The financial sector is undergoing significant transformation thanks to the integration of Artificial Intelligence (AI) solutions and open banking. These advancements enable strategic vision, improve operational efficiency, and offer more personalized services. Modernization includes integration, API-fication, and new open banking models, driving innovation and market adaptation.
Intelligent automation is key for companies seeking efficiency without losing control. It not only reduces costs but also redesigns processes to minimize errors, improve traceability, and enable data-driven decision-making. Unlike traditional automation, it integrates artificial intelligence, real-time analytics, and continuous learning to dynamically optimize operations. Additionally, it strengthens control by providing greater visibility and standardization. Its impact spans areas such as finance, operations, and customer service. The real value lies in automating with purpose, prioritizing critical processes and aligning them with business objectives.
AI agents are evolving into a new phase with the ability to act economically thanks to blockchain. They can now operate wallets, execute payments, interact with smart contracts, and manage assets without direct human intervention. This removes barriers from the traditional financial system, where they were not recognized as entities. New infrastructures are emerging, such as wallets designed for machines, temporary permissions (EIP-7702), and automated machine-to-machine payments. Real-world use cases already exist in DeFi and decentralized identity systems. However, risks remain, including key security, fraud, latency, and regulatory gaps. This convergence is redefining the financial system by enabling software to act as an autonomous economic agent.
Multi-Agent architecture enables the effective implementation of BIAN (Banking Industry Architecture Network) principles by introducing an intelligent layer that orchestrates the bank’s Service Domains. Artificial intelligence agents interpret requests from customers or employees, identify intent, and coordinate interactions with banking capabilities exposed through APIs, while keeping the experience, business, and data layers decoupled. This approach also enables Composable Banking, where capabilities are built as independent services that can be dynamically combined to support different customer journeys, allowing banks to build more modular, scalable, and service-oriented platforms.
AI in Costa Rica has evolved from experimentation to a strategic priority. The challenge is no longer testing tools, but generating measurable impact. With strong leadership, reliable data, and integration into key processes, AI moves beyond pilot projects and becomes a sustainable competitive advantage.
In 2026, the most competitive companies will not be those that adopt the most technology, but those that use it with purpose, measurable returns, and risk control. There are no universal formulas: each organization must choose technology based on its reality, data, and priorities. Trends such as practical AI, automation, strategic data use, cybersecurity, and scalable solutions will define the year. The focus will be on a few high-impact use cases, well-prepared data, and clear governance.
Generative artificial intelligence has rapidly evolved from experimentation into a strategic priority. While creating proofs of concept is relatively easy, moving them into production and scaling solutions in a secure, well-governed, and business-integrated way remains a major challenge. This challenge is even greater with agentic AI, where multiple agents collaborate and require a clear strategy to avoid failures when scaling. Success depends on a lifecycle that includes ideation, value modeling, a strong data strategy, and a focus on minimal usable solutions that evolve with security and responsibility.
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