AI infrastructure is having a moment. Headlines celebrate rising GPU counts and scaling from watts to megawatts, but inside the enterprise, success hinges on something harder: getting data, scale, security, and operations to work together across real production environments with real business and operational constraints.

The gap in enterprise AI infrastructure preparedness is visible. McKinsey Global Institute estimates AI could generate up to$4.4 trillion in corporate profits, yet according to the Cisco AI Readiness Index, only 13 percent of enterprises say they're ready to support AI at scale, and most AI initiatives stall early-not because the models fail, but because the underlying infrastructure can't support them.

The enterprise AI infrastructure gap

Most production data centers were never designed for GPU-dense, data-hungry, multi-stage AI pipelines. Model training, fine-tuning, and inference introduce new stresses on the IT environment. Here are some of those stresses and their resulting infrastructure requirements.

• GPUs that are fed with the data they need to handle AI workloads require high-throughput, low-latency, east-west traffic at scale.
• Heterogeneous stacks that mix bare metal, virtual machines, and Kubernetes workloads must be supported.
• Massive data gravity from huge datasets requires cost-effective storage performance, optimized for localization and movement.
• Precise management of operational overhead must incorporate fragmented tools across compute, fabric, and security domains.
• Risk posture must include protection for regulated data, intellectual property, and model integrity.

Customers say the hardest part isn't standing up AI infrastructure, but operating AI as a reliable service in the face of these challenges.

Cisco's AI focus

Earlier this year, Cisco introduced the Secure AI Factory with NVIDIA, a scalable, high-performance, secure AI infrastructure developed by Cisco, NVIDIA, and other strategic partners. It combines validated architectures, automated operations, ecosystem integrations, and built-in security.

AI PODs are how many customers start. You can think of them as modular building blocks-pre-validated infrastructure units that bundle compute, fabric, storage integrations, software, and security controls so teams can stand up AI applications quickly and grow them methodically. For organizations moving beyond a lab into production, Cisco AI PODs provide a controlled, supportable path.

A new option in Cisco AI PODs is Cisco Nexus Hyperfabric AI-a turnkey, cloud-managed AI infrastructure solution for multi-cluster, multi-tenant AI. For customers seeking to scale across multiple domains or data center boundaries, Hyperfabric AI provides a fabric-based model for AI POD-based deployments.

Five operational goals driving enterprise infrastructure optimization

1. Time-to-results: Pre-validated builds and lifecycle automation-using Cisco Intersight, Cisco Nexus Dashboard, and Hyperfabric AI-cut deployment cycles and shorten the path from data prep to model output.
2. Performance at scale: GPU-optimized Cisco UCS servers and non-blocking, low-latency Nexus fabrics keep expensive accelerators fed.
3. Unified operations: Unified management and observability-using platforms like Splunk and ThousandEyes-reduces the use of separate silos across compute, network, and workload layers. Whether you're starting with inference or growing to distributed training, the operational model remains the same.
4. Responsible use of data anywhere: Integrations with storage partners-like NetApp, Pure, and VAST Data Platform-support high-bandwidth, secure data processing and pipelines without locking customers in.
5. Built-in security and trust: Controls from Cisco AI Defense, Cisco Hypershield, and Isovalent eBPF help protect data, models, and runtime behavior, which is critical for regulated sectors.

Real deployments, mission-critical outcomes

Global customers in healthcare, finance, and public research are already using Cisco AI POD architectures in their production environments to:

• Run secure GenAI inference next to governed data
• Fine-tune domain models without moving sensitive intellectual property
• Burst workloads across AI PODs and facilities as projects scale

AI infrastructure readiness

Ask your team:

• Can we provision GPU capacity in days, not quarters?
• Is our east-west network designed for GPU saturation?
• Do we have policy, telemetry, and security across data, models, and runtime environments?
• Can we support inference now and add training later without re-architecting?
• Are operations unified or stitched together from point tools?

If any of these are "not yet," a modular approach like an AI POD is a fast on-ramp to AI infrastructure readiness.

Built for AI. Ready for what's next.

Enterprise AI success depends on infrastructure that's smart, secure, and operationally simple. With modular AI PODs and fabric-scale expansion when you need it, Cisco is helping organizations turn AI ambition into execution-without rebuilding from scratch.

Find out more about Cisco AI PODs.

Additional resources:

• What is an AI Data Center?
• What is AI in Networking?