Artificial intelligence is no longer a novelty in the global business landscape. Yet for Mexico’s small and medium-sized enterprises (SMEs), the challenge has shifted from access to adoption. As international supply chains reconfigure and nearshoring opportunities expand, the ability of Mexican SMEs to integrate AI into core operations—not just peripheral tasks—has become a critical determinant of national competitiveness.
A recent OECD report, prepared under the Canadian presidency of the G7, underscores a persistent structural gap: while 40% of large firms (250+ employees) across OECD countries report using AI, only 11.9% of small firms (10–49 employees) do so. Even among SMEs that have adopted generative AI, just 29% apply it to core business functions. In Mexico, where SMEs form the backbone of industrial supply chains, this disparity risks capping the productivity gains expected from nearshoring.
Between 2020 and 2024, AI adoption among firms with ten or more employees in OECD economies rose from 5.6% to 14%. However, less than 10% of G7 firms used AI in production-related functions in 2024, with Japan reporting as low as 1.9%. This trend reflects a broader pattern: AI is often deployed at the margins—chatbots, document drafting—rather than embedded into operational processes such as inventory optimization or predictive maintenance.
AI adoption is no longer about access—it’s about embedding intelligence into the operational core of Mexico’s industrial SMEs.
The OECD’s SME AI Adoption Blueprint, endorsed by G7 industry and digital ministers in December 2025, offers a taxonomy to guide policy: firm maturity, use-case complexity, and integration scope. For Mexico, this framework highlights that generic interventions—like software subsidies or broad training programs—are insufficient. What SMEs require are foundational enablers: reliable connectivity, access to computing infrastructure, data readiness, and workforce training tailored to specific business functions.
Open-source AI tools may offer a cost-effective entry point, helping SMEs avoid vendor lock-in and experiment with minimal investment. But without integration into real business processes—such as reducing scrap rates or improving machine efficiency—their impact remains limited. Moreover, short-term return-on-investment expectations can deter firms from undertaking the process redesigns necessary for meaningful AI integration.
The stakes are macroeconomic. The OECD estimates that AI could add between 0.2 and 1.3 percentage points to annual labor productivity growth in G7 economies over the next decade. For Mexico, realizing even a fraction of this potential depends on whether thousands of SMEs can move from experimentation to transformation. That means shifting from using AI to write emails to deploying it in quality control or logistics optimization—areas where inefficiencies are both measurable and costly.
Absent targeted support mechanisms—patient capital attuned to long adoption cycles, training aligned with operational roles, and infrastructure that supports edge or cloud computing—SMEs risk falling further behind. In a nearshoring context where responsiveness, quality, and efficiency are paramount, this lag could blunt Mexico’s industrial upgrading ambitions. The challenge is not adopting AI for the sake of innovation but embedding it where it counts: in the daily routines that define productivity.
