AI-Ready Data Infrastructure

How Technology Partners Position Solutions for AI Success

Artificial intelligence has moved beyond the experimental phase. Organizations worldwide are actively deploying AI initiatives, with 81% already piloting or scaling AI projects according to recent studies. However, a sobering reality check reveals that 85% of AI projects fail to reach production. The primary culprit? Inadequate data infrastructure that simply isn't AI-ready.

The foundation of successful AI lies in robust, intelligent data infrastructure that can handle the unique demands of machine learning workloads, generative AI applications, and real-time analytics. Leading technology vendors recognize this critical need and are positioning their solutions to address the complex data requirements that fuel AI innovation.

NetApp's Intelligent Data Infrastructure: Security-First AI Enablement

NetApp has taken a comprehensive approach to AI readiness with their Intelligent Data Infrastructure, focusing heavily on data security and governance as foundational elements for AI success.

Built-in Security at the Storage Layer

NetApp's solution embeds security, privacy, and compliance directly at the storage level rather than layering it on afterward. This approach addresses a critical challenge: many enterprises build AI on fragmented, poorly governed datasets with unclear permissions and inconsistent protections.

Key capabilities include:

• Data classification to identify suitable training data

• Guardrails ensuring only authorized users access appropriate data

• End-to-end posture management across hybrid multicloud environments

AI-Powered Resilience and Threat Detection

NetApp leverages AI itself for anomaly detection, automatically flagging unusual access patterns and potential vulnerabilities before they escalate. Machine learning algorithms analyze data flows to predict and prevent breaches, ensuring sensitive information remains encrypted and isolated during AI training processes.

Cisco: Embedding AI into Compute and Networking Infrastructure

Cisco is positioning itself at the intersection of compute and networking for AI workloads, with significant investments in their Unified Computing System (UCS) platform and advanced networking solutions designed specifically for AI demands.

UCS Platform AI Integration

Cisco has embedded AI capabilities directly into their UCS compute platform, recognizing that traditional compute architectures struggle with the intensive parallel processing requirements of AI workloads. The UCS platform now offers:

• AI-optimized compute configurations designed for training and inference

• Intelligent Packet Flow that dynamically steers traffic using real-time telemetry and congestion awareness across AI fabrics

• End-to-end visibility across networks, GPUs, and distributed AI jobs for proactive issue detection

Networking for AI Workloads

Cisco's networking strategy for AI focuses on handling the massive bandwidth and low-latency requirements of modern AI applications. Their approach includes:

• Expanded AI PODs that enhance flexibility and scalability for diverse AI workloads

• 400G bidirectional optics enabling cost-efficient transitions to higher-speed networks while preserving existing infrastructure

• Partnership with NVIDIA showcasing the first technical integration of Cisco G200-based switches with NVIDIA NICs, demonstrating NVIDIA Spectrum-X Ethernet networking based on Cisco Silicon One

The company has also introduced new converged access and edge router devices powered by Cisco Silicon One.

Dell's AI Data Platform: Comprehensive AI Enablement

Dell has developed a holistic approach with their AI Data Platform, designed to address the entire AI data lifecycle from ingestion to model deployment. Their solution recognizes that successful AI requires seamless data placement, processing, and protection.

GPU-Accelerated Data Processing

Dell's most significant recent innovation is the integration of NVIDIA RAPIDS Accelerator for Apache Spark into their Data Lakehouse platform.

The platform eliminates traditional CPU bottlenecks by harnessing GPU parallel processing for essential tasks like Extract, Transform, Load (ETL), advanced analytics, and AI model training.

Dell's AI Data Platform is built on three core capabilities:

• Data Placement: Advanced ingestion and aggregation with continuous streaming, intelligent load balancing, and scalable petabyte-level storage with GPU optimization.

• Data Processing: MetadataIQ for indexing unstructured data, metadata enrichment, and curation of comprehensive data product sets from diverse sources.

• Data Protection: Role-based access control, real-time threat detection, data masking, and secure data isolation capabilities.

Built-in Resilience

Dell has also introduced robust disaster recovery features with Active/Passive nodes spanning separate data centers, ensuring business continuity for mission-critical AI workloads.

IBM's watsonx.data: Harnessing Structured and Unstructured Data

IBM's approach to AI-ready data infrastructure centers on their watsonx.data platform, which addresses the fundamental challenge of managing both structured and unstructured data for generative AI applications.

Unified Data Access

IBM watsonx.data serves as an open, hybrid data lakehouse that enables unified access to enterprise data regardless of location. Key capabilities include:

• Single entry point for accessing data across clouds and on-premises environments

• Multi-modal and multi-engine support for ingesting and processing diverse data formats

• Vector embedding and retrieval based on open-source Milvus for Retrieval Augmented Generation (RAG) applications

AI-Powered Data Fabric

IBM's Data Fabric is specifically designed for generative AI requirements. The platform addresses the complex challenge of reconciling document-level governance models for unstructured data with fine-grained models used for structured data, ensuring comprehensive data governance across all data types.

Real-Time Policy Enforcement

IBM's solution includes the Common Policy Gateway, which integrates with policy engines like Apache Ranger to enforce granular access control through row-level filtering and column masking, ensuring governance policies align with organizational frameworks.

The Competitive Landscape: Unified Strategies for AI Success

These technology partners share several common themes in their AI-ready infrastructure approaches:

• Security as a Foundation: All vendors recognize that AI amplifies both opportunities and risks, making security a non-negotiable foundation rather than an add-on.

• Hybrid and Multi-Cloud Support: Modern AI workloads span multiple environments, requiring infrastructure that seamlessly operates across on-premises, cloud, and edge locations.

• Unified Data Access: Breaking down data silos is critical for AI success, with each vendor offering solutions to unify access to diverse data sources without complex data movement.

• Performance Optimization: GPU acceleration, intelligent networking, and optimized storage are table stakes for handling AI workload demands.

• Governance and Compliance: As AI becomes business-critical, robust data governance ensures models are built on trusted, compliant data foundations.

Moving Forward: Building AI-Ready Infrastructure

The convergence of these vendor strategies points to a clear market direction: AI success depends on intelligent, secure, and unified data infrastructure. Organizations evaluating their AI readiness should consider how these solutions address their specific requirements for data placement, processing, and protection.