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 is very limited in that second occupation,” Shahidi said. “How these tasks appear in an occupation’s workflow becomes important.”

That’s where the concept of task chaining becomes critical. Rather than using AI for isolated steps, organizations can link together multiple tasks so AI executes them as a continuous sequence.

Not all chains are equal, however. When adjacent tasks are well suited to AI, they can be bundled effectively. When even one step is difficult for AI, it can break the chain, Shahidi said. “If one of them is super hard for the AI, that single task is going to undermine the entire operation,” he said.

This finding leads to a new work design principle: How tasks are clustered matters as much as which tasks are automated.

Why system-level efficiency beats task-level perfection

One of the most counterintuitive findings in the research is that AI doesn’t need to outperform humans at every individual task to create value. In fact, organizations may benefit from assigning entire chains of tasks to AI even when humans could perform some steps better.

The reason is coordination cost. Each time work passes from AI to human, it requires review, validation, and adjustment. Those checkpoints slow the overall system. In contrast, allowing AI to handle a sequence end to end can eliminate friction, reduce handoffs, and accelerate output — even if the quality of individual steps is slightly lower.

“You’re saving on human time cost,” Shahidi said, noting that removing repeated oversight can outweigh marginal differences in performance.

This reframes how leaders should evaluate AI: They should focus less on whether it excels at each individual step and more on whether it improves the efficiency of the entire workflow. It also reinforces the importance of task adjacency. When AI-friendly tasks are clustered together, they can be executed in a single flow. When they’re scattered or interrupted by tasks that AI struggles with, the benefits diminish.