The reality is that most enterprise data estates are not equipped to support AI at scale. Fixing the problem starts with a unified data platform.
Organizations are all in on artificial intelligence (AI); 64% plan to fund AI and machine learning initiatives over the next 12 months, and 88% have invested — or will invest — in tools to build AI capabilities internally, according to 2025 Foundry studies.
Yet, despite a significant outlay of time and money, many enterprises struggle to turn AI pilots into production-scale projects.
The problem is straightforward: AI eats data, and most enterprise data estates cannot support AI in production environments. In fact, Gartner predicts that through 2026, 60% of AI initiatives that are unsupported by AI-ready data will be abandoned.
Fortunately, the solution is equally straightforward: an intelligent data infrastructure powered by NetApp’s enterprise-grade data platform for AI that speeds up development, shrinks data prep from months to days, and enables your IT teams to scale from pilot to production without rebuilding what already works.
The true barrier to AI success
AI systems require massive volumes of accurate, governed, traceable information, but most enterprise data isn’t AI-ready. Rather, it is scattered across on-premises systems, software-as-a-service (SaaS) apps, edge devices, analytics platforms, and multiple public clouds. It spans file, block, object, structured, semistructured, and unstructured formats. Some of it is sensitive and cannot legally or ethically be fed into a model, and some resides in silos that your data engineers and scientists can’t access.
All of these factors slow — and often stop — AI projects. Forrester reports that enterprise AI teams spend up to 70% of their time on data preparation and pipeline management rather than model development. When data is scattered or poorly governed, AI teams simply cannot build reliable models or validate outputs, making it difficult to safely rely on the results for decision-making.
Symptoms of a data bottleneck
When data is not ready for AI prime time, your teams typically encounter four challenges:
- Obstructed data mobility: Data must cross multiple boundaries before it ever reaches a model, but persistent data silos and lack of native integrations introduce latency, inconsistency, and risk.
- Escalating storage and compute costs: As AI multiplies data volumes, fragmented architectures force companies to replicate or move data unnecessarily.
- Security and governance blind spots: Without clear visibility into what data exists and where it resides, organizations face the risk of cyberattacks and inadvertently feeding sensitive or noncompliant data into AI models.
- Inability to scale from pilot to production: Data pipelines that work for prototypes collapse under production demands, especially across hybrid or multicloud environments.
Production AI needs a unified data platform
By 2027, 75% of enterprise AI workloads will run on hybrid, fit-for-purpose infrastructure, according to IDC, which means that companies will need to modernize data management practices to support the change.
Successful AI depends on a unified data architecture designed for hybrid and multicloud operations. Key elements include the following:
- Unified access across environments: A consistent platform that spans on-premises and cloud and removes fragmentation across file, block, and object data — with native integrations with Amazon Web Services (AWS), Azure, and Google Cloud
- Enterprise-grade resilience and security: Built-in data protection and security at the data layer, eliminating the compliance, security, and governance gaps that slow AI deployments
- Intelligent automation everywhere: Embedded observability, optimization, and policy-driven operations so data can be consumed efficiently by AI workloads
- An autonomous control plane: An AI-driven layer that manages, optimizes, and protects data across the entire estate, enabling closed-loop operations
- Compatibility with AI ecosystems: Native support for accelerator platforms such as NVIDIA, plus seamless integration with AI services and tooling from major cloud providers
The business outcomes
Taking an intelligent data infrastructure approach powered by NetApp’s enterprise-grade data platform for AI speeds up development, shrinks data prep from months to days, and enables your IT teams to scale from pilot to production without rebuilding what already works.
AI innovation is limited by the quality, accessibility, and readiness of enterprise data. By modernizing the data infrastructure first, CIOs and other IT leaders can transform AI from a high-risk experiment into a resilient engine of long-term business value.
Learn more about the NetApp AI data platform — the unified, enterprise-grade foundation for an intelligent data infrastructure.