MIT research shows AI's biggest impact comes from reshaping entire workflows, not individual tasks

Researchers at MIT Sloan, Yale University, and Microsoft published a paper titled "Chaining Tasks, Redefining Work: A Theory of AI Automation" arguing that AI value emerges from how tasks are sequenced and connected in workflows, rather than from task-level productivity gains alone.

The research introduces the concept of task chaining — linking multiple tasks so AI executes them as a continuous sequence. However, if even one step in the chain is difficult for AI, it can break the entire operation, making task clustering as important as which tasks are automated.

Organizations may benefit from assigning entire chains of tasks to AI even when humans could perform some steps better, because each handoff between AI and human introduces coordination costs (review, validation, adjustment). Eliminating these handoffs can accelerate output despite slightly lower individual step quality.

Meaningful gains from AI adoption often emerge only after organizations have adapted their workflows and built sufficient capability, requiring patience — until reaching that threshold, adoption costs dominate the gains.