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Beyond Hybrid Cloud How AI and GenAI are Decentralizing Telco Workloads

As AI and GenAI (including agentic AI) continue to mature, they are no longer just enhancing intelligence within telecom networks — they are redefining the very foundation of how and where network workloads are hosted and managed.

In my previous post, I explored how telecom operators are embracing a hybrid cloud strategy — combining the strengths of on-premises infrastructure with the scalability of public cloud. This shift is driven by the need to balance performance, agility, and regulatory compliance.

But the real question now is:
What tangible impact will this have on the telco infrastructure ecosystem?

Understanding this will be key to shaping future-ready architectures.

What’s Changing?

Let’s start with AI/ML model training — it has traditionally thrived in the public cloud. But now, we’re seeing telcos selectively keep sensitive models on-prem to protect sovereignty and control. Similarly, data lakes and analytics, which used to live almost exclusively in public clouds, are moving toward hybrid architectures — keeping sensitive data local while using federated learning models to run cloud-based analytics.

On the network infrastructure side, functions like vRAN, UPF, and edge computing (MEC) remain rooted on-prem due to performance demands. But these are no longer isolated systems — they’re becoming smarter with AI inference running at the edge to optimize traffic, latency, and efficiency in real time.

Meanwhile, customer experience systems, OSS/BSS, orchestration layers, and Dev/Test environments are leveraging GenAI agents in the public cloud to boost agility, introduce autonomous workflows, and scale faster — while maintaining fallback provisions for on-prem control when compliance or latency requires it.

What Does This Mean for Telcos?

We’re moving into a world of:

  • Workload decentralization — from central core to distributed edge.
  • Hybrid intelligence — where training happens in the cloud, and inference lives at the edge.
  • Compliance-conscious GenAI — with on-prem deployments becoming the norm in regions like the EU.
  • AI-native system architectures — making legacy modernization a necessity, not a choice.

The telco cloud is no longer just a platform — it’s becoming an intelligent, adaptive fabric, optimized for both performance and autonomy. For telecom leaders, the key is to design cloud strategies that are AI-aware, compliance-aligned, and edge-ready.

As we move forward, I’d love to hear from telecom leaders:

  1. How are you prioritizing workload placement as AI-driven use cases scale across the telecom network?
  2. What steps are you taking to ensure your cloud strategy aligns with long-term autonomy, compliance, and performance needs?

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