AI in financial services is moving beyond the pilot phase as banks, capital markets firms, mortgage lenders and other organizations begin deploying the technology more broadly. The challenge is shifting toward managing AI responsibly at scale while meeting strict requirements for security, compliance and customer trust.

The AWS Financial Services Symposium highlighted that transition in May. As financial institutions expand their use of AI, they have opportunities to test new applications without committing to large-scale deployments, according to Scott Mullins, former general manager of AWS Worldwide Financial Services at Amazon Web Services Inc.

“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.”

This feature is part of SiliconANGLE Media’s ongoing coverage of the AWS Financial Services Symposium. (* Disclosure below.)

From AI pilots to regulated production

Moving AI from experimentation into production requires financial institutions to address a different set of challenges. Beyond demonstrating that the technology works, organizations must establish the governance and operational safeguards needed to support AI across regulated business processes, according to Paul Nashawaty, practice lead and principal analyst for application development at theCUBE Research.

“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 addressing those requirements through its AI platform, which incorporates safeguards and controls intended to help financial institutions deploy AI while managing risk, according to Rajaram R.K., vice president and head of sales for financial services at Infosys.

“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

Financial institutions face stringent regulatory requirements, making security, auditability and recovery essential to their operations. As banks, insurers and capital markets firms expand their use of AI, those requirements take on additional importance, particularly when automated systems begin influencing business decisions. Despite these challenges, financial institutions are among the sectors leading the way in AI adoption.

“The organizations that treat AI as a regulated business capability, not simply a technology initiative, will be best positioned to accelerate innovation while maintaining resilience and meeting evolving regulatory expectations,” Nashawaty said.

Applying those established risk-management practices to AI requires organizations to consider how automated systems access data, make decisions and operate within existing policies. The challenge is extending governance and operational controls to AI-enabled workflows without undermining the efficiency and innovation those systems are intended to deliver.

Customer experience and employee productivity move AI into daily workflows

Financial institutions are finding practical applications for AI in customer service and employee productivity. At New York Life Insurance Co., reducing routine manual work is giving employees more time to provide personalized service and respond proactively to customers’ needs, according to Kristina Welke, vice president at New York Life.

“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 using conversational AI to improve customer interactions and reduce average call-handling time. The bank is using Amazon Connect to support its customer-service operations, according to Kevin Anderson, product manager on the AI transformation team at Fifth Third Bank.

“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

For financial institutions, scaling AI depends in part on modernizing the infrastructure that supports data-intensive workloads. Cloud platforms can provide the flexibility to deploy new capabilities while maintaining access to existing systems, making modernization an important consideration as organizations expand their AI initiatives.

Mortgage lender PennyMac Financial Services recently expanded its agreement with Amazon Web Services to accelerate modernization of its proprietary mortgage servicing platform, Plaisse. The partnership also includes plans to deploy enterprise-grade AI capabilities and further develop the platform.

As financial institutions modernize their infrastructure, they also need ways to move and manage data without disrupting existing operations. NetApp Inc. is addressing those requirements through storage and data-management capabilities designed to support migration, resilience and performance in AWS environments, according to Jacob Lim, cloud solutions architect at NetApp.

“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 financial institutions incorporate AI into daily operations, managing the complexity of those deployments is becoming an increasingly important challenge. According to PagerDuty’s 2026 State of AI-First Operations report, 59% of surveyed banks were actively incorporating AI into their operations, while another 34% were discussing implementation. As adoption expands, institutions must determine how to govern these systems, maintain data quality and integrate AI into existing workflows.

“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?” Todd McNabb, chief revenue officer of PagerDuty Inc., told theCUBE.

Technology providers are responding to the demand for faster enterprise AI deployment. AWS’ recently announced $1 billion investment in forward-deployed engineering, for example, is intended to help customers develop and deploy agentic AI applications more quickly. For financial institutions, however, the measure of success will extend beyond deployment speed to how effectively AI can operate within existing systems, regulatory requirements and business processes.

(* Disclosure: TheCUBE is a paid media partner for the AWS Financial Services Symposium event. Neither AWS, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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