The AI Divide: SMEs Caught Between Labor Shortages and Skills Gaps

# The AI Divide: SMEs Caught Between Labor Shortages and Skills Gaps

Artificial intelligence (AI) is emerging as a double-edged sword in the labor market. On the one hand, 40% of small and medium-sized enterprises (SMEs) are adopting it to address labor shortages, seeing it as a solution to structural recruitment difficulties. On the other hand, an identical percentage of employers across all sectors are hesitant to take the plunge, held back by a glaring lack of internal skills. This contradictory dynamic reveals a deep divide within OECD economies: an AI skills gap that isolates large, agile, and pioneering companies from SMEs that are struggling to keep up. This article aims to analyze the multiple dimensions of this divide, examine the training policies implemented to close it, and assess the impact of this gap on overall productivity.

The Great Divide: A Multifaceted Skills Gap

Data from the Organisation for Economic Co-operation and Development (OECD) paints an unequivocal picture of the situation. A 2025 report on AI adoption by SMEs highlights an alarming disparity: half of the SMEs surveyed in four G7 countries acknowledge that their employees lack the skills required to use generative AI. This finding is compounded by the fact that about a third of these same SMEs have faced labor shortages and a general lack of skills or experience among their staff over the past two years [1].

This skills deficit has a direct impact on adoption rates. In 2024, while 40% of large companies (with 250 or more employees) had integrated AI into their operations, only 11.9% of small businesses (10-49 employees) had done the same. This gap of nearly 30 percentage points illustrates a significant lag that is not limited to general adoption but extends to specific applications. SMEs are particularly behind in the use of technologies like autonomous robots, where the adoption gap with large companies is most pronounced (7.2% vs. 0.7%). The gap is slightly less pronounced for tools like natural language generation (16.7% vs. 4.6%), which suggests that SMEs are prioritizing the most accessible and least expensive AI applications [1].

Source: OECD, "AI adoption by small and medium-sized enterprises", 2025.

The purposes for using AI also differ. While large companies deploy AI in strategic areas such as R&D, logistics, or information systems security, SMEs are more likely to confine it to marketing or sales functions. An OECD survey from 2025 shows that, although a significant share of SMEs use generative AI, they do so mainly for peripheral tasks that support operations without radically transforming production processes. Only 29% of user SMEs report using it in their main activities [1]. This deficit in deep adoption and integration is a major brake on the competitiveness and innovation of SMEs.

Training Policies with Uneven Results to Bridge the Gap

Aware of the urgency, several governments have launched ambitious training programs to try to bridge this skills gap. The analysis of these initiatives, with their varied success, offers valuable lessons.

Singapore's Model: SkillsFuture

Singapore has established itself as a benchmark with its SkillsFuture program. In 2025, the country recorded record participation with 606,000 people engaged in training supported by SkillsFuture Singapore (SSG), an increase from 555,000 in 2024. Notably, more than 105,000 of these training courses were related to artificial intelligence, demonstrating the program's ability to align with the needs of a rapidly changing labor market. The program's success is based on a system of individual credits that has encouraged more than half of eligible Singaporeans (aged 30-75) to train. However, this individual success contrasts with a decline in employer-initiated training, attributed to a more conservative economic climate [2].

Germany and the Qualifizierungschancengesetz: A Promise Awaiting Confirmation

Germany has opted for a targeted approach with the Qualifizierungschancengesetz (Qualification Opportunities Act). This measure aims to finance the continuing training of employees whose jobs are directly threatened by digital transformation and automation. The support is particularly generous for very small businesses (fewer than 10 employees), which can obtain a full reimbursement of training costs. Although the initiative is promising, its actual effectiveness is still difficult to assess. According to the Institute for Employment Research (IAB), the law has not yet led to a significant jump in the number of subsidized training courses, and no complete official evaluation has been published to date [3].

The Compte Personnel de Formation (CPF) in France: A Two-Speed Tool

In France, the Compte Personnel de Formation (CPF) has succeeded in democratizing access to training for millions of people. It has proven particularly effective in disseminating basic digital skills and proficiency in common software. Nevertheless, the CPF is struggling to stimulate the acquisition of cutting-edge skills. In 2021, a paltry 90 trainees took artificial intelligence courses through this scheme. The analysis of the CPF also reveals that it reproduces the gender imbalances present in the French education system, with more specialized STEM fields remaining overwhelmingly male. The CPF therefore seems to be an excellent tool for a general upgrade, but it is not yet the right lever to train the AI experts the country needs [4].

Beyond Skills: Other Barriers to AI Adoption by SMEs

While the skills gap is the most frequently cited barrier, it is not the only one. The OECD report identifies three other major obstacles that hinder AI adoption by SMEs: connectivity, access to data and computing resources, and financing.

