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Overcome data latency and sovereignty risks in enterprise AI

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Centralized cloud environments struggle with enterprise AI demands. The latency, bandwidth constraints and scalability challenges in sending massive data volumes to a distant cloud for processing can hinder performance and delay critical insights for real-time AI applications. 

Additionally, the growing need for data sovereignty requires organizations to maintain control over their data, ensuring compliance and privacy through localized processing. 

Read this white paper to see how a well-defined enterprise edge AI strategy enables smarter decision-making with real-time data processing at the edge—closer to users and data. Discover how high-performance data centers help maximize the potential of AI at the edge with low-latency, scalable and resilient infrastructure.

What you'll find inside:

  • A clear roadmap: A five-step guide to planning and implementing your edge strategy.
  • Real-world examples: See how edge infrastructure supports innovation in logistics, healthcare and smart cities.
  • Practical benefits: Learn how data processing at the edge reduces latency, improves data security and optimizes costs.
  • Myth debunking: Get clarity and insights on common misconceptions about the cost, complexity and security of edge deployments.
  • Partner selection: Discover what to look for in an edge infrastructure partner to support your growth.

Frequently Asked Questions

 

Edge infrastructure enables real-time data processing closer to its source, reducing AI latency and driving quick, informed decision-making. This is critical for AI applications that require speed, security and compliance.

By localizing compute resources near data sources, edge infrastructure eliminates delays caused by sending data to distant cloud servers, ensuring sub-millisecond response times for time-sensitive AI tasks.

Processing data locally helps organizations maintain control over sensitive information, meet regional compliance requirements and minimize risks associated with cross-border data transfers.

Yes. Distributed edge architectures reduce transmission risks and offer modular deployments that lower upfront costs while delivering long-term savings through optimized bandwidth and processing.

Begin with an actionable roadmap: integrate edge into business strategy, adopt hybrid architectures, identify optimal edge locations, ensure secure interconnection and deploy in high-performance data centers.

Hybrid Multicloud Networking Edge Computing Sustainability Colocation Interconnection Digital Transformation