Why Financial Services Needs AI-Ready Infrastructure

Within the financial services industry, AI has matured far beyond the pilot stage, evolving into a mission-critical capability that institutions can no longer treat as optional.

As AI has evolved into a core technology for the finance sector, its rapid growth will be a core focus of our upcoming breakfast roundtable in New York, where industry leaders will examine what’s next for AI-driven transformation in the industry.

More than half of finance industry leaders now view AI as a strategic priority, with nearly 98% planning to increase investments in AI infrastructure by 2025, according to the NVIDIA 2025 Financial Services AI Report.

Yet many financial organisations still rely on legacy data centre architectures designed for static storage rather than dynamic, real-time insight.

This infrastructure gap presents a significant challenge. Financial institutions today compete on the strength of their AI data platforms, not merely on traditional banking capabilities.

Those that move first with AI-ready infrastructure will unlock new revenue streams, reduce operational risk and establish competitive moats that are difficult to replicate. Those that lag risk being outpaced by more nimble competitors – whether traditional rivals or emerging fintech challengers.

Speed at the speed of markets

The demands placed on modern financial AI infrastructure are unforgiving.

High-frequency trading systems require sub-millisecond latency to capture microsecond gains. Fraud detection pipelines must process millions of transactions in real time, identifying suspicious patterns before they cause harm. Algorithmic trading systems need terabyte-per-second throughput to ingest and analyse vast datasets instantaneously.

Generic IT systems or outdated data centre infrastructure might lack the architectural intelligence to support the velocity and volume of financial AI workloads. This is not an abstract concern – delayed fraud alerts or failed transactions can cost millions, damage institutional reputation and trigger regulatory penalties.

Speed and resilience are now operational requirements, not luxury additions.

“Generative and agentic AI will reshape competitive dynamics across every industry, and we are embracing these tools as we have embraced robotic process automation and machine learning for years,” says Charlie Scharf, Chairman and CEO of Wells Fargo.

“The past year has been exciting as our world-class technology team has led us in building the technical foundation, training over 90,000 employees, deploying AI tools to over 180,000 desktops and we are now beginning to implement use cases more broadly.”

The hybrid infrastructure reality

Most financial institutions eventually discover that cloud-only approaches prove insufficient for sensitive operations.

Regulatory constraints, data residency requirements and compliance obligations demand that critical information remains under institutional control. Yet the agility and scalability of cloud computing remain valuable for experimentation, model retraining and burst capacity.

This reality has driven the adoption of hybrid infrastructure models – a blended approach where regulated data stays on-premises while cloud resources handle computational experimentation and scaling.

Institutions pursuing this path require GPU infrastructure capable of supporting both on-premises and cloud environments seamlessly.

Modern AI-ready platforms must integrate native support for leading AI tools and enable unified orchestration across edge, core and hybrid environments.

Compliance by design

Financial regulators increasingly expect compliance mechanisms to be embedded within infrastructure itself rather than bolted on afterwards.

Encryption, role-based access controls, immutable audit logs and data residency controls must be foundational design principles. Regulations including PCI-DSS, GDPR and regional banking laws directly shape infrastructure architecture decisions.

Progressive financial institutions are adopting governance frameworks that allow multiple teams – AI and compliance functions alike – to securely share infrastructure without operational contention.

Event-driven architectures trigger AI workflows based on live transactions and market shifts, whilst maintaining complete transparency for regulators. This represents infrastructure as a compliance accelerator, not merely a technical enabler.

“Agentic AI represents a step-change in how financial services organisations operate and innovate,” says Matt Cloke, CTO at Endava.

“The opportunity is clear, but so is the responsibility. Our research shows that those who build AI-native operating models, backed by strong governance, will be the ones to lead the next era of financial services.”

The path forward

Financial institutions that invest in AI-ready infrastructure today position themselves to capture tomorrow’s opportunities.

The opportunity is substantial, but so are the consequences of inaction. The firms that win will be those moving first with infrastructure built for speed, scale and real-time decision-making at the pace financial markets demand.

This topic will be part of the wider discussion on how Generative AI is reshaping enterprise content, customer communications and compliant financial services operations.