Banks bet on AI to bridge old and new—but legacy systems still hold the keys

AI integration is fundamentally transforming banking cloud contracts and core system modernization by exposing the limitations of legacy agreements and architectures originally designed for predictable workloads. As highlighted in the 2026 analysis "Everything Is AI Now," banks face mounting pressure to renegotiate cloud contracts to accommodate AI's compute-intensive demands, emphasizing sovereignty, flexibility, and interoperability to manage regulatory compliance and avoid vendor lock-in. Legacy core systems, often siloed and lacking robust data governance, are increasingly seen as impediments to effective AI deployment, necessitating modernization efforts that prioritize well-managed data environments to unlock AI’s full potential.

Despite the push for modernization, legacy banking infrastructure remains the backbone of trust, resiliency, and scale, with approximately 75% of transactions still processed on mainframes, according to Paymentus' Garrett Baird. Rather than wholesale replacement, banks are adopting AI-driven orchestration strategies that intelligently layer new capabilities atop existing systems—described metaphorically as 'Russian nesting dolls' of middleware and APIs—thereby unlocking intelligence trapped within complex, layered infrastructures. This approach balances the reliability of legacy platforms with the need for speed, personalization, and regulatory compliance in a competitive landscape.

AI’s unique ability to analyze relational context across fragmented data ecosystems offers incumbent banks a strategic advantage by leveraging accumulated customer insights rather than merely competing on speed or user experience. However, as Garrett Baird notes, entrenched internal silos and narrow modernization efforts focused on rip-and-replace strategies often hinder large financial institutions from fully capitalizing on AI’s potential. By embracing AI-driven orchestration and fostering partnerships to extend capabilities, banks can preserve the trust embedded in core systems while modernizing customer engagement layers and navigating complex regulatory environments.

The increasing compute demands of AI inference and privacy-sensitive data pipelines compel banks to reconsider data residency and flow, requiring tighter integration and control than traditional workloads. This shift underscores the critical need for unified, high-quality data environments, as poor data quality and fragmentation significantly limit AI effectiveness. As Mladen Vladic of FIS emphasizes, AI is only as good as the data it consumes, making data governance and orchestration between legacy and AI-driven technologies essential pillars of successful core banking modernization.

Speed to value has emerged as the paramount metric in modern banking, shifting the focus from traditional cost-cutting to rapid revenue generation through swift product launches. This transformation is powered by modular, cloud-based, and low-code bank operating systems that enable iterative deployments, unlocking organizational capacity and accelerating growth. For instance, some banks leveraging sidecar core architectures have boosted digital adoption by 80%, illustrating how platforms like Finzly BankOS serve as control planes for real-time, tokenized economies, decoupling innovation cycles from legacy cores to meet modern customer demands.

Fully integrated bank operating systems that unify CRM, core banking, payments, and compliance data create a single source of truth critical for effective AI-driven automation. Firms with such integrated platforms report up to 10x better growth performance and ROI measured in weeks, overcoming the biggest barrier to AI effectiveness—fragmented data. Backbase’s AI-native banking OS exemplifies this trend by orchestrating omnichannel customer journeys and employee workflows on a core-agnostic platform that seamlessly blends legacy systems with fintech capabilities, enabling rapid innovation and deployment.

The evolution toward autonomous, AI-powered bank operating systems is redefining workflows and frontline operations by leveraging AI agents for touchless support and fully automated customer data management, while maintaining essential human oversight. This shift creates a 'flywheel of capability' that allows banks to do more with the same workforce, dramatically reducing processes like loan origination from weeks to hours. Leaders such as Infosys emphasize moving beyond pilot AI projects to platform-scale transformations that reimagine entire operations and customer experiences, underscoring the critical role of partnerships in accelerating AI-driven modernization and speed to value.

Traditional procurement and implementation cycles of six to nine months are rapidly giving way to iterative release models that prioritize continuous value realization, enabling banks to stay agile in a fiercely competitive landscape. This approach, championed by forward-thinking institutions, supports modular innovation and faster product launches across multiple channels, effectively unlocking organizational capacity and enhancing customer responsiveness. As Rajaram R.K. from Infosys notes, the platform approach is not just about efficiency but about reimagining processes and engagement to scale AI-powered transformation.

By early 2026, banks have reframed AI from a potential risk to a critical risk mitigation engine, proactively anticipating and neutralizing threats before they materialize. This evolution is coupled with a commitment to transparent AI governance, as institutions increasingly test AI capabilities directly with customers, fostering trust and regulatory alignment. Such openness not only empowers users but also strengthens banks’ roles as secure anchors in the emerging autonomous commerce ecosystem, providing reliable identity verification and real-time liquidity.

Revolut exemplifies how AI can deftly navigate the labyrinth of global financial regulations, employing large language models to parse and parameterize rules across 39 countries. This AI-driven regulatory interpretation enables a unified app experience while significantly boosting compliance and financial crime prevention. By automating routine transaction monitoring and KYC reviews with AI that statistically outperforms humans, Revolut frees expert reviewers to focus on complex cases, illustrating a powerful synergy between machine efficiency and human judgment.

Macquarie Bank’s deployment of AI-powered fraud detection tools highlights how AI not only enhances security but also deepens customer engagement and trust. With a reported 40% increase in usage of self-service fraud detection features, customers are empowered to identify suspicious transactions in real time, transforming AI from a backend safeguard into an interactive, trust-building interface that aligns with banks’ broader risk management and compliance strategies.

While AI adoption in banking is widespread—with McKinsey's 2025 survey noting 88% of respondents use AI regularly—scaling AI to generate enterprise-wide value remains elusive due to entrenched legacy operating models and fragmented systems. Banks often excel in pilot projects but struggle to embed AI deeply into workflows and operating systems, a challenge underscored by OpenAI's finding that 'the value is in the work that changed because of it,' not mere tool usage. This gap between individual productivity gains and organizational performance highlights the need for systemic transformation beyond isolated AI applications.

Legacy core systems, broken integrations, and poor data quality stand as the primary barriers preventing banks from moving AI initiatives from pilots to full-scale production, as emphasized in analyses from early 2026. Regulatory pressures from frameworks like DORA, CRR3, CRD6, and the EU AI Act further complicate adoption by demanding rigorous AI explainability and governance, elevating AI from a mere technology project to a comprehensive operating model challenge. Consequently, successful AI scaling requires banks to prioritize data cleansing, workflow integration, and early governance establishment to transition AI from experimental demos into core infrastructure.

A strategic pivot toward platform-based, unified AI-native operating models is emerging as banks seek to overcome legacy fragmentation and siloed data. Concepts like Backbase's 'unified frontline' illustrate this shift, envisioning an operating system that seamlessly integrates customers, employees, and autonomous AI agents in real time to enhance decision speed, operational resilience, and cost optimization. This transformation is not only technological but cultural and organizational, requiring coordinated data strategies, governance frameworks, and cultural alignment as outlined in Kaufman Rossin's four-pillar execution framework for sustainable AI transformation.