Enterprises today are striving to become Frontier organizations. They want to move beyond AI experimentation and begin embedding AI into the way they operate, make decisions, and serve customers.
But there is a common misconception that AI success is primarily about models or compute infrastructure. In reality, the biggest barrier is usually much simpler: data.
AI models, retrieval-augmented generation (RAG) applications, copilots, and agentic systems all depend on fast, secure, governed access to enterprise file data. Yet for most organizations, that data is scattered across on-premises infrastructure, multiple clouds, SaaS platforms, and edge environments. As a result, valuable information often remains trapped in silos, disconnected from the AI services designed to create business value.
The challenge becomes even more difficult at scale. Organizations frequently find themselves moving, duplicating, and transforming data just to make it available for AI. This increases costs, creates governance concerns, introduces security risks, and slows innovation.
Building the data foundation for AI
Microsoft and NetApp are helping customers overcome these challenges by providing a hybrid cloud data foundation designed for the AI era.
Together, Microsoft and NetApp enable organizations to securely connect data across on-premises environments and Azure while maintaining performance, governance, and control. The goal is simple: make enterprise file data AI-ready wherever it lives.
At the center of this approach is NetApp ONTAP, which creates a unified data fabric spanning on-premises infrastructure and cloud environments. Rather than forcing organizations to consolidate all data into a single location, ONTAP enables consistent data management, protection, mobility, and access across hybrid cloud environments.
This gives organizations the flexibility to use the right data, in the right place, at the right time for AI initiatives.
Diagram: Make enterprise file data AI-rady wherever it lives
Bringing AI to the data
Many AI projects stall because organizations spend months moving and preparing data before they can begin creating value.
A better approach is emerging: bring AI to the data instead of moving data to AI.
Microsoft Fabric, OneLake, Azure AI Foundry, and Microsoft Purview provide the services needed to prepare, govern, and operationalize data for AI. Azure NetApp Files extends this vision by serving as a high-performance enterprise file data platform that can make file available to Microsoft's AI ecosystem while allowing organizations to maintain their existing data architectures.
This approach helps reduce unnecessary data movement, minimize storage sprawl, and simplify governance.
Instead of creating additional copies of enterprise data, organizations can leverage existing data assets and make them available to AI workflows through a unified data architecture.
Accelerating AI, ML, and HPC workloads
For AI training, machine learning, and high-performance computing (HPC), data performance matters.
GPUs are only as effective as the data feeding them. When storage cannot keep pace, expensive compute resources sit idle waiting for information.
Azure NetApp Files provides the performance, scalability, and reliability required for demanding AI and analytics workloads. Organizations can support data-intensive environments while maintaining enterprise-grade availability and operational simplicity.
Whether running large-scale analytics, model training, engineering simulations, or scientific workloads, high-performance storage becomes a foundational requirement for maximizing AI investments.
Powering generative AI and RAG applications
Many organizations have valuable data stored in file shares, documents, knowledge repositories, engineering systems, and business applications. This information often represents the institutional knowledge that generative AI applications need to produce accurate and relevant responses.
With a hybrid cloud data architecture, organizations can make enterprise data available to AI workflows through file and object access models without creating costly duplicate data stores.
This enables organizations to build RAG applications and knowledge driven AI experiences that leverage trusted enterprise content while maintaining governance and security controls.
The result is faster time to value, lower storage costs, and better utilization of existing data investments.
Creating the foundation for agentic AI
As organizations move toward agentic AI, the requirements become even more demanding.
Agents need persistent access to shared data, reliable state management, governance controls, and the ability to operate consistently across environments.
This is where enterprise data services become critical.
Capabilities such as snapshots, cloning, replication, business continuity, sovereignty controls, and data protection help provide the trusted foundation that agents need to safely interact with enterprise information.
As AI systems become increasingly autonomous, organizations will need confidence that their data remains protected, compliant, and accessible regardless of where it resides.
The path to Frontier transformation
The future of AI will not be defined solely by better models. It will be defined by how effectively organizations can connect trusted enterprise data to those models.
Organizations that can securely unify, govern, and activate their data across hybrid cloud environments will be better positioned to scale AI from pilot projects to real business transformation.
The bottom line: Frontier transformation depends on trusted enterprise data. Microsoft and NetApp help organizations securely connect data to AI across hybrid cloud environments, enabling them to move faster, reduce complexity, maintain control, and turn their most valuable data assets into measurable business outcomes.
When data is ready for AI, AI becomes ready for business.
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