High-quality connectivity is the foundation of any digital transformation. However, significant disparities persist between urban and rural areas in many G7 countries, penalizing SMEs located outside major economic centers. Likewise, access to quality data and the computing power needed to train and deploy AI models remains a major challenge. Large companies have vast proprietary datasets and the means to invest in computing infrastructure, an overwhelming competitive advantage.

Finally, financing remains the sinews of war. SMEs face structural difficulties in accessing bank credit, due to asymmetric information, a lack of collateral, and a limited credit history. In a context of tightening credit conditions, financing long-term investments in emerging technologies like AI becomes an almost insurmountable challenge for many, forcing them to focus on short-term financing needs for their immediate survival [1].

The Productivity Paradox and the Threat of a Two-Speed Economy

The potential of AI to boost productivity is colossal. The OECD puts forward promising estimates, with annual labor productivity growth that could reach between 0.2 and 1.3 percentage points in G7 economies over the next decade. The highest gains are expected in the United States and the United Kingdom, while Japan and Italy could see more modest growth. Generative AI, in particular, is seen as a general-purpose technology capable of reinventing entire swathes of the economy [1].

However, this potential remains largely theoretical for a large part of the economic fabric. The productivity paradox, where a revolutionary technology fails to spread its effects throughout the economy, threatens to create a two-speed economy. On the one hand, large companies, with the necessary human and financial resources, are capitalizing on AI to increase their efficiency and market dominance. On the other, SMEs, unable to overcome the barriers to entry, risk stagnation and loss of competitiveness. This scenario is not only detrimental to SMEs themselves; it threatens the resilience and dynamism of the entire economy, which relies heavily on the vitality of its fabric of small and medium-sized enterprises.

OECD research shows that the most productive companies are also the ones that adopt AI the most. In France, in 2018, the adoption rate of companies in the top productivity decile was 40% higher than that of companies in the bottom decile. This gap reached 120% in Germany and even 240% in Italy in 2020. This correlation is partly explained by a selection effect: companies that are already more competitive and digitized are more inclined to adopt AI. But it also suggests that AI could become a multiplier of inequality, widening the gap between leaders and the rest [1].

Furthermore, the productivity gains linked to AI are not immediate. They often follow a J-curve: an initial drop in productivity due to investment and reorganization costs, followed by a rise once the complementary investments (training, new processes) bear fruit. SMEs, with their shorter time horizons and more limited resources, are less able to absorb this initial dip, which is an additional barrier to investment [1].

Conclusion: A Call for Coordinated Action for a Just Transition

The AI skills gap is not inevitable, but a market and public policy failure that requires a coordinated and multidimensional response. The examples of Singapore, Germany, and France, despite their imperfections, offer food for thought. It is clear that there is no one-size-fits-all solution. Training policies must be both ambitious in their objectives and finely targeted in their implementation. They must aim not only to improve basic digital skills for the entire workforce, but also to cultivate cutting-edge AI expertise within a pool of specialized talent.

To ensure that SMEs are not left behind in the AI revolution, it is imperative to go beyond training alone. Resolute action is needed to improve connectivity throughout the country, to democratize access to data and computing infrastructure, and to create innovative financing mechanisms tailored to the needs of SMEs. This could involve the creation of dedicated investment funds, sectoral data-sharing platforms, or shared competence centers where SMEs could experiment with AI at a lower cost.

The stakes are high. It is a matter of ensuring that the promise of productivity and innovation from AI is a rising tide that lifts all boats, not a wave that only benefits the largest ships. The future of our economies' competitiveness and social cohesion depends on it. The time for observation is over; it is time for action.

References

[1] OECD. (2025). AI adoption by small and medium-sized enterprises. https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/12/ai-adoption-by-small-and-medium-sized-enterprises_9c48eae6/426399c1-en.pdf

[2] The Straits Times. (2026, February 9). Over 1 in 2 S’poreans aged 30 to 75 used SkillsFuture credit, surge driven by year-end deadline: SSG. https://www.straitstimes.com/singapore/parenting-education/1-in-2-sporeans-aged-30-to-75-used-skillsfuture-credit-surge-driven-by-year-end-deadline-ssg

[3] Cedefop. (n.d.). Qualification Opportunities Act. https://www.cedefop.europa.eu/en/tools/matching-skills/all-instruments/qualification-opportunities-act

[4] Bruegel. (2023, December 20). Promoting STEM skills: a brief assessment of French individual learning accounts. https://www.bruegel.org/analysis/promoting-stem-skills-brief-assessment-french-individual-learning-accounts