Cisco’s Jeetu Patel joins The View from Davos to discuss why infrastructure, security, and data readiness now determine whether enterprise AI can scale.
AI is advancing fast. The real friction is in scaling the systems behind it.
From Davos, Patrick Moorhead and Daniel Newman sit down with Jeetu Patel, President and Chief Product Officer at Cisco, to talk about what changes when AI shifts from experimentation into production. The focus is not on whether AI works, but on whether networks, security, and data systems are ready to support AI operating at machine speed.
Jeetu breaks down three constraints that now define enterprise AI scale: infrastructure limits around power, compute, and bandwidth; a growing trust gap as AI systems become non-deterministic; and a widening data gap as organizations exhaust publicly available training data and turn to machine and synthetic sources. Together, these pressures are reshaping how enterprises think about networking, security, and observability as foundational AI capabilities.
Key Takeaways Include:
🔷 AI scale is constrained by infrastructure, not imagination: Power availability, network bandwidth, and compute distribution now set the ceiling for what AI systems can realistically deliver.
🔷 Trust and security are prerequisites, not add-ons: As AI systems become non-deterministic, enterprises must secure both the network and the AI itself to enable adoption.
🔷 Data strategy is becoming a limiting factor: Enterprises are running out of usable public data, increasing the importance of machine data, observability, and correlation at scale.
🔷 Networking is shifting from scale-out to scale-across: Connecting AI clusters across locations is becoming essential as power and capacity fragment geographically.
🔷 Edge inferencing is no longer optional: Latency, autonomy, and operational needs are pushing more AI workloads closer to where data is created.
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