A new dataset shows that U.S. workers using generative AI tools can complete core job tasks in a fraction of the time. Based on a national survey of 4,278 adults, conducted in late 2024 by researchers at Stanford and the World Bank, the figures offer a sharp snapshot of how AI is reshaping work patterns. For every task included in the study, workers finished the job at least 60% faster with AI than without it.
The most dramatic gain came in troubleshooting, where the average task shrank from nearly two hours to less than thirty minutes, a 76% drop. Technical assignments like programming and technology design also saw heavy reductions, with task times falling more than 70% on average. Similarly, critical thinking and complex problem solving, cognitive tasks that previously consumed up to two hours, could be completed in under half an hour with AI tools in hand.
Writing Sees the Steepest Drop in Time
Among all 18 tasks measured, writing showed the largest absolute time drop. Without AI, workers reported needing roughly 80 minutes on average to complete a writing task. With generative tools, that figure fell to just 25 minutes. That 69% reduction placed writing among the top three most time-efficient gains, suggesting that AI is particularly effective when used to draft or edit textual content.
Other tasks that involve structured thinking, such as mathematics, operations analysis, and systems evaluation, also saw time cutbacks in the 64% to 73% range. In these areas, the tools often function as accelerators, helping users reason through steps more quickly or reduce time spent on calculations and formatting.
Even Human-Facing Roles Are Being Transformed
Notably, the impact of AI is not limited to technical domains. The survey revealed that roles involving direct human management, judgment, and instruction also experienced consistent time savings. Personnel management, decision-making, and instructing tasks each saw time reduced by at least two-thirds when AI was used. While AI did not perform these tasks autonomously, its integration helped guide decisions, draft communications, and check for inconsistencies, which collectively improved pace.
Time management itself, how workers plan, allocate, and track their hours, also became more efficient. With AI support, the average task time in this category dropped from 77 to 29 minutes, a 62% reduction.
Efficiency Gains Tied to Workflow Assistance, Not Full Automation
Despite the sharp drops in task duration, most workers did not rely on AI to complete tasks entirely. According to the broader study findings, fewer than one in five used AI to fully offload their work. Instead, the tools were often deployed to guide or streamline the process. Workers typically engaged with AI for about a third of their workweek, using it intermittently to plan, assist, or accelerate routine assignments.
This pattern of usage suggests that generative AI currently operates more as an enabler than a replacement. Many workers said they used AI to check their logic, offer phrasing suggestions, or propose alternatives, especially for writing, analysis, and planning-related work.
Task Completion Times with and Without AI
Here’s a breakdown of the average time (in minutes) workers reported spending on each task, with and without AI:
| Task | Time Reduction | Time With GenAI (minutes) | Time Without GenAI (minutes) |
|---|---|---|---|
| Writing | -69% | 25 | 80 |
| Active Learning | -66% | 26 | 76 |
| Critical Thinking | -74% | 27 | 102 |
| Troubleshooting | -76% | 28 | 115 |
| Judgement and Decision Making | -65% | 28 | 79 |
| Management of Material Resources | -70% | 28 | 92 |
| Mathematics | -73% | 29 | 108 |
| Time Management | -62% | 29 | 77 |
| Complex Problem Solving | -75% | 30 | 122 |
| Instructing | -67% | 31 | 93 |
| Operations Analysis | -68% | 31 | 98 |
| Systems Analysis | -64% | 31 | 87 |
| Management of Personnel | -69% | 32 | 103 |
| Programming | -74% | 33 | 129 |
| Equipment Maintenance | -73% | 34 | 124 |
| Quality Control Analysis | -65% | 36 | 103 |
| Management of Finances | -64% | 38 | 106 |
| Technology Design | -73% | 39 | 142 |
These results reflect consistent productivity acceleration across all categories. Whether the task was technical, analytical, creative, or administrative, the average worker needed less than half the time when AI was incorporated.
Productivity Shifts Reflect Workforce Trends
In the broader findings from the Stanford–World Bank study, workers who adopted AI tended to be younger, more educated, and higher earning. AI use was most common in industries such as information services, professional management, and software development. Those working in public sector or manual labor roles used AI tools less frequently, reflecting practical limitations in those fields.
Yet even with uneven adoption, the survey found that about 43% of working adults had used AI tools by early 2025, a sharp increase from just 30% three months earlier. This growth indicates that AI is no longer niche, it is becoming a regular feature of modern work.
Conclusion
The task-specific data offers clear evidence that generative AI tools are driving widespread efficiency across a diverse set of professional roles. Although not all jobs or industries can benefit equally, most white-collar tasks now show measurable gains when AI is used for planning, problem-solving, or communication.
As these tools continue to evolve, employers and policymakers may need to revisit assumptions about what work requires human labor, and which parts of the job can be augmented through intelligent assistance.
Note: This post was edited/created using GenAI tools.Read next: Where Social Media Engagement Is Moving in 2025, and What’s Driving It
