What is AI-Ready Data Storage Infrastructure (AI-RDSI)?

At a recent industry event, I found myself in a room full of technology leaders, data scientists, and infrastructure teams. The energy around agentic AI was unmistakable. When the speaker asked how many of the 400-odd people in the audience were experimenting with AI agents, a significant number of hands went up.

Then came the follow-up: how many had moved those agents to production – securely, reliably and at enterprise scale. Not a single hand went up.

As the Chief Platform and Technology Officer, I see this gap everywhere: organizations are excited about what agentic AI can do, but moving from pilot to production requires more than a compelling demo.

Why the disconnect? The answer lies in AI-Ready Data Storage Infrastructure (AI-RDSI)—a concept recently defined by the analyst firm IDC.

What is AI-ready data storage infrastructure?

Per the IDC Special Study AI-Ready Data Storage Infrastructure: Definition, Taxonomy, Ontology, and Future Outlook, sponsored by NetApp, “AI-ready data storage infrastructure (AI-RDSI) is defined as: The hardware, software, and services necessary to ingest, prepare, store, manage, protect, secure, govern, and transform data to address the requirements of artificial intelligence applications. AI-RDSI also encompasses service levels pertaining to AI workloads, including performance and system availability; data quality related attributes, such as trust and provenance; and technologies for the disposition of data post-analysis.”

Before an AI agent can reason, act, and make decisions on behalf of your business, it needs secure access to data that is current, trustworthy, and consistently available. But to understand why this is such an important concept, we need to step back and look at the current state of enterprise AI.

Why do enterprise AI projects fail?

Enterprise AI projects primarily fail because the underlying data is not properly prepared, governed, or consistently available to feed your models. According to the IDC Special Study, fewer than half of AI pilot projects advance to production.

AI isn’t failing because the models aren’t good enough; it’s failing because the data feeding them isn’t ready. For most enterprises, achieving well-governed, AI-ready data is more challenging than the AI itself. In fact, Gartner^® predicts that “through 2026, organizations will abandon 60% of AI projects that are unsupported by AI-ready data.”

IT leaders today are scrambling to prepare AI-ready data for their AI initiatives. This is where AI-RDSI comes in.

What does the research say about AI-RDSI?

The IDC study tackles what AI-ready data storage infrastructure means for enterprise growth and operational excellence.

Organizations need to approach AI from a data-centric perspective. Data quality, timeliness, and governance—all enabled by AI-RDSI—are foundational to an AI project's success or failure.

What are the five characteristics of AI-RDSI?

For leaders tasked with balancing rapid scale and operational precision, AI-RDSI is built to go beyond legacy approaches. AI-RDSI requires a combination of hardware and software that builds on traditional technologies and embeds AI to enhance system use, performance, reliability, and efficiency. The five key characteristics include:

1. A single source of truth. Having a copy data management (CDM) capability within AI-RDSI is vital for AI workloads.
2. Performance that keeps pace with compute. Storage system performance must support AI workload needs, including data throughput, input/output operations per second (IOPS), latency, and network bandwidth.
3. Service levels that deliver nonstop data availability. Gaps in data availability don’t just stop access; they can degrade AI models while wasting compute cycles.
4. Policy engines that automate data logistics. Policy engines ensure that data is delivered to the right place at the right time for AI optimization.
5. Data governance to ensure data quality and trust. As you scale AI from pilot to production, having the appropriate policies and procedures in place to reduce data contamination or tampering is essential to preserving data quality and trust.

How does NetApp deliver AI-ready data infrastructure?

NetApp enables organizations to deploy AI-RDSI through an intelligent data infrastructure that’s silo-free, secure, and ready for AI. Two of our most important products form a unified data foundation that’s purpose-built for enterprise AI:

• NetApp AFX is a disaggregated, high performance storage system built for the most demanding AI workloads. AFX runs on NetApp ONTAP—the same data management foundation our customers have relied on for decades—but with a new architecture that independently scales performance and capacity, supports terabytes per second of bandwidth across up to 128 nodes, and integrates seamlessly across on-premises and cloud environments.
• NetApp AI Data Engine (AIDE) is a unified, end-to-end data service that simplifies and secures the entire AI data lifecycle. AIDE provides:
  • Real-time metadata indexing
  • Automated data curation
  • Built-in guardrails for privacy and compliance
  • Integrated vectorization for GenAI applications

Consult the IDC study for a complete list of NetApp products that enable AI-ready data infrastructure.

Why is NetApp the enterprise choice for AI-RDSI?

NetApp has a 40-year track record serving enterprise customers—including 84% of the Fortune 500—for whom data integrity, security, and availability are paramount. We have been trusted stewards of enterprise data for decades, and we continue to deliver on our promise to our customers with our latest AI-RDSI solutions.

At every step of the AI journey, from data preparation to model training to deployment, NetApp has the technology and a deeply integrated partner ecosystem that enables enterprises to build, refine, and scale impactful AI solutions. Enterprises choose NetApp for AI-RDSI for three key reasons:

• Native hybrid multicloud integrations. Cloud is a critical component for developing and deploying AI. With native first-party cloud storage in AWS, Azure, and Google Cloud, NetApp delivers the seamless core-to-multicloud data connectivity and mobility enterprises need to power any AI workload wherever it resides.
• Built-in data protection and security. NetApp enterprise AI security and governance includes real-time AI-powered ransomware protection with 99% detection accuracy, guaranteed recovery, post-quantum cryptography, authentication controls, comprehensive encryption, immutable snapshots, and FPolicy file blocking. And it’s all built in, not bolted on.
• NVIDIA and AI partner ecosystem. NetApp and NVIDIA deliver proven, validated reference architectures and infrastructure solutions, including NVIDIA DGX SuperPOD with NetApp ONTAP storage, NetApp AIPod for NVIDIA DGX, NeMo Retriever, and more. Our partnerships with other AI leaders, including Cisco, Intel, Lenovo, AWS, Google, and Microsoft, enable us to deliver integrated solutions that accelerate AI deployments.

Bridging the AI gap

The gap between AI pilots and payoffs is a data infrastructure problem. And it’s solvable now. Read the IDC Special Study to learn more about the future of AI-ready data infrastructure. Take the AI maturity self-assessment to see how IDC’s framework, best practices, and considerations can help you assess where your data infrastructure stands — and take steps to close the gap.

Over the next 6-12 months, I expect to see our most innovative customers pushing ahead into this space where the possibilities are endless. Will you be one of them?

Citations

1. IDC Special Study: AI-Ready Data Storage Infrastructure: Definition, Taxonomy, Ontology and Future Outlook, #US53709325, January 2026
2. Gartner Press Release, Lack of AI-Ready Data Puts AI Projects at Risk Q&A with Roxane Edjlali, Senior Director Analyst, February 26, 2025. GARTNER is a trademark of Gartner, Inc. and/or its affiliates.