In 2020, the World Economic Forum predicted that by 2025, 50 percent of all employees would need to reskill because of technology change. Well, it’s now 2025, and this seems highly relevant, especially now as employees across most disciplines are expected to understand AI and use it in their jobs. Job ads increasingly call for a data-driven approach to leading teams and projects.

So, where does this leave the IT professional, particularly those managing data storage and IT infrastructure? Until recently, those managing data storage – whether it is on premises or in the cloud – have been able to continue their focus on performance, capacity, and data protection.

Times are changing. Storage IT professionals are increasingly managing data movement and access across complex hybrid cloud and multi-vendor environments. Addressing security threats from AI and cyberattacks is critical, as is managing infrastructure and data workflows for AI.

Reimagining work with data at the center

Storage architects and engineers should consider how they can energize their careers by moving from a storage steward – managing and procuring storage technology – to being a data steward. This entails reducing risks, using cost modeling and FinOps tools, preparing data and infrastructure for AI, and delivering data services to departments.

Below are some ways that the work of storage IT teams will change, as data-centric and AI-centric processes spread across organizations.

1. Collaborate and listen: Thank you, Vanilla Ice, for elevating the words that matter. Data stewards need to adopt a cross-disciplinary mindset. The job of managing technical configurations and resolving issues now requires a broader understanding of the full IT infrastructure. Storage professionals will thrive as trusted advisors to other IT and business roles to help set the requirements for data management vis-à-vis business objectives. This will require a collaborative mindset and business-savvy approach to meet user and departmental needs while satisfying IT objectives for standards, security, compliance, and cost efficiencies.

2. Get data on your data: As Peter Drucker said, “You can’t manage what you don’t measure.” While this is not entirely true, it certainly helps to have metrics that inform data management decisions and serve internal customers. In the AI age, storage professionals will need to work with data scientists, data analysts, project teams, and department leaders, bringing more knowledge about data in storage. This entails identifying, segmenting, and defining data types and managing that data granularly, according to business and user needs. For instance, you will need to know data volumes and growth rates across the organization by department, common file types and sizes, and lead efforts at data classification via metadata enrichment.

3. Cost modeling and FinOps: Large organizations are spending millions of dollars annually on data storage, backups, and disaster recovery. On balance, there’s nothing wrong with that since data is the center of everything today – but all data should not be treated the same. Using cost modeling tools, the storage manager can enter actual storage costs to determine upfront new projected storage costs and actual usable capacity, based on data growth rates. These costs must factor in backups and disaster recovery, which can be 3X of storage spending, and should compare on-premises versus cloud models. An unstructured data management system that indexes all data across all storage can supply metrics on data volumes, costs, and predicted costs, and then model plans for moving less-active data to lower-cost archival storage, such as in the cloud. Then, they can select the optimal storage platform for hot or active data when it’s time for a storage refresh.

4. Improve SLAs and data metrics for business: Service level agreements for storage typically include data availability and uptime [a.k.a. the Three Nines or Four Nines], latency for hot and cold storage, backup frequency and windows, RTO and RPO, and response times for issues such as critical data loss. There may be new metrics, however, that storage professionals need to start tracking as they deliver more services to businesses. These could include departmental chargeback or showback, percent of non-compliant data in storage (such as personal videos or legacy system files that must be deleted), top data owners by department and individual, and the amount of duplicated and orphaned data reduced.

5. Reduce risks from ransomware: Storage teams must mitigate ransomware risks associated with file data. One way to do this is by implementing hybrid tiering strategies that offload infrequently accessed (cold) files to immutable cloud storage, which reduces the active attack surface by as much as 70 or 80 percent. Immutable storage ensures that once data is written, it cannot be altered or deleted, providing a robust defense against ransomware attempts to encrypt or corrupt files.

6. Prepare infrastructure for AI: Storage teams play a crucial role in AI by ensuring the reliable, scalable, and efficient storage and management of the massive datasets required for AI model training and deployment. Procuring the right infrastructure to run AI workloads will vary significantly depending on the size and budget of the organization, the level of AI customization needed, and other considerations such as security. Launching an AI initiative in your enterprise may require model development and training if you need to build your own generative AI model. This typically begins with acquiring adequate high-performing computational resources—the pricey CPUs, GPUs, and TPUs that are required to host machine learning models and process data at warp speed.  While pre-baked infrastructure, public models, and cloud services offer cost and ease-of-use benefits, IT organizations must also weigh the benefits of keeping AI in-house or developing a hybrid cloud AI model for better controls.

7. Prepare data for AI: Big data analytics and AI projects often involve processing large and diverse unstructured data sets, such as images, videos, documents, messages, and sensor data. Storage teams are responsible for classifying and preparing this data for model training.  The first step is gathering basic insights about the data sets – such as its size, location, file type, access, and usage patterns, and if data needs enrichment (via additional metadata tagging) for better context and classification. Storage leaders also need to organize and manage data so that end users, such as data scientists and researchers, can easily search for and leverage the specific data required for their projects. Using technology for automated data workflows streamlines this process of discovering, enriching, copying, and/or moving data to the optimal location for analysis.

8. Protect data for AI: Storage professionals need to prevent sensitive data leakage to AI, as employees use GenAI and other tools daily. This begins with identifying and segregating sensitive and proprietary data into a private, secure domain where it can’t be shared with commercial applications. It’s also vital to maintain an audit trail of your corporate data that feeds AI applications. A healthcare organization, for instance, would need to verify that no patient PII data has been leaked to an AI solution per HIPAA rules. Storage IT managers will need to help institute an AI governance framework that covers privacy, data protection, ethics, and more.

Embracing the future: A new mindset for storage IT professionals

The age of AI and data is not just transforming technology and business models — it’s redefining careers. For IT storage professionals, the shift from managing infrastructure to orchestrating data presents both challenges and incredible opportunities.

Now is the time to ask yourself: Are you simply maintaining storage, or are you preparing data to drive business success? By expanding your skills in data intelligence, automation, FinOps, security, and AI governance, you can position yourself as an indispensable leader in the modern enterprise.