AI workloads are reshaping the technological infrastructure of companies, leading to significant changes in storage strategies. According to McKinsey, AI will be a pivotal factor in the anticipated tripling of data center capacity demand by 2030. Current storage methods, designed for predictable traffic, fail to meet the needs of AI systems that handle irregular and large datasets. Without redesigning these architectures, companies risk wasting substantial investments due to inefficiencies.
The existing corporate storage solutions have been rooted in predictable workloads over the past decades, enabling IT leaders to manage storage comparatively flexibly and efficiently. However, the demands of AI are quickly outpacing these conventional approaches. AI processing relies heavily on systems capable of reading and processing vast unstructured datasets quickly and simultaneously, which one can liken to the challenges faced in high-performance computing (HPC) environments. Lessons from these past experiences suggest a transition to hybrid storage systems tailored to specific learning and inference needs is essential for future success.
Data durability is another critical challenge, as it directly affects the success of AI projects. Gartner projects that by 2026, 60% of organizations lacking optimized data environments will abandon their AI projects. The cost implications of poor data quality are severe, resulting in significant annual losses. Effective technical and operational responses are vital, including the integration of advanced coding techniques and automated systems for data integrity checks, to safeguard the operational success of AI initiatives.
👉 Pročitaj original: CIO Magazine
