AI Turns Every Employee Into a Utility Player
For decades, the org chart worked like a map of who knew what. Marketing didn’t touch contracts. Customer service didn’t run financial calculations. Those boundaries held because expertise lived in specific people, and getting specialize work done meant calling in the right one. New research from OpenAI suggests generative AI is dissolving those lines fast enough that job titles are starting to lag behind what people actually do all day.
The evidence comes straight from OpenAI. The company examined more than 800,000 work-related ChatGPT messages from U.S. users and found that 43.5% of occupation-specific requests involved tasks traditionally associated with a different profession, according to the company’s report, the first installment in a new “Work at the Frontier” series published July 27. OpenAI has a name for the phenomenon: task crossover.
Customer Experience Crossover Hits 77%, the Highest of Any Role
Crossover reached 77% among customer experience workers, the highest of any role in the study, followed 75% among designers, 69% in human resources, 56% in legal and 53% in marketing, OpenAI’s research found. In practice, that’s a customer experience worker running a financial calculation once handled by an analyst, or an HR professional troubleshooting a problem once sent to IT.
Direction matters as much as size, and design shows the pattern most starkly. Even counting every message they send, not just occupation-specific ones, designers pulled outside work in 35.2% of their prompts. Design expertise almost never travels the other way: just 1.7% of workers in other fields touched on design at all, OpenAI found.
Engineering runs the opposite direction: only 18.5% of engineering messages involve outside fields, the tightest boundary of any role OpenAI measured. But engineering-style tasks such as troubleshooting account for 7.4% of messages among non-engineers, suggesting more employees are patching technical problems themselves rather than filing a help desk ticket.
Marketing lands in the middle, and it’s the most porous role in either direction. Marketers put 24.3% of their messages toward other occupations’ work, while marketing tasks bled into 8.9% of messages from other fields, the highest outward share OpenAI recorded.
Company size plays a role too, though not a simple one. Crossover ran higher at smaller workspaces (18.9%) than at larger ones (16.3%), OpenAI found, a gap that tracks with small companies inability to afford a dedicated specialist for every function. But among the platform’s heaviest users, company size stops predicting crossover almost entirely. Something else is doing the work.
The Longer Workers Use AI, the More Boundaries Blur
That something is tenure. PYMNTS Intelligence’s latest Consumer AI Benchmark Report found that 61% of workplace gen AI users have logged for at least a year with the technology, a share that climbs to 75% among the heaviest users and falls to just 42% of light users, PYMNTS reported.
Tenure doesn’t just build comfort with the tools, it also changes what people trust them to do. Personal users with at least one year of experience complete an average of 11 tasks with gen AI, 68% more than the 6.6 tasks newcomers manage. The stakes rise too: 31% of one-year-plus users call gen AI essential for managing finances and banking, compared with 13% of newcomers, a 129% jump, PYMNTS found.
Put the two datasets side by side, and a fuller picture forms. OpenAI’s crossover numbers are a snapshot of what people are already doing with AI. PYMNTS’ tenure data explains why that snapshot keeps growing: The longer someone uses the technology, the more of their own job, and increasingly someone else’s, they fold into it. Crossover compounds with experience.
OpenAI is careful to note none of this proves AI is eliminating jobs or dissolving departments outright. What it shows is that the boundary between roles is loosening faster than job titles or org charts can catch up. A marketer who can now review a contract isn’t doing a lawyer’s full job, but they’re doing a meaningful slice of it without waiting for that specialist to become available.
That gap, between what employees already do and what their job descriptions still say, is where the real decision now sits: How quickly companies redraw the lines to match it?
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