Banks need cross-functional teams, robust controls for AI rollout – S&P webinar

➤ Financial institutions face mounting challenges in balancing rapid AI adoption with robust governance frameworks and cybersecurity controls.

➤ Successful AI implementation requires data, technology, risk, compliance and business teams working together from day one.

➤ AI mentions by Europe's top 50 banks surged from eight in Q1 2023 to 520 in the same quarter of 2026.

European banks must create cross-functional teams spanning technology, risk and compliance functions, while implementing robust governance frameworks, to successfully deploy AI at scale, experts said during a Sept. 22 S&P Global Market Intelligence and European Banking Federation webinar.

Banks need more than just data scientists and machine learning engineers, but also people who understand the business, the risks, and the regulatory environment, according to Andreas Widegren, head of public affairs at Nordea Bank Abp and a member of the European Commission's expert group on critical and emerging technologies.

"You need to have the data people, you need to have the tech people, you need to have the risk people, you need to have the compliance people, and you need to have the business people all working together from day one to deliver a successful AI product," Widegren said.

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Furthermore, the full human experience needs to be applied to the models; therefore, banks need people of different generations, he said.

"AI is going to enable us to do more with the same amount of people, not to remove everyone," Widegren said.

AI an integral part of banks' strategies

Mentions of AI by Europe's top 50 banks — based on machine-readable transcripts from earnings calls, investor days and other corporate events — increased from zero in Q4 2022 to a high of 520 in Q1 2026.

"We all know AI is important, but this is the data backing it up and showing how this is top of mind for bank executives," Mohsin Ali Khan, director of EMEA sell-side business development at Market Intelligence, said.

The rapid rise of AI is forcing banks to move beyond adding the technology to existing workflows, and to push through fundamental operational redesign, according to Marco Bressan, chief scientist at Banco Santander SA.

Bressan, who is leading his third large-scale data and AI transformation at a major European bank, said current AI adoption differs from waves that came before due to the wide availability of capable general-purpose models that are easily adopted by business units and employees.

"For us ... AI is not simply a technology that is added to the business, but it is increasingly shaping the business itself," Bressan said. "The challenge is not to get people to use AI, but to build the capabilities required to redesign the business around it."

AI changes mean security concerns are mounting

As banks accelerate AI deployment, establishing robust governance frameworks and security controls has become critical. Banks must balance rapid adoption with conservative, controlled rollouts to maintain oversight, the experts said. This means establishing clear governance processes, particularly as AI becomes embedded in internal operations and vendor-supplied processes.

"We need to make sure that we have the right governance in place, that we have the right security controls in place, and that we're able to demonstrate to regulators that we know what's happening with these AI systems," Widegren said.

The rapid pace of AI evolution, with new models and capabilities emerging every few months, compounds the challenges, creating difficulties in making long-term technology commitments while ensuring systems are secure and compliant.

Banks must develop flexible architectures that adapt to new AI capabilities without requiring complete system overhauls, Bressan said.

"You can't rebuild your entire infrastructure every six months when a new model comes out," he said.

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