DN Report: 65% of Organizations Struggle to Achieve AI Success

Why It Matters

Without streamlined infrastructure, AI initiatives deliver lower ROI, slower time‑to‑value, and higher operational risk, threatening competitive advantage across sectors. Companies that adopt unified, energy‑efficient, partner‑backed solutions can accelerate innovation while curbing costs.

Key Takeaways

• 65% cite AI infrastructure complexity as barrier
• 97% say cloud essential for scaling AI
• 93% aim to cut AI energy footprint
• 72% rely on third‑party partners for AI infrastructure

Pulse Analysis

The AI boom is no longer defined by model size or GPU horsepower; it is increasingly a data‑infrastructure challenge. Organizations that continue to cobble together legacy storage, networking, and compute layers create hidden friction that stalls model training and inference. Unified platforms—like DDN’s AI‑optimized storage—consolidate data movement, reduce orchestration overhead, and deliver the high‑throughput, low‑latency fabric needed for generative AI workloads. By abstracting the underlying hardware, these solutions let data scientists focus on model development rather than system integration, shortening the path from prototype to production.

Cloud adoption is the logical entry point for most enterprises, with 97% of respondents confirming its critical role in scaling AI. Public‑cloud providers now offer managed Lustre, NVMe‑backed block storage, and GPU‑ready instances that eliminate the need for on‑prem hardware procurement cycles. This agility enables rapid experimentation, faster GPU onboarding, and seamless access to the latest accelerator generations. Moreover, a cloud‑agnostic strategy—leveraging multi‑cloud or hybrid models—helps firms avoid vendor lock‑in while optimizing cost and performance across workloads.

Energy efficiency has emerged as the new currency of AI, as 93% of leaders actively seek to reduce power consumption. Metrics such as "tokens per watt" quantify how effectively compute translates into model output, driving investments in high‑density, low‑power architectures. Coupled with a pronounced skills shortage—98% cite talent gaps—companies are turning to ecosystem partners like Cognizant, Google Cloud, and NVIDIA for expertise, reference architectures, and managed services. These collaborations accelerate knowledge transfer, improve GPU utilization, and mitigate the risk of stalled projects, positioning firms to capture AI‑driven growth while meeting sustainability goals.