Eliminating Data Movement Bottlenecks: Why AI Pipelines Need an Operating System Built for In-Place Processing

Artificial intelligence has entered a phase where model sophistication is no longer the main barrier to enterprise success. The real limitation comes from underlying data architecture. Most organisations still rely on legacy systems that separate storage, compute, and data services. This separation forces data to move repeatedly throughout the AI pipeline. Every transfer adds latency, cost, operational drag, and risk.

As a result, innovation slows down even when advanced models and high-performance compute resources are available.

Recent studies reinforce this challenge. A 2025 Fivetran report found that 42 percent of enterprises have seen more than half of their AI projects delayed or underperforming due to poor data readiness. According to another industry review, nearly 70 percent of enterprise AI projects fail to reach production, primarily due to data infrastructure limitations and inefficient pipeline design.

These findings make one fact clear. The performance bottleneck for enterprise AI is no longer GPU capacity. It is the cost and inefficiency of moving data across different layers of the technology stack.

Why Data Movement Is Becoming the Silent Bottleneck

Data has grown in both scale and diversity. AI training, fine tuning, real time inference, and retrieval augmented generation rely on rapid access to structured, unstructured, streaming, and vector data. Traditional architectures treat each of these data types differently. This creates friction at every stage of the workflow.

Key constraints created by data movement:

Latency and throughput issues – In distributed AI workloads, communication overhead often dominates runtime.

An arXiv study confirms that inter node data transfer frequently prevents linear scaling of AI performance.

High operational and energy costs – Research on large scale training workloads shows that I O operations can consume more energy than computation when data pipelines are not optimised.

Pipeline fragility – Traditional ETL processes and multi-step data copying create brittle systems that are hard to maintain and slow to modify.

Limited ability to achieve real time AI – Workflows that depend on copying data from storage to compute struggle to meet the latency requirements of real time analytics, RAG based systems, and agentic AI.

These constraints are symptoms of an outdated assumption. Historically, compute was fixed and data was moved to the compute layer. The arrival of modern AI has reversed this equation. Data

volumes are too large and too diverse. The only sustainable model is one where compute moves to the data.

AI Pipelines Need an Operating System Built for In-Place Processing

Enterprises need a unified operating system for AI. This type of platform integrates storage, data services, compute, and orchestration into a single fabric. It allows organisations to process data directly where it resides, without copying it into external compute clusters.

An AI operating system should provide the following capabilities:

Unified support for structured, unstructured, vector, and file or object data.
Ability to execute transforms, analytics, indexing, and lightweight compute directly inside the data layer.
High throughput access for both CPU and GPU workloads.
Event driven pipeline automation that eliminates complex ETL chains.
Built in governance, security, and multi tenancy for safe enterprise scale AI.
Independent scaling of storage and compute for predictable performance.

This approach is the only way to remove fragmentation and create a future ready architecture for AI.

VAST Data AI OS: A Strong Example of In-Place AI Processing

A good real-world example of this architectural shift is VAST Data and its AI OS. It has been designed specifically to remove data movement bottlenecks and to unify the entire data lifecycle for AI.

VAST AI OS brings together storage, database services, analytics engines, and workflow orchestration into a single platform. It is built on a disaggregated shared everything architecture that supports both scale out compute and exabyte scale data.

Key Features

Unified DataStore that supports file, object, and block interfaces under a global namespace.
DataEngine that provides serverless in-place compute for python functions, indexing, vector processing, and containerised analytics.

Native support for structured, unstructured, and vector data for simplified RAG and agentic AI workflows.

High performance flash-based architecture that delivers extremely low latency access at scale.

Workflow engines such as SyncEngine, InsightEngine, and AgentEngine that enable continuous pipelines from ingestion to inference.

Independent scaling of compute and storage for predictable performance across dynamic AI workloads.

Architectures like VAST AI OS remove the need for the repetitive pattern of storing data, copying it to compute, processing it, and copying it back. This transforms both the speed and cost of enterprise AI operations.

In conclusion, Enterprises cannot build future ready AI systems on foundations that were designed for older analytical workloads. The next generation of AI requires an operating system approach. Compute must move to data rather than forcing data to travel through costly and complex pipelines. In place processing, unified data services, and integrated orchestration are no longer optional. They are essential for scale, reliability, and real time intelligence.