FIDA and AI: Relevance – not regulation – will determine value creation
With the EU’s Financial Data Access regulation (FIDA), access to financial data across sectors will soon become standardized. While this addresses long-standing infrastructural challenges and breaks down historical data silos, it does not, by itself, create value for businesses or consumers.
As data access becomes regulated and uniform, the strategic bottleneck shifts decisively from availability to relevance. Competitive advantage no longer stems from owning data, but from the ability to turn data into meaningful customer value.
This is where artificial intelligence takes on a fundamentally new role. Under FIDA, AI is not a supporting technology, but a decisive strategic value layer. It determines which data is meaningful in a given situation, which services are actually relevant, and whether data usage is economically viable.
“As data access becomes standardised, organisations face a clear choice. They can either continue to manage data – or they can actively design relevance," says Stefan Schnitzler, Senior Manager at Eurogroup Consulting.
AI as a context engine
In an environment where data is abundant, relevance becomes the scarce resource. Organisations that focus primarily on regulatory compliance may meet minimum requirements, but they risk becoming interchangeable suppliers in ecosystems where others capture the real value.
“The strategic role of AI begins where traditional data processing reaches its limits. As a context engine, AI does not generate more information. It creates situational decision context," Schnitzler argues.
By combining data from multiple sources and identifying patterns that point to concrete life events or decision moments, AI prioritises what actually matters. Crucially, this also includes the ability to deliberately trigger no action at all. Under FIDA, deciding when nothing should happen becomes a defining quality of effective digital services.
AI as a service designer
This shift fundamentally changes how financial services are designed. Traditional product-centric models rely on predefined offerings and stable usage patterns. In an open data environment, this logic becomes increasingly fragile.
AI enables adaptive services that emerge during operation rather than at the point of initial conception. Humans define strategic guardrails, quality criteria and economic boundaries. Within these guardrails, AI dynamically combines, adapts or withholds service components based on real-world behaviour and situational context.
Differentiation no longer comes from the number of products offered, but from the ability to orchestrate services in a way that is relevant at a given moment.
AI as an economic filter
FIDA makes data usage legally possible, but not automatically economically sensible. Standardised access often creates the temptation to use data simply because it is available, leading to rising complexity and costs without corresponding value.
Here, AI acts as an economic filter. It continuously assesses which data sources actually contribute to value creation and which generate effort without impact. Economic viability is not assumed by default, but actively steered in operation.
“Not every possible use of data creates value. Without economic filtering, costs scale with data volume, while margins stagnate," ,” Schnitzler notes.
From regulation to strategic choice
Ultimately, FIDA establishes the technical tracks. What runs on them is determined by the ability to design relevance.
Financial institutions now face a strategic choice. They can remain functional data providers, focused on compliance and data supply. Or they can leverage AI as a strategic value layer to actively shape customer journeys, services and outcomes.
Success in the landscape will not be measured by the volume of data handled, but by the deliberate design of services that reduce complexity, support real decision-making and create tangible customer advantage.
