Why your AI stalls at production, and what fixes it?

Most enterprise AI projects fail at production scale because of data architecture, not the model. A new GigaOm CIO decision brief: From AI pilots to production, commissioned by NetApp, shows the fix: Build an intelligent data infrastructure that delivers true hybrid cloud mobility, built in cyber resilience, and consistent data management across every AI workload.

Enterprise AI spending will top $300 billion globally this year, according to the GigaOm brief. Yet most organizations report no measurable return from their generative AI deployments. The pattern is consistent. A model performs beautifully on curated data in a sandbox, then collapses the moment it meets production volumes, pipeline latency, and governance rules.

Here's the part most vendors won't tell you: the bottleneck isn't the model. It's the data. AI workloads need fast, governed access to data spread across on-premises systems, clouds, and the edge, with consistent security and the freedom to access data without being forced to move it or make copies. When that foundation is missing, every AI project has to solve it independently. Most don't.

So what separates the AI projects that reach production from the ones that stall? The answer sits beneath the surface. This article breaks down the GigaOm findings and shows why NetApp helps you run not just your AI workloads, but your entire enterprise data estate.

Why do enterprise AI projects fail at production scale?

The GigaOm brief is direct: AI projects rarely fail because the model was wrong. They fail because the data architecture was never designed to support any model at production scale.

Picture enterprise AI as an iceberg. The model, use case, and business outcome your board tracks sit above the waterline. Everything that decides whether the AI initiative survives sits below it: hybrid cloud data mobility, cyber resilience at the data layer, and unified data management. Without that crucial mass beneath the surface, the visible initiative could capsize.

Most current data estates can make matters worse. Data lives everywhere, governed and protected inconsistently, and managed by teams that have never collaborated on AI. Run five or ten parallel pilots that each build their own infrastructure, and you're not five or ten times closer to production. You're more likely to be five or ten times deeper in technical debt.

This is where the comparison with other vendors matters. Many show off a narrow, specific AI use case. Whereas NetApp helps you execute your entire enterprise data strategy, because the same platform handles every workload, not just AI. NetApp ONTAP, the foundation, spans on-prem arrays, edge deployments, and first-party native cloud services across AWS, Azure, and Google Cloud, with one operating system and consistent data services everywhere.

Let's look at the three pillars beneath the waterline.

How does NetApp unify data across hybrid and multicloud environments?

NetApp unifies data across hybrid and multicloud environments through NetApp ONTAP, a single operating system that spans on-premises arrays, edge deployments, and first-party native cloud services in AWS, Azure, and Google Cloud. The same data management tools work everywhere, so AI workloads have access to the data wherever it may live.

AI workloads need data wherever compute runs best: on-prem GPU clusters for training, cloud instances for inference, edge locations for real-time decisions. In most enterprises, data sits locked in environment-specific silos. Moving it means egress fees, slow syncing, and broken governance chains. The result? Your AI is constrained to wherever the data happens to live, not wherever compute makes the most sense.

NetApp is the only storage vendor providing first-party native services in all three major hyperscalers: Amazon FSx for NetApp ONTAP, Azure NetApp Files, and Google Cloud NetApp Volumes. First-party means the cloud provider fully manages it, bills it, and integrates it into native cloud APIs. It's not a marketplace appliance you have to manage yourself.

Here's what that unlocks. You can work across cloud boundaries using the same protocols your storage teams run on-premises. A training dataset on AWS can serve Azure AI compute at local-read speeds, with no full-volume replication. Your AI workloads have access to the data wherever it may live, without rebuilding pipelines for each environment.

The competitive edge: No other storage vendor offers unified hybrid cloud infrastructure through first-party cloud storage in all three major cloud providers. That single fact moves hybrid multicloud from aspiration to operational reality.

“NetApp also holds an advantage with its hybrid cloud portfolio. IDC recognizes NetApp as the only storage provider offering first-party integrations across all major cloud providers. The NetApp ONTAP platform remains one of the most widely adopted and technically mature storage technologies, with value for developers seeking consistency and control across environments.” – IDC Link: NetApp Focuses on Enterprise-Grade AI Infrastructure with New AFX Storage System and NVIDIA-Fueled AI Data Engine.

