Why Modern Issuing Platforms Will Determine Which AI Strategies Succeed
Artificial intelligence (AI) is moving from experimentation to practical deployment across financial services. As agentic models exceed merely generating recommendations, issuers are discovering that AI itself is no longer the limiting factor for autonomous action. AI readiness varies significantly across issuer segments. Digital banks, FinTech issuers and institutions operating modern…
Incident reports that 80% of organizations face moderate to severe operational disruption due to fragmented payments data. This challenge is particularly acute for issuers, as the value of AI becomes increasingly apparent. Organizations continue investing in AI because it generates measurable business outcomes, including increased revenue and reduced costs.
However, despite years of experimentation, enterprise deployment of AI remains limited. Only 16% of banks have fully deployed production use cases, while 52% have only piloted agentic AI. The reason for this limited adoption lies in the fact that scaling AI's value across an enterprise requires more than just model intelligence.
Organizations need to access reliable data, connect workflows, and operate within clear controls. The organizations leading the way in AI adoption possess mature environments that support these requirements. Visa's research reveals that while 75% of European banks have a defined AI strategy, only 43% have an organization-wide approach to identifying and testing AI use cases.
Coordinating data, workflows, governance, and operational execution across historically siloed systems is a complex process. The challenge of creating an operational environment where AI can reliably execute work while maintaining security, regulatory compliance, and customer trust explains why many AI initiatives stall. While general-purpose consumer tools may be suitable for individual use, operational AI requires access to an institution's transaction histories, customer records, risk signals, policies, and real-time account information.
Forrester's survey indicates that 50% of banks and issuers identified legacy payments infrastructure and technical debt as key modernization challenges. The impact of fragmented operations is evident in the payments ecosystem, with 80% of organizations experiencing moderate to severe disruptions due to fragmented payments data. Removing individual bottlenecks can help, but AI demands a larger transformation.
The next step for issuers is to modernize their architectures, allowing AI-driven processes to operate reliably across the card lifecycle.
Written by urgent.news from PYMNTS's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
