4. Governing execution-layer AI

As AI moves from insight to execution, governance can no longer remain retrospective.

Many banks still rely on after-the-fact controls, reconstructing decisions, auditing outcomes and validating compliance once actions have already been taken. That model struggles to scale in an environment where decisions are continuous and automated.

The shift underway is toward embedding governance directly into execution. Policy enforcement, monitoring and explainability are becoming integral to system design, rather than external overlays.

Approaches such as AI/works™ support this by enabling transparent model behavior and system-level explainability within modernized domains.

5. Cost is an architecture problem

Cost pressures across banking are persistent, but often misdiagnosed. They are typically treated as financial problems, addressed through periodic cost programs.

In reality, they are architectural.

Duplicated systems, redundant logic and fragmented ownership create structural inefficiencies that cannot be resolved through surface-level interventions. Without addressing these underlying issues, cost reductions tend to be temporary.

A more durable response is emerging: simplifying at the design level. This involves clearer domain ownership, more modular architectures and the systematic elimination of duplication.

AI is increasingly used to support this process, identifying overlap, quantifying technical debt and enabling more targeted decisions. In this context, cost reduction is not the objective, but the outcome of greater clarity.

The executive mandate

Taken individually, these five tensions can be managed within IT. Taken together, they point to a broader shift in how banks compete.

As capital becomes more automated and regulatory expectations move toward continuous resilience, performance will depend on how well institutions can operate at speed while maintaining control. For many, the limiting factor is not access to new technology, but visibility into existing systems. While high-level consultancy frameworks focus heavily on process redesign and workforce upskilling to prepare for this shift, they ignore a stark reality: you cannot upskill a workforce for agentic workflows that a batch-based mainframe physically cannot execute. Visibility is the mandatory engineering prerequisite to any competitor's strategy.

The “stranger core” is unlikely to fail abruptly. Its effects are more gradual and harder to isolate — slower execution, delayed integrations and capital flowing to institutions that can respond more quickly. This is where the mainstream industry narrative falters. Competitor perspectives largely treat the agentic shift as a front-office, productivity-enhancement story. But this isn't a matter of building shinier chatbots; it is a question of structural, systemic survival. You cannot safely encode governance boundaries or deploy autonomous agents onto an unmapped black box.

For the C-suite, this places architecture firmly in the realm of enterprise strategy. Decisions about the core now shape growth, risk and competitiveness. 

Overcoming this architectural debt requires strict operating discipline: establishing clear domain ownership, governing emerging AI behavior at runtime and replacing retrospective compliance with real-time explainability. This transition must be sequenced deliberately, evolving the architecture domain by domain based on hard, verifiable evidence.

By 2026, the distinction will be clear: some banks will be operating on systems they understand and can adapt with confidence. Others will still be working around infrastructure that constrains them.