Confidently moving enterprise AI from proof of concept to production

I'm excited to announce NetApp^® AI Data Engine (AIDE), our new end-to-end AI data service, integrated into NetApp ONTAP^® software, that makes AI simple, affordable, and secure—from finding and preparing data to serving it to GenAI apps. This announcement comes at a critical time when most enterprise AI initiatives are struggling to move from proof of concept to production, often due to the lack of AI-ready data. AIDE, which will be available in the coming months, is here to help in a big way.

There’s a scenario I hear repeatedly: A data scientist at a Fortune 100 financial services company builds three different AI models that could revolutionize customer service. All three are sitting in development limbo, because the team can't get reliable access to the data they need—and when they finally do, the compliance team won't sign off.

Stalled projects like this aren’t unique. In fact, analysts predict that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.​ After spending time with hundreds of customers across industries, I've seen the same pattern emerge: It’s not the AI applications or models that are causing the headaches. It’s the data that feeds them.

The excitement around AI is palpable in boardrooms and engineering teams alike. But between the initial proof of concept and production deployment lies a minefield of data infrastructure challenges that most organizations are discovering the hard way.

Data discovery becomes a treasure hunt. Data engineers tell me they're spending too much of their time just finding, accessing, and wrangling the data that their data scientists need, which is often scattered across on-premises systems, multiple clouds, and various business units. By the time they locate relevant datasets, the business requirements have often evolved.

Tool friction creates deployment roadblocks. Companies grapple with multiple tools and experiments that they find hard to implement and unify. Managing disparate platforms slows down deployment early on, forcing teams to spend more time on integration challenges than on actual AI development.

Data preparation turns into data multiplication. To get AI-ready data, teams typically end up creating an average of least seven (and often more) copies across different stages of the pipeline—raw data, cleaned data, transformed data, vectorized data, multiple versions for different models, and so on. And each copy needs storage, synchronization, and governance oversight. 

Security and compliance teams become bottlenecks. Without clear data lineage and governance frameworks built for AI workloads, security and compliance officers have no choice but to slow down or block projects. They can't risk exposing sensitive data or violating regulations like HIPAA or GDPR in AI pipelines they can't fully audit. 

Vector data bloat kills performance and budgets. When organizations finally get to the vectorization stage for their GenAI applications, they often discover that their data storage requirements have ballooned 10–20x, creating cost and performance problems at scale.

The result? Projects that show incredible promise in the lab fail to cross the production finish line, leaving organizations frustrated and questioning their AI strategy.

The fundamental issue isn't that AI is too hard. It's that we're trying to force AI workloads through data infrastructure designed for traditional applications. Enterprise AI needs purpose-built data infrastructure and data management that address the unique challenges of AI workloads from day one.

This realization led us to develop AI Data Engine, which tackles these challenges at the infrastructure level. Instead of requiring organizations to stitch together a dozen or more different tools and platforms, AIDE provides unified data management specifically designed for AI workloads such as GenAI, retrieval-augmented generation (RAG), agentic AI, and AI factories. This turnkey approach eliminates the deployment friction and tool integration challenges that slow down AI initiatives.

The service starts with global data discovery, creating a structured, searchable view of your entire NetApp data estate, regardless of where the data lives. This eliminates the treasure hunt and gives data scientists immediate visibility into available datasets with rich metadata and semantic search capabilities.

AIDE uses automated change detection and synchronization to maintain a single, always-current view of your data. Instead of managing multiple copies, teams work with one authoritative source that automatically updates downstream consumers when source data changes.

On the governance side, AIDE embeds policy-driven guardrails directly into AI workflows, as shown in the following image. Data moves securely with permissions and access controls intact, giving compliance teams the visibility and control they need without creating bottlenecks. This helps you accelerate enterprise AI with confidence.

Perhaps most important for scaling AI initiatives, AIDE addresses the problem of vector data bloat head-on. Using automated transformation—powered by NVIDIA NIMs—coupled with NetApp’s advanced compression and deduplication technologies, organizations will typically see up to 10x reduction in storage requirements for vectorized data, making large-scale AI deployments economically viable. After the data is efficiently transformed into vector embeddings, data scientists get a secure RAG endpoint that they can simply copy and paste into any RAG-based GenAI application.

The following image illustrates how you can start the embedding pipeline with a few clicks and flexible configuration options.

NetApp recognizes that no single vendor can deliver everything needed for enterprise AI. That's why AIDE is an open, scalable solution that integrates natively with major cloud AI platforms like Amazon Bedrock, Google Vertex AI, and Azure AI, while also supporting popular commercial and open-source MLOps tools and frameworks that data teams already use.

AIDE works seamlessly with leading ISV partners across the entire data pipeline and AI landscape. Whether you're using solutions such as Domino Data Lab for model development and deployment, LangChain for building enterprise-grade GenAI applications with secure RAG, Starburst for federated query capabilities across data silos, Informatica for data integration and cataloging, and many others, NetApp provides the intelligent data infrastructure layer that ties it all together. These aren't just integrations—they're strategic collaborations designed to accelerate time to value. From preparing and unifying data to training models and deploying in production, our AIDE partner ecosystem integrations help you build, refine, and scale AI solutions by using the tools you already know and trust, while gaining the infrastructure foundation needed to scale.

The organizations that successfully scale AI aren't necessarily those with the most advanced algorithms or the largest data science teams. They're the ones that have solved the data infrastructure challenges early, creating reliable pipelines that data scientists can trust and compliance teams can approve.

For storage administrators, this means thinking beyond traditional storage metrics to consider AI-specific requirements like vector data efficiency and real-time synchronization. For data engineers, it means having infrastructure that eliminates the complexity of data movement and preparation. For data scientists, it means spending time on models and insights rather than data pipeline maintenance. And for all roles, it means having a single solution to make workflow coordination and handoff across teams seamless.

Most important, for executive teams, it means having the confidence that AI investments will actually make it to production and deliver business value.

The AI revolution is real, and it's being built on data infrastructure. Organizations that recognize this and invest accordingly won't just join those that successfully deploy AI—they'll lead their industries in the transformation ahead.