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Workers seek AI automation for low-value repetitive tasks, study finds

Stanford research reveals 69% cite freeing time for high-value work as primary automation motivation.

Luis Rijo

• 6 min read
Workers prefer AI automation for repetitive tasks like data entry while keeping creative work themselves.
Workers prefer AI automation for repetitive tasks like data entry while keeping creative work themselves.

Workers overwhelmingly want artificial intelligence to handle repetitive and tedious tasks rather than creative or strategic work, according to new research from Stanford University published June 11, 2025. The comprehensive study of 1,500 U.S. workers reveals that freeing up time for high-value activities drives 69.38% of positive automation responses.

The research, spanning January through May 2025, created the WORKBank database examining 844 occupational tasks across 104 professions. Workers consistently favor AI automation for administrative functions like scheduling appointments, maintaining files, and processing routine adjustments while strongly resisting automation for creative and interpersonal work.

Tax preparers scheduling client appointments received the highest automation desire rating at 5.0 on a 5-point scale. Public safety telecommunicators maintaining emergency call files scored 4.67, while payroll clerks processing pay adjustments rated 4.60. These tasks share common characteristics: they involve routine data management with minimal creative input.

Summary

Who: Stanford University researchers surveyed 1,500 workers across 104 occupations, focusing on attitudes toward automating routine versus creative tasks.

What: The study reveals workers primarily want AI automation for repetitive, low-value tasks like data entry, file maintenance, and routine processing, with 69.38% citing time savings for high-value work as primary motivation.

When: Research conducted January through May 2025, published June 11, 2025, capturing current worker attitudes toward task-specific automation preferences.

Where: Comprehensive analysis of U.S. workforce covering computer-compatible occupations from administrative roles to creative professions, drawn from Department of Labor task classifications.

Why: Workers seek to eliminate tedious routine responsibilities while preserving creative and strategic work, indicating AI adoption should focus on administrative efficiency rather than replacing human judgment and creativity.

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According to Yijia Shao, the study's lead researcher, "The primary motivation for automation is freeing up time for high-value work, though trends vary significantly by sector." Workers across multiple industries identified repetitive tasks as prime automation candidates, with 46.6% citing task repetitiveness as a key factor.

Administrative data entry emerged as a clear automation target. Desktop publishers converting file formats scored 4.50, while online merchants creating customer databases and calculating revenue using spreadsheet software both received 4.40 ratings. Computer support specialists maintaining transaction records also scored 4.50, indicating strong worker acceptance for routine technical documentation.

Quality control activities involving regular monitoring appeal strongly to workers seeking automation relief. Quality control systems managers directing defect tracking received 4.50 ratings, while statisticians reporting analysis results through graphs and charts scored equally high. These findings suggest workers view standardized reporting processes as suitable for AI handling.

The pattern extends across professional services sectors. Court clerks instructing parties about hearing schedules rated 4.33 for automation desire, while compliance officers preparing licensing correspondence scored 4.25. These administrative communication tasks represent clear automation opportunities in traditionally human-centered fields.

Workers demonstrate markedly different attitudes toward creative and strategic functions. Editors writing articles, editorials, or newsletters received the lowest automation desire score at 1.60. Graphic designers creating concepts and layouts scored 1.78, while librarians locating unusual information requests rated 1.80. The resistance patterns indicate strong preference for human involvement in creative problem-solving.

Customer service tasks requiring personal interaction face significant automation resistance. Travel agents tracing lost baggage scored 1.50, matching logistics analysts contacting vendors for material availability. These interpersonal tasks highlight worker recognition that human judgment and empathy remain essential for complex service situations.

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Audio analysis revealed specific worker concerns about AI limitations in human-centered roles. One art director with 6-10 years experience stated, "I don't want it to be used for content creation. If anything, I want it to be used for seamlessly maximizing workflow and making things less repetitive and tedious."

The research identifies task complexity as a key factor in automation acceptance. Simple data manipulation tasks consistently receive high ratings, while complex decision-making processes face resistance. Workers appear comfortable delegating routine cognitive work while maintaining control over strategic and creative functions.

