How AI is reshaping workflows and redefining jobs

Source Domain: mitsloan.mit.edu

Most organizations have approached artificial intelligence as a tool for boosting productivity at individual tasks, such as drafting emails, summarizing documents, or generating code. But new research suggests that this task-by-task mindset may be limiting the true value of AI.

A new research paper, “Chaining Tasks, Redefining Work: A Theory of AI Automation,” argues that AI’s biggest impact comes from how it reshapes entire workflows — specifically, how tasks are sequenced, grouped, and handed off between humans and machines.

“The central question is no longer just how AI improves a single task,” said Peyman Shahidi, a PhD candidate at the MIT Sloan School of Management and co-author of the paper. “We’re trying to understand AI’s effect at a broader system level, not just as spotty productivity tools at the task level.”

The paper is co-authored by Mert Demirer and John Horton of MIT Sloan, Nicole Immorlica of Yale University and Microsoft, and Brendan Lucier of Microsoft.

AI value emerges at the workflow level

Traditional approaches to automation have focused on task-level gains, such as whether AI can perform specific activities faster or better than a human. The new research created models of how tasks are sequenced and connected in real-world workflows to create a new framework for how work actually happens, which is as sequences of interdependent tasks.

That shift matters because even when two roles involve similar activities, the way those tasks are arranged can dramatically affect how much value AI can deliver. The researchers highlight lecture-based teaching and tutoring as an example. Both involve similar tasks, but their workflows differ. Teachers, for example, prepare content in advance, making it easier to automate parts of the process. Tutors operate in a continuous back-and-forth with students, limiting opportunities for automation.

“The extent to which you can automate your workflows using AI…

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