Agentic AI: An Asset for SMEs Against Banking Giants

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Key Takeaways

1About 78% of banks are already using SaaS platforms to integrate AI and process data in real time.

2Small and medium-sized enterprises benefit from the adoption of SaaS, avoiding the semantic weight of large institutions.

3Headless and composable architectures facilitate the portability of operational definitions through reusable APIs.

💡Why it matters — The adoption of SaaS and agentic AI enables SMEs to compete more effectively with large banks by optimizing their operations without structural burdens.

Introduction to Agentic AI

Agentic artificial intelligence is often perceived as a domain reserved for large companies due to the high costs associated with its deployment. However, a recent study reveals that nearly 78% of banks have already adopted SaaS-based core banking platforms. These platforms enable real-time data management, thereby facilitating the integration of AI. By the end of 2026, SaaS and hosted models are expected to account for about two-thirds of the core banking market, which could profoundly transform the structure of financial institutions.

The Paradox of Tier 1 Institutions

Large financial institutions, often at the forefront of AI innovation, paradoxically find themselves hindered by their own semantic complexity. Years of internal development have led to divergences in operational definitions across their various branches and functions. In contrast, small and medium-sized enterprises (SMEs) do not carry this burden, as they have not developed such complex internal infrastructures. For them, SaaS has replaced custom development, thereby simplifying their operations.

SaaS as an Accelerator of Semantic Architecture

Modern SaaS platforms do more than just enhance productivity; they redefine operations. For example, nCino structures workflows for commercial loans, Salesforce Financial Services Cloud organizes customer relationships, and ServiceNow manages case routing and operational escalation. These systems already integrate a significant portion of the operational definitions of institutions, thereby standardizing definitional meaning, a crucial aspect for agentic AI.

Headless Architecture and Its Impact

Headless and composable architecture has become a key element in the evolution of enterprise AI. By decoupling workflow logic from interfaces, it allows operational definitions to be accessible via reusable services and APIs. This approach facilitates the portability of institutional definitions across different workflows, thereby increasing the efficiency and flexibility of systems.

The Importance of the Semantic Control Plane

One of the major challenges of enterprise systems is the diversity of definitions for similar concepts. For instance, a fraud investigation workflow may be interpreted differently across systems. The Semantic Control Plane plays a crucial role in harmonizing these definitions before orchestration, thus ensuring consistency in process execution.

The Governance Layer Under Enterprise AI

With the expansion of orchestration across different systems, reasoning becomes an essential component of enterprise governance. The challenge lies not only in the accuracy of the results produced by AI but also in verifying that the reasoning follows an approved definitional meaning before execution. The 3 Cs framework of agentic, which includes Context, Control, and Coordination, is fundamental for managing orchestration at the reasoning level in enterprise environments.