Meaningful work as shaped by employee work practices in human-AI collaborative environments: a qualitative exploration through ideal types

Purpose This study aimed to investigate how employees perceive meaningful work in tasks co-generated by Microsoft 365 Copilot, an AI-powered workplace assistant. Specifically, it explored how its adoption influences work practices, autonomy and decision-making, identifying patterns of user experiences that shape attitudes toward AI integration in professional settings. This offered an opportunity to further theorise the notion of meaningful work as it is constructed and reconfigured through emerging patterns of human–AI collaborative environments. Design/methodology/approach Data were collected through a survey administered to 802 employees of a multinational company who were given a Microsoft 365 Copilot licence to test this AI-powered assistive tool in their daily tasks, yielding 357 responses. The survey included both multiple-choice and open-ended questions, with this study focusing on the qualitative empirical data. Specifically, we applied the qualitative ideal-type analysis method to identify typologies of user adoption practices with the artificial intelligence (AI)-powered assistive Microsoft 365 Copilot tool. Findings Three Ideal Types were identified: Ideal Type [1] – the Efficiency-Seeking Type – perceives Microsoft 365 Copilot as a straightforward task-assistance tool, Ideal Type (2) – the Pragmatic Integrator Type – views it as a smarter assistant, and Ideal Type (3) – the Collaborative Optimiser Type – considers it an expert-like teammate. The results indicate that meaningful work is not a static construct; rather, it evolves through the dynamic interplay between objective dimensions of meaningful work in human-AI collaborative environments – such as task discretion and organisational structures –and subjective experiences, including users’ perceived role and expertise. Additionally, we underscore how cognitive prompts and metacognitive prompting become not only a technical competence to effectively interact with technology, but a reflective and interpretive practice through which workers negotiate relevance, value and purpose in their tasks. Practical implications Understanding diverse employee perspectives through ideal-type analysis enables tailored strategies for reskilling and upskilling, supporting individual needs and fostering adaptive work practices. It also informs the design of personalised development programmes and awareness initiatives that highlight human expertise, ensuring meaningful work and engagement in human-AI collaborative environments. Originality/value This article advances the discourse on meaningful work within human–AI environments by examining the factors that support or constrain employees' capacity to find significance and fulfilment in their roles, as influenced by the interplay between individual agency – reflected in users’ decision-making, engagement and role adaptation – and organisational contexts, including technological integration, workplace structures, and human-AI collaborative practices. The use of Ideal Types in the qualitative approach strategy helps maintain the uniqueness of users' perspectives, capturing diverse experiences and patterns of AI adoption while preserving individual meanings and interpretations of meaningful work.