Open ecosystems—not technological dependence—will define the region’s AI future By: Christiaan Smits, Head of Public Policy EMEA at Cloudflare.
Artificial intelligence is rapidly becoming a defining factor in national competitiveness. Across the Middle East and Africa, governments increasingly view AI not only as a tool for innovation but also as a strategic capability that will shape economic growth, digital resilience, and public service transformation. Yet as AI adoption accelerates, policymakers are confronting an important question: who ultimately controls the technologies powering the AI economy? Today, most foundational AI models and large-scale computing infrastructure are developed primarily in the United States and China. For many countries across the Middle East and Africa, the challenge is not necessarily to build every layer of the AI ecosystem domestically. Instead, the priority is ensuring strategic control over how AI technologies are deployed, governed, and integrated into national economies. Rather than pursuing technological isolation, governments across the region are increasingly focusing on a pragmatic goal: building resilient and open AI ecosystems that avoid unilateral dependencies while preserving flexibility and choice.
AI as a driver of economic transformation: Artificial intelligence is already central to many national economic strategies across the region. Countries such as the United Arab Emirates and Saudi Arabia have placed AI at the heart of long-term development plans like the UAE’s National Artificial Intelligence Strategy 2031 and Saudi Arabia’s Vision 2030. These initiatives aim to accelerate innovation, improve public services, and diversify economies beyond traditional sectors. The economic potential is significant. According to PwC, artificial intelligence could contribute up to $320 billion to Middle Eastern economies by 2030, with the UAE and Saudi Arabia expected to capture a large share of that value. At the same time, McKinsey estimates that AI could add up to $1.2 trillion to Africa’s GDP by 2030 through productivity gains and new digital services. However, as AI becomes embedded in critical sectors—from financial services and healthcare to national infrastructure—governments are also becoming more aware of the risks associated with over-reliance on external technology ecosystems. This has led to growing discussions around AI autonomy, or the ability for countries to shape how AI technologies are used within their economies.
Strategic control through freedom of choice: In practice, AI autonomy does not mean building every component of the technology stack locally. Only a handful of countries have the resources to independently develop the full ecosystem—from semiconductor manufacturing to large-scale model training infrastructure. Instead, autonomy increasingly means maintaining freedom of choice. Governments, enterprises, and institutions should be able to choose the technologies that best fit their needs, retain control over their data, and deploy AI solutions without becoming locked into a single provider or technological approach. Achieving this requires diversified infrastructure, open standards, and resilient digital supply chains that allow multiple platforms and providers to coexist. This diversity helps foster innovation while reducing the risks associated with technological dependence.
Infrastructure beyond hyperscale data centers: Global discussions about AI infrastructure often focus on massive, centralized data centers used to train large AI models. While these facilities are important, they represent only one part of the infrastructure required for widespread AI adoption. Many real-world AI applications depend on distributed computing infrastructure located closer to users. Edge networks—where computing resources are deployed across geographically distributed locations—enable AI applications to operate with low latency, high reliability, and strong security. This architecture is particularly valuable for use cases such as smart cities, financial transactions, healthcare diagnostics, and industrial automation. Demand for such infrastructure is growing rapidly. According to IDC, spending on edge computing across the Middle East and Africa is expected to grow at more than 15 percent annually through 2028, driven by the need for real-time data processing and AI-enabled services.
Expanding access to AI innovation: Ensuring that AI benefits the broader economy—not just large corporations—is another key priority across the region. Small and medium-sized enterprises represent more than 90 percent of businesses in the Middle East and Africa, according to the World Bank, yet many face barriers when adopting advanced digital technologies. Usage-based computing models can help address this challenge. Serverless platforms and pay-per-use services allow organizations to experiment with AI solutions without making large upfront infrastructure investments. Governments are also investing heavily in AI ecosystems. Saudi Arabia’s National Strategy for Data and AI aim to position the kingdom among the world’s leading AI economies, while the UAE’s national AI strategy focuses on integrating artificial intelligence across government services and industries. Across Africa, initiatives such as Smart Africa and the African Union’s Continental AI Strategy are supporting digital infrastructure and innovation ecosystems across the continent.
