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AI can help with parts of a job without taking over the whole role. The International Labour Organization (ILO) concluded in 2023 that generative AI is more likely to augment than destroy jobs because it is more likely to automate some tasks than entire roles. That is a broad finding about possible task effects—not a guarantee that any particular job is safe or will improve.
For workers, the useful question is not simply whether a job is “exposed” to AI. It is which tasks may change, what human responsibilities remain, and how an employer chooses to use the technology.
What AI exposure says—and what it does not
An occupation is considered exposed when some of its tasks overlap with tasks AI could theoretically perform. Exposure is not a forecast of layoffs: adoption, workflow design, and employer decisions all affect what happens in practice. The OECD notes that some older AI exposure measures were developed before recent generative AI advances, so they should not be read as precise predictions of current workplace outcomes.
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In a November 2024 policy brief, the OECD reported that about one-third of vacancies in a sample spanning 10 OECD countries were in occupations classed as highly exposed to AI. The country range was 31% in Austria to 45% in the United Kingdom. “Highly exposed” used an occupational measure at least one standard deviation above the mean; it did not mean those vacancies were expected to disappear. OECD policy brief, November 2024
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How AI can change a job
The OECD describes three forces that can operate at the same time. Their combined effect on employment is uncertain, and exposure figures alone cannot reveal which force will dominate in a particular workplace.
- Task displacement: AI can automate tasks people previously performed, reducing the need for some kinds of labor.
- Productivity and demand: If workers or organizations produce more efficiently, lower costs or faster service may increase demand for the work or its output.
- New tasks: Adoption can create work that did not previously exist, including tasks needed to use, check, integrate, or govern AI systems.
These effects can differ within the same occupation. An automated task may be a small part of one person’s work but central to another’s. Employers also decide whether to retain people for remaining tasks and oversight, redesign roles, or use productivity gains in other ways. OECD Employment Outlook 2023 and the ILO’s AI topic page discuss these mechanisms.
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Jobs where AI may assist with some tasks
The examples below are occupations identified among those with high AI exposure in the OECD’s 2024 policy brief. That label identifies potential task overlap; it does not establish that AI will benefit or replace these workers. Specific duties vary by workplace, and no occupation-wide employment outcome is established here.
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| Occupation | Possible task-level change | What remains important to assess |
|---|---|---|
| Secretaries and administrative assistants | Some information-handling and routine communication tasks may be candidates for AI assistance or automation. | How much of the role consists of those tasks, and how much depends on coordination, context, discretion, and interaction with people. |
| Accountants and financial analysts | Some information processing and analysis tasks may be assisted or automated. | Where human interpretation, professional judgment, accountability, and review remain in the workflow. |
| Software developers | Some work involving code or related information may be assisted by AI tools. | How the employer assigns design, testing, integration, security, and responsibility for the resulting software. |
| Managers | AI may affect some information-processing or administrative tasks. | How the role handles decisions, people, accountability, and coordination—and whether the organization changes those responsibilities. |
| Human resources professionals | Some information-handling tasks may be candidates for assistance or automation. | How the employer handles context, sensitive decisions, communication, and responsibility for outcomes. |
These are questions for evaluating a particular role, not claims that a specific task is automated in every workplace. The ILO’s global analysis, focused particularly on generative AI such as GPT-4, found that most jobs and industries are only partly exposed. ILO, “Generative AI likely to augment rather than destroy jobs,” 21 August 2023
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- Realistic Movements: 12 powerful servos enable 32 actions, including walking, sitting, standing, shaking its head, wagging its tail, and performing playful tricks, closely mimicking a real and providing an engaging experience
- Rich Sensor Suite for Interactive Experiences: features ultrasonic, touch, gyroscope, sound, camera, speaker and microphone. These provide it with advanced hearing, vision, and touch, enabling it to see, detect obstacles, respond to touch, and recognize sounds, making interactions highly engaging
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Why the same tool can help one worker and unsettle another
Whether automation complements workers depends in part on three factors:
- Task centrality: How central is the affected task to the occupation? Automating a peripheral task may free time for other work; automating a core task may reshape the role more substantially.
- Workflow integration: Is AI fitted into a process with clear human review and responsibility, or does it change how the whole process is staffed and organized?
- Management choices: Does the employer retain workers to perform or oversee remaining work, and how are productivity gains allocated?
Those choices can influence job quality as well as headcount. The ILO warns that generative AI may change work intensity and worker autonomy, so “not replaced” does not necessarily mean “unchanged.” ILO, Artificial intelligence
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Skills and working conditions may change too
OECD research by Andrew Green examined online vacancies across 10 OECD countries over the past decade. In highly AI-exposed occupations, the share of vacancies asking for at least one emotional, cognitive, or digital skill rose by 8 percentage points. A separate panel of establishments in the same work showed evidence that demand for these skills was beginning to fall. The mixed results are not a universal trend or proof that AI caused a particular worker’s outcome; they describe different evidence about changing skill demand. OECD, 10 April 2024
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The same study found that management and business skills were among the skills most demanded in highly AI-exposed occupations. This is evidence about vacancy patterns, not a checklist that guarantees job security. Workers assessing a role can look for changes in its actual responsibilities: what the AI produces, who checks it, who makes decisions, and who is accountable.
Workers’ reported experiences also vary. In an OECD 2024 survey summary, four in five workers said AI improved their performance at work and three in five said it increased their enjoyment of work. These are survey responses, not proof that AI caused the reported outcomes or that all workers share them. OECD, “Using AI in the workplace: Opportunities, risks and policy responses,” 2024
How to judge what AI may mean for your own job
- Break the role into tasks. List the recurring work rather than treating the job title as a single unit.
- Identify possible task overlap. Ask which tasks an AI system could assist with, which it might automate, and which require human interaction, judgment, physical work, or accountability.
- Check how central each task is. A change to a minor task may have a different effect from a change to a core responsibility.
- Look at the employer’s workflow and staffing decisions. Find out who reviews AI output, who remains responsible, and whether the organization is changing the role or how many people perform it.
- Watch the work, not just the tool. Responsibilities, skill expectations, autonomy, and work intensity may shift even when the occupation remains.
The OECD’s account of AI and jobs covers automation, complementarity, productivity, and the creation of new tasks; none can be inferred reliably from an exposure label alone. OECD Employment Outlook 2023
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