Your Business Is Probably Overspending on Data Storage. Here’s How AI Fixes That.

When was the last time you roughly calculated how much storage your business would need over a half-year period? Maybe you had a look at the growth you achieved last year and then added extra amounts to try to make sure you are safe.

This is the approach people usually take, and it is driven by anxiety. Fear not if you do this because people have been doing it for decades. IT leaders today have learned that running out of storage is something that can cost a lot of profits. At the same time, buying too much is not ideal and can lead to money wastage if you are overly cautious.

Data storage is not cheap. Global data creation is currently estimated to be around 175 zettabytes. Buying too much data is not financially savvy nor sustainable. Fortunately, there is a new alternative. Today, AI can predict how much data backup your business needs and when it will be needed.

The high cost of buying too much

Typically, your systems warn you when you are running low on available data storage. Usually, a heads-up comes when you are at 70% full, and then a message with a critical warning comes at around 80%. When this happens, most people buy more storage. However, this approach can increase your unplanned expenses by as much as 30%.

The need for data is never the same, and this makes things complicated. You need more when you are focusing on marketing. During the holiday season, when business can dry up as expected, you need less. Then, suddenly, when you plan to launch a new product, you need a whole lot of it.

This is something that the human mind or traditional ways of thinking cannot work around, and it often leads to overspending or panicking when you have underestimated the amount of data that you need.

How AI tells you how much you need

AI helps optimize things by analyzing your historical usage of data and telling you things that are hard for humans to clearly see. It will not only look at how much data you stored but also consider when you did so, how often you accessed the data and how useful it actually is. We all have a tendency to store data we don’t actually need or will never use. AI can help to reduce this.

Two powerful tools used by AI for these purposes are Long Short-Term Memory (LSTM) networks and Transformer architectures. They are able to differentiate between season trends, yearly/monthly patters and long-term plans.

Cloudera’s Observability Planning is one example of something that uses such a feature in order to predict how data will be used when it comes to storage, RAM and CPU. It can make forecasts six months in advance, and the planning it gives is delivered on either a weekly or daily basis. This helps organizations make well-thought-out decisions.

Teradata also uses machine learning in order to focus on storage and how to increase quality performance over a period of time. This allows companies to identify problems with infrastructure and fix them before they are reality.

Beyond planning

The power of AI in this scenario is not just about telling you how many terabytes of data you will need over the next few months. Modern systems will optimize and tell you how you can compress data and use it to its maximum potential based on your needs and limitations.

While doing physics experiments at CERN’s ATLAS project, researchers used AI models that helped them forecast which data they would actually use and which they would probably never look at again. With this information in mind, they stored needed data on fast SSDs, and the data unlikely to be touched was on tape storage, which saved money.

This thinking can be used for most businesses as well. They can move files from last year, which is only there for reference to cheaper storage options, while keeping critical data needed in the immediate future on high-performance systems. By doing this, the AI will also teach itself how to conserve your data based on your habits and access patterns.

People are moving to AI-powered storage at a very fast rate. In 2025, the value for AI-driven storage was estimated to be around $34.71 billion, and this is expected to reach around $149.51 billion by 2032, meaning a compound yearly growth rate of 23.2%. These numbers show how the ways in which companies are viewing data and data infrastructure are changing at a very fast rate.

Big names in the IT industry, such as Dell Technologies, Hewlett Packard Enterprise, IBM, Pure Storage and NetApp, are all working hard to develop and invest more time and resources into AI-driven and powered storage solutions.