Hitachi Vantara has published research showing that financial institutions are making data growth their top storage priority, while giving less attention to AI-ready storage and centralised data platforms. The findings are based on a survey of 100 financial services decision-makers worldwide.
The study found that 35% of respondents identified managing data growth as a leading storage priority, the highest share in the survey. By contrast, 10% said enabling AI-ready storage and data platforms was a top priority, while 9% pointed to centralised data hubs for governance, reporting, AI and machine learning, and data reuse.
The figures suggest many banks, payments groups and investment firms are dealing first with rising data volumes, while placing less emphasis on the infrastructure needed to organise and use that data across larger systems. Other priorities centred on governance, access and modernisation rather than AI-specific storage preparation.
Data sovereignty, regulatory compliance and policy-driven governance ranked second at 30%. That focus also appeared in responses on AI deployment, with 99% saying data sovereignty concerns influence where they run AI workloads.
Nearly one in five respondents, or 19%, said sovereignty concerns significantly limit AI workload scalability or performance. The survey also found that 23% restrict AI workloads to specific regions, 21% train models centrally while keeping data local, and 16% split training and inference across locations because of sovereignty rules.
Cost pressure
Cost emerged as the dominant factor in object storage decisions. Some 65% of financial institutions cited cost or total cost of ownership as the most important consideration when selecting object storage platforms, well ahead of data resilience and availability at 46%.
That gap points to the budget pressures facing technology and data leaders as they weigh storage spending against broader demands from compliance, operations and AI programmes. In practice, institutions are trying to balance immediate financial discipline with the need to maintain access to growing volumes of structured and unstructured data.
Object storage has become more relevant as firms build analytics environments and prepare data for AI use, while block storage remains closely tied to core banking systems, payments infrastructure, databases and transaction processing. The survey indicates that many firms are still working out how to connect those layers in a way that supports governance and operational resilience.
Octavian Tanase, Chief Product Officer at Hitachi Vantara, said the survey showed a gap between rising complexity and current infrastructure choices.
"Financial institutions clearly recognise that data management is becoming more complex, but many are not yet fully addressing what their environments require," Tanase said. "As data volumes grow, organisations need unified data platforms that can span block, file and object storage to reduce fragmentation, improve visibility and support consistent governance for the mission-critical data that financial institutions depend on. This includes unstructured data used for analytics and AI, as well as the mission-critical databases and transaction systems that support core operations. That activation depends on the data availability and resilience needed to keep information accessible, protected and ready for use."
Split adoption
The research also pointed to uneven adoption of object storage across the sector. While 35% of respondents said they had deployed object storage at enterprise scale across multiple workloads and teams, 36% remained in early-stage or pilot phases.
That split suggests a two-track market. One group of firms is moving towards broader use of integrated storage and data management systems, while another is still testing deployment models and limiting adoption to smaller projects.
For financial institutions, the issue goes beyond storage volume alone. Regulated firms must retain control over where data sits, how it is accessed, and how it is combined for analytics or model training, while maintaining resilience for systems that support daily operations.
Those requirements make storage architecture a strategic issue for banks and other financial groups as AI moves from experimentation towards wider use in areas such as analysis, reporting and operational decision-making. They also help explain why governance concerns rank so highly even when AI-ready systems have not yet become a leading investment priority.
Tanase said firms are being asked to manage a growing mix of operational, analytical and AI-related demands.
"Financial institutions are being asked to manage more data, in more places, while maintaining control, resilience and compliance," he said. "Modern storage platforms, such as Hitachi Vantara's Virtual Storage Platform One, feature block, object and file storage systems that can help organisations address that complexity by supporting scalable, governed and accessible data environments that serve both traditional retention needs and emerging AI-based demands, including data lakehouse architectures supported by open table formats and native S3 Tables capabilities."