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Help a team adapt to AI by first identifying which tasks and responsibilities are changing, then involving affected workers in redesigning the workflow, training people for the skills they need, and checking whether the change improves work without creating avoidable harms. AI may assist people with some tasks, automate others, and create new responsibilities; it does not affect every role in the same way. This practical approach synthesizes OECD and ILO guidance and evidence, rather than offering a formula proven to work for every workplace.
Start with tasks, not job titles
A job title can conceal very different day-to-day work. AI may take on a specific activity, support a worker’s judgment, or change how tasks are divided without replacing an entire occupation. The International Labour Organization says AI is more likely to augment human capabilities than lead to widespread automation across many roles, while noting that exposure varies by occupation and demographic group. That is a broad finding, not a guarantee that any particular job is safe from change.
OECD guidance recommends that managers understand what a system can and cannot do, then decide which activities belong with the system and which remain with people. Make the division explicit: who checks outputs, who handles exceptions, who can override a recommendation, and who remains accountable for the result?
Map the changed workflow
- List the tasks employees perform now, including review, communication, and exception handling.
- For each proposed AI use, record whether it supports a task, performs part of it, or changes how work is prioritized.
- Identify the human responsibilities that remain, such as judgment, customer interaction, approval, or escalation.
- Describe what happens when the AI output is wrong, incomplete, or unavailable.
One OECD example illustrates why this task-level view matters: an insurer used AI to prioritize accounts likely to escalate, shifting sales agents’ time away from file analysis and toward customer interaction. It is an example of a possible workflow change, not a forecast for all insurers or other workplaces.
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Involve affected workers before the design is settled
Workers often see practical problems that are hard to spot in a system plan: duplicated checks, unrealistic time targets, unclear boundaries between roles, or data collection that changes how their work is monitored. Ask employees and their representatives for input while their feedback can still influence the design. Invite specific comments on workload, staffing, training, responsibilities, data use, and how employees can challenge AI outputs.
OECD evidence associates worker consultation and training with better workplace outcomes, but consultation does not guarantee agreement or remove risks. In a laboratory experiment involving three German manufacturing firms, participants were able to agree on algorithmic-management designs they judged could preserve productivity gains while improving job quality. The OECD describes the findings as limited and calls for broader research, so they should not be treated as proof that consultation will produce the same result in every workplace.
Match training to the work people will do
Training should address the actual tasks and decisions changing, not assume that every employee needs advanced technical expertise. Distinguish foundational AI and digital literacy from specialist AI skills, then add the complementary capabilities people will need in the redesigned role.
| Training area | What it helps people do |
|---|---|
| AI and digital literacy | Understand the system’s intended use, basic limits, and when to seek help or verify an output. |
| Role-specific practice | Use the system in the tasks it is meant to support, review its output, and handle exceptions. |
| Human and organizational skills | Apply judgment, problem-solving, critical thinking, communication, teamwork, and socioemotional skills when work changes. |
| Specialist AI expertise | Build deeper technical capability where a role actually requires it, rather than making it the default course for everyone. |
| Manager preparation | Develop a working understanding of system capabilities and limits, risks, workflow redesign, and change management. |
The ILO’s 2026 skills report treats AI literacy as foundational and describes rising demand for cognitive, socioemotional, digital, and AI skills. It emphasizes higher-order skills, adaptability, resilience, and human agency; it does not provide a numeric growth rate for those demands. OECD guidance also stresses that managers need enough understanding of AI to make responsible decisions about work and skills.
Monitor job quality as well as productivity
A system can meet a productivity target while making work worse in other ways. Set a review date and check whether the new workflow is delivering its intended benefit and whether it is affecting employees’ working conditions. The sources identify risks, but do not prescribe one universal set of measures.
- Workload and intensity: Has the change increased pace, reduced recovery time, or added hidden review work?
- Autonomy and accountability: Can people exercise judgment, question an output, and understand who is responsible for decisions?
- Privacy and data use: What employee data is collected, for what purpose, and how is it used?
- Fairness and access: Are the effects or opportunities uneven across roles or groups?
- Health and safety: Has the new process introduced physical, psychological, or other workplace risks?
- Employment effects: Are tasks, staffing needs, or job boundaries changing in ways employees should understand?
Use employee feedback alongside operational results, and revise the workflow when the evidence points to problems. Check applicable law, collective agreements, and workplace policies in the relevant jurisdiction; the OECD and ILO materials cited here are guidance and evidence, not legal advice or a statement of one global rule. The OECD AI Principle puts the balance plainly: “It is important to allow for flexibility at the workplace while safeguarding workers’ autonomy and job quality.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available figures do—and do not—show
OECD findings offer context, but their populations and measures matter. The 2024 OECD workplace survey covered 5,334 workers and 2,053 firms in manufacturing and finance in Austria, Canada, France, Germany, Ireland, the United Kingdom, and the United States. In those survey findings, four in five workers said AI improved their performance at work, and three in five said it increased their enjoyment of work. These are reported responses from that survey, not estimates for all workers worldwide or proof that AI will improve every person’s job.
The OECD also reported that about 27% of employment in OECD countries was in occupations at highest risk of automation, citing its 2023 Employment Outlook. That figure concerns occupations assessed as being at highest risk across automating technologies; it is not a prediction that 27% of jobs will disappear.
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In an OECD analysis of vacancies in occupations most exposed to AI, 72% demanded at least one management skill, 67% at least one business skill, and 58% at least one digital skill. The brief also reported a three-percentage-point decline over the prior decade in vacancies demanding those skills in workplaces most exposed to AI, describing the change as relatively small. These are vacancy findings, not recommended training targets for an individual team.
The evidence behind workplace adaptation combines surveys, policy guidance, an illustrative workflow, and a small laboratory experiment. It supports careful task redesign, worker voice, relevant training, and ongoing checks; it does not establish one intervention that will work for every team, sector, AI system, or jurisdiction.
Quick Recap
Sources and further reading
- OECD, Using AI in the workplace: Opportunities, risks and policy responses (2024)
- ILO, Artificial intelligence adoption and its impact on jobs (2025)
- OECD, How is AI changing the way workers perform their jobs and the skills they require? (2024)
- OECD AI Principle: Building human capacity and preparing for labour market transformation
- OECD Employment Outlook 2023, Skill needs and policies in the age of artificial intelligence
- OECD, Bridging the AI skills gap: Is training keeping up? (2025)
- ILO and partner agencies, Changing landscape of skills in the age of AI (2026)
- OECD, How widespread is algorithmic management in workplaces? (2025)
- OECD, Exploring win-win outcomes of algorithmic management (2025)
- ILO, ILO adopts first-ever conclusions on AI in manufacturing work (2026)
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