Telco AI: Why the Real Value Starts Now, Not at the Edge

*This blog is co-authored by Dr. Rajarajan Sivaraj, Group Director of Telco AI Architecture; and Mandeep Singh, Senior Director of Product Management, Mavenir.

Recent industry conversations—like those explored in Dell’Oro Group’s latest blog—highlight the growing anticipation around AI’s role in transforming radio access networks. Much of the current focus is on deep infrastructure integration: embedding AI directly into the baseband, deploying it at the edge, or relying on tightly coupled vendor stacks.

This is an important and exciting vision, especially for near-real-time use cases that demand extremely low latency—such as channel emulation, scheduling, or continuous retraining of large digital twins. These scenarios may eventually benefit from GPU-based baseband architectures, but scaling them today is both impractical and cost-prohibitive for most operators.

What’s missing in this narrative is making AI an integral part of day-to-day telco operations (be it real-time, near-real-time or non-real-time) with a flexible, unified one-stop-shop AI architecture —a domain where AI-native platforms are already solving real-world network and business challenges without requiring a new stack, standard, or chipset. The narrative seems to be trying hard to make a “case” for AI – but, do we really need to search for a reason to be “intelligent”?

The narrative in the blog risks turning AI-RAN into a long-term science project. It delays the benefits of AI until after yet another wave of hardware deployment or standards alignment. But what if that’s the wrong starting point? What if the value of AI doesn’t need to wait?

Intelligence Is Not an Afterthought — It’s the Architecture

At the core of our approach is a fundamental shift: AI is not a feature layered on top of the network—it’s the intelligence that shapes how the network operates, evolves, and performs.

NIaaS (Network Intelligence as a Service) is a full-stack, AI-native telco capability. It’s built with intelligence at its core—delivering real, operational AI, not a dev kit or toolkit for someone else to figure out. It offers:

• Zero-code deployment
• Generic intelligence services that can be provisioned flexibly:
  • In centralized management clusters
  • At the edge (NF cluster)
  • At local data centers or even cell sites
  • Embedded as microservices or containers within NFs

NIaaS isn’t just a bolt-on, it isn’t yet another platform. It is a capability, where compounding intelligence layers work in sync across the telco stack to answer critical operational questions:

Each layer is driven by dedicated AI agents working in a cloud-native, CUPS-based disaggregated architecture, exchanging intelligence to enable closed-loop, autonomous optimization. The result? A smarter, faster, more adaptive network with meaningful improvements in performance and customer experience.

Beyond SON: What Worked, What Didn’t, and What’s Next

Let’s acknowledge that Self-Organizing Networks (SON) had their place. For years, operators relied on SON to automate repetitive configuration tasks and optimize handovers. Many of these were rules-based systems that delivered incremental gains—mostly in siloed, single-vendor environments. There are some very distinctive differences between SON and NIaaS:

• Functionality-centric SON vs Objective-centric NIaaS:
  • SON is “functionality”-centric. This means SoN solutions are too focused on optimizing the “means” or “actions” (that is, the functionality), and the “ends” or “goals” (objective KPIs) would be a consequence of optimizing the “means”.
    • As an example, SON solutions focus on optimizing a functionality, such as antenna tilt settings or A3 offset settings, which would subsequently improve objective KPIs, such as coverage or mobility KPIs. However, the magnitude of improvement in objective KPIs due to functionality optimization may vary from cell-to-cell, site-to-site, market-to-market, due to a multitude of underlying factors.
     But telco AI is “objective”-centric, where the “ends” or “goals” are the cause for the “means” or “actions” that follow, guided by intelligence. This means NIaaS is focused on providing quantified intelligence about the “end goals” and optimizing “whatever means” it takes to meet the “ends”.
    • As an example, based on the identified target objective KPIs, NIaaS provides deep-dive quantified intelligence about the causal factors impacting the target KPI in live operations on a cell-by-cell, site-by-site, market-by-market basis, following which, it accordingly optimizes relevant functional parameters associated with these causes towards KPI improvements in auto-pilot mode.
  • NIaaS thus enables service assurance guaranteeing gains with its compounding layers of causal intelligence, whereas SON offers only best-effort gains.

