Seagate report finds 43% of firms lack AI storage infrastructure

The immediate implication is a looming bottleneck for streaming firms attempting to integrate generative AI into content recommendation and encoding workflows. As data volumes explode, the reliance on legacy architectures creates a performance ceiling that could stall innovation in real-time video processing. Within the broader ecosystem, this infrastructure deficit suggests that the true cost of AI adoption is significantly higher than software licensing alone, requiring a fundamental shift toward intelligent, tiered storage strategies. Watch for a surge in enterprise investment toward hybrid cloud solutions as companies attempt to reconcile their AI ambitions with the physical realities of data management.

Seagate's findings arrive as streaming and video delivery companies grapple with the same storage pressure across their own pipelines. The report's 43% infrastructure gap figure aligns with broader industry data showing that video workloads, particularly AI-driven encoding and recommendation systems, are among the fastest-growing consumers of enterprise storage. In its 2026/2027 Video Developer Report, Bitmovin found that 98% of video professionals now use AI or ML somewhere in their workflows, with 46% employing AI tools daily, a level of compute and data throughput that directly stresses storage architectures. The report's emphasis on low-latency live streaming as the top challenge for 36% of respondents further underscores how real-time video processing demands storage systems that can keep pace with ingest, transcode, and delivery simultaneously. On the business side, Seagate's warning that 99% of leaders expect storage costs to rise mirrors pricing dynamics already visible in the managed video API market. A 2026 analysis of streaming platform economics found that Mux remains the priciest option on a per-gigabyte basis among major video API providers, a cost structure partly driven by the storage and compute overhead of built-in AI features like auto-chaptering, semantic search, and generative clipping. For streaming companies evaluating whether to build or buy AI-powered video infrastructure, the storage cost trajectory Seagate identifies becomes a material input into total cost of ownership models, particularly for firms running large content libraries that require re-encoding or AI-enriched metadata at scale. From a technical standpoint, the storage infrastructure challenge Seagate highlights intersects directly with how video delivery platforms are architecting their AI pipelines. Mux launched its Robots product in 2026, running AI analysis natively alongside stored video assets rather than requiring developers to shuttle data between external AI providers and their own infrastructure. That design choice reflects a broader industry recognition that co-locating AI compute with video storage reduces egress costs and latency, but it also concentrates storage demand on the platform side. For companies building on-premises or hybrid DAM storage architectures, Seagate's data suggests that the gap between AI ambition and storage readiness is not merely a cloud-vendor problem but a structural one that affects every tier of the video delivery stack, from origin servers to edge caches serving AI-personalized streams.