SDG 8: Decent Work and Economic Growth | SDG 9: Industry, Innovation and Infrastructure | SDG 17: Partnerships for the Goals
Ministry of Electronics & IT (MeitY) | NITI Aayog | Ministry of Skill Development and Entrepreneurship
The OECD Working Paper (2026/01), titled ‘Digital technology diffusion in the age of AI: Cross-country evidence from microdata’ discusses the adoption of advanced digital tools as a multi-layered process that depends heavily on foundational infrastructures and human expertise. It establishes that advanced technologies such as Artificial Intelligence (AI), big data analysis, and the Internet of Things (IoT) are rarely adopted in isolation. These high-level tools typically build upon enabling “foundational” technologies, including cloud computing, Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP) systems. This creates a critical path-dependency where a firm’s prior level of digitalization directly determines its ability to effectively integrate and benefit from advanced AI-driven systems.
Sectoral Specialization and Firm-Level Disparities
The diffusion of these technologies exhibits significant sectoral heterogeneity across the 15 OECD countries studied.
Sectoral Concentration: AI and big data are most prevalent in the ICT and professional services sectors, whereas robotics and 3D printing are concentrated within manufacturing and utilities.
The Size Gap: Larger firms are substantially more likely to adopt advanced digital technologies, with large firms being on average 20 percentage points more likely to adopt AI than small firms, likely due to superior scale advantages and financial capacity.
Productivity Gains: Adopters of advanced technologies generally exhibit higher productivity than non-adopters, with the notable exception of 3D printing.
Human Capital as the Primary Enabler
The report identifies human capital—specifically ICT skills, training, and education—as a fundamental prerequisite for technological diffusion.
Skilled Workforce: Firms with higher shares of tertiary-educated workers and employees in technical “techie” occupations are significantly more likely to adopt advanced tools.
Complementary Investments: The research highlights that productivity premia often diminish when controlling for human and technological capital, suggesting that technology alone cannot drive growth without accompanying investments in specialized skills and organizational changes.
Policy Imperative: For policymakers, this underscores the need for a comprehensive policy mix that balances digital infrastructure development with aggressive upskilling for both ICT specialists and the general workforce.
What is “Distributed Microdata Analysis” in the context of global technology tracking? Distributed microdata analysis is a decentralized research methodology where researchers run a common statistical code on national firm-level datasets in their respective countries. This allows for the generation of harmonized cross-country insights while strictly complying with data confidentiality constraints, as the sensitive raw data never leaves the national statistical offices.
Policy Relevance
Although the paper focuses on 15 OECD countries, its findings serve as a strategic roadmap for India’s Digital India and Skill India initiatives.
Strategic Infrastructure: Strengthening high-speed broadband and cloud services is a mandatory prerequisite for enabling Indian firms to move into high-value AI and big data analytics.
SME Support: India must address the scale barriers faced by its vast SME base through financial incentives and shared access to foundational digital tools.
Deep-Tech Skilling: Policy attention should shift toward deep-tech education to ensure the workforce can manage AI-driven manufacturing and services effectively.
Interoperable Governance: As India expands its AI footprint, international cooperation on trustworthy AI governance is essential to maximize economic returns and minimize risks.
Follow the full paper here: Digital technology diffusion in the age of AI: Crosscountry evidence from microdata