AI is creating new market access barriers to trade

The Organization for Economic Co‑operation and Development (OECD) predicts that artificial intelligence could increase global exports by as much as 40% by 2040. As AI tools are adopted across industries and geographies, they are reshaping how business and government leaders approach trade, economic growth, and national security.

At the same time, a new class of market access barriers is emerging. Unlike traditional barriers such as tariffs or physical borders, these obstacles stem from regulatory fragmentation, unequal access to digital infrastructure, and uneven availability of capital and advanced computing resources. AI adoption in advanced economies is expanding nearly twice as fast as in developing countries. As a result, countries and firms capable of building and deploying AI systems are pulling further ahead of those that cannot.

This growing divide is creating structural challenges for global trade and digital supply chains. Governments and supply chain leaders must now navigate a world in which competitiveness increasingly depends on access to data, computing power, and AI capabilities.

The AI divide and the cost of keeping up

As artificial intelligence adoption accelerates, an AI divide is emerging between organizations and countries that can deploy these technologies and those that cannot. This divide is creating a new set of operational and strategic challenges for supply chain and government leaders worldwide.

Building and running AI systems requires substantial capital investment. Data centers depend on components whose costs have risen roughly 40% over the past five years. Access to electrical grid connections is also becoming increasingly constrained. In some regions, connection waitlists now extend up to seven years.

Several countries—including Singapore, Ireland, China, the Netherlands, and Canada—have introduced restrictions on data center development, location, or energy use in response to concerns about power demand and environmental impact. At the same time, electricity demand from AI computing infrastructure is projected to grow dramatically over the next decade.

Advanced economies and large enterprises are far better positioned to absorb these costs. Many emerging economies and smaller firms lack the infrastructure, capital, and technical talent required to deploy AI systems at scale. According to the OECD, approximately 66% of firms in high-income economies report using AI, compared with 27% in lower-income economies.

This widening capability gap will increasingly shape supply chain performance and global trade flows. Regions with strong AI capabilities are likely to see greater efficiency, faster innovation, and improved resilience. In contrast, regions that lag in digital infrastructure may experience slower productivity gains and persistent operational bottlenecks.

Dive deeper

Dravida Seetharam and Sarah Lahti join the Talking Supply Chain podcast to talk about AI-driven trade barriers.

For governments, the implications are equally significant. Countries that fail to invest in digital infrastructure and AI capacity risk slower economic growth and declining competitiveness in global markets.

Cybersecurity risks

The rapid integration of AI into supply chains is also increasing cybersecurity risks.

The World Economic Forum’s Global Cybersecurity Outlook report found that 87% of respondents identified AI-related vulnerabilities as the fastest-growing cyber risk. In addition, 64% of organizations now account for geopolitically motivated cyberattacks in their risk mitigation strategies.

As AI becomes embedded in logistics platforms, digital trade systems, and cross-border data exchanges, the attack surface for cybercriminals expands. AI tools can also be exploited by attackers to conduct more targeted and automated attacks.

For supply chain leaders, this means cybersecurity strategies must evolve rapidly. Risk management frameworks, security monitoring systems, and governance policies must continuously adapt to an operating environment where both threats and technologies are advancing quickly.

How governments are responding

Governments around the world are still determining how best to manage the economic and security implications of AI.

According to the Global Cybersecurity Outlook survey, 31% of respondents reported lacking confidence in their country’s ability to respond to a major cyber incident, up from 26% the previous year. This uncertainty has prompted many policymakers to explore stronger digital sovereignty measures.

Europe, for example, has launched EuroStack, an initiative aimed at reducing dependence on foreign technology providers and strengthening regional digital infrastructure.

While such policies may improve security and technological independence, they also risk fragmenting the global digital economy. Differing regulatory frameworks for AI systems, data governance, and cloud infrastructure can create additional compliance burdens for companies operating internationally.

A joint survey conducted by the World Trade Organization and the International Chamber of Commerce found that nearly one-fifth of surveyed firms identified regulatory fragmentation and uncertainty as major barriers to adopting AI technologies.

If regions pursuing digital autonomy cannot match the resources currently provided by global technology ecosystems, they may inadvertently restrict access to the large datasets and computing scale required to fully realize AI’s economic benefits.

Steps to mitigate AI-driven market access barriers

While many AI-related barriers are driven by national policy and infrastructure constraints, supply chain leaders can take several proactive steps to protect their organizations’ market access and operational resilience.

The strength of a supply chain depends on the capabilities of every partner within it. Organizations should assess the digital readiness of key suppliers, logistics providers, and markets to identify vulnerabilities before they become operational failures. Equal attention should be given to strengthening internal capabilities by ensuring supply chain professionals are equipped to work effectively with AI tools, data systems, and digital risk frameworks.

Companies should also diversify their digital infrastructure partnerships (for example, by adopting regional cloud strategies) to reduce exposure to regulatory or geopolitical disruptions. Organizations that address these priorities with urgency will be better positioned to manage disruptions and sustain a competitive advantage in the years ahead.

Conclusion

Artificial intelligence is reshaping the foundations of global trade faster than many organizations can adapt. Rising infrastructure costs, the widening AI divide, growing cybersecurity risks, and increasingly fragmented regulatory environments are all creating new challenges for supply chain leaders.

There is no single solution that addresses all of these issues. However, leaders who recognize these emerging barriers and proactively adjust their strategies will be better positioned to maintain competitiveness and resilience.

As AI continues to transform the global economy, the distinction between countries and companies that help shape the rules and those that simply follow them may prove as important as the technology itself.

About the authors

Dravida Seetharam, is a fellow at the Center for Global Enterprise.

Sarah Lahti is the Director of Operations and Program Management for the Digital Supply Chain Institute.

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