Why 80% of leaders feel the pressure to deploy AI
The pressure for enterprises to deploy generative AI is undeniable, with 80% of leaders feeling increased urgency. While creating AI models is more accessible than ever, the real challenge is operationalizing them according to the Cisco AI Readiness Index 2025. Moving a model from a lab to full-scale production often takes seven to twelve months, a timeline that hinders innovation and cedes competitive ground.
This delay stems from complex operational hurdles. Organizations face poor data quality, siloed information, and a persistent shortage of skilled AI talent. Additionally, significant concerns around cybersecurity, integration with existing IT estates, and data center network performance create substantial roadblocks. Success requires more than just powerful models; it demands a unified, scalable, and secure infrastructure designed for the unique demands of AI workloads.
Why AI initiatives stall: Closing the operationalization gap
The journey from a data scientist’s lab to a live production environment is where most AI initiatives falter. Key obstacles contribute to this gap:
- Data management and governance: AI models are only as effective as their training data. Fragmented data sources and inconsistent quality cripple model performance. Modernizing data pipelines is foundational for successful AI.
- Integration with existing IT: AI systems must integrate securely and efficiently with existing applications and workflows. This requires careful architectural planning to avoid creating new silos or introducing security risks.
- Network performance: AI and machine learning workloads generate massive, high-volume traffic. Traditional network architectures cannot handle these “elephant flows,” leading to bottlenecks. Low latency and high throughput are essential for optimal AI performance.
- Cybersecurity and compliance: AI introduces new security complexities, from protecting sensitive training data to securing the models themselves. Addressing these concerns from the outset is critical.
- Lack of specialized skills: A significant talent gap exists for professionals who understand both AI and enterprise infrastructure. Upskilling teams in areas like MLOps and AI-ready networking is essential.
AI PODs: The key to scalable, secure AI infrastructure
To overcome these challenges, enterprises need a cohesive infrastructure strategy. Cisco AI PODs are a transformative concept in this regard. An AI POD is a pre-validated, ready-to-deploy building block that integrates all necessary compute, networking, storage, and software components required to run AI workloads.
By leveraging a standardized architecture, Cisco AI PODs and trusted partners like Red Hat simplify deployment, reduce risk, and accelerate time to value. This approach provides a clear path for scaling from pilot projects to enterprise-wide production. A unified infrastructure ensures that GPU compute power is matched by a high-performance network fabric, all managed under a consistent operational framework with Red Hat OpenShift AI.
Five practical steps to build an AI-ready data center
Preparing your organization for enterprise-grade AI requires a structured approach.
Step 1. Conduct a readiness assessment. Begin by evaluating your current data infrastructure, network capabilities, security policies, and team skill sets. This assessment will identify critical gaps and help create a prioritized roadmap.
Step 2. Prioritize networking for AI. Your data center network is the central nervous system of your AI strategy. Modernize it to deliver the low latency and high throughput required for demanding workloads. Ethernet-based solutions from Cisco provide the performance needed to ensure your GPU resources are fully utilized.
Step 3. Modernize data pipelines. Establish a robust data foundation. Implement modern data pipelines that deliver high-quality data to your AI models and enforce strong governance to ensure data integrity, security, and compliance.
Step 4. Plan for MLOps and LLMOps. Operationalize AI with a disciplined approach to managing the model lifecycle. Plan for machine learning operations (MLOps) and large language model operations (LLMOps) from the start to automate training, validation, and deployment.
Step 5. Invest in upskilling teams. Bridge the skills gap by investing in training and development. Equip your IT, data science, and security teams with the knowledge they need to collaborate effectively on AI initiatives.
Your blueprint for AI success
The journey to enterprise AI is about building a resilient, scalable, and secure foundation. By focusing on the critical task of operationalization, you can harness the transformative potential of AI. A unified infrastructure approach, built on proven solutions from Cisco and Red Hat, lays the groundwork for success.
To gain deeper insights into creating a future-ready AI infrastructure, watch our on-demand webinar. Join my colleagues from Cisco and Red Hat as they explore these topics and provide a strategic guide for your enterprise AI journey.
