Artificial intelligence has quickly moved from innovation labs into core business operations.
Banks are using AI to strengthen fraud detection. Trading organizations are deploying AI to analyze market activity in real time. Insurance providers are accelerating underwriting, claims processing, and risk assessment. Across financial services, institutions are investing aggressively in AI to improve efficiency, deliver better customer experiences, and uncover competitive advantages.
Yet as many organizations move beyond pilots and begin scaling AI into production, a fundamental question is emerging:
Where should AI actually run?
The answer carries significant implications for performance, security, regulatory compliance, operating costs, and long-term business agility.
For years, the cloud was viewed as the logical destination for advanced analytics and emerging technologies. However, AI is changing the equation. Large-scale AI workloads create new power, cooling, networking, and governance requirements that force organizations to reassess traditional infrastructure strategies. At the same time, financial institutions face growing pressure to control sensitive data, meet regulatory obligations, and support mission-critical operations without introducing additional risk.
Many financial services organizations are discovering that infrastructure decisions have become business decisions.
For applications such as fraud detection, transaction monitoring, algorithmic trading, and customer interactions, milliseconds matter. AI systems often need to process information close to where data is generated to deliver real-time insights and responses. Moving everything to centralized environments can create challenges around latency, costs, and operational control.
Data sovereignty adds another layer of complexity.
Financial institutions manage some of the most sensitive information in the world. Regulatory expectations around data privacy, governance, and auditability continue to evolve. As AI becomes more deeply embedded in financial operations, organizations must evaluate how deployment models affect security, compliance, and control over business-critical data.
The result is that many organizations are moving toward a more deliberate infrastructure strategy.
Rather than asking whether AI should run in the cloud or on-premises, leading institutions are evaluating a broader range of options, including cloud services, colocation providers, modernized data centers, and hybrid deployment models. Each approach carries different advantages depending on business priorities, workload requirements, and risk tolerance. According to Schneider Electric's research, factors such as data sovereignty, latency, time-to-market, scalability, staffing expertise, cash flow preferences, and total cost of ownership should all be considered before determining where AI workloads should reside.
For many organizations, AI success will not be determined solely by algorithms or models. It will depend on whether infrastructure can support AI's growing demands while preserving resiliency, security, and operational control.
The organizations that succeed will be those that align infrastructure decisions with business outcomes, creating an environment where AI can scale efficiently without compromising governance, performance, or customer trust.
AI Workload Deployment in Data Centers: Retrofit, Outsource or Build New?
Explore a strategic framework for determining where and how to deploy infrastructure for high-density AI workloads. Compare the trade-offs of retrofitting existing facilities, building new data centers, or outsourcing capacity, while considering total cost of ownership, time-to-market, scalability, data sovereignty, and operational capabilities. Gain practical guidance to assess whether existing sites can support AI infrastructure and make more informed, AI-ready capacity decisions.
