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AI can make some work faster and better, but using it indiscriminately can also make work less reliable. The practical move is to choose AI task by task: use it where its capabilities fit and you can check the result, and keep human judgment central where context, uncertainty, or correctness matter most.
AI capability is uneven, even across tasks that seem similar
Researchers call the boundary between tasks AI handles well and tasks it does not a “jagged technological frontier.” The important point is that the boundary is not obvious from a task’s apparent difficulty: a tool may help with one kind of work and falter on another that looks comparable to a person.
In a preregistered experiment involving 758 knowledge workers, participants using GPT-4 completed 12.2% more tasks and finished 25.1% faster on average across 18 tasks within the study’s capability frontier; the researchers also found improved quality. Those results apply to the experiment’s tasks, participants, and GPT-4 conditions—not to every job or every AI tool. The findings were published in the 2025 Organization Science study “Navigating the Jagged Technological Frontier.”
Speed gains can come with a correctness cost
In the same experiment, on one complex managerial task selected as outside the frontier, participants using AI were 19% less likely to produce correct solutions than participants without AI. That is a result for one task in a designed experiment, not a general estimate of how much AI harms work. It does, however, show why faster completion is not enough to establish that a tool improved productivity.
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Productivity has to include the quality and usefulness of the result, along with the effort needed to review and correct it. If an AI draft saves minutes but introduces errors that are difficult to catch, the apparent time saving may not translate into better work.
Decide whether to use AI by checking the task
Before handing work to an AI tool, consider three questions. This is a practical decision aid, not a checklist validated by the cited experiment.
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- Is there evidence the tool can do this kind of task? A task’s resemblance to something the tool handled well does not guarantee the same result; capability can vary across seemingly similar work.
- How much specialized judgment or context does the task require? Work that depends on tacit knowledge, relationships, uncertainty, or details unique to an organization may be difficult to delegate safely.
- Can you check the output cheaply and reliably? AI is more useful when you can verify important claims or results without spending more effort than the tool saves.
If the answer to the last question is no—or if a mistake would be consequential—keep a person responsible for the judgment and verification. Depending on the task, that can mean using AI for a bounded part of the work, treating its output as a draft, or not using it at all.
Measure saved effort, not just time to first draft
One way to find out whether AI helps with a recurring task is to run a small, controlled comparison. For a defined period, record how long the task takes, assess the result against the same quality criteria, and include the time spent checking or fixing AI output. Compare AI-assisted work with your usual approach. This is suggested practice, not a protocol tested by the study.
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Keep the task and success criteria narrow enough that the comparison is meaningful. A quicker first draft is not a productivity gain if review, rework, or errors erase the saving. Also note that a short trial may not capture longer-term changes to coordination or workflow.
Workplace results depend on how AI is used
There is no single workplace result that applies to every role or organization. Microsoft Research’s July 2024 synthesis of more than a dozen workplace studies cautions that effects vary by role, function, organization, adoption, and utilization. Human-factors researchers have also described possible workflow costs: effort can shift from creating work to evaluating it, new tools can disrupt established workflows, interruptions can increase, and tasks can become polarized in complexity.
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Gallup reported that 65% of employees at organizations that had implemented AI said it had a positive effect on productivity and efficiency, based on survey data from May 2026. That is a report of employee perceptions, not a causal experimental estimate. It should not be read as evidence that AI will improve productivity for 65% of workers or that adopting AI alone produces a gain.
Productivity estimates also have limits when work depends on tacit knowledge, relationships, judgment under uncertainty, or interdependent tasks. Anthropic’s research note on estimating productivity gains discusses these measurement difficulties, including the limits of model-estimated task times. A time estimate that leaves out review, coordination, or downstream effects can overstate a real-world benefit.
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Make the tool serve the work
Using AI less indiscriminately does not mean rejecting it. It means treating each use as a choice with a possible benefit and a verification cost. Delegate bounded work when the tool is capable and the answer is checkable; preserve human ownership when correctness depends on context or judgment. The result worth aiming for is not maximum AI use, but better work with less total effort.
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