FEATURE Business & partnerships
By investing in domestic infrastructure, securing data assets, and enabling seamless access to information, nations can reduce external dependency while fostering innovation from within
PROTECTING OUR DIGITAL FUTURE
Ravi Shankar, Senior Vice President and Chief Marketing Officer, Denodo, explores how AI Sovereignty is the next frontier of global power, as the Artificial Intelligence race gathers speed
A
s the global race for artificial intelligence (AI) accelerates, a new form of geopolitical and economic competition is
emerging; AI sovereignty. AI sovereignty is broader than data residency alone. It encompasses control over the infrastructure, governance frameworks, compute power, and data ecosystems that underpin AI innovation. While discussions around sovereignty have traditionally focused on data residency and governance, the conversation must now expand to include control over computing infrastructure, data ecosystems, and the ability to innovate at scale. Nations that fail to act decisively risk becoming structurally dependent on a handful of dominant players, most notably the United States, which is rapidly consolidating its leadership in AI infrastructure.
The scale of that dominance is hard to
ignore. The United States currently operates more than 5,000 data centres, with an additional 3,000 more being planned; this is more than the rest of the world combined. Because AI is inherently compute-intensive, countries without sufficient domestic infrastructure face a binary choice: constrain their ambitions or outsource their AI workloads to foreign providers. That dependency comes with risks. Outsourcing compute limits innovation velocity, and organisations also face latency, cost pressures, and regulatory friction. More critically, reliance on foreign infrastructure introduces systemic exposure. With shifting geopolitical dynamics, access to essential compute resources could become a lever of influence, restricting not just growth, but sovereignty itself.
28 October 2026 | Automation For the UK, this challenge has become
increasingly strategic. The government’s ambition to position Britain as a global AI leader will depend not only on world-class research and talent, but also on ensuring organisations have access to the compute infrastructure needed to develop and deploy AI domestically. Without sufficient sovereign capability, even the most innovative businesses risk becoming dependent on overseas providers for the computing power that underpins AI. But compute is only half the equation. Data is the lifeblood of AI, and it introduces an equally complex set of challenges. Many countries have enacted stringent data sovereignty regulations that mandate keeping sensitive data within national borders. Yet if compute resides elsewhere, how can those countries reconcile compliance with their AI ambitions? Moving data across borders increases exposure to privacy risks, governance challenges, and foreign legal frameworks. Retaining data within national boundaries is not just about compliance; it is about ensuring that citizen data is not repurposed to train external models or subjected to jurisdictional overreach. In the UK emphasis is already growing
around responsible AI governance. The challenge now is enabling organisations to make better use of sensitive data for AI while maintaining public trust, security and regulatory compliance. As AI adoption accelerates, data governance will become an increasingly important competitive advantage, rather than simply a compliance requirement. The macroeconomic implications are equally
profound. AI is expected to expand from contributing just 0.3% of U.S. GDP today to more than 15% within the next decade. This represents one of the largest redistributions of economic
value in modern history. Countries that are constrained in how they operationalise AI will inevitably limit their share of this opportunity. Building modern AI infrastructure is capital-intensive, with hyperscale data centres in the U.S. costing upwards of US$5 billion each. Technology giants are investing at unprecedented levels. Microsoft alone has committed more than US$200 billion to expand its data centre footprint. Even large national investments pale in comparison. India, for example, has allocated approximately US$200 billion toward AI development, underscoring the scale of the global investment gap. However, there is a viable path forward. AI sovereignty is achievable, but it requires intentional investment and a rethinking of national strategy. Countries must prioritise the development of domestic data centre infrastructure, leveraging regional advantages such as lower energy costs and labour efficiencies to build at scale. We are already seeing early signs of this shift. The UK has already recognised data
centres as Critical National Infrastructure, highlighting their growing importance to economic resilience and national security. As demand for AI compute continues to increase, the focus must now shift towards expanding capacity while balancing energy requirements, sustainability goals and long-term resilience. However, infrastructure alone will not unlock the full value of AI. Nations must also address the fragmentation of their data ecosystems. Today, critical data remains siloed across organisations and systems, limiting its usability for AI. A logical approach to data integration, one that enables secure, governed access to distributed data with zero-copy, offers a powerful alternative. By adopting such a strategy, organisations can accelerate AI adoption while remaining compliant with sovereignty requirements.
As McKinsey has noted: “CDOs who want
to scale AI throughout their organisations will need to treat data as a core enterprise asset.” That principle also applies to AI proper, and at a national level as well. The countries that succeed will be those that treat data not as a constraint, but as a strategic asset that is unified, accessible, and governed.
Denodo
www.denodo.com
automationmagazine.co.uk
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