The AI-Enabling Bank: Why Infrastructure Matters More Than Features in the Agentic Era

The banking industry stands at a pivotal inflection point. For years, financial institutions have focused on integrating artificial intelligence into customer-facing tools: chatbots for support, fraud detection algorithms, and personalized loan recommendations. While these internal improvements represent significant progress, they only address one side of the AI revolution.

I believe the next frontier of competition will not be defined by which bank has the best internal AI features, but by which bank becomes the most effective AI-enabling infrastructure for the broader ecosystem.

What is the difference between an AI-enabled and an AI-enabling bank?

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An AI-enabled bank consumes artificial intelligence to optimize its own operations or user experience. You see this today in AI-powered chatbots handling routine queries, machine learning models refining credit scoring, and predictive analytics forecasting corporate cash flow. These enhancements boost efficiency and polish the user interface, but they remain largely inward-facing.

An AI-enabling bank, by contrast, designs its core systems so external AI agents, Large Language Models (LLMs), and autonomous workflows can securely and seamlessly interact with financial data and execute actions.

This transition goes far beyond traditional API banking. It involves adopting standardized protocols like the Model Context Protocol (MCP). Pioneered by innovators like Mercury, MCP acts as a secure bridge that allows AI models to read transactions, manage accounts, and trigger payments with real-time context. MCP has been described as “Bluetooth for AI”: a universal connector that transforms fragmented banking data into actionable intelligence for agentic workflows.

Why is enabling AI infrastructure more strategic than building features?

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Becoming a part of the AI infrastructure stack offers several structural advantages that often outweigh the benefits of standalone feature integration:

1. Platform Stickiness and Ecosystem Lock-In: When the AI agents managing a company’s accounting, treasury, or business operations rely on your bank for data and execution, the switching costs become massive. Startups building AI-native workflows will naturally default to banks that “just work” with their custom agents.
2. New Revenue and Partnership Opportunities: Enabling banks can monetize through premium API access, MCP hosting, and embedded finance plays. They become the essential rails for the exploding agent economy rather than just another node in it.
3. Defensibility in a Commoditizing Market: Sleek apps and robo-advisors are easily replicated. Deep, secure AI integrations requiring regulatory trust and real-time data pipelines are much harder to copy. Banks that enable AI position themselves as indispensable infrastructure, similar to how cloud providers did for the software era.
4. Future-Proofing for Autonomous Finance: As AI shifts from assistance to autonomy, banks must support natural language commands and multi-step reasoning. Early enablers will capture high-growth segments like tech-forward SMEs and enterprises running AI financial operating systems.

Mercury’s recent $200M raise and its emphasis on MCP and AI Insights highlight this shift. Traditional banks risk becoming “dumb pipes” if they ignore the infrastructure layer in favor of internal features.

How does the Philippine banking landscape measure up in AI readiness?

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While Philippine banks have made strides with Open Finance PH pilots and various API initiatives, most remain far from achieving AI-enabling status. There is a noticeable gap in public developer portals and a total absence of MCP support or CLI tools optimized for AI agents.

Based on current market observations as of mid-2026, here is a summary of the readiness landscape for top universal and digital banks in the Philippines:

Key takeaway: While UnionBank, Maya, and Tonik lead in API accessibility, there is a clear deficiency in AI-native tooling across the board. This leaves Philippine businesses reliant on foreign banks or inefficient workarounds for seamless AI financial workflows.

What is the path forward for Philippine financial institutions?

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To move from legacy systems to AI-enabling infrastructure, I suggest that banks prioritize the following:

• Open Developer Portals: Publish comprehensive documentation and robust sandbox environments.
• Standardized AI Access: Implement support for MCP servers to enable standardized, secure tool-calling by AI agents.
• Agent-Centric Interfaces: Offer CLI tools and natural language interfaces that complement traditional REST APIs.
• Trust and Consent Management: Prioritize security and auditability to build the necessary trust for autonomous AI interactions.

Banks that act now can leapfrog their competitors and position the Philippines as a regional hub for AI-native finance in Southeast Asia. The future of banking isn’t just about smarter apps: it is about the banks that power the intelligence layer itself.

Appendix: Philippine Banking AI Readiness (Full Dataset)

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The following table provides a comprehensive view of the current state of API accessibility and AI-native infrastructure across the top 10 universal banks and all 6 licensed digital banks in the Philippines as of mid-2026.

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