Tech • IA • Crypto
Customer Ignite Talk: Antonio Bravo Acin (Global Head of AI Transformation, BBVA) & OpenAI
8/10TL;DR
BBVA is embedding AI across its entire business through a structured “eight-part” strategy, combining enterprise-wide deployment with specialized systems to transform banking operations and customer experience.
KEY POINTS
AI as an Operating Layer
BBVA, one of the world’s largest banks, has shifted from treating AI as a tool to making it a core operating layer across the organization. More than 120,000 employees have access to AI systems, enabling integration into daily workflows rather than isolated use cases. The initiative is designed to support critical business functions, not just automate simple queries.
Six Specialized “Robots”
The bank’s strategy is organized into six domain-specific AI systems, referred to as “robots,” each aligned with a major business function. These include retail banking, advisory services in corporate and private banking, risk analysis, back-office operations, software development, and employee productivity. Each robot is co-led by business executives, ensuring alignment with operational priorities.
Retail and Advisory Transformation
In retail, AI is being used to reshape digital customer interactions across mobile and future devices. In advisory-heavy segments, such as corporate and investment banking, AI aims to increase the time bankers spend with clients from roughly 20 percent to significantly higher levels. The goal is to enhance relationship-driven services rather than replace them.
Efficiency Gains in Risk and Operations
AI is improving risk analysis by enabling faster and more accurate assessments, streamlining underwriting processes and enhancing customer experience. In back-office functions, automation of document processing and classification is reducing manual workloads in areas like mortgages, insurance, and consumer finance.
Developer Productivity and Backlog Reduction
AI-powered coding tools are being deployed to software teams to increase productivity and address longstanding development backlogs. This is particularly গুরুত্বপূর্ণ for a large institution managing complex systems and continuous feature demands.
Enterprise-Wide Enablement
A sixth “connected” system focuses on democratizing AI use across all roles. Employees are encouraged to build their own tools and automations for tasks such as email, scheduling, and internal systems. The approach has already produced over 100 internal tools, some delivering 70–80 percent time savings for thousands of employees.
Two Foundational Pillars
The strategy is supported by two core pillars: becoming a data-rich organization and building orchestration capabilities to manage a growing network of AI agents. These ensure that systems can access high-quality data and operate cohesively at scale.
Top-Down Strategy with Bottom-Up Adoption
Leadership alignment has been critical, with the strategy developed collaboratively across the executive team. At the same time, widespread employee access encourages experimentation, allowing successful use cases to emerge organically and inform broader priorities.
Governance, Training, and Cultural Change
Dedicated teams oversee AI adoption, providing hands-on training across global offices and tracking usage metrics. Monthly dashboards compare adoption rates among leaders, including the CEO, creating accountability and internal pressure to engage with the technology.
Partnerships and Rapid Iteration
Operating in a fast-evolving environment, BBVA emphasizes partnerships with external technology providers to stay current. The bank follows an iterative approach, delivering measurable “aha moments” every few months to demonstrate value and sustain momentum.
CONCLUSION
BBVA’s AI strategy illustrates how large financial institutions can integrate AI at scale by combining strong executive alignment, enterprise-wide access, and targeted operational use cases to drive measurable impact.