AI infrastructure services will be the new foundation of the enterprise computing stack for banking and the financial services sector.
For banking and financial services companies, artificial intelligence was once a feature layered onto existing IT systems. But it has evolved into a driver of design, deployment, and operation of modern computing environments.
As generative AI and new AI agents go from the pilot-testing stage to becoming mission-critical tools for many businesses, the underlying infrastructure that supports them is being redesigned.
It is such a fundamental shift that AI infrastructure services can be seen as the next foundation of computing, and something that will redefine cloud architecture, data center design, and enterprise strategy for the next decade and beyond.
And for highly regulated, data-intensive industries like banking, the implications will be profound.
GPUs and CPUs
A common question related to this transformation is whether GPUs will ultimately replace CPUs in AI-enabled environments. It’s not a simple yes-or-no question.
CPUs are and will remain indispensable for general-purpose computing, orchestration, control logic, and other tasks. But AI workloads, for example, model training and high-volume inference, are very much optimized for accelerators such as GPUs, TPUs, FPGAs, and specialized ASICs.
This doesn’t mean CPUs will vanish, but heterogeneous infrastructure is certain to become the norm. In this model, accelerators handle heavy computational tasks while CPUs coordinate and execute supporting functions.
The ‘AI Factory’
The rapid adoption of accelerators introduces new variables into the economics of computing.
GPUs boast exceptional throughput but also introduce higher power consumption, more serious cooling requirements, and faster hardware refresh cycles. As businesses scale their AI ambitions, efficient utilization and modular upgrade strategies will be essential to avoid wasted resources and capital expenditures.
These and other shifts are contributing to a broad reimagining of the traditional data center. The impact will not be immediate or disrupt every operation, but the shift is unmistakable.
A new class of AI factories, or AI-native facilities, is emerging. These are built from the ground up around dense GPU clusters, advanced cooling systems such as direct-to-chip or immersion cooling, ultra-high-speed networking, and modular configurations that can adapt to fast-moving hardware cycles.
See also: How AI Is Forcing an IT Infrastructure Rethink
Growth Ahead
Large infrastructure players forecast massive growth in this segment: estimates range up to 75 gigawatts of new AI data center capacity that may be added worldwide over the next decade, bringing total capacity to more than 80 gigawatts by 2034.
These facilities require far more than compute hardware. They will depend on expanded electrical transmission, specialized power distribution, and advanced thermal management systems.
The economics of the upcoming shift are complex. AI-optimized infrastructure-as-a-service (IaaS) models are making high-performance compute more accessible, reducing the need for enterprises to invest heavily in on-premises hardware. But the operational costs of AI facilities will still be high.
Many traditional data centers will retrofit their existing buildings with enhanced power capabilities, liquid cooling technologies, or accelerator clusters, while others will partner with niche GPU hosting providers. Renewable power sourcing, modular energy-efficient designs, and waste-heat reuse strategies are becoming competitive differentiators, as the pressure to be sustainable continues to mount.
The AI Impact on Financial Companies
No sector stands to be transformed more substantively than banking and financial services. Massive historical datasets, real-time transactional flows, and strict regulatory oversight make BFSI both fertile ground and a high-stakes proving environment for AI.
AML detection, risk modeling, and fraud analytics are already benefiting from high-speed infrastructure. AI models can scan millions of transactions or documents in seconds, spotting anomalies or patterns that legacy systems would miss.
Personalized financial products are also evolving. Wealth managers, insurers, and retail banks are now deploying Large Language Models (LLMs ) to offer tailored guidance, automate certain advisory functions, and streamline customer support.
But these innovations will always raise questions about governance, transparency, and model risk. These considerations will increasingly influence infrastructure design.
Making the AI Investment
With AI-optimized IaaS expected to reach nearly $40 billion in annual spend by 2026, banks are turning to on-demand GPU services to avoid large capital costs. Firms that scale AI infrastructure effectively will be able to deliver real-time personalization, deploy agentic AI systems, and run predictive analytics at a pace that competitors who haven’t made the same investments will not be able to match.
Institutions that underinvest may lose ground to fintech companies and AI-native challengers.
The next decade of AI infrastructure services will see extraordinary growth, diversification, and specialization. Trillions may be spent over the next decade across chips, data centers, connectivity, and power. GPU-as-a-service platforms alone could grow from approximately $30 billion in 2025 to more than $250 billion by 2034, analysts say.
Opportunities and Challenges
The massive transformation in computing will be exciting, but it will also introduce significant challenges.
The energy demands of AI data centers are projected to grow exponentially, raising questions about grid stability. For many organizations, capital intensity will bring the possibility of stranded assets as hardware evolves.
Operational complexity will increase, meaning teams will need to be skilled in accelerator management, cooling engineering, high-speed networking, and other emerging disciplines.
And for regulated sectors like BFSI, governance, security, and auditability will not be optional. These will be core design requirements.
It is clear that AI infrastructure services will not be a supporting layer. These services will make up the new foundation of the enterprise computing stack.
Over the next decade, the organizations that thrive will be those that embrace heterogeneous systems, invest in AI-optimized data centers or cloud models, adopt robust lifecycle management practices, and integrate governance and sustainability into their infrastructure strategies.
And for the banking and financial services sector, the stakes will always be higher.