• Platform-specific vendor-dependent SON vs Platform-agnostic vendor-independent NIaaS
  • SON solutions are platform-specific and vendor-dependent, whereas NIaaS is a platform-agnostic and vendor-independent capability. NIaaS can interface with any NF vendor and cloud provider solutions. NIaaS can tap into the data and configuration pipelines of any vendor solution and can inter-work with vendor-specific data models, proprietary counters, proprietary configuration parameters, proprietary call trace data, etc., towards generating holistic telco intelligence in live operational networks.
  • NIaaS works seamlessly in multi-vendor deployments without any change in 3^rd party vendor solutions, where SON struggles.

• Policy-based SON vs AI-native NIaaS
  • SON solutions are based on pre-configured policies, and adaptation of such policies is not automated, updated and managed online. NIaaS is comprised of stacked-up layers of data-driven intelligence powered by AI agents operating in each layer, based on powerful state-of-the-art ML algorithms. Each layer of intelligence has its own life cycle management of its AI agents for automated adaptation and online update of its models, based on disaggregated CUPS services-based architecture, anchored by its control plane.
  • NIaaS henceforth performs well in network deployments where patterns shift faster requiring dynamic adaptation, where SON fails with preconfigured business logic.

• Generation-aware SON vs Generation-agnostic NIaaS
  • Since SON solutions are domain- and functionality-centric, they do not scale seamlessly across multi-G deployments, often requiring substantial upgrades to existing solutions. NIaaS, in contrast, is data- and objective KPI-centric. With 0-code domain-agnostic data engineering and AI science pipelines, and with a fully-configurable domain-aware business logic component, NIaaS scales seamlessly across multi-G deployments WITHOUT code change or software upgrades.
  • NIaaS therefore benefits in multi-G environments, where SON struggles.

SON is thus not equipped for the dynamics, diversity, densification and democratization of today’s networks.

AI-native systems like NIaaS are not “SON + ML.” They are architecturally different—able to uncover deep network behaviors and adapt in real time. Especially in domains where heuristics have failed (e.g., dynamic interference, 4G/5G ducting, cross-layer dependencies), AI doesn’t need to rewrite the baseband—it can overlay and integrate intelligence on the live network.

Use Cases: From Efficiency to Revenue Impact

Legacy views of AI in RAN still gravitate toward energy savings or spectral efficiency—areas historically addressed by SON or vendor-specific heuristics.

NIaaS expands the frontier. By chaining intelligence across domains, it delivers use cases that align with both operational KPIs and business outcomes:

• Churn prediction and suppression: Detect degradations before they trigger customer complaints
• SLA protection: Proactively mitigate issues that affect premium services
• Root-cause automation across RAN + Core + BSS: Especially useful in multi-vendor environments
• Dynamic experience optimization: Personalized tuning per user, device class, or location

These are not merely theoretical. They reflect the day-to-day pain points of performance, RF, and operations teams—made worse by silos and legacy tooling.

Why Now? Because You’re Already Sitting on the Data

AI doesn’t need to wait for idealized network conditions. Operators already have vast amounts of underutilized network data—from OSS, PM, CM, trace logs, call data records, and more.

The barrier is not technical—it’s architectural. Instead of expecting 3GPP, TIP, or a new interface spec to unlock value, operators can deploy overlay intelligence today, whether the underlying RAN is legacy, C-RAN, or O-RAN. After all, one doesn’t need to wait for standardization before embracing intelligence, does he?

With smart ingestion pipelines, onboarding data into NIaaS can be achieved in weeks—not months—and without needing baseband or network function vendor coordination.

Conclusion: AI You Can Experience, Not Just Speculate About

NIaaS isn’t a platform for operators to develop their own AI. It is a capability to experience the outcomes of AI, today.

• It doesn’t require changing your RAN.
• It doesn’t depend on real-time edge integration.
• And it doesn’t wait for another wave of network transformation.

It’s intelligence, built-in.

So while the industry continues to explore the long-term vision of AI-RAN or telco AI in general, let’s not lose sight of the immediate, tangible value already within reach.

With this intro blog, we welcome you to our blog series on telco AI. For further more on NIaaS, stay tuned!