Weak Data Management Undermines Generative AI Success and Profitability:
An analysis has revealed that investing billions of dollars on AI is pointless, most companies are not seeing their return on investment. According to this new analysis, the problems are not in the ambition or algorithm but in the hidden layer which we call data infrastructure.
Experts have said that storage, scalability and performance bottlenecks can prevent enterprise AI from moving much beyond the pilot stage and generating profits.
According to the latest study by MIT (Massachusetts Institute of Technology), U.S companies have spent approx. Some $30-40 billion has been invested in AI, but 95% of companies have yet to see any measurable results, and only 5% of organizations have successfully deployed their AI tools on a large scale.
Researchers say that current AI systems still lack features such as memory, adaptability, and workflow integration, making them ineffective in business processes.
The major finding of the MIT study is that there is a significant gap between some companies that have earned millions from AI and those that do not see a measurable impact in their profit and loss statements. Four key reasons are given to support this conclusion.
Top Reasons Why Generative AI Projects Fail:
Most generative AI projects are unsuccessful not even at the pilot stage. Of over 250 public implementations, only 5% have successfully reached the pilot stage, with measurable impact. The problem isn’t with the quality of the AI models—but with enterprise integration. Tools that fail to learn from feedback and don’t integrate well with regular operations.
Another major reason for low ROI (Return on Investment) is that employees use “shadow AI economy” tools like ChatGPT, etc., without integrating them into official systems.
Another reason is the mismatch in AI budget allocation – where sales and marketing receive more than 50% of the funding, while back-office automation could have a more measurable ROI.
According to Quobyte CEO Björn Kolbeck, ROI failure is visible at every corporate layer. Too often, CEOs push AI products that are of no use to users, or release incomplete “AI features.”
There are also issues on the technical side – models are delayed or not trained properly, often due to weak data infrastructure and storage bottlenecks.
Why Storage Bottlenecks Block AI Profitability:
Kolbeck states that companies that are investing billions in AI infrastructure, but do not pay attention to the brawl strong system, and this is a big mistake that is causing their AI projects to fail. According to him, this negligence has three main consequences: the creation of data silos, uptime issues, and low performance.
For AI, its most important resource is its data training. When companies store their data in multiple silos, the data scientist has access to the important point.
He explained that “unified access must be provided, and storage systems must be scalable, so that a centralized AI data lake can serve as the most efficient storage for the entire company.”
Storage systems face performance issues if they can’t keep pace with GPU training demands. This costs resources, leaving projects delayed and frustrating data scientists.
He warned, “If storage solutions aren’t designed for maximum performance and availability—like many traditional HPC storage systems—the same problem repeats: delayed projects and wasted investments.”
Outdated Storage Solutions Threaten Critical AI Performance:
According to MIT reports, AI deployments are successful only when they are integrated at a large scale, and fault-tolerant storage is crucial.
Kolbeck explained that traditional storage is reliable but cannot scale out. Meaning, AI projects run smoothly initially, but as the project grows, it consumes more GPUs, causing system crashes and halting mission-critical workflows.
He explained that scale-out architecture is a good option for the massive data loads of modern ML and AI, while scale-up approaches fail.
Quobyte company has said that they developed a parallel file system in which they convert normal servers into much more high performance, scalable, robust ones. This concept is applicable in HPC, modern CPUs, and cloud as well and scale out system is very effective everywhere.
The logic is also applicable for AI transactions. If our storage does not scale out then only our limited models will be able to be trained. Kolbeck has said that when we add GPUs and storage should also scale out at the same speed.
Solving Critical Data Problems in Modern AI Systems:
AI workflows are a mix of large and small files. When multiple GPUs are simultaneously accessing data, performance demands become very high. Furthermore, managing the varying requirements of different users on the same storage system is a major challenge.
Kolbech explained that the training and development of AI technology is still an experimental process, requiring rapid adaptability of storage infrastructure. As data scientists test new ideas, the storage system must adapt quickly.
Real-time performance analytics plays a crucial role here. Storage administrators need to understand which applications (such as AI training or data pipeline stages) are impacting storage. Data scientists often have limited storage visibility, so administrators need accurate data to make optimization and scale decisions.
Quobyte’s policy-based data management system addresses all of these issues—and adapts quickly to changing business and workload needs. This allows users to easily decide where and how to store files—in just a few clicks.
Fixing Legacy Technology Problems:
Kolbeck explained that even traditional enterprise storage is based on 30-year-old technology, such as the NFS protocol, designed by Sun Microsystems in 1984. These old-school systems can’t compete with tomorrow’s AI scale-out requirements.
He cited the examples of Yahoo and Google. Yahoo built its infrastructure on NFS-based storage, while Google developed its software-based storage on distributed systems and cheap servers.
Kolbeck explained that if people think the same old technology will successfully build AI, it’s an illusion.
Building a successful AI infrastructure requires thinking like hyperscale. This is Quobyte’s core principle. Their software-defined storage system uses a distributed algorithm, which delivers optimal performance and scales seamlessly—whether it’s a small machine or an entire data center.
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