AI is no longer a promise, it's a $67 billion strategic mandate for banking. Ahead of Sibos 2025, a new report from SAS distills perspectives from top executives at Banorte, Intesa Sanpaolo, Millennium BCP, and Old National Bank, revealing five crucial lessons for integrating AI responsibly to drive both profitability and trust.
Artificial intelligence is no longer a futuristic concept for financial services; it’s a current-day strategic imperative. With global banking sector AI spending projected to more than double from an estimated $31 billion in 2024 to nearly $67 billion by 2028, banks are rapidly moving From Algorithms to Impact. This massive investment, however, carries significant risks without a clear, responsible roadmap.
Just ahead of Sibos 2025 in Frankfurt, a new report from SAS—a global leader in data and AI—distills the hard-won wisdom of executives from institutions like Banorte, Intesa Sanpaolo, Millennium BCP, and Old National Bank. These leaders offer five non-negotiable lessons for integrating AI in a way that builds both profitability and, crucially, trust.
The time for treating AI as an isolated pilot project is over. According to the executives, successful AI adoption demands its full integration into the core business strategy—not as a technical add-on, but as a pillar of long-term resilience and competitiveness.
Abraham Izquierdo, Managing Director of Trading and Treasury Risks at Banorte, emphasized that “Innovation and AI must be recognised as a pillar of the institution’s strategy.” This top-down leadership is deemed nonnegotiable. For treasury and risk professionals, this means AI deployments must directly align with critical goals, such as capital calculations, liquidity management, and preparing for adverse economic scenarios.
As José Miguel Pessanha, Chief Risk Officer at Millennium BCP, noted: “We are developing models to help us better prepare for adverse scenarios in the next five years… we can more effectively mitigate the effects of a rise in defaults.” This focus grounds AI in real-world risk mitigation, underscoring its strategic value.
Despite the rapid evolution of AI tools, human judgment remains indispensable. The report stresses that investment in a skilled workforce and a data-driven culture is just as important as investment in the underlying AI platforms.
AI can automate tasks, but it cannot replace the strategic, creative problem-solving capabilities of leadership. Pessanha explained that AI cannot definitively answer key questions like: “How will I minimise the impacts for the bank? What will be the risk strategy afterwards?”
Intesa Sanpaolo’s Head of Data Science and Responsible AI, Andrea Cosentini, highlighted the cultural shift, stating that recognizing data-driven successes creates a culture where data is valued as a strategic asset. This people-first approach is also driving financial inclusion, using historical loan data to develop new lending models for underserved market segments.
The consensus among banking leaders is clear: robust foundations are essential for any advanced AI use case, including the light, low-code applications powered by GenAI. This foundation includes a cloud-native platform, coupled with strong data governance and clear synthetic data principles.
Harmonizing data for different needs—from provisioning to capital and liquidity calculations—is a crucial first step. As Pessanha put it: “Walking before you run is essential.”
Furthermore, Banorte’s Izquierdo warned that AI and cloud deployments must be multi-year and step-by-step. Banks must recognize their long-term ambition but focus on short-term, precise goals to maintain compliance and build trust.
The focus must shift from merely procuring innovation to actively driving it from within. Equipping internal IT and developer teams with AI tools unleashes significant productivity gains, freeing employees from tedious manual work for higher-value, deeper analysis.
Andrew McCammack, Data Science Officer at Old National Bank, cited a compelling example where AI was used to generate 90% of the code for a new web-based workflow for loan data entry, eliminating the use of “Microsoft Excel sheets.” This has allowed staff to pivot to more rewarding and valuable deep analysis.
This shift fosters an environment where asking ‘why’ is valued, allowing data scientists to design experiments that validate—or disprove—assumptions, often revealing breakthrough solutions for market volatility or complexity, as seen at Banorte.
AI is not a destination but an ongoing journey of experimentation, feedback, and adaptation. Banking leaders stress the need to stay connected with internal stakeholders and external partners—including academics and startups—to keep pace with the rapid evolution toward agentic and quantum AI.
The deployment of Generative AI (GenAI) is currently stretching innovation boundaries across the sector, from accelerating code base development to strengthening cybersecurity posture and fostering business continuity.
However, Intesa Sanpaolo’s Cosentini offered a necessary caution: GenAI’s full potential is only realized when guided by human oversight, ensuring that creativity, responsibility, and strategic judgment remain at the heart of the process.
The full report, “From Algorithms to Impact: Banking’s AI Future,” offers a deeper dive into these executive insights.