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As enterprises accelerate AI adoption, the conversation has largely focused on GPUs, models, and compute power. In his latest article for The AI Journal, Julian Jacquez, President & COO of BCN, explores an emerging challenge that could have an even greater impact on enterprise AI: the cost and complexity of moving data.
As AI workloads become distributed across cloud environments, data centers, edge locations, IoT devices, and autonomous systems, networks must handle growing volumes of machine-to-machine traffic, inference requests, and real-time data exchanges. This increases demands on bandwidth, latency, security, resiliency, and cloud costs.
Julian explains why modern networking and edge computing are becoming critical components of the AI infrastructure stack. By processing intelligence closer to where data is generated and intelligently determining when and where information needs to move, organizations can reduce latency and transport costs while improving AI performance and scalability.
The takeaway: AI success will increasingly depend on more than compute. Enterprises need an infrastructure capable of moving intelligence securely, efficiently, and in real time.
Read the full article:
https://aijourn.com/the-hidden-cost-of-enterprise-ai-why-data-movement-will-cost-more-than-compute/
Connect with Julian Jacquez on LinkedIn:
https://www.linkedin.com/in/julian-jacquez-a988457b/
