What senior leaders are asking as AI moves into banking operations

AI is moving quickly from discussion to execution in financial services. 

At Oradian’s executive roundtable in Manila, The AI-Native Bank: Competing in the Agentic Era, senior banking and fintech leaders came together to discuss what this shift means in practice; how financial institutions can use AI to improve execution, strengthen operations, respond faster to market change, and compete in a more dynamic environment. 

One message stood out: banks and fintechs are no longer asking whether AI matters. They are asking: How do we embed AI into the way we operate, while maintaining control, governance and human oversight? 

AI is becoming part of how financial institutions operate 

For many financial institutions, AI has moved beyond isolated use cases. 

The focus is shifting from individual tools to operational capability. Leaders are looking at how AI can support decisions, improve workflows, reduce manual effort, and enable teams to act faster across the institution. 

Given the complexity of banking operations, this requires the right infrastructure, governance and ways of working built in. Product changes, customer journeys, compliance requirements, reporting processes and back-office workflows all depend on systems that need to be accurate, controlled and reliable. 

It is only by building the right foundations for AI that an institution can become truly AI-native and reap the benefits of AI-enabled operational efficiency. 

Competitive advantage will come from continuous improvement 

Everyone has access to similar tools and models. The real difference will come from how quickly they can turn those capabilities into better products, faster workflows, and stronger customer experiences. 

Future market leaders will not differentiate once. They will need to keep improving. That means launching new products faster, adapting workflows more easily, responding to customer needs sooner, and reducing dependence on long vendor roadmaps. 

For banks and fintechs, this puts new pressure on the core banking platform. 

The core cannot only be a system of record. It must also support controlled change. Institutions need a foundation that gives them stability and governance, while allowing them to adapt quickly when the market moves. 

Governance matters as much as innovation 

In banking, innovation only creates value when institutions can control it. 

AI adoption must come with clear permission controls, auditability, approval flows and human oversight. Leaders need to know what AI can do, who has authorised it, what action it has taken, and how decisions can be reviewed. 

This becomes even more important as AI moves from generating insights to supporting execution. 

The next stage of AI in banking will not only be about recommendations, summaries, or analysis. It will be about AI helping to complete operational work within defined limits. 

This is where agentic operations begin. 

Agentic operations mean AI can support or execute tasks safely, but it must operate inside a governed environment. The governance aspect is particularly important for regulated financial institutions, as AI cannot sit outside the systems, permissions and controls that protect the institution, its customers and its regulators’ expectations. 

In 2026, most institutions are still early in their AI journey 

Interest in AI is high, but adoption remains pragmatic. 

Leaders are focused on business outcomes: faster execution, better decisions, operational efficiency, stronger control and the ability to keep improving. 

The institutions that make progress with AI will not be the ones that try to apply it everywhere at once. They will be the ones that identify where AI can solve real operational problems, then build the infrastructure and governance to support it safely, as AI can only enhance a core system that is set up to support it. 

AI-native banking enables agentic operations and continuous differentiation 

The Manila discussion pointed to two strategic outcomes that will define the next stage of banking infrastructure. 

1. Agentic operations: AI safely supporting or executing operational work under human oversight. 

2. Continuous differentiation: the ability for financial institutions to keep building, adapting and improving products, workflows and capabilities as the market changes. 

Agentic coding will also shape how institutions achieve this. As AI begins to support software development, financial institutions will be able to move faster from idea to implementation, whether they are improving workflows, building integrations or adapting customer experiences. 

In banking, however, that speed must sit inside a governed environment. Institutions need to know what has been built, who approved it, how it affects the core system, and how it can be reviewed, tested, and maintained. 

Together, agentic operations, agentic coding and continuous differentiation show where AI-native banking is heading: faster execution, continuous improvement and greater adaptability, without losing the control, auditability and oversight that regulated financial services require. 

The next advantage will come from execution 

While AI will not replace the fundamentals of banking. Institutions will still need resilience, compliance, trust, security and strong systems of record, it will raise expectations for how quickly financial institutions can act. 

The next competitive advantage will come from the ability to move from idea to production faster, improve operations continuously, and put AI to work safely at scale. 

For banks and fintechs preparing for the agentic era, the question is no longer whether AI will change financial services; the question is whether their operating model and core infrastructure are ready for what comes next. 

Build the foundation for AI-native banking 

For a deeper look at the infrastructure, data and governance financial institutions need to make AI work in practice, read Oradian’s Digital-first bank’s guide to AI in 2026.