In July 2026, OpenAI published Work at the Frontier. Researchers analyzed more than 800,000 chat messages, real requests from people trying to get work done, and tried to match each one to an occupation.
Three outcomes were possible. The request matched the sender’s own occupation: 21.8 percent. It matched a different occupation: 16.8 percent. It matched no occupation at all: 61.5 percent.
The research question was about job boundaries, so only the first two outcomes could answer it. OpenAI reported the 61.5 percent, then excluded it from the calculation. Of the requests that could be tied to an occupation, 43.5 percent crossed into somebody else’s. That is the number being quoted everywhere.
The number nobody is quoting is the one that got set aside.
Now look at what the same company builds. OpenAI’s research measures work as tasks rolled up into occupations. Its products run on skills: ChatGPT and Codex both adopted the open agent-skill format, where each file holds one capability and the standard for performing it. The research and the products use different units. The research uses the one that cannot hold most of the behavior.
Three definitions
A task is a piece of work to be done. Build the Q3 revenue forecast for the EMEA region.
A skill is a capability that gets a task done. That forecast takes an analyst four of them: financial modeling, variance analysis, reading the pipeline for what is real, and defending a number in front of a CFO. One task, four skills. The task ends. The skills remain: they repeat across hundreds of other tasks and travel with the person into the next role at a higher proficiency.
An AI skill is a file. Developers and, increasingly, employees use it to give an AI system instructions for one job. It is software configuration, not a human capability.
Throughout, skill means human capability. AI skills are named explicitly.
The skills people use every day are not on their job descriptions
That analyst’s job description probably says she supports regional planning and reporting. None of her four capabilities appear on it.
That is the 61.5 percent, and it is not an artifact of OpenAI’s dataset.
Job descriptions are duty lists, and duty lists are always incomplete. Work shifts week to week. Much of what someone did last month was never assigned in a document and never will be.
Capabilities are different. They are finite and you can name them. You cannot list every negotiation a commercial lead will run this year. You can state that the role requires commercial negotiation at a defined proficiency, and that holds across all of them.
That is the difference between a job description and a skill profile. Stop enumerating the work. Name what the work requires.
Companies have been moving to skills for years. It is hard.
Mercer’s 2025/2026 Skills Snapshot found 38 percent of organizations now maintain a single enterprise-wide skills library, up from 30 percent in 2023. It also found 55 percent map skills directly to jobs, up from 47 percent.
Then look at what the library is used for. In a 2024 Gartner poll of HR leaders, 2 percent said their organization had adopted skills-based approaches across all their processes.
Nearly two in five have built the inventory. Almost none run the business on it.
Buying a taxonomy can take an afternoon. Getting the business to agree on what a skill means, at what proficiency, and for which roles takes quarters. Resolving every role, job, program, and pathway to the same canonical skills, then keeping that system current as work changes, is where these programs stall. Most stop at a library that nothing downstream reads.
Why the same skill shows up in hundreds of roles
Career Highways analyzed roughly 3,500 roles inside one organization. About 800 of them shared the same top strategic skills.
That is not sloppy job design. It happens for two reasons.
Skills deepen across roles rather than getting replaced. An analyst, a manager, and a director may all require financial forecasting. That is one capability at three proficiency levels. People do not swap skills out when they move up. They get better at what they hold and add a few.
Strategic skills are needed in many places at once. If a company competes on customer retention, customer-needs analysis appears in sales, service, product, and marketing. Breadth is what makes a skill strategic.
This is why “43.5 percent of AI use crosses job lines” sounds more alarming than it is. When a designer’s AI use resembles a market researcher’s, the job-based view reports a boundary crossing. Both roles run structured analysis of qualitative feedback. One capability, two subjects, and it was always in both jobs. Nobody wrote it down in either.
The two views lead to opposite decisions. Crossing tells you to redraw job boundaries. Sharing tells you to build one capability deliberately for everyone who needs it.
Get skills right and everything else follows
Skills are not another HR program. They are the foundation the others depend on.
Once every role, person, program, and pathway is expressed through the same governed skills, compensation can tie to capability and market value instead of title and tenure. Development can target the specific gap between what someone holds and what the next role requires. Internal mobility becomes visible because you can see who is already most of the way to a role nobody offered them. Workforce planning can show where capability is concentrated and how long it would take to rebuild.
The biggest implication is AI impact.
In a skills-based organization, AI impact is not a study you run once. It becomes an attribute of every skill: whether AI automates the capability, augments it, or leaves it as a human advantage; how much can be delegated to a system; and which AI capability is driving the change. That attribute is governed and versioned, just like proficiency and scarcity. When AI shifts, you update the skill. Every role and person expressed through that skill updates with it.
How the research decides what AI can do
This matters because the headline numbers rest on it.
The method behind much of this work comes from Eloundou, Manning, Mishkin, and Rock in 2023. It works like this. Take the government’s catalogue of task statements for an occupation, one-line descriptions such as “prepare financial reports.” For each one, answer a single question against a rubric: would access to an LLM cut the time to do this by at least half, without losing quality? Code the answer three ways. Add up the results to get the occupation’s percentage.
GPT-4 rated all of them. Humans rated a sample. The rubric given to GPT-4 was, in the authors’ words, “modified to enhance agreement with a set of human labels.”
No model is run on the actual work. Nothing is checked for correctness. The output is a prediction about a sentence.
The authors are direct about the weaknesses. Their annotators were “not occupationally diverse” and may hold “biased judgments regarding LLMs’ reliability” in unfamiliar domains. They state that obtaining high-quality labels requires workers engaged in those occupations. They also note that the results are sensitive to the rubric’s wording, the prompt’s order, and which examples are included.
So “90 percent exposed” does not mean AI can do 90 percent of a job. It means a model predicted AI would at least halve the time on 90 percent of the catalogued task statements.
Overhang, or gain
That estimate is one side of the comparison people quote. OpenAI’s April framework sets it beside observed AI use, roughly 23 percent in the most exposed occupations, and calls the distance the capability overhang.
Two problems. The estimate and the observation are different kinds of numbers, yet subtracting one from the other produces something that looks like a measurement. And both are matched against occupation-based definitions, where the 61.5 percent already told us most of the work is missing.
The useful measure runs the other way: not the shortfall against a rated hypothetical, but the gain you have actually realized. It is the share of work now automated or augmented by AI, measured at the skill level, from work that actually happened.
That number is computable in a skills-based organization and nowhere else. Every canonical skill already carries its category. Roll those skills up to a role and you get the role’s position. Roll them across the organization and you get the enterprise position. The measure carries a date, expires, and is re-derived when the underlying skills change.
What you measure it with
Two signals. Both are already inside your company.
The first is what people use AI to help them do. Map that activity to job titles and most of it disappears. OpenAI’s 61.5 percent makes that visible. Map it to canonical skills and you can see which capabilities people are exercising, where AI is landing, and whether your AI impact assessment matches what is happening in the building.
The second is stronger. When an employee builds an AI skill to help them work, they are writing a specification of a capability: what it is, when it applies, and how it is done correctly here, precise enough for software to execute.
Now set that against what the exposure researchers said about their own method. High-quality labels, they wrote, require workers engaged in those occupations. Those workers are now producing exactly that, unprompted, every time they author an AI skill to get work done. But the signal is readable only if you have a skills foundation to attach it to.
Where this lands
OpenAI measured how people actually use AI at work and found that 61.5 percent of the requests did not map to any occupation. It then set that finding aside because it could not be used in the occupation-based calculation.
That is the point. Work happens at the skill level, not the title level. Measure by job title and you cannot see most of what AI is doing to your workforce.