Sector-specific patterns emerge from the data analysis. Computer and mathematical occupations show 53.8% of tasks receiving positive automation ratings, reflecting high comfort with technical automation. Management roles reach 51.1%, while business and financial operations follow at 48.4%. Arts, design, and media sectors trail significantly at 17.1%.

The stressfulness factor influences automation preferences across multiple professions. Workers selected "stressful or mentally draining" as automation motivation in 25.5% of positive responses. This suggests AI adoption could address workplace stress by handling burdensome routine tasks.

Income levels correlate with automation acceptance patterns. Higher-earning workers generally express greater willingness to automate routine functions, possibly reflecting greater exposure to efficiency tools and technologies. Those earning over $529,000 annually show significantly more positive attitudes toward delegating repetitive work.

Current AI investment patterns show notable misalignment with worker preferences. Analysis of Y Combinator companies reveals 41.0% focus on areas workers consider low priority or face resistance. Many investments target software development and business analysis rather than the routine administrative tasks workers most want automated.

The Human Agency Scale introduced by researchers provides framework for understanding automation preferences. Tasks receiving H1 and H2 ratings (minimal human involvement) align closely with routine, repetitive work. Workers prefer collaborative H3 partnerships for complex tasks requiring human judgment.

Marketing professionals face particular implications from these findings. Content marketing strategies are already shifting as AI capabilities expand, while performance marketing fundamentals remain challenging despite technological advances.

Database maintenance activities represent prime automation targets across industries. Bioinformatics scientists manipulating genomic databases scored 4.17, while network administrators performing routine startup procedures rated equally high. These technical maintenance tasks align perfectly with worker automation preferences.

File management and backup operations consistently rank high for automation desire. Web developers backing up site files scored 4.20, matching web administrators handling disaster recovery procedures. The pattern suggests workers readily accept AI assistance for protective and maintenance functions.

Budget and financial processing tasks show strong automation acceptance where routine calculations dominate. Computer research scientists managing operational budgets scored 4.17, indicating comfort with AI handling standardized financial procedures. Workers distinguish between routine number processing and strategic financial decision-making.

The research methodology employed audio-enhanced interviews capturing nuanced worker reasoning. This approach revealed that workers make sophisticated distinctions between task types, readily embracing automation for routine functions while protecting creative and strategic responsibilities.

Professional development implications extend beyond simple task delegation. As routine work becomes automated, workers anticipate focusing more on interpersonal skills, creative problem-solving, and strategic thinking. The study suggests traditional information-processing skills may become less valuable while human interaction capabilities gain importance.

Geographic analysis shows consistent patterns across different regions, with workers nationwide expressing similar preferences for automating repetitive tasks. The 104 occupations studied provide comprehensive coverage of computer-compatible work environments.

Expert assessments often rate technological feasibility higher than worker comfort levels for complex tasks. However, alignment appears strong for routine administrative functions where both workers and experts agree AI capabilities match automation desires.

Time allocation emerges as a critical factor driving automation preferences. Workers spending significant portions of their day on routine tasks show highest automation desire. This pattern suggests AI adoption could substantially improve job satisfaction by eliminating tedious responsibilities.

The implications for workforce development center on preparing workers for higher-value activities as routine tasks become automated. Marketing automation adoption increased 17% in recent surveys, reflecting broader trends toward efficiency improvements.

Quality improvement represents another motivation for automating routine tasks. Workers selected "automating this task would improve the quality of my work" in 46.6% of positive responses, suggesting AI could enhance output consistency for repetitive functions.

The research provides systematic framework for evaluating which tasks workers genuinely want automated versus those they prefer to control. This distinction proves crucial for successful AI implementation that aligns with worker values and preferences.

Future research directions include tracking attitude changes as AI capabilities expand and workers gain direct experience with automation tools. The WORKBank database establishes baseline measurements for ongoing analysis of workplace AI integration.

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