The enterprise AI evolution has become a revolution

The pace of AI is changing rapidly as cloud and mission-critical enterprise applications become inextricably linked. Most organizations are finding that a hybrid approach, combining cloud services with on-premises infrastructure, offers the most effective and flexible path for AI success. 

As a result, there is a fundamental shift in how organizations approach AI implementation, data management, and enterprise storage, according to a recent report by Futurum, “Enterprise AI Evolution: Maximizing Data Value in a Hybrid World.” 

With these new production deployments, there are higher demands for manageability, cost efficiency, and collaboration across teams.  

“To maximize the value of their data for AI initiatives, businesses are focusing on data integrity, governance, and accessibility within their current systems, rather than creating separate AI infrastructure,” Futurum wrote.  

In the context of AI, these priorities require teams to instantly access and operationalize data — powering real-time intelligence at scale.

This renewed emphasis on data, the lifeblood of almost every AI project, means faster innovation with actionable insights. Nothing improves AI ROI faster than using a unified data platform to ensure high-quality, accessible data across your AI pipeline — from cloud to edge to core. 

But it’s not just about moving data from point A to B. A robust metadata management process helps elevate the data for improved analytics.  

To capitalize on these diverse data requirements and AI workloads, organizations need to recognize if their older niche storage systems are not up to the task. Modern workloads require flexible infrastructure that can handle multiple applications, particularly as AI expands to include inference, vector databases, and robust data preparation.  

The impacts to infrastructure and cloud spending will be significant. 

Futurum says that “71% of organizations plan to comprehensively reevaluate their cloud workloads in 2025 to optimize placement between private and public environments. This signals a pivotal moment in enterprise infrastructure strategy, with AI requirements driving much of this reassessment.” 

NetApp has anticipated these changes and offers a future-ready, unified approach that provides consistent, high-performant workload management, including security and governance controls regardless of where data physically resides.  

Companies need a secure, scalable, and governed data infrastructure across on-premises and multiple cloud environments for successful AI initiatives.

NetApp’s AI solutions can help companies take full advantage for their data: 

• Seamlessly serve data to AI across any ​hybrid multicloud environment​. 
• Streamline AI data pipelines with best-in-class infrastructure and intelligent data services for AI​. 
• Confidently deploy and protect AI workloads ​with the most secure storage on the planet​. 

 Fuel AI innovation with enterprise-ready data: 

•  Remove data silos: We help companies break down data silos by providing a single, intelligent data infrastructure that spans file, block, and object storage across data centers and all major public clouds (AWS, Azure, Google Cloud). This enables seamless data movement, management, and protection, ensuring AI teams have access to the right data wherever it resides. 
• Gain unmatched flexibility: By leveraging a hybrid multicloud approach, organizations can select the best cloud provider for each AI workload, optimizing for performance, compliance, and cost, without being locked into a single ecosystem. 
• Support advanced use cases: We integrate enterprise data with custom AI models, supporting advanced use cases such as retrieval-augmented generation (RAG) and generative AI. Our solutions are designed to accelerate AI pipelines from data preparation to model training and deployment, both on-premises and in the cloud. 
• Achieve performance and scalability: We deliver high-performance, scalable storage (including all-flash and hybrid-flash systems) that supports demanding AI workloads, such as those involving high performance NVIDIA GPUs and DGX SuperPOD systems. This helps enterprises efficiently manage large, complex datasets required for AI. 
• Built in security and governance: Recognizing the dual challenge of AI opportunity and cybersecurity threats, NetApp builds robust security and governance into its storage solutions, including ransomware protection and policy-driven data lifecycle management. 
• Simplicity and Efficiency: Our goal is to help you reduce operational complexity and cost by automating data classification, movement, and provisioning, optimizing GPU utilization, and simplifying integration with MLOps platforms. 

 As a leader in hybrid cloud storage management, we enable organizations to unify, protect, and accelerate data pipelines across environments, and to deploy AI responsibly, securely, and at scale. Explore more about NetApp AI solutions and hybrid multicloud storage – the foundation for enterprise AI.