Seagate Says 99% of Companies Expect AI to Drive Up Storage Demand, but Only 38% Feel Fully Prepared

Seagate Technology released the global research report, "Data Infrastructure Readiness Report 2026." The report analyzes the state of data infrastructure readiness as companies expand AI adoption and use. As AI use moves from the early stages to broad deployment, companies are increasingly focusing on building data infrastructure that can support large-scale data storage, management, and use.

According to the report, about 99% of companies expect AI to increase storage demand. In contrast, the share of companies that said they are thoroughly prepared came to 38%. The report shows that while the importance of data storage, management, and utilization continues to grow amid the spread of AI, storage demand is a near-universal expectation, but relatively few companies believe they are sufficiently prepared.

Seagate said the full-scale rollout of AI adoption in companies has confirmed a trend in which data infrastructure is seen as a core strategy.

Market research firm Recon Analytics independently surveyed 2,712 corporate technology decision-makers across 7 major markets worldwide. The survey asked global corporate technology decision-makers about both the effects of AI investment and the infrastructure changes that come with adoption.

The survey found that about 86% of companies had experienced at least moderate ROI from AI investments. In addition, 33% said they had achieved a significant level of measurable results from AI investment.

The share of companies expecting AI to increase storage demand over the next 3 years stood at 99%. Among them, 32% said the increase in storage demand would exceed 50%.

Data quality and readiness ranked first among the main challenges to AI adoption, at 53%. Storage infrastructure came in second at 43%.

Seagate explained that the 43% for storage infrastructure was higher than 27% for computing availability and 24% for energy constraints. It also said the share of respondent companies agreeing that AI has transformed storage into strategic business infrastructure stood at 98%.

The survey results showed that companies' AI infrastructure strategies have expanded from computing resources to the broader data infrastructure stack. This indicates that companies view AI infrastructure not simply as a matter of computing resources, but as a challenge spanning the entire data infrastructure.

Accordingly, companies' key tasks were presented as responding to rising storage demand, strengthening data accessibility, reinforcing governance, and laying the groundwork for sustainable data scaling. Companies are moving to address these issues together.

Seagate defined this approach as "Sustainable Scaling." "Sustainable Scaling" means improving AI capabilities, enhancing business performance, and operating support infrastructure efficiently.

The standards for infrastructure investment and operations were presented as expanding infrastructure to meet rising AI data demand, improving efficiency, strengthening sustainability, and maximizing the long-term value of data. Melissa Banda, executive vice president of edge storage and solutions at Seagate, said AI is changing how companies design, build, and operate infrastructure, and that value creation is expanding in proportion to data growth. She explained that the importance of data infrastructure, which can efficiently store large volumes of data and make it available when needed, is growing.

Sustainable Scaling was described as a concept that increases operational efficiency and sustainability while maximizing the long-term value of data.

AI is already producing visible results at many companies, and the importance of data infrastructure is also increasing as AI spreads. According to the report, 98% of respondent companies agreed that AI is transforming storage into strategic business infrastructure.

The report also said 99% of respondent companies expected AI to increase storage demand within the next 3 years. In contrast, only 38% said they are sufficiently prepared for long-term data demand.

Against this backdrop, AI use is expanding across companies. At the same time, companies are facing 2 changes: growing volumes of data to be stored and managed, and increasing value that data can generate.

The report explained that data is not merely an input resource for AI. It also said data is establishing itself as a core asset that contributes to companies' long-term competitiveness and growth.

As AI-driven storage demand rises, companies are reexamining their data infrastructure as a whole, beyond simply expanding storage capacity. According to the report, the focus of corporate AI strategies is expanding from simple adoption to strengthening data-based capabilities.

However, the share of companies saying they are sufficiently prepared for long-term AI data demand came to just 38%. As a result, companies were seen checking their ability to respond to long-term demand as they expand the scope of AI use.

As they broaden AI use, companies are treating data readiness, storage infrastructure, governance, budgets, and AI strategy development as review items. The report said companies' attention has shifted beyond AI adoption itself to building a data management system and infrastructure foundation for stable operations and sustained use.

Against this backdrop, 76% of respondent companies included data center investment among their top 3 infrastructure investment priorities. Of those, 20% said data center investment was their top priority, highlighting it as a major infrastructure priority.

The survey presented, in numerical terms, both the obstacles encountered while building AI readiness and the core challenges at the AI adoption stage. The biggest obstacle to improving AI readiness was AI strategy maturity, at 16%. The second-tier obstacles to improving AI readiness included lack of budget and resources at 14% and data management and governance at 14%.

The top challenge in AI adoption was data quality and readiness at 53%, followed by storage infrastructure at 43%. The 43% for storage infrastructure was higher than 27% for securing computing resources and higher than 24% for energy constraints.

Based on these findings, the report said corporate AI strategies are expanding from the simple adoption stage toward strengthening data-based capabilities. It also analyzed that companies' focus is shifting from whether to adopt AI to building a data management system and infrastructure foundation for stable operations and sustained use.

The report also said companies are placing importance on efficiency, sustainability, and lifecycle management in response to data growth driven by AI expansion. Accordingly, while storage demand is rising, companies are reviewing ways to improve operational efficiency in response, and are also examining ways to extend the usable life of their systems.

The survey found that companies see extending infrastructure lifespan and improving storage operations as important ways to strengthen sustainability. 97% of companies recognized the benefits of extending infrastructure use and said it would greatly help improve sustainability. In addition, 94% of companies expected storage operation sustainability to improve further within the next 5 years.

At the same time, the survey found that sustainability and energy issues directly affected the timing and method of AI infrastructure expansion plans. 77% of companies said they had delayed or adjusted AI infrastructure expansion plans because of sustainability and energy concerns. Among those 77%, 36% significantly revised their plans.

The main environmental concerns were concentrated on energy use and the resulting emissions. The main environmental concern related to energy use from AI was 52%. Carbon emissions resulting from energy use were also included among the major concerns, at 51%.

Against this backdrop, the report described sustainability not as a separate goal but as a core element of infrastructure strategy. It presented sustainability's role as balancing data demand with improved energy efficiency and resource utilization.

Banda, executive vice president, said the next phase of AI will require greater capacity. However, she said capacity expansion alone would not be enough. She stressed that the key going forward is the efficient scaling, structuring, preservation, and use of data for AI.

The speaker said that for a company to create continuous value through AI, data infrastructure must become central to business strategy, emphasizing that for the results of AI use to be sustained rather than one-time, companies must treat data infrastructure as a core element of strategy and linking the question of whether AI creates value to the way management approaches data infrastructure.

Source: TECHWORLD · Lee Gwang-jae
Original: https://www.epnc.co.kr/news/articleView.html?idxno=406956

References

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