Digital banking platform Plumery AI has introduced a new technology called “AI Fabric” aimed at helping financial institutions integrate artificial intelligence into their daily operations. This initiative seeks to overcome the persistent challenge of moving beyond experimental proofs of concept to full-scale AI deployment, all while strictly adhering to crucial aspects like governance, security, and regulatory compliance.

Plumery AI positions its “AI Fabric” as a standardized framework designed to connect generative AI tools and models directly to core banking data and services. The company emphasizes that this product is intended to reduce the need for custom, bespoke integrations and to foster an event-driven, API-first architecture that can seamlessly scale with the growth of financial institutions. This approach directly addresses a recognized industry-wide dilemma where significant investments in AI experimentation often fail to translate into widespread production use, largely due to fragmented data estates and outdated operating models.

Research from McKinsey highlights that while generative AI holds substantial potential for boosting productivity and enhancing customer experience in financial services, many banks struggle with full deployment. The consultancy suggests that enterprise-level AI adoption necessitates shared infrastructure, robust governance, and the creation of reusable data products. Ben Goldin, Plumery’s founder and CEO, affirmed that financial institutions prioritize tangible production use cases that improve customer experience and operations, but are unwilling to compromise on governance, security, or control. He stressed that the event-driven data mesh architecture employed by AI Fabric fundamentally transforms how banking data is produced, shared, and consumed, rather than simply adding another AI layer atop disjointed systems.

Data fragmentation remains a significant impediment to the operationalization of AI in banking. Many institutions rely on legacy core systems coexisting with newer digital channels, which creates silos across various products and customer journeys. This fragmentation means that each new AI initiative often requires extensive integration work, security reviews, and governance approvals, leading to increased costs and slower delivery times. Academic and industry studies further support this diagnosis, noting that fragmented data pipelines complicate the traceability of AI-driven decisions, thereby increasing regulatory risk, particularly in sensitive areas such as credit scoring and anti-money-laundering. Regulators consistently demand that banks must be able to explain and audit the outcomes of their AI models, regardless of where they were developed.

Plumery asserts that its AI Fabric effectively tackles these issues by presenting domain-oriented banking data as governed streams that are reusable across multiple applications. The company argues that by separating systems of record from systems of engagement and intelligence, banks can innovate more securely and efficiently.

Despite these challenges, AI is already deeply integrated into various facets of the financial sector. Industry case studies show widespread adoption of machine learning and natural language processing in areas such as customer service, risk management, and compliance. For instance, Citibank utilizes AI-powered chatbots to manage routine customer inquiries, thereby reducing call center pressure and improving response times. Other major banks employ predictive analytics to monitor loan portfolios and anticipate potential defaults, while Santander has publicly detailed its use of machine learning models for assessing credit risk and strengthening portfolio management. Fraud detection is another well-established application, with banks increasingly relying on AI systems to analyze transaction patterns and identify anomalous behavior more effectively than traditional rule-based systems. However, technology consultancies note that the efficacy of such models hinges on high-quality data flows, and integration complexity continues to be a limiting factor, especially for smaller institutions. More advanced applications, such as conversational AI powered by large language models, are emerging, but these remain largely experimental and subject to strict scrutiny due to their significant regulatory implications.

Plumery operates within a competitive landscape of digital banking platforms, positioning itself as an orchestration layer rather than a replacement for core systems. The company has forged strategic partnerships, such as with Ozone API, an open banking infrastructure provider, to enable banks to deliver standards-compliant services more rapidly without extensive custom development. This strategy aligns with a broader industry trend towards composable architectures, where vendors promote API-centric platforms that allow banks to integrate AI, analytics, and third-party services with their existing core systems. Analysts generally concur that such architectures are better suited for incremental innovation than for large-scale system overhauls.

However, readiness for large-scale AI adoption remains uneven across the sector. A report by Boston Consulting Group revealed that fewer than a quarter of banks feel prepared, citing gaps in governance, data foundations, and operating discipline. Regulators have responded by establishing controlled environments for experimentation, such as the UK’s regulatory sandbox initiatives, which allow banks to test new technologies, including AI, to support innovation while reinforcing accountability and risk management. For providers like Plumery, the opportunity lies in delivering infrastructure that bridges technological ambition with regulatory realities. While demand for operational AI is evident, the ultimate success of new tools like AI Fabric will depend on their proven safety and transparency. As banks transition from experimentation to production, the focus is increasingly on the underlying architectures that support AI. In this evolving context, platforms that can demonstrate both technical flexibility and unwavering governance adherence are poised to play a pivotal role in the next phase of digital banking.