Is your data infrastructure ready for AI?

The AI revolution isn’t coming—it’s already here, transforming the business landscape at lightning speed. Companies that fail to adapt risk falling behind as AI reshapes how we operate, make decisions, and compete in the market. However, AI’s effectiveness is only as good as its data. Which begs the question: Is your underlying data infrastructure ready to meet the demands of AI?

Many organizations are eagerly diving into AI projects without first evaluating whether their existing data systems can handle the demands of AI workloads. The result? Bottlenecks, security vulnerabilities, and usually failed AI initiatives. To avoid these pitfalls, it’s crucial to assess your data infrastructure’s readiness. But where do you start? By asking the right questions. Below, we’ll explore the key areas—hybrid multicloud integration, data mobility, robust security, and seamless scalability—and the essential questions to ask your vendors to ensure your infrastructure is truly AI-ready.

AI workloads are not confined to a single location. Data must flow effortlessly from the edge where it’s created, to the core data center for processing, and across multiple public clouds (like AWS, Azure, and Google Cloud) to leverage specific AI services. The IDC AI-ready data infrastructure report —a trusted resource for IT leaders—warns that the lack of seamless data movement can significantly hinder AI initiatives – "The majority of organizations are hybrid multicloud with on-premises and private cloud data repositories... often geographically distributed, perhaps globally... leading to data silos, which inhibit data leverage accuracy. An AI-ready data infrastructure addresses these issues using a common data plane across repositories."

Key question for your vendor: “How does your solution provide a unified data platform that spans on-premises, private, and public clouds, allowing us to move and manage data from a single control plane?”

What to look for:

• Seamless integration: Look for deep, native integration with all major cloud providers. Your AI data shouldn’t be siloed to only one cloud environment.
• Data mobility: Ensure the solution enables you to copy, clone, and tier data across your entire hybrid environment without friction. Efficient data mobility is crucial to avoid latency issues, operational bottlenecks, and added costs.

AI models are valuable intellectual property, and the data they train on is often highly sensitive. Security measures that many vendors offer are insufficient in this context. You need end-to-end protection that safeguards data at rest, in transit, and in use. The IDC report highlights the increased security risks associated with AI data pipelines and how “security, data trust, and protection are foundational to an AI-ready data storage infrastructure.”

Key question for your vendor: “What enterprise-grade, built-in security features does your platform offer to protect our AI data pipeline from creation to archive?”

What to look for:

• Autonomous ransomware protection: Seek proactive, AI-driven detection and response to threats at the data layer, not just the perimeter.
• Robust encryption: Ensure end-to-end encryption and support for the highest industry and government security certifications (e.g., NSA CSfC validation).
• Multi-Admin Verification: Look for controls that prevent unauthorized changes or deletions, protecting against both external and internal threats.

AI data sets and models are growing exponentially. Your infrastructure must scale not just in capacity but also in performance to feed data-hungry GPUs without creating bottlenecks. A system that can’t scale efficiently will quickly become obsolete. “The proliferation of data, especially unstructured, in both AI ingest and output can lead to massive and irregular capacity requirements” as noted in the IDC report.

Key question for your vendor: “How does your architecture support both scale-up and scale-out to meet growing performance and capacity demands without requiring a complete overhaul?”

What to look for:

• Flexible architecture: The ability to scale performance (throughput) and capacity independently, ensuring you only pay for what you need.
• Linear, predictable scaling: Performance should increase predictably as you add resources, with no surprise bottlenecks or performance degradation.
• High throughput for data pipelines: Proven ability to deliver the high-bandwidth performance required to keep expensive GPU clusters fully utilized.

Building a successful AI practice starts from the ground up. Before you invest heavily in AI models and applications, ensure your data infrastructure is prepared. This means demanding seamless hybrid multicloud integration, uncompromising security, effortless data mobility, and limitless scalability.

The right infrastructure doesn’t just support your AI strategy—it accelerates it, turning data into a true competitive advantage. Ensure your foundation is solid, and you’ll be ready to unlock the full potential of AI.

Watch the on-demand webinar Building the AI-ready enterprise with NIVDIA and IDC to learn more about preparing your data infrastructure and how you can unleash the power of enterprise AI with NetApp AI solutions.