2025年生成式人工智能与中小企业劳动力研究报告_53页_2mb

Summary of "Generative AI and the SME Workforce: New Survey Evidence"

Core Content

This report by the OECD presents new survey data on the adoption and impact of generative AI among Small and Medium-sized Enterprises (SMEs) in seven OECD countries: Austria, Canada, Germany, Ireland, Japan, Korea and the United Kingdom. It explores how SMEs are using generative AI, its effects on skill and labour needs, and the extent to which SMEs are preparing their workforce for AI integration.

Main Findings

Generative AI Adoption

• Demographics: Over 5,232 SMEs were surveyed, with generative AI adoption ranging from 24% in Japan to 39% in Germany.
• Sectoral Use: Generative AI is most commonly used in the information and communication sector, with 42% of SMEs in this sector using it.
• Company Size: Larger SMEs are more likely to use generative AI than smaller ones. For example, one-person companies are half as likely to use generative AI as the largest SMEs.
• Task Types: Generative AI is mostly used for peripheral tasks such as marketing, sales, and content creation, rather than core business activities.

Impact on Labour and Skills

• Skill Gaps: Generative AI helps SMEs compensate for skill shortages. Among SMEs that use it and have experienced a skill gap, 39% report that it has helped. This rises to 46% where AI is reported to improve employee performance.
• Workload Reduction: For one-third of SMEs, generative AI has reduced staff workload, and for 14%, it has reduced reliance on external contractors.
• Skill Needs: Despite AI automation, SMEs associate generative AI with increased skill needs. Twice as many SMEs (20%) say it increases skill needs compared to those (9%) who say it decreases them.
• Important Skills: Data analysis and interpretation, as well as creativity and innovation, are seen as the most important skills that have become more relevant due to generative AI.

Barriers to Adoption

• Unsuitability: The most common barrier to using generative AI is that it is not suited to the SME's work, reported by 57% of non-adopters.
• Concerns: Other barriers include concerns about copyright, legal, and regulatory issues (54%), concerns about data privacy (52%), and lack of employee skills (50%).
• Attitudes: 86% of SMEs hold neutral or positive attitudes towards generative AI, while only 2% prohibit its use.
• Training and Guidelines: Only a third or fewer of SMEs using generative AI are taking measures to train staff, set internal guidelines, or research legal and regulatory issues.

Government Role

• Governments can help SMEs leverage generative AI's potential by supporting training programs, financial assistance, information campaigns, and business mentoring.
• SMEs are particularly interested in government-led initiatives to improve AI literacy and address the challenges of AI adoption.

Key Information

• SMEs as Labour Market Pillars: SMEs account for over 99% of companies and 60% of business sector employment in OECD economies.
• Potential Benefits: Generative AI is perceived as a tool to enhance employee performance, reduce workload, and compensate for skill gaps.
• No Job Reduction: Generative AI does not appear to lead to job cuts. 83% of SMEs report no change in overall staff need.
• Digital Divide: SMEs are less likely to adopt AI than large companies, which are more than twice as likely to use AI tools.
• Growth of Generative AI: Generative AI has seen rapid growth since its inception, with USD 18 billion in global venture capital investments in 2023 and USD 20 billion in 2024.

Conclusion

This report highlights the growing role of generative AI in SMEs and its potential to enhance productivity and performance. However, it also underscores the digital and skill gaps between SMEs and larger firms, as well as the need for government support to help SMEs effectively adopt and integrate AI technologies. The findings aim to inform policy decisions to ensure that the benefits of generative AI are widely shared across the workforce and the economy.