Artificial Intelligence has ceased to be a futuristic promise and has become the tangible engine of financial transformation. However, beyond the media noise surrounding Generative AI, the true revolution lies not just in algorithms, but in an organization’s capacity to govern its data and, crucially, in the human judgment that guides those decisions.
In a recent conversation with David Roldán, Chief AI Officer and expert at the intersection of technology and business, we explored how the industry is moving from initial fascination to the implementation of real value.
David Roldán,“The problem with Generative Artificial Intelligence is not in the answers, the real problem is in the questions.”
Chief AI Officer and AI Researcher.
Beyond the GenAI hype
It is often assumed that adopting AI is as simple as opening a chat application and asking questions, but the enterprise reality is far more complex. Artificial Intelligence is merely the “tip of the iceberg”; beneath the surface, success depends entirely on data quality and robust infrastructure.
As Roldán points out, the current impact of Generative AI sometimes falls short of expectations precisely because this foundation is ignored: “AI comes with data. The data has to be of quality. If the data isn’t quality, the AI results won’t be either”.
“The finance of the future will be more instant and invisible, but everything will rest on well-governed data and algorithms.”
From isolated innovation to “Data + AI First”
Traditionally, financial institutions competed on capital and branch scale. Today, the competitive advantage lies in the capacity for data integration and governance. It is not just about technology; it is about having a holistic vision that connects the CTO, the data manager, and the compliance director.
To make the user experience simple (hiding complexity behind a click), entities must first resolve the fragmentation of their information. Roldán highlights the importance of integrations: if data is distributed across the organization’s “digital universe,” it is vital to be able to consume it and expose the results efficiently.
Use cases: Start simple to scale value
Deploying AI in banking and fintech does not require starting with massive projects. The key to avoiding “analysis paralysis” is to identify simple use cases that provide fast, tangible value.
David Roldán suggests clear focus areas to move from experimentation to impact:
- Process Innovation: AI allows us to question the established order. If the standard way to solve a problem is “1, 2, 3,” AI enables us to simulate what happens if we alter that sequence, driving creative innovation.
- Operational Efficiency & HR: From chatbots for customer service to tools supporting personnel selection, freeing teams from repetitive tasks.
- Risk Management & Prediction: Using models for risk prediction in home insurance, mortgage granting, or sales plan support.
Infrastructure and Governance: The new strategic core
To enable these use cases, the data infrastructure must be solid. Roldán, with his background in integration architecture and APIs, emphasizes that the problem is rarely the technology itself, but the governance of it.
AI Governance is not just about complying with regulation (like the AI Act); it is about establishing internal standards that guarantee traceability. We need to know “who does what, how, and with what tools” to ensure that AI usage is ethical, responsible, and aligned with business objectives. Without this layer of control, which acts as the foundation of the “data lake”, it is impossible to generate the necessary trust for clients to adopt these solutions.
People at the center of transformation
Perhaps the most critical point Roldán highlights is that, in the midst of a technological revolution, the most valuable skills are human ones. While hard skills and repetitive cognitive tasks are increasingly automatable by AI, soft skills such as empathy and critical thinking remain irreplaceable.
💡David said…
“AI allows us to automate repetitive tasks that don’t add much value, leaving us space for creativity.”
The banking of the future will require professionals who act as a “quality filter,” questioning AI results to avoid hallucinations and bias. As Roldán concludes, technology may process the credit score, but empathy, ethics, and the ability to understand the client’s context will remain the exclusive domain of people.
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