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AI use is consistently lower among SMEs than among large firms across all G7 countries. The adoption gap between large firms and SMEs is evident across AI technologies and applications. In fact, across the OECD, the share of large firms using AI (40%) is more than three times that of small firms (11.9%). Sectoral differences are pronounced, with information and communication technologies (ICT) and professional services leading in adoption. Similar patterns of sectoral heterogeneity are evident across G7 countries. Overall, the adoption rate of AI by firms is increasing but remains relatively low compared to other digital technologies. Between 2020 and 2024, the share of firms using AI rose from 5.6% to 14% across OECD Member countries.
AI adoption has the potential to enhance productivity of SMEs. An increasing body of evidence suggests a strong positive association between AI use and firms' productivity. At the macroeconomic level, a recent OECD study estimates potential gains from AI ranging from 0.2 to 1.3 percentage points in annual labour productivity growth across G7 economies over the next decade. Generative AI in particular shows promise as a general-purpose technology, but its potential has yet to be fully realised. Maximising aggregate productivity will require not just broader adoption, particularly among SMEs, but also complementary investments in workers’ skills and other assets that enable firms to realise the full benefits of AI.
This discussion paper identifies four critical enablers for AI adoption in SMEs: i) connectivity, ii) AI-enabling inputs, iii) skills, and iv) finance. While there has been progress in the expansion of high- quality connectivity across OECD Member countries, including G7 members, disparities remain across firms of different sizes and between urban and rural areas. Access to AI-enabling inputs, such as quality datasets and compute resources is essential for adopting certain types of AI applications and for improving SMEs’ ability to develop custom AI tools to harness AI’s innovative potential. Skill shortages are a major barrier to AI adoption, consistently identified by SMEs as one of the main hurdles they face. Financial constraints hinder long-term investment, prompting governments implement policies and actions that improve and expand access to a broad range of financing instruments and fintech, enabling SMEs to secure the resources needed for digital transformation.
Recognising different SME adoption profiles can help design more targeted and effective policy support. The discussion paper proposes a taxonomy for AI adoption in SMEs, distinguishing four categories of SME adopters according to their digital maturity, complexity of AI use, and the scope of AI application: AI Novices, AI Explorers, AI Optimisers, and AI Champions. This taxonomy can support governments in designing targeted policy design aligned with the specific needs and capabilities of SMEs at different stages of their AI adoption journey.
Case studies of SMEs from G7 countries illustrate diverse adoption pathways. AI Novices typically rely on embedded tools for peripheral tasks, while AI Optimisers integrate multiple tools across functions. AI Explorers develop bespoke solutions, and AI Champions embed AI across operations and strategy.
Despite the benefits of using AI, SMEs across all categories experience challenges and risks related to AI adoption, and reported issues concern accuracy, harmful content, and legal uncertainty.
G7 governments are implementing multi-pronged strategies and diverse sets of policy measures to enhance AI adoption by SMEs, addressing the four key enablers identified in this paper. Common instruments include infrastructure investments, skills development programmes, financial support, data access initiatives, and regulatory guidance. Country-specific programmes reflect national priorities but share a common goal of enabling inclusive and productive AI diffusion across SMEs.
Further policy efforts can help accelerate adoption of AI among SMEs and contribute to closing the gaps between small and large firms. Policymakers should enhance connectivity, facilitate access to digital resources and AI inputs, raise awareness of potential use cases, benefits and risks of AI, and strengthen workforce capabilities through targeted training. Improving investment readiness, expanding financing options and tailoring interventions to diverse SME profiles are essential, alongside promoting AI use in core business functions. Enhanced international co-operation on AI, aligned with the OECD AI Principles, will promote knowledge-sharing, certainty, and harmonisation of regulatory approaches and technical standards, helping accelerate adoption by firms of all sizes.
AI use is consistently lower among SMEs than among large firms across all G7 countries. The adoption gap between large firms and SMEs is evident across AI technologies and applications. In fact, across the OECD, the share of large firms using AI (40%) is more than three times that of small firms (11.9%). Sectoral differences are pronounced, with information and communication technologies (ICT) and professional services leading in adoption. Similar patterns of sectoral heterogeneity are evident across G7 countries. Overall, the adoption rate of AI by firms is increasing but remains relatively low compared to other digital technologies. Between 2020 and 2024, the share of firms using AI rose from 5.6% to 14% across OECD Member countries.
AI adoption has the potential to enhance productivity of SMEs. An increasing body of evidence suggests a strong positive association between AI use and firms' productivity. At the macroeconomic level, a recent OECD study estimates potential gains from AI ranging from 0.2 to 1.3 percentage points in annual labour productivity growth across G7 economies over the next decade. Generative AI in particular shows promise as a general-purpose technology, but its potential has yet to be fully realised. Maximising aggregate productivity will require not just broader adoption, particularly among SMEs, but also complementary investments in workers’ skills and other assets that enable firms to realise the full benefits of AI.
This discussion paper identifies four critical enablers for AI adoption in SMEs: i) connectivity, ii) AI-enabling inputs, iii) skills, and iv) finance. While there has been progress in the expansion of high- quality connectivity across OECD Member countries, including G7 members, disparities remain across firms of different sizes and between urban and rural areas. Access to AI-enabling inputs, such as quality datasets and compute resources is essential for adopting certain types of AI applications and for improving SMEs’ ability to develop custom AI tools to harness AI’s innovative potential. Skill shortages are a major barrier to AI adoption, consistently identified by SMEs as one of the main hurdles they face. Financial constraints hinder long-term investment, prompting governments implement policies and actions that improve and expand access to a broad range of financing instruments and fintech, enabling SMEs to secure the resources needed for digital transformation.
Recognising different SME adoption profiles can help design more targeted and effective policy support. The discussion paper proposes a taxonomy for AI adoption in SMEs, distinguishing four categories of SME adopters according to their digital maturity, complexity of AI use, and the scope of AI application: AI Novices, AI Explorers, AI Optimisers, and AI Champions. This taxonomy can support governments in designing targeted policy design aligned with the specific needs and capabilities of SMEs at different stages of their AI adoption journey.
Case studies of SMEs from G7 countries illustrate diverse adoption pathways. AI Novices typically rely on embedded tools for peripheral tasks, while AI Optimisers integrate multiple tools across functions. AI Explorers develop bespoke solutions, and AI Champions embed AI across operations and strategy.
Despite the benefits of using AI, SMEs across all categories experience challenges and risks related to AI adoption, and reported issues concern accuracy, harmful content, and legal uncertainty.
G7 governments are implementing multi-pronged strategies and diverse sets of policy measures to enhance AI adoption by SMEs, addressing the four key enablers identified in this paper. Common instruments include infrastructure investments, skills development programmes, financial support, data access initiatives, and regulatory guidance. Country-specific programmes reflect national priorities but share a common goal of enabling inclusive and productive AI diffusion across SMEs.
Further policy efforts can help accelerate adoption of AI among SMEs and contribute to closing the gaps between small and large firms. Policymakers should enhance connectivity, facilitate access to digital resources and AI inputs, raise awareness of potential use cases, benefits and risks of AI, and strengthen workforce capabilities through targeted training. Improving investment readiness, expanding financing options and tailoring interventions to diverse SME profiles are essential, alongside promoting AI use in core business functions. Enhanced international co-operation on AI, aligned with the OECD AI Principles, will promote knowledge-sharing, certainty, and harmonisation of regulatory approaches and technical standards, helping accelerate adoption by firms of all sizes.