How does NetApp protect AI training data?

NetApp protects AI training data by building detection, immutability, and recovery directly into the storage layer. With NetApp, AI workloads inherit real-time ransomware detection, tamper-proof snapshots, and minute-scale recovery automatically, with no separate security toolchain.

AI training datasets represent an enormous compute investment. A corrupted or encrypted training set doesn't just need restoring; it needs you to rerun training jobs that may have burned weeks of GPU time. Model artifacts are high-value targets precisely because they're expensive to reproduce. Yet most organizations protect AI workloads less rigorously than their production databases, often because the AI infrastructure was built outside the standard security architecture.

NetApp embeds detection, immutability, and recovery directly into the storage layer:

• Real-time ransomware detection. ARP/AI spots threats through behavioral analysis at the point of data creation. It earned an AAA rating from SE Labs, with greater than 99% recall for file-based scenarios.
• Tamper-proof immutability. SnapLock Compliance enforces immutability that resists administrative override, with multi-admin verification.
• Recovery in minutes, not days. SnapRestore recovers from immutable snapshots using pointer-based restoration rather than bandwidth-constrained backup recovery.

When ransomware hits, the difference between pointer-based recovery in minutes and backup restoration over days is the difference between an AI pipeline that resumes and one that needs weeks of retraining. Your AI workloads inherit the same protection as every other enterprise workload, automatically, with no separate security toolchain.

The competitive edge: NetApp delivers NSA-validated safeguards and native, real-time ransomware detection built right into the data layer. No bolted-on third-party solutions that add another layer of complexity.

You’ve heard that NetApp has the most secure storage on the planet, but did you know: In its inaugural report Novel Threat Detection and Response, IDC identified 20 different novel solutions/technologies addressing purpose-driven detection and response solutions. The IDC report adopted a classification spectrum for use cases in three categories: Novelty, Uniqueness in Market, and Potential Impact. All technology evaluations are based on expert analyst opinion derived from primary research. Each use case is classified along a 1-5 spectrum to indicate how innovative or novel the detection is, how unique it is in market, and how impactful it is operationally.

According to the IDC report, NetApp Ransomware Resilience was classified as follows:

• Potential Impact to the market – 5- Cutting Edge: Implementation of the technology stops threats before they happen. The detection or response is good and does not require much human intervention. The better the capability to find or respond to a zero-day threat, the better
• Uniqueness in market – 5- Unicorn: The solution provides technological uniqueness that IDC has not seen in other technology solutions, and there are currently no competitors
• Novelty – 5 - Breakthrough: Fundamentally new detection approach or category not easily replicated.

How does modernizing legacy IT infrastructure help scale AI?

Modernizing legacy IT infrastructure helps scale AI by replacing fragmented, ungoverned data silos with a unified, governed data layer. With NetApp, AI pipelines get classified, compliant data on demand, and automated tiering cuts the storage costs that block economic scaling.

The most common structural blocker for enterprise AI isn't missing data. It's ungoverned data. Critical information scatters across systems, clouds, and legacy environments, each managed by different teams with different standards. AI pipelines need unified access to classified, compliant data. Instead, they get a patchwork of ETL jobs, manual approvals, and ad-hoc copies that add latency and governance gaps at every handoff.

NetApp Console acts as the central control plane for AI data governance, giving you visibility, policy management, and coordinated enforcement across NetApp storage. It surfaces data sensitivity and applies controls before data enters AI workflows, rather than relying on audits after models are already running. The Enkrypt AI integration extends this to real-time enforcement, blocking unauthorized access at the storage I/O layer based on workload behavior and data sensitivity.

There's a cost angle too. Siloed systems force redundant copies across environments, inflating storage costs with every new project. NetApp's automated data tiering intelligently moves data to the most cost-effective, performance-optimized tier across on-premises, cloud, and edge. According to the GigaOm brief, automated tiering can reduce flash capacity requirements by up to 80%. You scale AI economically while keeping governance intact.

