62% of firms are not fully ready for AI-driven storage growth, Seagate says

Survey Scope and Demographic Reach

The 2026 Seagate report draws its conclusions from a sample of 2,712 technology decision‑makers who were surveyed during May and June across seven major markets: the United States, China, India, the United Kingdom, Germany, France and Japan. This geographic spread provides a broad view of enterprise attitudes toward storage demands linked to artificial intelligence, while the sample size offers statistical relevance for the statements that follow.

Projected Storage Growth and Current Readiness

According to the respondents, an overwhelming 99 % anticipate that artificial intelligence will increase their storage requirements within the next three years. Despite this near‑universal expectation, only 38 % consider themselves fully prepared to meet the upcoming demand. The gap between expectation and preparedness highlights a potential bottleneck for enterprises that depend on AI‑driven workloads.

The magnitude of the expected increase varies among the participants. A proportion of 32 % foresee a rise in storage needs that exceeds 50 % of their current capacity, while 70 % anticipate an increase of at least 26 %. These figures suggest that a substantial majority expect a double‑digit growth trajectory, yet the confidence in handling such growth remains limited.

Key Barriers Identified by Executives

When asked to rank the primary obstacles to scaling AI‑related storage, 53 % of respondents cited data quality and preparation as the most significant hurdle. Storage infrastructure itself was identified as a barrier by 43 % of the sample, indicating that hardware and capacity concerns are prominent but not the sole issue. Additionally, 27 % pointed to compute availability and 24 % to energy constraints as limiting factors.

These barrier percentages illustrate that, while hardware is a critical component, the broader ecosystem—including data pipelines and power considerations—plays a decisive role in shaping readiness. The interplay among these factors could affect the speed at which organizations expand their storage ecosystems.

• Data quality and preparation (53 %)
• Storage capacity constraints (43 %)
• Compute availability (27 %)
• Energy supply limitations (24 %)

The prominence of data‑related challenges aligns with the view that AI effectiveness depends heavily on the underlying dataset. If data are not properly curated, the additional storage may not translate into usable intelligence, thereby reducing the expected return on investment.

Economic Expectations and Infrastructure Priorities

Despite the readiness gaps, 86 % of the surveyed executives describe the return on investment from AI as moderate or significant, with 33 % reporting a notably important and measurable ROI. This optimism suggests that firms perceive AI as a value‑generating technology even when they acknowledge preparedness shortfalls.

Infrastructure priorities reflect this optimism. A substantial 76 % rank data centre capabilities among their top three infrastructure concerns, and 20 % place data centres at the very top of their agenda. The emphasis on data centre readiness may indicate that organizations are channeling resources toward the environments that host both storage and compute resources.

Energy and sustainability considerations also shape planning decisions. According to the survey, 77 % have postponed or restructured an infrastructure expansion due to energy or sustainability issues, with 36 % describing the impact as significant. These adjustments could delay the deployment of additional storage capacity, potentially widening the gap between demand and supply.

Limitations of the Study and Open Questions

It is important to note that the Seagate study was commissioned by a storage manufacturer, and the findings capture self‑reported statements from senior officials rather than audited assessments of existing infrastructure. Consequently, the data reflect perceived readiness rather than verified capability.

The reliance on subjective confidence levels raises questions about the accuracy of the preparedness metric. Future research could compare declared readiness with actual deployment metrics to gauge the predictive value of these surveys.

Another open question concerns the interaction between energy constraints and storage scaling. While 24 % cite energy as a barrier and 77 % have altered expansion plans for sustainability reasons, the precise trade‑offs between power consumption, cooling requirements and storage density remain to be quantified.

The impact of compute availability, noted by 27 % of respondents, also warrants deeper investigation. As AI models become more demanding, the synergy between storage throughput and processing power could become a decisive factor for overall system performance.

Finally, the regional composition of the sample—spanning North America, Asia and Europe—suggests that cultural and regulatory differences might influence the reported barriers. Comparative analyses across these markets could reveal divergent strategies for addressing AI‑driven storage growth.