Building an AI-Ready Foundation With Modern Infrastructure

Artificial intelligence (AI) has moved from experimental pilots to real-world deployment almost overnight. But for many enterprises, the focus isn’t on training large models from scratch — it’s about running AI applications reliably, securely and at scale. That’s where inference becomes the priority.

To support inference at scale, enterprises need infrastructure that’s agile, efficient and built for distributed AI workloads. That evolution spans both networking and virtualization, the foundational layers that power the performance, security and manageability of AI pipelines.

Modernizing these layers is no longer optional. From smart, segmented network fabrics that enable scalable AI services, to GPU-aware hypervisor platforms that support containerized MLOps pipelines, every component of the stack must align with the demands of enterprise AI inference. Organizations that move early to modernize will gain speed, operational efficiency and a lasting competitive advantage.

The organizations that can get this right early stand to accelerate their insights, improve operational efficiency and gain a long-term edge amongst their competition.

By 2026, Gartner predicts 30% of enterprises will automate more than half of their network operations with AI, up from less than 10% in 2023. Meanwhile, IDC expects global spending on AI systems to exceed $400 billion by 2027.

These numbers tell a clear story: companies that delay building an AI-ready foundation will fall behind on innovation, agility and cost efficiency.

AI workloads are uniquely demanding:

• Dynamic traffic patterns: Agentic AI and real-time inference drive bursty east-west traffic that legacy networks can’t always prioritize efficiently.
• Security and segmentation: Isolating sensitive AI services from other workloads is essential for compliance and resilience.
• Elastic infrastructure: Inference clusters must scale out cleanly with predictable resource control and automation.

According to Cisco, 89% of organizations plan to deploy AI workloads within two years, yet only 14% say their current network is AI-ready.

The good news is that the upgrades needed for AI inference are both achievable and highly impactful:

• Build segmented, smart fabrics: Separate front-end LAN, out-of-band management and inference traffic to improve security, observability and performance.
• AI-driven network operations: Tools that ingest real-time telemetry and provide natural language interfaces make it easier for IT teams to manage AI traffic patterns, troubleshoot faster and respond proactively.
• Close the skills gap: With the help of AI copilots and automated config assistants, organizations can scale their network operations even without deep command-line expertise on staff.

While full-scale model training often avoids hypervisors for performance reasons, virtualization and container orchestration are critical to inference operations and machine learning operations (MLOps) workflows:

• Standardized pipelines: GPU-aware container strategies help orchestrate inference services and model updates consistently across environments.
• Secure, scalable design: Virtualization layers enable strong isolation between tenants and workloads while simplifying resource allocation.
• Platform engineering enablement: Internal developer platforms (IDPs) allow DevOps and MLOps teams to build and deploy AI services quickly — while ensuring governance and security.

CDW bundles deep expertise, robust partner ecosystems and proven methodologies to make every step of your AI-ready journey seamless with:

• AI infrastructure assessments: CDW’s 140+ point checklist benchmarks readiness across networking, storage and compute.
• High-performance fabric design: Architectures validated for NVIDIA, AMD and Intel AI inference platforms.
• Virtualization and container strategy: Roadmaps, phased migrations and container workshops to streamline DevOps and MLOps workflows.
• Platform engineering services: Tailored enablement for DevOps, SecOps and NetOps teams.
• Managed AI operations: network operations center (NOC)/ security operations center (SOC) services, capacity planning and continuous optimization.

Whether it’s modernizing your network for secure AI segmentation or aligning your virtualization stack with container-first pipelines, CDW is ready to accelerate your journey — so your teams can focus on what’s next.