Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
Our framework features an audio-enhanced mini-interview to capture nuanced worker desires and introduces the HumanAgency Scale (HAS) as a shared language to quantify the preferred level of human involvement. Using this framework, we construct the WORKBank database, building on the U.S. Department of Labor’s ONET database, to capture preferences from1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation “Green Light” Zone, Automation “Red Light” Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics.
Domain workers want automation for low-value and repetitive tasks (Figure 4). For 46.1% of tasks, workers express positive attitudes toward AI agent automation, even after reflecting on potential job loss concerns and work enjoyment.
Notably, 41.0% of Y Combinator company-task mappings are concentrated in the Low Priority Zone and Automation “Red Light” Zone. Current investments mainly center around software development and business analysis, leaving many promising tasks within the “Green Light” Zone and Opportunity Zone under-addressed.
45.2% of occupations haveH3(equal partnership) as the dominant worker-desired level, underscoring the potential for human-agent collaboration.
traditionally high-wage skills like analyzing information are becoming less emphasized, while interpersonal and organizational skills are gaining more importance.
Traditional technology impact studies often ask: To what degree can this task be automated? Besides this view of automation, we consider the view of augmentation—where technology complements and enhances human capabilities (Autor, 2015), as this new wave of technology holds significant potential to augment human workers through human-agent collaboration, enhancing both productivity and work quality.
• H1: AI agent handles the task entirely on its own.
• H2: AI agent needs minimal human input for optimal performance.
• H3: AI agent and human form equal partnership, outperforming either alone.
• H4: AI agent requires human input to successfully complete the task.
• H5: AI agent cannot function without continuous human involvement.
Unlike SAE driving automation levels (Committee et al., 2014) that adopt an “AI-first” perspective, HASprovides a human-centered lens for assessing both task properties and appropriate agent development approaches. Importantly, higher HAS levels are not inherently better—different levels suit different AI roles. Tasks at H1-H2 favor automation approaches, while H3-H5 tasks benefit from augmentation strategies.
As shown in Figure 5 b, the company-task mappings are relatively evenly spread across the four zones. Most mapped tasks are concentrated in occupations related to software development and business analysis, with the top five occupations being: Computer and Information Systems Managers, Computer Programmers, Computer Systems Engineers/Architects, Software Quality Assurance Analysts and Testers, and Business Intelligence Analysts. 41.0% of YC companies are mapped to Low Priority and Automation “Red Light” Zone; while many promising tasks within the “Green Light” Zone and Opportunity Zone remain under-addressed by current investments.
While this trend in expert assessments might reflect current technological limitations—i.e., AI agents are not yet capable of fully replacing human involvement in most tasks—it is notable that workers in many domains also prefer a balanced, collaborative partnership with AI. H3 emerges as the dominant worker-desired level in 47 out of 104 occupations analyzed.
Shrinking demand for information-processing skills. Skills related to analyzing data and updating knowledge—while common in today’s high-wage occupations (as shown in the left side of Figure 7 in red color)—are less prominent in tasks that demand high human agency.
Greater emphasis on interpersonal and organizational skills. Skills involving human interaction, coordination, and resource monitoring are more frequently associated with high-HAS tasks (as shown in the left side of Figure 7 in green color), even if they are not currently prioritized in wage-based evaluations.
High-agency skills span diverse aspects. The top 10 skills with the highest average required human agency encompass a broad range, from interpersonal and organizational abilities to decision-making and quality judgment.