As AI has matured from consumer prototypes to real-world enterprise initiatives, IT operations teams are feeling the pressure to build high-performing infrastructure, including GPU-ready storage. That’s needed to handle AI’s massive data crush, yet enterprise AI uses mostly existing organizational data. Performance, while important, is less critical than creating the right unstructured data management strategy to deliver sound governance, data hygiene, and data classification for AI. Data management for AI entails moving beyond storage optimization to deliver fully managed data services for different audiences. These services include reporting and analytics on data usage and spending, cost-effective archive and migration, granular data search and classification, AI data workflow and ingestion services, and cybersecurity such as ransomware protection and sensitive data management.
Enterprises across industries are storing more data than ever. More than 5 petabytes (PB) is the new normal and more than 10PB is increasingly common. The theme for unstructured data management in 2026 is more of everything: more data, more investment, more pains and more AI security and risk concerns. Unstructured data growth is being driven by accelerated AI adoption, exploding digital exhaust and massive increases in rich media and sensor data. Yet more data also means more potential opportunity, if enterprises can efficiently identify, filter and classify data for the right use cases while also mitigating risks from AI. Next year, we predict that as AI inferencing adoption grows, organizations will realize the critical role unstructured data curation plays in AI ROI.
Consumers and employees have jumped onto the AI bandwagon, finding vast time-saving benefits of integrating these new tools into daily tasks. Yet senior IT and leaders will not tolerate a breach or leakage of sensitive data or increased ransomware exposure. At the same time, few limitations exist today. The Komprise survey found that only 14% of organizations are restricting AI in their workforce. Meanwhile, our research and countless other studies indicate that sensitive data leakage, hallucinations and unclear data provenance are dire concerns of IT and business leaders. IT executives will need to strike the proper balance between access to cutting-edge technology and encouraging experimentation among employees with the need to vigilantly protect corporate and customer data. The ability to audit what data goes in and out of tools while closely monitoring app usage for risk will be a growing best practice developed jointly by IT, legal and security teams.
Unstructured data is one of the most valuable assets in organizations today, yet it is still largely unknown because it has grown so fast and lives across many boundaries of systems and storage. Without a systematic way to classify and filter unstructured data, such as to spot outdated versions, search based on its contents and relative value, IT remains in the dark to protect it and deliver precisely the right data sets to stakeholders for AI. However, by enriching file metadata with automated file scanning and tagging tools, IT teams can exclude sensitive, irrelevant, and outdated data from AI workflows. This also makes it easier for employees to search for the exact data they need for a project. Preparing and classifying data for AI will be a top data management priority in 2026, right behind storage cost optimization.
3. IT leaders will adopt unstructured data classification to improve data security and AI ROI
IT leaders are bullish on investing in the future, despite a rollercoaster global economy. Executives’ expectations for AI’s impact on the bottom line are high as the technology moves from pilot to production. Financial and tech analysts are projecting anywhere from 4 to 10% growth in IT budgets in 2026. These funds will not be spent across the board but in key areas for optimization: data, AI and infrastructure including storage. security and data management. Many IT organizations will need to upgrade data storage and data management platforms, as reported by 64% of IT leaders in the Komprise survey. Beyond technology, IT and business executives will seek IT leaders with expertise in AI infrastructure along with technical staff who can design, implement and manage AI agents and data workflows. Nearly half of IT teams will be adding staff and 55% will engage in reorgs and re-skilling for AI, according to our research.
5. IT infrastructure priorities pivot to AI-ready data management
Unstructured data growth has reached a tipping point this year in the enterprise. According to the Komprise 2026 State of Unstructured Data Management, most enterprises are storing more than 5PB and 40% store more than 10PB. Managing this data using the same methods of 5 to 10 years ago is no longer viable due to high costs for managing and protecting data and the emerging, unpredictable needs for AI data preparation, curation and auditing. Capabilities that allow IT infrastructure and operations teams to see, understand, clean up, filter, classify and move data across storage and backup silos will become essential to manage risk and improve data visibility and access for departments.
By Krishna Subramanian
4. GenAI risks become the ultimate balancing act for leaders
1. Uncontrolled data growth creates a perfect storm for dramatic change
2. IT budgets survive a tenuous year and will support new technologies and headcount for AI
Here are the 5 leading trends that we see playing out in the coming year:
