Data Storage Costs: A New Challenge for Corporate IT Strategy The rise of artificial intelligence (AI) has brought about changes that extend far beyond mere technological advancement. AI is now comprehensively reshaping our daily lives and businesses, with few industries remaining untouched by its adoption. However, behind this technological innovation lies a crucial, yet easily overlooked, issue. This issue is the explosion of data driven by AI and the consequent surge in data storage costs. According to a recent announcement by Komprise, a global data management specialist, a startling forecast suggests that data storage costs will double by the end of 2026. As of March 2026, signs of rising data storage costs are already evident. Komprise's report analyzes that as the volume of data generated by AI technology grows exponentially, the costs required to store and manage it are also rapidly increasing. Gartner, a global IT market analysis firm, predicts that prices for DRAM (Dynamic Random-Access Memory) and SSDs (Solid-State Drives) will rise by up to 130% by the end of this year, attributing this to the surging demand for High Bandwidth Memory (HBM) and high-capacity NAND flash storage required by AI. A particularly noteworthy indicator is the sharp decline in DRAM inventory levels. According to Komprise's report, current DRAM inventory has plummeted from historically healthy levels to just a two-week supply. This is having a widespread impact on hardware manufacturers (OEMs) and data center operational plans, acting as a factor that increases tension across the entire supply chain. All these indicators suggest that data management costs will be a critical variable determining corporate profitability and competitiveness in the future. MIT Technology Review, in a recent analysis, pointed out that AI model training and inference processes demand an unparalleled amount of memory bandwidth compared to traditional computing environments. Specifically, Large Language Models (LLMs) and generative AI require real-time processing of hundreds of gigabytes to terabytes of memory, making High Bandwidth Memory (HBM) essential. The Economist also analyzed that the increase in memory and storage costs, alongside data center power consumption, is one of the key factors threatening the sustainability of the AI boom, emphasizing that companies must urgently develop strategic responses. So, how will this escalating data storage cost issue affect businesses? Firstly, fundamental data management steps such as backup and recovery—in other words, disaster preparedness—will consume a significant portion of IT budgets. According to Komprise data, over 30% of current IT budgets are spent on data storage and management, a proportion projected to increase further as AI adoption accelerates. This is likely to place a substantial burden on domestic small and medium-sized enterprises (SMEs) in particular, as they struggle to afford data storage facilities or cloud costs. Looking at the situation in Korea, as the adoption rate of AI among domestic companies rapidly increases, so too does the burden of data management costs. While large corporations are likely to respond by establishing optimized data governance strategies, SMEs may have relatively weaker countermeasures. According to a 2025 survey by the National IT Industry Promotion Agency (NIPA), 65% of Korean SMEs cited increased cloud and data storage costs as a major impediment to digital transformation. This raises the possibility of another wave of change in the future structure of Korea's IT industry. The Rise of AI Technology Shakes Up the Storage Market Among these, the surging prices of next-generation data storage technologies such as DRAM, SSDs, and High Bandwidth Memory (HBM) are particularly noteworthy. Gartner's analysis indicates that AI-driven applications, demanding vast memory and high speeds for tasks like high-resolution image analysis, video processing, and real-time data streaming, are intensifying this hardware dependency. HBM, in particular, offers more than five times the bandwidth of conventional DRAM, but its high production complexity and limited supply exert even greater upward pressure on prices. While South Korean companies Samsung Electronics and SK Hynix boast world-class competitiveness in HBM and NAND flash memory, these cost-increasing factors stem from global demand and supply chain complexities, making it difficult to overcome them solely through technological prowess. Rather, Korean companies face a dual challenge: being both suppliers of memory semiconductors and operators of AI services and data centers. In the semiconductor manufacturing sector, expanding HBM production capacity is urgent, yet companies are simultaneously in a dilemma of needing to manage the costs of building their own AI infrastructure. So, what strategies should companies adopt to overcome these rising costs and the challenges of the AI era? Komprise offers concrete soluti
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