AI Growth Impacts Enterprise Storage

A new wave of solutions, dubbed Generative AI, is quickly moving from concept to implementation. One ripple effect is these applications require enormous amounts of data storage. Consequently, enterprises need to account for the change, something that may require that they work with a third party specialist.

AI is a general purpose solution and can be used for any application. Computer vision for manufacturing, machine learning for supply chain applications, automation to streamline data center system troubleshooting, and chatbots to improve customer satisfaction are a few examples of where it has been making progress.

The technology has been reaching a key inflection point because Generative AI systems offer quantum leaps in computing power and capabilities. They work with much larger volumes of information (hundreds of billions of words) and larger data models (hundreds of billions of parameters) than previous AI systems.  Consequently, they possess impressive and unprecedented power and can perform very sophisticated functions, like pass the law bar exam and Advanced Sommelier (wine tasters) tests.

So, companies are investing in it. The global generative AI market size was valued at $10.14 billion in 2022 and is expected to reach $115.90 in 2030, a compound annual growth rate (CAGR) of 35.6%.

Enterprise Generative AI Gains Traction

Much of the attention to date has focused on open source models, like ChatGPT. However in this case, businesses enter their data into someone else’s data models. Therefore, they lose control over corporate information and how well third parties will protect such information is unclear.

Also, large enterprises usually need to build customized large-language models created by individuals with deep expertise in their unique business domains. Only then will the models deliver the results that they need to improve their business.

Storage Volumes Swell

Regardless of the applications, AI relies on massive amounts of structured and unstructured data and objects. The more and better data they are fed, the smarter data models become. Therefore, Generative AI models generate large amounts of text, images, videos, and AI models. Data analytics worked with gigabytes of information, machine learning pushed data analysis to petabytes (1 pb is about 500 billion pages of standard typed text) of information, and generative pushes the numbers to hundreds of P bytes and beyond.

Enterprise data volumes were already growing at healthy double digit rapid rates before Generative AI took hold.  The fallout is a significant increase in the amount of data that enterprises need to store. Consequently, they must increase storage capacities to accommodate the new content.

Before generative AI is widely deployed, organizations must rethink, rearchitect, and optimize their storage architecture to avoid potential, last-mile storage bottlenecks. They usually cannot just add onto what they already have. Why?

Their storage requirements morph.  One challenge is different types of data are created, so Generative AI often does not mesh with legacy solutions. Companies need flexible technologies like object storage, distributed file systems, and tiered storage architectures.

Real-time data is another requirement. Flash storage, which is expensive but offers fast retrieval times, is a good fit. Unstructured data often requires large scale, software-defined repositories.

In sum, Generative AI storage deployments must support a variety of different storage media, such as memory, flash, disk drives and tapes. Storage arrays require various access protocols, based on the type of workload.

AI Creates New Challenges

Cloud storage is not a good fit. More data means more data movement to and from the cloud. The change escalates ingress and egress costs and creates more latency, making a cloud-first approach infeasible for Generative AI applications.

Data backup and recovery is another challenge. With the generation of large volumes of data, robust backup and recovery mechanisms become crucial. Enterprises must develop strategies to regularly back up the generated content and ensure quick and reliable recovery in case of data loss, which is difficult to do given the large data volumes.

Data retention is another key consideration. Enterprises need to establish clear data retention policies to manage the ever-increasing amounts of data. This process includes determining how long to retain generated content, which data should be archived, and which can be safely stored where.

These challenges highlight the importance of having a robust on-premises infrastructure, especially when considering the trend of cloud repatriation.

AI Storage Best Practices Emerge

Companies need to develop best practices. Trying to maximize system lifecycles is a good place to start. Enterprises need to manage the entire data lifecycle, from creation to archival to optimize storage utilization, and eventually to data retirement.

Repurpose Storage:

In many cases, corporations replace storage systems that were working fine. Instead, redeploy them and use them for the AI applications. Enterprises can even extend the useful life of storage beyond vendor end of life support by finding a third party partner, like Top Gun, that will service the equipment.

Storage System Selection’s Growing Importance

Companies rely on these systems to run their business. Problems arise, so they have to design their storage systems, so they remain operational even if one component fails. Enterprises need to balance redundancy and additional costs and find a happy medium.

Simplify Support

Rather than take on the burden of managing a number of different solutions themselves, companies can hand that work over to a third party specialist. As noted, these AI applications support critical enterprise applications, so make sure that your partner has a track record of delivering top support to enterprises.

Generative AI significantly impacts enterprise storage by increasing data volumes, diversity, and complexity. Enterprises need to adapt their storage strategies to accommodate the new demands. They need to examine the full lifecycle and make decisions for both the short term and long term.

Don’t let generative AI catch your enterprise off guard. We understand how Generative AI is impacting enterprises and leverage our experience to put you on the best path moving forward.

Contact Top Gun today for a free consultation on optimizing your storage architecture for AI workloads. Our experts can assess your current infrastructure, project future needs, and deliver customized solutions to support your AI initiatives.

We simplify AI storage so you can focus on leveraging generative AI to transform your business. Reach out now to get ahead of the curve.

Blog Author Details

Donna Pizarro

Senior VP, Storage & Transition Services

Top Gun

Donna’s LinkedIn Profile