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Businesses should build a workforce that can use AI safely, judge its outputs and adapt as work changes—not assume any skill or job can be made “AI-proof.” Start with a shared AI-literacy baseline, then add technical, data and domain training where particular roles require it. Critical thinking, communication, creativity and collaboration also matter, but their value depends on the work people do alongside AI.
Why AI changes skills rather than producing one workforce outcome
AI can automate some tasks, help people do others more productively and create new work. Those effects can occur together, and they vary by occupation, sector, region and how an organization introduces the technology. The OECD’s 2026 analysis of skills in the AI age discusses these different channels; exposure to AI alone does not establish that a job will disappear.
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The International Labour Organization (ILO) describes changes across cognitive, socioemotional, physical, digital and AI-related skills. In many settings, higher-order cognitive and socioemotional capabilities are becoming more important. That does not make them a fixed shield from change: their importance depends on the tasks and decisions involved.
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“Future-proofing” is therefore better understood as building the capacity to keep learning and adjust tasks as tools and workflows evolve. Training can support that readiness, but it cannot guarantee that a role will remain unchanged or prevent displacement.
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Which skills should businesses future-proof?
A practical skills framework combines broadly useful foundations with capabilities matched to the work. Most employees need a blend; advanced AI engineering is not a universal requirement.
| Capability | What it enables at work | Who needs it |
|---|---|---|
| Foundational learning skills | Literacy, numeracy and the ability to learn support participation in further digital learning. | Everyone; these underpin other skills. |
| Digital and AI literacy | Understand what AI systems can and cannot do, use them safely and ethically, and critically check their outputs. | A baseline for employees who encounter AI at work. |
| Human and socioemotional capabilities | Critical thinking, creativity, communication, collaboration and judgment help people make decisions and work with others in AI-enabled tasks. | Develop in relation to actual responsibilities, rather than treating these as “AI-proof” traits. |
| Digital, data and domain capability | Apply digital and data skills together with knowledge of the business, customers, processes and relevant rules. | Tailor to role and workflow; many jobs need this combination. |
| Specialist AI and machine-learning skills | Develop, implement, manage or maintain AI systems. | Roles with those responsibilities, not necessarily the whole workforce. |
| Adaptability and agency | Learn, adjust tasks and act with sound judgment as tools and responsibilities change. | Useful across roles as work evolves. |
The ILO calls AI literacy “a foundational skill – an essential enabler of human agency and inclusion in AI-augmented environments.” A baseline should enable employees to question outputs and use systems responsibly, not just operate a tool. The OECD’s 2026 executive summary estimates that workers with advanced AI skills represent around 1% of the workforce, underscoring the distinction between broad workplace literacy and specialist expertise; see its executive summary.
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How to upskill employees for AI
- Map tasks before choosing courses. Identify where AI could assist, change a workflow or create new responsibilities. Ask workers and managers who understand the work to help identify tasks, risks and required judgment. This is a practical implementation approach, not a single audit method prescribed by the cited sources.
- Set a shared literacy baseline. Cover basic AI concepts, appropriate use, checking outputs, data and safety considerations, and how to raise concerns. Reserve deeper technical instruction for roles that need to build, implement or maintain systems.
- Teach through real work. Use relevant examples and supervised practice with appropriate tools. Connect lessons to the decisions employees actually make rather than relying only on abstract explanations or tool demonstrations.
- Make learning reachable and modular. Offer flexible pathways for different roles, schedules and starting skill levels. Integrate learning into work and peer support; the OECD recommends flexible, modular lifelong-learning pathways.
- Set responsible-use expectations. Clarify which uses are approved, who checks outputs, how sensitive information is handled and where employees can escalate problems. Leadership support and clear responsibility help make training usable in daily work.
- Review learning and workflow outcomes. Define what employees should be able to do, then assess progress and revisit training as tasks change. Measure outcomes in the organization’s context rather than assuming training produces a particular productivity gain.
- Plan for access and capacity. Account for employee time, equipment, tool access and internal support. A programme that assumes every team has the same infrastructure or capacity may exclude the people it is meant to reach.
What UK employer evidence says about AI training
The UK Department for Work and Pensions and Skills England’s 2026 employer guide to AI upskilling draws on 23 workshops, 10 case studies and 536 survey responses. Within that programme’s evidence, over 44% of surveyed organizations said they used AI tools daily, and 97% reported providing some AI training. Respondents also identified gaps: 51% cited flexibility and 34% cited practical, contextualized learning. These are findings from the guide’s evidence, not rates for all UK employers or businesses elsewhere; reported training provision does not by itself show that training was sufficient.
The guide’s PRIMES framework describes effective training as practical, reachable, integrated, modular, expandable and sustainable. In concrete terms, employees need opportunities to practise; access that fits different roles and circumstances; learning connected to daily work; modules that can be adapted; scope to grow as needs change; and support that can continue over time. The guide also emphasizes responsible use, leadership support, skills frameworks and measuring impact.
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A separate UK source, the Department for Science, Innovation and Technology’s 2025 AI Labour Market Survey executive summary, reports that 97% of respondents identified at least one AI labour-market skills gap: 57% identified a technical gap and 30% a non-technical gap. This is a different survey and population from the employer-guide programme; it should not be read as a universal estimate of businesses or employees.
How company size and adoption affect the plan
OECD analysis indicates that AI uptake among firms in OECD countries rose from around 7% in 2021 to 20% in 2025. The OECD’s 2026 executive summary also says larger firms and start-ups tend to lead adoption, while small and medium-sized enterprises (SMEs) face cost, infrastructure and skills barriers. These OECD-wide figures do not describe every country or individual business.
For an SME, a workable first step may be a focused literacy programme and task-specific practice rather than trying to reproduce a large employer’s training structure. More broadly, compare training options on how well they fit real tasks, how accessible and flexible they are, the practice they allow, their treatment of responsible use, their integration with daily work and peer learning, whether they support recognition or progression, and whether the organization can assess and sustain learning. There is no universally best delivery format established by the cited guidance.
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AI can contribute to automation and displacement risks as well as productivity improvements and new tasks. The balance differs by industry, place, occupation and deployment choices. Skills training can help workers adapt to changing tasks, but the available evidence does not establish that training alone prevents job loss or guarantees a particular return. Businesses should plan for changing work while being clear with employees about decisions and responsibilities that training cannot settle.
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