Seagate Report Cites AI Storage Demand Amid 4% Stock Drop

Seagate Technology reported surging enterprise storage demand driven by artificial intelligence workloads, even as IT leaders face significant hurdles regarding data readiness and energy constraints, according to a report from Investing.com.

The findings arrive as Seagate Technology Holdings PLC (STX) stock closed down 4.19% at $771.81, compared to its previous close of $805.55, as of September 15, 2026, according to Finnhub market data.

"AI is reshaping the way organizations plan, build and operate infrastructure," said Melyssa Banda, senior vice president of Edge Storage Business at Seagate Technology, in a statement detailing the findings. "As data volumes grow, so does the value organizations can derive from the data."

Global Survey Findings on IT Preparedness

The 2026 Data Infrastructure Readiness Report shows that 99% of IT leaders expect increased storage requirements, while only 38% consider themselves fully prepared for the surge.

The research is based on a survey of 2,712 enterprise technology decision-makers across seven global markets, including the United States, China, India, the United Kingdom, Germany, France, and Japan. The data was collected by Recon Analytics between May and June 2026.

Key Bottlenecks in AI Deployment

While compute power has dominated discussions surrounding generative artificial intelligence, corporate infrastructure bottlenecks extend further into data management and physical resources. Survey respondents identified specific primary challenges facing their deployments:

• Data quality and readiness ranked as the primary challenge at 53%.
• Storage infrastructure followed as a primary obstacle at 43%.
• Compute availability was cited by 27% of decision-makers.
• Energy constraints were noted by 24% of respondents.

Sustainability and Energy Hurdles

Power limitations are increasingly dictating corporate capital allocation and project timelines. The report indicates that 77% of organizations have delayed or restructured their artificial intelligence infrastructure expansion due to sustainability or energy concerns.

Among those organizations, 36% reported significantly restructuring their expansion plans to account for power availability. These physical and structural constraints highlight the gap between soaring enterprise data generation and the operational readiness required to harness it effectively.