THE IMPACT OF GENERATIVE AI CO-PILOT INTEGRATION AND TASK-TECHNOLOGY FIT ON INDIVIDUAL CREATIVE PRODUCTIVITY

Abstract

The proliferation of generative artificial intelligence tools across professional domains has prompted organizations to invest substantially in these technologies, yet the conditions under which they genuinely enhance individual creative productivity remain inadequately understood. Drawing upon task-technology fit theory, this study examined the interactive effects of generative AI co-pilot integration and task-technology fit on creative productivity among knowledge workers. A quantitative cross-sectional survey was conducted with 200 knowledge workers employed across technology, marketing, design, and consulting firms in Karachi, all of whom utilized generative AI tools such as Microsoft 365 Copilot, ChatGPT, GitHub Copilot, or Midjourney as part of their daily professional routines. Data were analyzed using multiple linear regression to assess the individual and combined predictive effects of the independent variables on creative productivity. The findings revealed that AI integration level emerged as a significant positive predictor of creative productivity (β = 0.344, p < .001), indicating that deeper assimilation of AI tools into daily workflows substantially enhances creative output. Task-technology fit, however, demonstrated a non-significant relationship (β = 0.179, p = .057), though the p-value approached conventional significance thresholds. These results suggest that within the context of creative knowledge work, the depth and frequency of AI tool integration may exert a more immediate influence on productivity than the perceived alignment between tool capabilities and task requirements. This study contributes to the growing body of literature on generative AI adoption by highlighting the primacy of integration processes over fit perceptions in predicting creative productivity outcomes. The findings offer practical implications for organizations seeking to maximize returns on generative AI investments through strategies that encourage consistent, deep integration of these tools into routine creative workflows, while acknowledging that fit considerations remain relevant and warrant continued attention in future research.