensemaking. AI appears to further amplify the importance of contextual interpretation and interaction within consulting work. The study therefore contributes to emerging discussions concerning hybrid human–AI collaboration and the future of knowledge work by demonstrating that expertise remains deeply embedded in human interaction and organizational contexts
The theoretical framework builds upon organizational research on AI and expert work, creativity and problem-solving, and management consulting research. Building on these perspectives, the study conceptualizes expertise as relational and context-dependent, emerging through ongoing interaction, interpretation, and collaboration within organizational problem-solving processes. Through this framework, AI is approached as part of collaborative and socially situated problem-solving processes.
The study adopts a qualitative research approach. The empirical data consists of semi-structured interviews conducted with management consultants working in different consulting organizations and roles. The data was analyzed through an inductive qualitative analysis process inspired by grounded theory principles and Gioia methodology.
The findings suggest that AI is increasingly embedded into consulting work as part of collaborative and iterative problem-solving processes. AI was perceived as particularly useful in exploratory search, ideation, and the development of alternative perspectives during ambiguous consulting projects. At the same time, the findings indicate that consulting expertise remains strongly dependent on contextual interpretation, organizational understanding, the legitimization of solutions, client interaction, and collaboration. Consulting problems were described as socially embedded and evolving processes that require ongoing negotiation, reframing, and contextual judgment together with organizational actors.
The findings further suggest that AI transforms the distribution of cognitive labor within consulting work. As routine tasks become automated, consultants’ roles appear to shift toward orchestration, coordination, critical evaluation, and sensemaking. AI appears to further amplify the importance of contextual interpretation and interaction within consulting work. The study therefore contributes to emerging discussions concerning hybrid human–AI collaboration and the future of knowledge work by demonstrating that expertise remains deeply embedded in human interaction and organizational contexts