Understanding GenAI adoption and use is crucial to ensure the best return on technology investment, particularly in government. This paper seeks to address a gap in current Generative Artificial Intelligence (GenAI) literature by examining the influence of role complexity upon knowledge worker adoption and use of GenAI. Drawing from concepts of role complexity and technology adoption and use, this research adopted a case study research method and used multiple data sources gathered from a large UK government organization piloting Microsoft Copilot. GenAI was found to be most suitable for entry-level and potentially mid-level job grades, while role complexity impacted usage type and job function did affect adoption rate. Limitations include measurements from a single point in time, low granularity of usage metrics, and single organizational context and toolset. Future research includes validation of these findings against other organizational contexts, longitudinal analysis, exploration of other toolsets, and the impact of Robotic Process Automation on GenAI adoption.