From Effort Reduction to Effort Management: An Expectancy Theory Perspective on Professionals’ Work Practices with Generative AI

Abstract Generative Artificial Intelligence (GenAI) is adopted by knowledge workers to boost productivity, yet its specific characteristics such as probabilistic outputs and human-level content generation may change how professionals think about their effort. Prior literature has warned about unintended side effects of AI, but experiments on effort reduction when working with AI – which could threaten performance – reported mixed results. GenAI’s rapid adoption combined with its specific characteristics make it critical and timely to clarify how GenAI influences knowledge workers’ effort in professional settings. The qualitative study draws on 21 interviews with knowledge workers who frequently use GenAI for work. A directed content analysis, guided by expectancy theory and social loafing frameworks, revealed that most interviewees do not simply reduce effort, but rather strategically reallocate or even increase effort. They continuously learn to steer GenAI, viewing themselves as process administrators. The traditional group-based mechanisms of reduced effort or diffused responsibility do not seem to be directly transferable to human–GenAI dyads in professional settings. By revealing that GenAI reshapes the factors that influence effort rather than simply eroding motivation, providing a multifaceted view of effort investment beyond mere reduction, and highlighting the interplay between human relationships and GenAI-facilitated work, this research advances the discourse on human-(Gen)AI dynamics and the unintended consequences of (Gen)AI. Recognizing these shifts when setting policies and expectations enables organizations to benefit from GenAI’s potential while mitigating potential risks to performance.