This summer, your AI has your back while you're at the beach

One in five workers admit they have skipped a vacation entirely because nobody could properly cover their work while they were gone. That statistic, cited in a recent Korn Ferry analysis, captures a problem HR leaders have managed for years: time off that turns into a logistics puzzle instead of a rest. This summer, more companies are running a live test on that puzzle. Instead of scrambling to redistribute the load across whoever stays behind, they are asking AI agents to fill the gap.

The vacation test

Tanyth Lloyd, global vice president of technology and transformation, says leaders are watching closely how their teams put AI agents to work over the summer months. "The remaining team shouldn't be scrambling to backfill while others are off," she says. The appeal makes sense on paper. Productivity studies show output can drop by as much as 20% during the summer, and mid-sized businesses can lose between 15% and 20% of revenue in July alone. Stress and burnout climb too, for the person on vacation and for whoever is holding their calendar, which helps explain why so many workers give up on time off rather than deal with the coverage problem at all.

Where AI actually helps, and where it doesn't

Stephen Lams, senior vice president of data and analytics, draws the line clearly. "AI can't fill in for judgment," he says. "If there's a human on the job, we don't want AI running by itself." The technology's real strength during this window is narrower than a full stand-in: summarizing meetings and emails, updating calendars, and flagging action items for whoever picks up the account next. How much value shows up depends on what a team already has running. "If there are tools you spun up, those components can provide value," Lams says.

The workflow question 

Mirka Kowalczuk, senior vice president of digital services, points to a bigger opening than coverage alone. Decisions about who or what fills in, she says, should rest on how the workload is actually structured, not on habit. "The interesting opportunity is that some of these holiday experiments could spark the beginning of proper workflow design," she says.

That reframes a scheduling headache into a diagnostic tool. When a team has to spell out exactly which tasks an AI agent can safely handle, they end up describing their own workflow with more precision than most job descriptions ever capture. That kind of clarity is the same raw material that makes manager coaching effective. Improving a workflow, or a person's role inside it, starts with someone mapping what actually happens day to day, and few exercises force that mapping as directly as figuring out what a machine can and can't take over for two weeks.

A culture signal

Lloyd frames the stakes in cultural terms. If AI can cover someone's job while they're at the beach, employees will reasonably ask why it couldn't cover the job permanently. Leaders building trust in AI adoption need to explain the difference in plain terms: the tool is there so people can disconnect and come back at a higher level, and that framing only holds if it never becomes the reason someone's role shrinks over time.

Lams sees a related twist worth sitting with. If AI genuinely makes a team more productive while someone is away, he says, "there is an argument that one individual going on holiday has a greater impact." Coverage stops reading as a liability and starts looking like proof that the system runs without someone hovering over it.

41% of workers in one survey say they have shortened or skipped vacation because of how much time it takes to dig back in afterward. Shanda Mints, Korn Ferry's vice president of AI strategy and transformation, points to a cost most calendars never show: the "reentry tax" that hits once someone gets back from time off. She says AI can ease it by summarizing meetings and emails, updating calendars, and assigning next steps before the returning employee even opens their inbox. 

So how do you prepare for vacations with AI?

Before assigning anything to an agent, sort the workload into three buckets: tasks repetitive and rule based enough to automate outright, tasks that benefit from an AI draft a person still checks, and tasks that need a human's judgment from start to finish. Define what "done" looks like for each one, and write down who owns the decision at every handoff, so nobody has to guess when they're clearing something with a machine and when they need a manager's sign-off.

The same clarity pays off on return. An AI coach that has tracked a manager's context all along (their priorities, open commitments, and the patterns in how their team operates) can act as a standing memory the moment they're back. That's what actually closes the reentry tax Mints describes.

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