Banking transformation in action: Banco do Brasil
Banco do Brasil in Brazil presented the incredible transformation work they have accomplished.
As part of the Qorus-Infosys Finacle Banking Innovation Awards 2026, we selected a few of the banks whose projects impressed us the most. Among them, Banco do Brasil in Brazil presented the incredible transformation work they have accomplished.
What transformation strategy has your organization led in recent years?
Banco do Brasil has led a structural transformation to become an AI-enabled and governance-led organization, repositioning data and artificial intelligence as core drivers of its business and operating model.
The strategy moves beyond digitalization by establishing an integrated system that connects enterprise data platforms, generative AI capabilities, and institutional governance mechanisms, enabling scalable and responsible AI adoption. This approach embeds AI into critical domains such as risk, pricing, legal operations, and customer management, while ensuring reliability and compliance at scale.
Simultaneously, the bank redesigned its operating model by aligning leadership development, experimentation, and solution delivery, transforming AI from isolated expertise into an organizational capability.
This transformation also reshapes culture and workforce, enabling leaders to make consistent, data-driven decisions and orchestrate cross-functional execution, positioning Banco do Brasil as a coordinated, enterprise-scale AI innovation system.
What prompted this strategic transformation?
This transformation was prompted by the need to respond to a more complex, data-intensive, and highly regulated financial environment. Banco do Brasil faced growing pressure to make decisions faster and more consistently across diverse business areas, while reducing manual effort, operational risk, and fragmented use of data and AI.
At the same time, generative AI created new opportunities to improve productivity, customer experience, risk management, and business performance, but also introduced challenges related to reliability, explainability, security, and compliance. Scaling AI without a structured model could increase inconsistency and reputational exposure.
The leadership team therefore initiated a long-term transformation to turn data and AI into coordinated organizational capabilities, supported by governance, workforce readiness, and responsible adoption mechanisms, ensuring that innovation could scale with trust, control, and measurable business value.
What have been the most significant changes implemented, and how were they led?
To enable this transformation, Banco do Brasil implemented three major structural shifts.
First, it moved from fragmented analytics to enterprise AI and data platforms, enabling large-scale decision-making and consistent insight generation across the organization.
Second, it shifted from isolated experimentation to governance-led scalability, introducing structured validation, risk controls, curated AI architectures, and certification frameworks for generative AI models, ensuring reliability and compliance as innovation scales.
Third, it redesigned its operating model, transitioning from specialist-driven execution to leadership-driven AI adoption, aligning leadership development, experimentation, and execution. Initiatives such as leadership upskilling and applied innovation challenges ensured AI became embedded in business routines.
These changes were led through strong coordination between leadership, AI specialists, and business units, ensuring alignment between strategy, governance, and execution.
What unique or key transformation practices do you follow to ensure success?
To ensure consistent execution of this transformation, Banco do Brasil follows a set of integrated practices that combine agility, governance, and scale.
First, initiatives are developed through agile, cross-functional teams that bring together AI specialists, business units, and technology areas, ensuring alignment between models and real operational needs.
Second, the bank adopts a governance-by-design approach, embedding validation, risk controls, human oversight, and certification frameworks for generative AI models into solutions from development to production.
Third, a leadership-driven operating model connects capability building and execution, enabling experimentation to evolve into prioritized, scalable solutions.
What differentiates this model is the integration of governance, leadership enablement, and AI delivery into a single operating system.
These practices are supported by enterprise platforms and cloud-based AI technologies, enabling continuous delivery, scalability, and reuse across the organization.
How has your workforce been engaged and transformed through this journey?
Workforce transformation has been a central pillar of Banco do Brasil’s strategy, ensuring that data and AI adoption scale beyond technology into daily decision-making.
The bank implemented a leadership-driven approach, combining capability building with real-world application. Programs such as Líder Digital and the BB Data Leaders Challenge engaged more than 2,000 leaders across strategic, tactical, and support units, connecting learning with practical AI use cases and business priorities.
This model integrates upskilling, experimentation, and governance, enabling leaders to understand AI implications, prioritize initiatives, and drive adoption consistently across the organization. At scale, training initiatives reached tens of thousands of employees through the corporate university, reinforcing a shared data-driven culture.
As a result, AI adoption shifted from specialized teams to a broader organizational capability, strengthening collaboration, decision quality, and execution consistency.
What measurable outcomes demonstrate the success of your transformation?
The transformation delivered measurable outcomes across business performance, operational efficiency, governance, workforce adoption, and ESG.
AI-driven pricing improved financial performance, generating +R$5.7 million in annual revenue uplift and reducing lost transactions by 15%. In operations, the automation of complex processes reduced manual workload and achieved up to 24% reduction in processing time, with minimal human intervention.
Enterprise platforms scaled adoption significantly: over 11,800 users actively use advanced analytics solutions, improving decision-making and increasing business effectiveness. Governance advancements eliminated AI vulnerabilities in critical applications, enhancing reliability and trust.
From a workforce perspective, more than 2,000 leaders were engaged, accelerating data-driven decision-making across the organization. The transformation also strengthened ESG governance in areas such as rural credit monitoring.
These results demonstrate the bank’s ability to scale AI with measurable impact, governance, and organizational adoption.
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