AI Adoption Set to Double Storage Demands in Three Years, Seagate Survey Reveals

AI will drive storage needs by 2x over next three years, according to survey funded by Seagate

The adoption of AI is driving explosive growth in data generation over the coming years. As available on-premise storage may not be enough for data generated by AI, cloud-based storage emerging as the top choice for businesses across industries, according to a global survey by Recon Analytics that was ordered and paid for by Seagate. According to the survey, storage demands will increase 2x in the next three years. AI adoption is widespread, according to the survey, with 72% of surveyed businesses already using the technology and 28% planning to adopt it within three years. Cloud storage has emerged as the preferred medium for handling AI-driven data growth, with 65% of AI-related data stored in the cloud in 2024, projected to increase to 69% by 2028, according to the survey. When it comes to storage capacity, it gets quite interesting: Among organizations using over 100PB of storage, 87% save AI training checkpoints in either cloud environments or a combination of hard drives and solid-state drives. Frequent checkpointing is common, with 28% of companies saving daily and 43% saving weekly, which adds to the rising demand for storage, the report says. Businesses are also prioritizing longer retention of AI training data, which is considered as essential for improving model accuracy, according to the survey. A significant 90% of respondents believe that extended retention periods lead to better AI outcomes. Companies storing daily checkpoints indicated that 32% retain this data for over 12 months, while 29% retain it for six to 12 months. This focus on data longevity reflects the increasing importance of historical data in refining AI models. When it comes to infrastructure priorities, security ranks as the most critical component, with 25% of respondents placing it at the top of their list. Meanwhile, storage capacity trails at 18%. Other notable infrastructure concerns include data management, computing resources, network capacity, and regulatory compliance. Companies are also actively adapting to the growing storage demands brought on by AI adoption. Among the measures taken, 61% have adopted scalable cloud storage solutions, 56% have implemented advanced data management software, and 55% have upgraded their existing infrastructure. Additionally, 49% are leveraging data compression techniques to manage the increasing volume of AI-generated data. These strategies highlight the focus on scalable and efficient solutions to accommodate exponential storage growth. Data replication has emerged as another strategy for ensuring data integrity and optimizing AI outcomes. Approximately 80% of respondents consider replication either very or moderately important for their AI strategies, particularly among organizations with larger storage capacities. Companies using over 100PB of storage are more likely to emphasize replication as critical for maintaining the accuracy and reliability of their AI models. Looking ahead, cloud storage is expected to dominate as businesses continue shifting operations from on-premises setups to cloud environments. This trend is driven by the scalability, cost efficiency, and convenience of cloud-based solutions. As AI adoption becomes more pervasive, storage requirements are anticipated to grow exponentially as companies transition from trial phases to full-scale implementations. Frequent checkpointing, longer data retention, and replication practices will remain essential, underscoring the need for robust storage infrastructure and advanced management tools to support the future of AI-driven operations.

AI adoption to double storage demands in just 3 years, Seagate survey finds

In a nutshell: A new global survey commissioned by storage giant Seagate reveals that businesses anticipate their data storage needs will double within the next three years, driven largely by the adoption of AI and machine learning. The survey, which included over 1,000 businesses across various industries, found that a whopping 72% have already jumped on the AI train, with another 28% planning to deploy it within the next three years. The data deluge is mainly being handled by the cloud. The survey indicates that 65% of all AI-related data is already being stored in the cloud as of 2024, and that percentage is projected to climb to 69% by 2028 as more workloads shift off-premises. Among companies using over 100 petabytes of storage, 87% are storing their AI model training checkpoints in the cloud or a hybrid cloud/on-premises setup. These checkpoints, which allow models to pick up where they left off, are being generated at a blistering pace: 28% of companies said they create new checkpoints every single day, while 43% do it on a weekly basis. It's no wonder nearly half of the companies surveyed (46%) believe their existing storage methods won't be enough to quench AI's thirst for data in the coming years. Keeping all those checkpoints around is also becoming a priority. A full 90% of respondents said they believe retaining lots of historical training data leads to better AI outcomes over time. 32% of companies creating daily checkpoints are holding on to that data for over a year, while 29% keep it for 6-12 months. To try to get ahead of any data bottlenecks, businesses are embracing a variety of strategies: 61% are expanding their usage of scalable cloud storage solutions; 56% have implemented enhanced data management software; 55% have upgraded their on-prem infrastructure; and 49% are turning to data compression to squeeze more into their existing storage. With AI proving to be an exponential data multiplier, businesses have no choice but to make scaling storage capacity a top priority over the coming years. Of course, cloud will likely continue gobbling up more of the storage load, but it seems on-premises optimization and hybrid setups will remain essential too.