Exploration and Optimization of Generative Variability in Future Work: A Mixed-Initiative Analysis

Exploration and Optimization are ubiquitous activities in human work. Exploration allows us to consider multiple design or solution possibilities. Optimization allows us to narrow and refine diverse possibilities. Given that generative AI produces diverse outcomes for the same input - an attribute known as generative variability - there is now a need to re-examine Exploration and Optimization in the context of generative AI and understand their implications for the future of work. The relationship between Exploration and Optimization has traditionally been portrayed as independent, inverse, or even contradictory. Guided by concepts from mixed initiative interfaces, we conducted a conceptual analysis of three future-work-centered generative AI use cases. We show that the relationship between Exploration and Optimization is not “one-size fits all.” We show how Exploration and Optimization may be combined into several integrated and inter-dependent work processes. Our use cases indicate that generative variability will increase the importance of Exploration and Optimization in future work tasks and applications.