By Kumar Goswami, co-founder and CEO, Komprise

IT leaders in enterprises should take pride in their critical role, as the demands they face continue to grow-cementing their value to the business. While budgets may be stable, the pressure to deliver is mounting. Many IT teams must overhaul their systems, retiring outdated technologies to create the powerful hybrid cloud infrastructure required for data processing and AI. Additionally, they must gain comprehensive insights into their vast, unstructured data across storage systems to leverage it for AI initiatives and ensure efficient, cost-effective, compliant data management. In some organizations, cost-cutting mandates will dictate which path to take for IT infrastructure and AI needs.

Here are a few "can't miss" predictions and trends to watch in 2025:

IT leaders will get creative to deploy AI on a budget

Many enterprise IT teams are not ready for AI. That's because it often requires new infrastructure, hard-to-find expertise on building and training learning models, unique governance and security solutions, employee training and more. In the 2024 Komprise State of Unstructured Data Management report, only 30% of IT leaders said they will increase the budget for AI. Organizations can go to the cloud for a more affordable approach by experimenting with AI services such as pre-trained AI models from AWS, Azure, Google, IBM and other cloud providers. Rather than developing and supporting a customized AI solution, an organization may get the benefits they need from upgrading to the latest version of their enterprise business applications which likely have AI built in-such as from Oracle, Salesforce or SAP. No-code or low-code AI platforms claim to allow non-technical staff to build AI models without extensive coding knowledge. Finally, being as efficient as possible with data storage by continually analyzing and right-placing unstructured data into the most cost-effective storage will free up funds for AI.

Unstructured data governance processes for AI will mature

Protecting corporate data from leakage and misuse and preventing unwanted, erroneous results of AI are top of mind for executives today. A lack of agreed-upon standards, guidelines and regulations in North America is making the task more difficult. IT leaders can get started by using data management technology for deep visibility on all their unstructured data across storage. This visibility is the starting point to understanding this growing volume of data better so that it can be governed and managed properly for AI. Unstructured data classification is a fundamental step in AI data governance; it involves enriching file metadata with tags to identify sensitive and regulated data that cannot be used in AI programs. Metadata enrichment and tagging also aids researchers, engineers and scientists who need to quickly curate data sets for their projects by searching on keywords that identify file contents. With automated processes for data classification and audit logging, IT can create workflows to continually send protected data sets to secure locations. Separately, they can use automated workflows to send AI-ready data sets to object storage for ingestion by AI tools. Automated data workflow orchestration tools will be important for efficiently managing these tasks across petabyte-scale data estates. AI-ready unstructured data management solutions will also deliver a means to monitor workflows in progress and audit outcomes for risk.

Systematic data ingestion for AI will be the first data storage mandate

AI mania is overwhelming, but so far, enterprise participation has been largely led by employees who are using GenAI tools to assist with daily tasks such as writing, research and basic analysis. AI model training has been primarily the responsibility of specialists, and storage IT has not been involved with AI. But this will change swiftly in the coming year. Business and public sector leaders know that if they get left behind in the AI Gold Rush, they may lose market share, customers and relevance. Corporate data will be used with AI for retrieval augmented generation (RAG) and inferencing, which will constitute 90% of AI investment over time. Everyone touching data and infrastructure will need to step up to the plate as a broader set of employees start sending company data to AI. Storage IT will need to create systematic ways for users to search across corporate data stores, curate the right data, check for sensitive data and move data to AI with audit reporting. Storage managers will need to get clear on the requirements to support their business, departmental and IT counterparts.

Hybrid cloud persists, mandating deep intelligence on data and costs

After years of ping-ponging between cloud-first strategies, then cloud repatriation and back again, one thing has become clear: hybrid cloud is here to stay for the foreseeable future. IT leaders have realized that a mix of on-premises, cloud and more recently edge computing is a sensible, risk-averse strategy to satisfy the needs of different workloads and departments. Data storage and cloud vendors will adapt to this reality while IT will need to collect and view intelligence on their data assets across silos so they can move data into the optimal storage over its lifecycle. Optimizing a hybrid cloud storage environment will be a moving target dependent upon real-time analytics on data types, data growth and access patterns and the flexibility to move data to secondary or cloud storage tiers as needed. Storage professionals can amp up their career by adopting an analytics and financial operations (FinOps) mindset in everything they do.

Role of storage administrator evolves to embrace security and AI data governance

Pressing demands on both the data security and AI fronts are changing the roles of storage IT professionals. The job of managing storage has evolved, with technologies now more automated and self-healing, cloud-based and easier to manage. At the same time, there is increasing overlap and interdependency between cybersecurity, data privacy, storage and AI. Storage pros will need to make data easily accessible and classified for AI, while working across functions to create data governance programs that shrink the potential attack surface of ransomware and prevent against the misuse of corporate data in AI. Storage teams will need to know where sensitive data lurks and have tools to develop auditable data workflows that prevent sensitive data leakage.

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ABOUT THE AUTHOR

Kumar Goswami is cofounder and CEO of Komprise

Kumar has spent 23+ years delivering products that solve complex IT problems with simplicity and cost-efficiency.