Still Your Problem

Delegating work doesn’t always free up as much time as we’d like. Even after someone else takes on a task, there’s effort involved in keeping track of it, answering questions, and making sure things are moving along. That’s common enough when working with other people, but AI introduces an interesting twist. It’s now possible to hand off work to systems that carry it out with relatively little involvement, sometimes for hours at a time.

The tricky part is knowing when to check in and how closely to look at what’s been done. With another person, you develop a sense of how much supervision they need. With AI, it’s possible to get a perfectly reasonable-looking result without knowing much about what happened along the way. And if something goes wrong, you’re still the one who has to explain it.

Two recent studies explore different sides of this problem. One looks at what happens to people after they’ve delegated work to AI. The other examines why people might be more willing to delegate to AI when the consequences of a decision become personal.

When the work follows you home

Researchers Yifan Zhang, Tianyou Li, and Chunming Chen studied over 600 remote and hybrid employees who used AI at least once a week. They wanted to understand how reliance on AI affected employees’ ability to disconnect from work.

Employees who depended more heavily on AI delegation reported greater concern about the work they’d handed over. They worried about its progress, the quality of the results, and whether they’d eventually have to correct something. Those concerns were associated with more work-related thoughts and greater difficulty relaxing after hours.

The researchers called this AI process vigilance. It’s a formal name for a familiar experience: handing off an assignment and continuing to worry about whether it’s being done properly.

The study doesn’t establish that AI delegation causes people to worry more. But it raises an interesting possibility: eliminating the effort involved in a task doesn’t necessarily eliminate the mental burden of being responsible for it.

A convenient place to put the blame

A second study, released in October by Syracuse University researcher Ashraf Haque, examined whether people become more willing to delegate decisions to AI when their own careers might be affected by the outcome.

In a survey experiment involving roughly 1,200 federal and local government officials, participants initially preferred delegating to another person rather than AI. When the scenario made personal career consequences explicit, that preference reversed. AI gained an eight-percentage-point advantage over human delegation.

Haque suggests that AI offers an opportunity to distribute responsibility. When a person makes a decision, the chain of accountability is usually reasonably clear. With AI, there’s the system, its developers, the organization that approved it, and whoever decided to use it. Sorting out responsibility becomes more complicated.

The experiment measured preferences in hypothetical situations, so I wouldn’t automatically assume government officials are routinely using AI to escape accountability. Still, it’s an interesting incentive, and I doubt it’s unique to government. There are plenty of decisions that become more appealing when someone else can share the blame. Especially when that someone isn’t actually a person.

Who’s watching the machine?

The two studies examine different aspects of delegation, but there’s an interesting tension between them. People may worry about work they’ve handed over to AI while also finding AI attractive when responsibility becomes inconvenient.

Consider a financial services operation using AI agents to investigate transaction exceptions. Agents gather records, reconcile information, and prepare recommendations. Analysts review the results and handle whatever needs additional attention.

There’s real value in that arrangement. But suppose an agent overlooks something important. The analyst could still be accountable for a decision based on information they didn’t assemble, using a process they may not fully understand. They’re expected to exercise judgment, but how much do they actually know about what happened?

Managers have always faced versions of this problem when delegating to other people. Over time, they learn someone’s strengths, recognize recurring mistakes, and develop confidence in how independently that person can work. Trust grows through experience.

Organizations need to develop something similar with AI, although the mechanics are different. A system might complete thousands of tasks correctly and still make an unexpected error on the next one. Knowing when to check its work, what evidence to examine, and when to intervene becomes part of the job.

That creates a measurement problem for automation initiatives. It’s relatively easy to calculate the time saved when an agent completes a task; the effort people spend supervising it, investigating questionable results, and deciding whether it deserves more independence is harder to capture.

Earning the right to be left alone

Good delegation always depends on clear expectations, appropriate authority, and trust built over time. AI makes it remarkably easy to assign work before that trust has been established.

For organizations deploying agents, that suggests looking beyond how many tasks the technology can automate. How much supervision does the work still require? What evidence would make people comfortable giving the system more independence?

These are practical questions. Agents need to make their work understandable, recognize when they’ve reached the limits of their authority, and provide enough evidence for people to evaluate the results. And organizations need to make sure whoever is accountable has the information and authority to exercise that responsibility.

I expect the next few years will bring plenty of attention to how much work agents can perform independently. That’s worth exploring, especially as their capabilities improve. But the more interesting measure of progress may be how much confidence organizations can place in the work being done, without having to continually check how it’s being done.

After all, handing something off is easy enough. Being able to forget about it is another matter.

References

1. Zhang, Y., Li, T., & Chen, C. (2026). When AI works, employees worry: The vigilance cost of AI delegation in hybrid work. Frontiers in Psychology, 17, 1914119. https://doi.org/10.3389/fpsyg.2026.1914119
2. Haque, A. (2026). Artificial Delegation: When Administrators Delegate More to AI than Humans. Center for Policy Research Working Paper No. 295, Syracuse University. https://www.maxwell.syr.edu/research/center-for-policy-research/research-publications/working-papers/artificial-delegation-when-administrators-delegate-more-to-ai-than-humans

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