From regulation to real-world: infrastructure will determine the success of the EU’s AI Act

The European Union’s roll out of the AI act, along with its wider push to accelerate AI and cloud adoption across Member States, reinforces Europe’s commitment to becoming a global leader in the field. These moves raise the stakes for organisations to ensure their infrastructure can keep pace with the growing operational demands, geopolitical pressures, and regulatory requirements. While the EU's efforts aim to strengthen competitiveness, their impact will depend on how effectively harmonisation is achieved in practise.

At the same time, the conversation around AI has evolved. It is no longer just about potential impact but also about building systems that are scalable, secure, and ready to support real-world deployments. Without robust, future-proof infrastructure in place, even the most advanced AI deployments risk stalling. The consequences extend beyond individual organisations and influence Europe’s standing in the global technology landscape.

Why AI won’t succeed without an effective cloud foundation

Businesses are investing heavily in generative AI, automation, and AI-driven decision-making, expecting transformative results – from operational efficiency to new services. The reality is that infrastructure underpins everything in AI deployment. Algorithms or data alone are not enough. AI workloads demand compute capacity, seamless data access, and robust compliance controls, all while managing costs effectively. Without an effective cloud foundation, the way systems are built, maintained, and optimised will determine whether these investments succeed or become another silo. It will also affect the EU’s ability to achieve its strategic objectives and support growth and AI developments across Europe. The stakes are high: 48 percent of EMEA IT leaders report wasting at least 25 percent of their cloud spend, and 90 percent prioritise cost predictability. Infrastructure can either accelerate AI adoption or create bottlenecks, leaving organisations grappling with underutilised investments, performance issues, spiralling costs and serious questions about regulatory compliance and sovereignty. In fact, 51% of global organisations are moving workloads back to private cloud over security or compliance concerns, underscoring the important of robust, well-governed infrastructure in realising AI’s potential.

Operational resilience at scale

AI workloads are dynamic, evolving with data and demand. Infrastructure must be equally agile – scaling flexibly to avoid constraints and ensuring rapid, secure data access. A system slowed by inefficient storage or fragmented data environments directly impacts the speed and reliability of AI insights.

Operational readiness extends beyond technical performance. It requires resilience, security, and the ability to handle demand surges. Organisations that prioritise these capabilities maximise the value and reach of their AI initiatives, turning infrastructure from a constraint into a competitive advantage. Resilience is not just an operational consideration but also a regulatory requirement. EU legislation for financial institutions such as the Digital Operational Resilience Act (DORA) mandates resilience in every aspect of the financial services information

technology infrastructure with emphasis on functions supporting critical services. The scalability of any AI application for the financial industry will need to factor not only the likelihood that it will support a critical service within the meaning of DORA, but the regulatory and compliance consequences that emerge from that determination.

How businesses can scale their AI strategy For IT leaders, the question is no longer whether to invest in AI infrastructure but how to do so in a way that supports scale, cost control and resilience. With 93% of organisations favouring private cloud for critical applications due to its financial visibility and predictability, there is a clear shift towards solutions that combine flexibility with strong governance. Private and hybrid cloud strategies offer the agility needed for high-demand AI workloads while meeting regulatory and sovereignty requirements, positioning them as a competitive alternative to hyperscaler models, which can raise concerns around cost, control and compliance.

Focusing on scalable infrastructure ensures AI initiatives can grow without limitations. The following sections outlines a practical approach to assess and align infrastructure for AI adoption. 1. Assess and align infrastructure

For organisations looking to adopt AI more widely, the first step is to assess current infrastructure against projected AI workloads, identifying gaps in compute capacity, data accessibility and cost management. Building or expanding infrastructure with a focus on scalability ensures that AI initiatives can grow without hitting bottlenecks.

2. Prioritise data integration and compliance

AI thrives on data, yet fragmented or siloed information can hinder both performance and compliance. Ensuring seamless data integration, secure access and audit-ready pipelines is fundamental. Leaders should prioritise architectures that support interoperability, secure storage and high-speed processing, enabling AI models to deliver actionable insights rapidly and reliably. Leaders should also assess the ways they plan to apply the technology comply with applicable rules and sector standards. Use cases that are captured by the EU AI Act are likely to require specific controls and governance that is linked with the data and the algorithms as they flow through the infrastructure. Requirements such as DORA and NIS2 that are linked to sectors are likely to prioritise organisational and technical controls on the infrastructure, the supply chain and the supply of data. Sovereignty will remain a political priority especially for public sector or critical infrastructure customers. Therefore, the ability to demonstrate independence from foreign interference in operating an AI infrastructure may become a key consideration in public procurement.

3. Continuous progress

AI infrastructure is not a set-and-forget investment. It requires ongoing tuning, testing and optimisation to remain aligned with evolving workloads and regulatory expectations. By adopting a proactive, forward-looking approach, enterprises can ensure their AI deployments remain both effective and compliant.

Europe’s changing AI landscape The need for continuous optimisation goes hand in hand with navigating a fast-evolving regulatory landscape that is redefining how AI is developed and deployed. It also affects the rules and obligations associated with specific applications or sector verticals. For European organisations, these pressures are particularly pronounced. The EU AI Act is a landmark piece of legislation that aims to create a consistent set of rules for AI use across member states. Its influence is already shaping enterprise priorities while more political initiatives aiming to promote cloud and AI utilisation are underway. In this complex environment, compliance is now a strategic imperative that can determine the success of one’s efforts. Businesses should ensure their infrastructure embeds governance, risk management, and transparency to meet regulatory demands and foster trust with customers, investors, and regulators.

Deploying AI in a non-compliant manner either because of the infrastructure choices or the lack of effective controls risks not only reputational harm but also financial penalties and legal action. By integrating compliance into infrastructure design, organisations can turn regulatory challenges into opportunities for trustworthy, ethical AI.

Next steps for Europe

Europe’s success in AI is not solely dependent on algorithms and data, but the readiness of the infrastructure that supports them. By leveraging regulatory foresight and a commitment to ethical technology, there is a unique opportunity for Europe to establish itself as a global leader in AI - but this position is far from guaranteed. Organisations must prioritise operational resilience, scalability, and regulatory alignment to realise the full benefits of AI and aid Europe’s position as a global leader.