For more than a decade, organizations stored nearly every unstructured file on the assumption that hardware capacity would stay cheap. That assumption no longer holds. AI initiatives demand massive computational power, hardware procurement cycles are stretching, and infrastructure costs are climbing. CTERA telemetry from 16 petabytes of enterprise NAS data shows that 83.1% of total capacity has not been modified in over a year, while the active working set accounts for just 4.4%.

Executive boards want immediate AI progress, with 51% of organizations prioritizing increased investment in generative and agentic AI tools for 2026, yet storage maintenance threatens to consume available capital before those projects show a return. High-performing enterprises respond by auditing their environments rather than requesting net-new funding, moving inactive cold data off Tier 1 storage, and funding AI directly out of optimized infrastructure.

In our latest Thought Leadership Brief, The Storage Squeeze: How Smart Organizations Are Funding Their AI Future, completed in partnership with CTERA, Futurum Research examines how unmanaged data growth drains AI budgets and lays out a cost-optimization playbook for funding AI through infrastructure savings.

In this brief, you will learn:

• How much enterprise capacity sits dormant on primary storage, and what paying Tier 1 prices for it costs AI programs
• How enterprise spending intentions are shifting from storage capacity toward ML/AI and data platforms
• How legacy hardware lock-in and cloud egress fees squeeze IT from both directions, and why hybrid flexibility is now mandatory
• Four plays for funding AI: consolidating and tiering data, building data resilience, classifying data at the source, and bringing compute to the data

If you are interested in learning more, be sure to download your copy of The Storage Squeeze: How Smart Organizations Are Funding Their AI Future today.