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Exploring generative AI’s impact on work practices and professional skills in knowledge work
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School of Business |
Master's thesis
Electronic archive copy is available via Aalto Thesis Database.
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en
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61
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Abstract
This thesis explores the impact of Generative Artificial Intelligence (GenAI) technologies on knowledge work, with a focus on how it is influencing daily work practices and professional skills. Through a qualitative and exploratory approach, this study addresses two research questions: 1) How do knowledge workers perceive and engage with generative artificial intelligence technologies and in what ways do these interactions influence their daily work tasks and practices? 2) How do these influences shape knowledge workers' perceptions of the impact of GenAI technologies on their professional skills? The primary data was collected through semi-structured interviews with five knowledge workers from the consulting industry and analyzed using mainly thematic analysis. The findings revealed that GenAI can augment and enhance daily work in tasks such as writing emails and analyzing data, and can support more complex activities like brainstorming and problem solving. GenAI can further enable more streamlined workflows and enhanced productivity, however, integration and utilization still requires active oversight to ensure outputs. This highlights the current collaborative nature of GenAI as an augmentation tool rather than a replacement for human workforce and expertise. The study identifies emerging skills like prompt engineering and AI literacy as crucial for effective use of GenAI and deskilling risks if for instance GenAI is over-relied upon. Other barriers such as data security concerns and skill gaps in using GenAI is currently challenging or hindering adoption but highlight the need for GenAI training within organizations. Whilst exploratory, this thesis contributes to the nascent stage of literature on human-AI work and develops understanding for how the human-AI dynamics are evolving and how that is impacting workers within knowledge work. Finally, accounting for the limitations within this thesis, future research could focus on such as quantitative impacts, assessment and evaluation of long-term impact, and more closely looking at the balance between automation, human expertise and AI augmented work in terms of how work will continue to shape in the future.