A global survey of 100 decision-makers from banking, payments and investment firms, published today, finds that over a third of respondents (35%) rank the growth of storage as their top challenge over the next 12 months.

Conducted by the market research firm FSTech on behalf of Hitachi Vantara, the survey finds other top priorities include ensuring data sovereignty, regulatory compliance, and policy-driven governance (30%), improving data accessibility and reuse across teams, platforms and distributed environments/locations (30%), modernizing existing storage platforms to simplify data management and reduce operational complexity (30%), cost reduction and improving storage efficiency at scale (29%) and improving hybrid and cloud integration (28%).

Surprisingly, however, the survey finds that only 10% of respondents are prioritizing storage and data platforms to handle the massive amounts of data that will be generated by artificial intelligence (AI) workloads, with even fewer (9%) making the deployment of centralized data hubs a priority.

Dia Ali, global platforms and solutions leader for data intelligence at Hitachi Vantara, said that despite the hype surrounding AI, many organizations still need to address data and storage management fundamentals. For example, 35% said they expect object storage to be better able to support generative and agentic AI applications in one to two years, while another 30% said they expect to see similar gains for machine learning workloads. More than half (57%) also said they expect object storage systems to be supporting some AI or analytics workloads in one to two years, compared to 43% that said object storage will be central to their AI or analytics strategy.

However, at the moment only slightly more than a third (35%) report they are widely using object storage across their IT environments, the survey finds.

There are, of course, multiple types of platforms for storing data, but given the expected volumes that will be generated by AI workloads, it should not come as a surprise that IT teams are instinctively looking to object storage to control anticipated costs. The challenge is ensuring that object storage systems will also be able to meet the performance requirements of those applications. Most IT teams that have deployed object storage systems are making a tradeoff between cost and performance. Many AI applications will ultimately, especially in the financial services sector, require both.

Regardless of approach, the one thing that can’t be overlooked is the need to be able to track the lineage of the data being accessed by AI applications, said Ali. At its core, AI is really a data management challenge that requires tools to classify and tag data in a way that ultimately creates the level of trust an AI application deployed in an enterprise IT environment requires, he added.

Unfortunately, many organizations are investing in AI at a rate that far exceeds what their data and storage management platforms can currently support. As such, many organizations may spend the next year or more waiting for next-generation integrated data and storage management platforms to be acquired and deployed before AI can be fully operationalized.