Are you using AI to eliminate work or are you simply adding AI management to an already overloaded job?
We have spent the last several years talking about how technology, automation, and artificial intelligence will make work more efficient.
But there is an uncomfortably honest question leaders need to ask:
What if we are making some jobs more technologically efficient while making the humans more overload across cognitive states?
Korn Ferry’s newly released Workforce 2026 Global Insights Report provides a compelling reason to pay attention. More than three in five employees—61%—say they are performing responsibilities of more than one role. At the same time, 52% of AI-weary workers say using AI has actually increased their workload, and 55% of managers who remain report being exhausted. The study surveyed more than 16,000 professionals across 11 global markets.
That is not simply a workload problem. It is a capacity-management problem.
There is a difference between temporarily stretching into a new responsibility and permanently absorbing another person's workload.
Organizations routinely experience restructuring, vacancies, layoffs, new technology implementation, and changing customer demands. Some redistribution of work is inevitable.
The problem begins when the temporary becomes permanent.
Someone leaves. Their responsibilities are distributed across the remaining team.
A new technology is introduced and instead of removing work, employees are expected to learn the technology, incorporate it into their workflow, review its output, correct its mistakes, and still complete everything they were doing before.
Now the employee isn't necessarily doing one job better.
They may be doing two jobs differently.
Korn Ferry describes this as the “Two-Job Job”as a situation in which responsibilities don't necessarily disappear when a role is eliminated or transformed. Instead, portions of that work can be redistributed to the people who remain.
There is an important management distinction here and it is judgment:
AI can automate a task without eliminating the workload associated with that task.
Here's how:
- Someone still has to determine whether the AI-generated output is accurate.
- Someone still has to provide context.
- Someone still has to make the decision.
- Someone still owns the consequences when something goes wrong.
This matters because human cognitive capacity is not infinite.
When demands continuously exceed perceived capacity, the brain has fewer resources available for higher-order thinking, problem-solving, learning, creativity, and effective decision-making.
This creates a dangerous organizational cycle:
More responsibilities → more cognitive load → less capacity → more errors → more correction → more work.
The organization may interpret the resulting behavior as a performance problem when the issue might more accurately be that the work has exceeded the person's sustainable capacity.
This is where managers need to step into metacognition and judgement.
One of the most important findings in Korn Ferry's report is the gap between how executives experience AI and how employees experience it.
While 79% of CEOs surveyed reported improved efficiency from AI, only 51% of individual contributors said the same. 52% of AI-weary workers said AI had increased their workload.
That gap should get every manager's and leader's attention.
Because handing someone an AI tool and saying “This should make your job easier” does not make the job easier.
The manager has to understand where the work actually changed.
Ask:
- What task is AI replacing?
- What task is AI adding?
- Who is responsible for reviewing the output?
- How much time does verification take?
- What decisions still require human judgment?
- What responsibilities should be removed now that the workflow has changed?
- What skills does the employee need to perform the new version of the job?
Without those conversations, organizations can accidentally create technology-enabled workload expansion.
This is where middle management becomes more and not less important.
Managers are often the first people who can see that the official job description and the actual job are no longer the same.
Korn Ferry found that 42% of organizations had cut management roles over the previous year, while 55% of remaining managers reported being exhausted.
That creates another capacity problem:
Who manages the people who are managing the capacity problem?
If managers are overloaded, they have less time to coach, clarify expectations, identify barriers, develop employees, and redesign work.
The organization then loses one of its most important mechanisms for detecting problems before they become expensive ones.
Stop measuring activity. Start Measuring capacity. This is part of the AI management shift.
Korn Ferry's research reinforces an important distinction in that activity is not the same as value.
Leaders should therefore begin asking different questions.
Effective accountability requires an accurate understanding of the environment in which performance occurs.
You need to be honest:
Are you actually using AI to create capacity—or are you using AI to justify adding more work to people who are already at capacity?
Ready to stop asking your managers to do more with less? Let’s build managers who have the capacity, clarity, and tools to lead. Book a Management Cues conversation today.
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