How AI is expanding what people do at work

What happened

AI changes the work that people do. S. 5% of occupation-specific messages are about tasks associated with another occupation. A small-business owner can independently draft copy, review a contract, or perform basic financial analysis. A salesperson can use AI to explore a customer dataset that might once have gone to an analyst. A marketer can troubleshoot a website without waiting for a developer.

Marketing and engineering tasks travel farthest, frequently showing up in messages from workers outside those fields. Task crossover is not evenly distributed when we zoom in from occupations to specific tasks. Financial calculation and technology troubleshooting each appear among the three most common outside tasks in all seven other occupation groups in the analysis.

Marketing work also travels broadly: creating marketing materials appears across five other groups and is especially prominent among design users. Looking at broader task bundles reveals two distinct directions of crossover. Some jobs bring in many tasks from other occupations. Others supply tasks that appear across many different jobs. Design illustrates the first pattern. 7% of messages from workers in other fields.

Designers draw heavily on outside tasks, but design work itself rarely appears elsewhere. Engineering is closer to the reverse. 4% of messages among workers in other occupations. Engineering is an important source of work that people elsewhere take on, from troubleshooting software to working with technical systems. Marketing stands out in both directions. 9% of messages among workers in other fields—the highest outward share in the sample.

Marketing workers combine tasks from multiple domains, and marketing work also spreads widely across the organization. The size and structure of a business shapes how AI changes work. In a large company, employees may have access to specialized teams, established workflows, and internal services. In a smaller organization, the worker closest to the problem is more likely to take on the problem rather than delegate.

Why it matters

In each case, AI changes not just how work gets done, but who does what. Our new report, Work at the Frontier: How AI is Expanding What People Do at Work, studies this shift. We call the resulting pattern task crossover: work historically associated with one occupation appearing in the AI use of people in another.

In our AI Jobs Transition Framework⁠(opens in a new window), we argue that many jobs are likely to reorganize: these are jobs whose day-to-day tasks could change substantially. This report is the first in our new Work at the Frontier series, which explores how AI is changing work in real time.

Using our unique window into how the world of work is changing, we will offer regular data-driven insights based on evidence to guide policy and practice. Many studies of AI and work begin with a fixed list of tasks associated with a given occupation and ask whether models can perform them. Our evidence suggests that AI is also changing who takes on which tasks.

To measure task crossover, we first separate work that is associated with an occupation from work that appears across many jobs. Some activities, such as writing, summarizing, and scheduling, are shared too broadly across occupations to be evidence of crossover. We classify these as generic. For the remaining messages, we ask whether the task falls inside or outside the user’s own occupation.

5% fall outside of the user’s occupation, offering an early window into how AI may be reshaping the task content of jobs before those changes appear in job descriptions or titles. This suggests that a substantial part of work-related ChatGPT use is from users expanding their role. The pattern is especially pronounced in several groups.

Once generic work is excluded, outside-occupation tasks account for: These occupations are “borrowing” these tasks from other roles. The evidence points to a changing division of work: some activities that once required a handoff can now be done by the person who first encounters the need. The heatmap below shows task crossover by asking which occupation’s job each non-generic message most closely resembles.

What to watch

3% for users in workspaces with over 100 seats. Among the heaviest users, we do not see the same monotonic pattern. One possible explanation is that moderate users in smaller organizations turn to AI when they encounter work that would otherwise require another function. Heavy users may instead have developed stable AI-supported workflows that look more similar across organizations, or may use AI more intensively within their core occupation.

AI may be especially useful as a generalist tool where specialist resources are scarce. AI usage data like this is an indicator of where work is shifting.