The competitive edge: NetApp's intelligent data tiering reduces costs and optimizes performance for AI and ML workloads, while a unified namespace lets data move through every AI workflow stage without forced duplication.

“At the heart of NetApp’s strategy to create silo-free intelligent data infrastructure is its ONTAP-based storage architecture designed to unify structured and unstructured data across on-premises and cloud environments, with a central management console to provide a consistent model for data management, security, and governance. NetApp has taken significant steps to strengthen its portfolio and become a serious contender in the rapidly evolving and increasingly competitive market for AI-ready data storage infrastructure.” – IDC Special Study: AI-Ready Data Storage Infrastructure.

What this means for your AI strategy

GigaOm frames the decision clearly: the path to production AI runs through your data infrastructure, not around it. Leaders who recognize that the AI problem is really a data architecture problem are the ones whose AI projects survive the production environment.

The business case compounds. The first AI project on a shared platform absorbs the full infrastructure cost. Every project after that inherits data mobility, security, and governance at near-zero marginal cost. Run ten AI initiatives on one platform, and you're amortizing a single investment across ten revenue-generating workloads, not paying ten times over.

AI is the visible initiative. What determines whether it reaches production is everything beneath the surface. Build that foundation once, and your entire enterprise data estate, AI included, moves with you.

Ready to take your AI from pilot to production?

Read the full GigaOm CIO decision brief

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Frequently asked questions

Why do most enterprise AI projects fail?

According to the GigaOm CIO decision brief, most enterprise AI projects fail at production scale because of data architecture, not the model. Fragmented data, inconsistent hybrid cloud operations, ungoverned pipelines, and security treated as an afterthought cause models that work in sandboxes to collapse in production.

What is an intelligent data infrastructure?

An intelligent data infrastructure consolidates storage, governance, and security under a single operating system that spans on-premises, edge, and all three major hyperscalers. With NetApp, ONTAP provides this foundation so AI workloads get governed data without project-by-project integration.

How is NetApp different from other AI vendors?

Many AI vendors focus on narrow, specific use cases. NetApp provides the infrastructure for your entire enterprise data estate, handling all data workloads rather than AI alone. It's also the only storage vendor with first-party native services in AWS, Azure, and Google Cloud.

How does NetApp provide secure, compliant data mobility between cloud and on-premises?

NetApp provides secure, compliant data mobility through ONTAP's single operating system and security model, which run identically on-premises and in AWS, Azure, and Google Cloud. SnapMirror and FlexCache move data across environments without breaking governance, while SnapLock immutability and ARP/AI ransomware detection travel with the data, so compliance controls stay consistent wherever workloads run.

How can I measure and improve ROI on digital transformation for AI?

Measure ROI by tracking infrastructure cost per AI project, storage capacity reductions, and the number of production workloads sharing one platform. With an intelligent data infrastructure approach, the first AI project absorbs the full infrastructure cost; every project after that inherits data mobility, security, and governance at near-zero marginal cost, and automated tiering can reduce flash capacity requirements by up to 80%, spreading one investment across many revenue-generating workloads.

What AI infrastructure solutions optimize IT costs without sacrificing performance?

NetApp optimizes IT costs through automated data tiering, which moves data to the most cost-effective, performance-optimized tier across on-premises, cloud, and edge. According to the GigaOm brief, this can reduce flash capacity requirements by up to 80%, while a unified namespace removes duplicate copies, so you cut spend without slowing AI and ML workloads.

Citations

IDC Link: NetApp Focuses on Enterprise-Grade AI Infrastructure with New AFX Storage System and NVIDIA-Fueled AI Data Engine (Doc #lcUS53899425, October 2025)

IDC Market Presentation: Novel Threat Detection and Response Report: Presenting 20 Novel Use Cases (Doc #US53936425, November 2025)

IDC Special Study: AI-Ready Data Storage Infrastructure: Definition, Taxonomy, Ontology and Future Outlook (Doc #US53709325, January 2026)