Banks Face New Hurdles as AI Moves From Testing to Live Operations

The financial services industry is advancing beyond initial AI experimentation toward broader operational deployment. Banks, capital markets organizations, mortgage companies and insurers are now grappling with the operational and regulatory demands of running AI systems in production environments. Security, compliance and customer confidence have become central concerns as these institutions scale their AI initiatives.

The AWS Financial Services Symposium in May underscored this shift. Scott Mullins, former general manager of AWS Worldwide Financial Services at Amazon Web Services Inc., noted that organizations can rapidly prototype and test AI applications within their policy frameworks. "You can spin this stuff up really quickly; you can experiment with it," he said during the symposium. "You can show that it actually achieves the things that you want to achieve within specific policy guidelines and frameworks, and you can actually evaluate whether that governance is holding."

From AI pilots to regulated production

Moving AI from testing phases into regulated production environments requires a different approach than demonstrating technical capability. Financial institutions must establish governance structures and operational safeguards that support AI across compliance-sensitive business processes. Paul Nashawaty, practice lead and principal analyst for application development at theCUBE Research, emphasized the gap between experimentation and production readiness.

"Our AppDev research shows that 92% of organizations now integrate AI into at least one stage of the software delivery lifecycle, yet only about one-third have established the governance, security, and operational maturity required for production-scale AI," he said. "In banking, capital markets and insurance, the gap is even more pronounced because compliance, explainability, and customer trust are non-negotiable. The next phase of AI adoption will be defined less by model performance and more by platforms that embed policy controls, auditability and security into AI-enabled workflows while improving customer experiences and operational efficiency."

Infosys Ltd. is developing solutions to address these production requirements through its AI platform, which incorporates safeguards and controls for financial institutions managing AI deployment risks. Rajaram R.K., vice president and head of sales for financial services at Infosys, described the platform's capabilities. "It has the securities [and] it has the guardrails. It has the token economics … all the capabilities that an organization would need," he told theCUBE.

Trust, governance and resilience become the real AI architecture

Regulatory constraints in financial services demand that security, auditability and system recovery be built into AI operations from the start. As banks, insurers and capital markets firms expand AI usage, these requirements grow more critical, especially when automated decision-making affects business outcomes. Notably, financial institutions are among the sectors most actively adopting AI despite these stringent constraints.

Nashawaty emphasized that institutions treating AI as a regulated business capability rather than purely a technology project will be best equipped to innovate while maintaining operational resilience and meeting regulatory expectations. Extending established risk-management practices to AI systems requires organizations to evaluate how automated systems access information, make determinations and function within existing policies. The real challenge lies in applying governance and operational controls to AI workflows without sacrificing the efficiency and innovation these systems promise.

Customer experience and employee productivity move AI into daily workflows

Practical AI applications are emerging in customer service and workforce productivity across financial services. New York Life Insurance Co. is using AI to reduce repetitive administrative tasks, freeing staff to deliver more personalized service and anticipate customer needs. Kristina Welke, vice president at New York Life, explained the strategy. "We really felt there was a lot of opportunity to support them, remove the manual work they do every day, enable them to provide that consultative support, be proactive versus reactive," she told theCUBE.

Fifth Third Bank is deploying conversational AI to enhance customer interactions and lower call-handling duration. The institution leverages Amazon Connect for customer-service infrastructure. Kevin Anderson, product manager on the AI transformation team at Fifth Third Bank, described the platform's role. "Amazon Connect really lays the foundation as it solves for not only messaging but voice as well, allows for AI and automation," he explained.

Cloud modernization becomes the foundation for AI deployment

Deploying AI at scale in financial services depends partly on updating the infrastructure supporting data-heavy operations. Cloud platforms offer the agility to introduce new capabilities while preserving connections to legacy systems, making infrastructure modernization essential as institutions expand AI programs.

Mortgage lender PennyMac Financial Services recently broadened its AWS partnership to modernize its proprietary mortgage servicing platform, Plaisse. The expanded agreement includes plans to introduce enterprise-grade AI and continue platform development.

As financial institutions upgrade their infrastructure, they require tools to move and manage data without interrupting current operations. NetApp Inc. offers storage and data-management solutions designed to support data migration, system resilience and performance in AWS settings. Jacob Lim, cloud solutions architect at NetApp, outlined the value proposition. "We create value for customers by making it simple to migrate and run their data in AWS by combining proven NetApp ONTAP capabilities, such as space-efficient snapshots, near-instant data cloning capabilities with minimal storage overhead and storage performance," he told theCUBE. "Then, there is also the highly efficient data replication and ransomware detection engine natively built into the operating system directly. Combined with AWS scale and elasticity, customers gained resilience, performance and operational simplicity in the cloud."

Scaling AI in financial services brings new challenges

As AI becomes embedded in daily financial operations, managing deployment complexity is emerging as a critical concern. PagerDuty's 2026 State of AI-First Operations report found that 59% of surveyed banks were actively deploying AI, with another 34% in planning stages. As adoption accelerates, institutions must address system governance, data quality assurance and AI integration with existing processes.

Todd McNabb, chief revenue officer of PagerDuty Inc., characterized the evolving focus. "I think you're going to see AI really become: How do we govern, how do we use [and] how do we make sure the data's clean? How does it become [about] helping us be more efficient in what we're doing in day-in and day-out operations?" he told theCUBE.

Technology vendors are responding to demand for faster AI deployment in enterprises. AWS announced a $1 billion investment in forward-deployed engineering to accelerate customer development and deployment of agentic AI applications. For financial institutions, however, success will be measured not just by deployment velocity but by how effectively AI operates within existing systems, regulatory frameworks and business operations.