AI now sits inside everyday business processes rather than in a single, centralized training environment. That means models need a constant flow of clean, timely data and enough compute power to learn and adapt in real time. A modern data foundation brings three things together: - **Unified, AI-ready data**: Data is organized, cleansed, and accessible across locations—from edge to cloud—so teams can use it without spending most of their time on preparation. - **High-performance compute**: GPU-enabled infrastructure can handle demanding AI workloads and real-time data pushes and pulls. - **Resilient architecture**: Built-in security, backup, and recovery keep AI services running even when there are cyber threats, hardware issues, or operational disruptions. HPE research shows a clear gap between confidence and capability: while **22%** of IT leaders say they’ve already operationalized AI and **41%** believe they’re close to running deep learning in production, only **45%** say they can run real-time data pushes or pulls, and fewer than **60%** feel they can fully handle most key stages of data preparation. This gap slows innovation, delays business benefits, and leads to frustration with AI outcomes. In short, without a modern, unified data ecosystem, AI initiatives struggle to move from promising pilots to reliable, scalable business value.

Most organizations are not struggling with AI algorithms as much as they are with the data feeding those algorithms. HPE research and customer feedback highlight several recurring challenges: 1. **Scattered and inconsistent data** Data is often spread across multiple systems and business units, with inconsistent formats and limited governance. A healthcare chief data officer described it this way: *“Our data is scattered across multiple systems and business units, with inconsistent formats and missing governance, making it very difficult to prepare it for AI projects.”* Only **41%** of surveyed IT leaders have set up shared data models with centralized business intelligence, which means access to data is uneven across the organization. 2. **Legacy applications and storage not built for AI** Many core systems were never designed with AI in mind. A manufacturing cloud architect noted that upgrading these legacy applications would require significant investment in storage and integration. At the same time, legacy storage platforms struggle with modern AI workloads. **86%** of IT leaders say their organization is focused on extending storage technology for AI, recognizing that current setups are not enough. 3. **Limited real-time data capabilities** Even among organizations that believe they are AI-ready, only **45%** report they can run real-time data pushes or pulls. This limits the ability to support AI that needs up-to-the-minute information, such as operational decisioning or adaptive customer experiences. 4. **Weak or manual data governance** Many teams still rely on manual processes to classify, cleanse, and approve data. Leaders acknowledge the need for stronger governance and clearly defined policies, but these are not yet consistently implemented. 5. **Security and resilience concerns** Almost **74%** of IT leaders are concerned about their ability to secure AI datasets. Without strong protections, backup, redundancy, and disaster recovery, AI initiatives are exposed to data leakage, cyberattacks, and operational failures. These issues combine to create a gap between perceived AI readiness and actual data maturity. The result is slower time to value, AI outputs that can’t be trusted, and difficulty scaling successful use cases across the business.

Modernizing your data for AI is less about a single tool and more about building a coherent, AI-ready ecosystem. Based on HPE research and best practices, four areas matter most: 1. **Improve data accessibility** - Move toward an overarching, data-first platform that unifies access to data across applications and locations—from edge to cloud. - Establish shared data models and centralized business intelligence so data scientists and analysts can find and use relevant data in real time. - Aim to close the gap where currently only **41%** of IT leaders have such shared models in place. 2. **Modernize data storage with hybrid cloud** - Bring data closer to compute, especially GPU-enabled environments, so AI workloads can run with low latency and high throughput. - Adopt hybrid cloud architectures that let you place storage where data is generated or ingested, rather than forcing everything into a single location. - Today, only **46%** of IT leaders are using hybrid solutions for AI data storage, leaving room to rethink how storage is designed for speed and flexibility. 3. **Automate data governance and preparation** - Use AI-driven tools to classify, cleanse, and standardize data before it is used to train or run models. - Define clear governance policies with your technology and data leaders, then automate enforcement so quality checks happen continuously, not just at project kickoff. - This helps address the current situation where fewer than **60%** of organizations feel confident in handling most key stages of data preparation. 4. **Build resilience and security into the data layer** - Implement strong protections for AI datasets, including encryption, access controls, and monitoring to detect unusual activity. - Design for continuity with backup, redundancy, and disaster recovery protocols so AI services can withstand cyber threats, hardware failures, and operational incidents. - This is especially important given that **74%** of IT leaders are concerned about securing AI data. By focusing on these four areas—accessibility, storage modernization, automated governance, and resilience—you can reimagine your data foundation as a strategic asset for AI. This creates the conditions for: - Faster, more reliable AI deployments - Better use of advanced analytics across teams - New business models and services built on trusted, real-time insights Ultimately, organizations that invest in a robust, unified data foundation are better positioned to adapt, innovate, and compete as AI becomes embedded in everyday operations.