How Generative AI Is Changing the Skills Required Across Different Industries

Generative AI is changing the skills employers expect from their workforce. The OECD report AI and Skills finds that around 40% of manufacturing and finance employers that have not adopted AI cite skill shortages as a major barrier. More than half of SMEs report a similar challenge. As AI adoption grows, employers increasingly need workers with strong digital, data interpretation and critical thinking skills. Employees may not need to develop AI systems, but they must know how to use AI outputs and apply sound judgement.

THE BIG TAKEAWAY

Generative AI is raising the importance of digital and data literacy, problem-solving, creativity and managerial skills across workplaces. Advanced AI expertise remains necessary for a small share of workers, while most employees need the ability to use AI effectively in their roles.

How Is Generative AI Changing Skill Requirements in Manufacturing and Finance?

Manufacturing and finance show how a lack of relevant skills can hold back AI adoption while the technology simultaneously increases demand for highly educated workers.

The OECD report identifies skills as a significant barrier for employers in both industries. Around 40% of manufacturing and finance employers that have not adopted AI say a lack of skills is the main reason preventing adoption.

However, businesses that have already adopted AI report a different change. More than half of employers in manufacturing and finance say AI has increased their need for highly educated workers. This indicates that AI adoption is not necessarily reducing the need for skilled employees. Instead, it can increase demand for workers who can handle more advanced responsibilities alongside AI tools.

The report also notes broader evidence that employment has grown fastest in roles most exposed to AI. In these jobs, AI tends to complement employees rather than replace them, increasing the importance of higher-level skills.

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Do Employees Need Advanced AI Skills to Work With Generative AI?

Not necessarily. The OECD estimates that fewer than 1% of workers will need advanced AI-specific skills, such as programming or model development.

For most employees, the changing skill requirements are more closely connected to using digital tools and working with information. As generative AI becomes more common, workers will increasingly need to:

• Use digital tools: Understand how to work with AI-enabled systems in their roles.

• Analyse data: Examine information and identify relevant patterns or findings.

• Interpret data: Understand what the information and AI-generated outputs mean before using them.

• Apply managerial skills: Use judgement and decision-making abilities when working with AI-supported processes.

• Solve problems: Assess challenges and identify suitable ways to address them.

• Use creativity and innovation: Develop ideas and approaches that go beyond the outputs generated by AI.

These abilities are relevant beyond specialised technology roles. The OECD highlights digital and data-related skills alongside managerial and human skills as important requirements for workers across industries.

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Are Human Skills Becoming Less Important as AI Adoption Grows?

The OECD findings show that problem-solving, creativity and innovation remain important, while the effect of AI on certain social and emotional skills is still developing.

Skills such as empathy, communication and teamwork continue to be essential in many jobs. However, the report points to some evidence that the use of algorithmic management tools may reduce the perceived need for certain social skills in parts of Europe.

For example, among managers using algorithmic management software in Germany, France, Italy and Spain, 20% believe these tools reduce the need for empathy, compared with 12% who believe they increase it.

The OECD cautions that it is too early to draw firm conclusions from these findings. They are signals that require further monitoring rather than proof that AI is making human skills less relevant.

Can Generative AI Help Businesses Address Existing Skill Shortages?

Generative AI can help businesses manage some workforce and skills gaps, even as a lack of relevant skills continues to limit wider adoption.

The OECD report notes that nearly two in five SMEs experienced a worker shortage in the previous two years, while around one-third reported a lack of skills or experience among their employees.

Among SMEs that experienced a skills gap, nearly 40% said generative AI helped compensate for it. A quarter also reported that it helped address worker shortages.

This suggests that generative AI can support businesses facing gaps in their existing workforce. However, using the technology effectively still depends on employees having the skills to work with it.

The report also highlights the importance of making AI-relevant training accessible to SMEs and workers who may otherwise fall behind as workplace requirements change.

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Why Are Upskilling and Employer-Funded Training Becoming More Important?

Training is emerging as a key employer response to changing skill requirements, with evidence linking it to better workplace outcomes for AI users.

Many businesses are investing in retraining and upskilling their existing employees rather than relying only on hiring workers with new capabilities. The OECD reports that more than half of workers using AI receive employer-funded training.

The findings also indicate that training can make a difference to how employees experience AI adoption. Workers who receive training are more likely to report positive outcomes, including:

• Better job performance.

• Improved working conditions.

The report recommends continued monitoring of changing skill requirements and greater access to AI-relevant training. It also highlights the need for lifelong learning and reskilling, with responsibility shared among employers, workers and governments.

Training must also form part of a wider approach to workplace AI adoption. The OECD points to the importance of social dialogue, transparency, explainability and accountability. AI systems should be safe and secure, protect worker privacy and produce objective results without reinforcing labour-market bias or discrimination.

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Key Takeaways

• Manufacturing and finance: Around 40% of employers that have not adopted AI cite a lack of skills as a major barrier. More than half of employers that have adopted AI report a greater need for highly educated workers.

• Advanced AI expertise: Fewer than 1% of workers are expected to need advanced AI-specific skills such as programming or model development.

• Digital and data skills: The ability to use, analyse and interpret data is becoming more important across workplaces.

• Human and managerial skills: Problem-solving, creativity, innovation and managerial abilities remain important alongside technical knowledge.

• SME workforce: Nearly 40% of SMEs experiencing a skills gap say generative AI helps compensate for it.

• Employee training: More than half of workers using AI report receiving employer-funded training, and trained workers are more likely to report positive outcomes.

Generative AI is changing the skills required across industries, but the shift is not simply towards more technical expertise. The findings point to a workforce where digital literacy, data interpretation, problem-solving and human judgement matter alongside the ability to use AI tools. 

As businesses continue adopting the technology, investing in employee training and making reskilling opportunities accessible will be important. The organisations and workers that combine AI use with the ability to understand, evaluate and apply its outputs will be better placed to adapt to changing workplace demands.