Making AI Work: Why Investing in Skills MattersSkills will be a key driver of AI’s impact on productivity and growth.
- GAB NEWS
- 2 days ago
- 5 min read
Skills will be a key driver of AI’s impact on productivity and growth. This blog examines how skills gaps are slowing adoption, how AI is reshaping employer demand, and why training is the key to ensuring AI delivers better outcomes for businesses and workers alike.
Artificial intelligence is often discussed in terms of breakthroughs, algorithms and investment figures. Yet as AI continues to transform workplaces, the decisive factor in whether economies benefit from this shift may not be the technology itself, but the skills of the people using it.
Recent evidence highlights where a lack of skills is holding back the use of AI, how AI is changing the kinds of skills employers are looking for, and why training has become the single most important lever for ensuring that AI boosts productivity while also improving outcomes for workers.
AI is ready, but the workforce isn’t
Many businesses, especially small to medium enterprises (SMEs), report that their ability to use AI is constrained by a lack of people with the right skills. Around 40% of employers in manufacturing and finance who have not yet adopted AI say that skills are the main reason, as do more than half of SMEs that are not yet using generative AI. If AI-related skills are not developed across the workforce, only a small group of well-equipped firms will reap the benefits.
Making training more widely available, especially for small businesses and workers who have fewer chances to learn new skills (e.g. low-skilled workers), will be essential to prevent the AI transition from deepening existing inequalities.
AI use increases with company size
% of companies reporting use of AI, by country and company size
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The chart has 1 Y axis displaying values. Data ranges from 2.24 to 70.4.
250 or more employees10 to 49 employeesFinlandBelgiumDenmarkKoreaSloveniaSwedenNetherlandsNorwayIrelandAustriaGermanyLuxembourgSpainPortugalCzechiaEstoniaLatviaPolandFranceItalyLithuaniaSlovak RepublicSwitzerlandNew ZealandCanadaGreeceHungaryTürkiyeJapanColombiaUnited StatesUnited KingdomAustraliaIsrael0255075
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Yet AI can help address skill shortages
Many firms are struggling to find workers with the skills they need. In fact, nearly two in five SMEs report having faced a worker shortage in the past two years, while a third report a lack of skills or experience among staff. Generative AI helps fill these gaps: nearly 40% of SMEs that experienced a skills gap say that generative AI helps compensate for it, and a quarter said it helps compensate for a worker shortage. So AI isn’t only creating demand for new skills, it’s also helping businesses manage existing shortages.
AI is raising the skill bar
As more employers adopt AI, many report that it is increasing the demand for highly skilled workers. In manufacturing and finance for example, more than half of employers that had adopted AI said it had increased the need for highly educated workers. This fits with broader evidence showing that employment has grown fastest in roles that are most exposed to AI. In these jobs, AI tends to complement rather than replace employees work and increase demand for higher-level skills. For policymakers, the implication is clear: countries that invest in developing a strong pipeline of high-skilled workers will be better placed to turn AI adoption into productivity gains and economic growth.
Most managers say algorithmic management increases the need for data analysis and digital skills
% of managers using AM tools saying skills become less/more important
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The chart has 1 Y axis displaying values. Data ranges from 2.6 to 65.97.
More importantLess important05101520253035404550556065Ability to use or interpret dataDigital skillsProblem solving skillsGeneral managerial skillsConflict resolution skillsCommunication skillsEmpathyActive listening skills
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Note: Algorithmic management is the use of technological tools which may include artificial intelligence (AI), to fully or partially automate tasks traditionally carried out by human managers
It's not just about coding: what are the skills needed to work with AI?
When people think about AI skills, they often think of coding, algorithms and data scientists. In reality, only a very small share of workers (less than 1%) will ever need advanced AI-specific skills such as programming or model development. Instead, AI is increasing the importance of digital skills, and the ability to use, analyse and interpret data.
Alongside this, managerial skills and human skills such as problem-solving, creativity and innovation will remain essential, helping workers apply AI effectively in real-world settings. Keeping track of how these skill demands evolve will be crucial for shaping education, training and skills policies, and for ensuring that workers are equipped to use AI confidently and responsibly at work.
Most SMEs say GenAI increases the need for data analysis and creativity skills
% of SMEs saying skills become less/more important due to GenAI
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Bar chart with 2 data series.
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The chart has 1 X axis displaying categories.
The chart has 1 Y axis displaying values. Data ranges from 13 to 46.4.
More importantLess important051015202530354045Data analysis and interpretationCreativity and innovationProgramming and codingCommunication and collaborationClerical and administrationCritical thinking and problem solvingCustomer service and Sales
End of interactive chart.
The dehumanisation of work?
Social and emotional skills, such as empathy, communication and teamwork remain essential in many jobs. However, there are some signals that in parts of Europe the demand for certain social skills may decline as AI becomes more widely used in the workplace. For example, in Germany, France, Italy and Spain, managers are more likely to believe that algorithmic management tools reduce their need for empathy (20%) rather than increase it (12%).
This raises an important and perhaps uncomfortable question: could AI be contributing to a gradual dehumanisation of work?
It is still too early to draw firm conclusions, and such signals should be interpreted with caution. That said, they highlight the need to monitor not only how AI affects productivity and skills, but also how it reshapes the human side of work, such as job quality, social interaction and well-being.
Training is effective and employers know it
Faced with changing skill needs, most employers are not standing still. Many firms are investing in retraining and upskilling their existing workforce, and more than half of workers using AI report receiving employer-funded training.
This investment pays off. Workers who receive training are far more likely to report positive outcomes, including better job performance and improved working conditions . This confirms that training is one of the most effective tools for improving both business productivity and worker well-being. When skills investment is neglected, AI adoption becomes patchy, widening gaps between firms and resulting in worse outcomes for workers.
There are still unanswered questions about exactly which skills will be needed in the future, and how best to develop them. But one point is already clear: without the right skills, AI cannot deliver on its promise to boost productivity, improve job quality, and support economic growth.
That means policy efforts should focus on:
· keeping a close eye on how skill needs are changing;
· making AI-related skills more widely available, especially for small and medium-sized firms as well as workers at risk of falling behind (e.g. the low skilled); and
· supporting lifelong learning and reskilling, with responsibility shared between employers, workers and governments.
Getting skills policy right will be essential to ensuring that AI adoption is not only faster, but also fairer and more sustainable.
Published by Raphael Amorim
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