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Modern day Telecom networks are highly complex and increasingly so. Given the huge diversity in network components, functions, and services, they demand significant investment of time and human resources for deployment and maintenance. Experts anticipate that by 2030, telecom networks would require integration of over 50 to 60 different types of devices! Due to the complexities involved, the volume of support calls inundating Network Operations Centers (NOCs) also continues to grow. All of this has reiterated the need to find innovative solutions.

Mr. Sagar Neve – Associate Director, Engineering at GS Lab | GAVS was on the panel for this episode of the webinar series “Everything Products” on the future of AI, Automation, and Telecom. The session was moderated by Mr. Ganesh Samant – Associate Director, Engineering at GS Lab | GAVS.

Traditional and Modern Challenges of Telecom Automation

Human-centric QA activities have evolved significantly, incorporating various tools, toolchains, and frameworks. However, Telecom QA complexity arises from dealing with intricate architectures, numerous network functions, and many network protocols like Diameter, GTP V2, ISAAC, PFCP, and more. The infrastructure is intricate, and interdependencies between components abound, particularly due to the proliferation of independently developed network functions that must work harmoniously with others.

Writing QA automation tests involves the interpretation of lengthy specification documents and their transformation into automated test cases. Despite the use of generic automation tools, toolchains, frameworks, and technological advances like the cloud, human effort remains indispensable. This brings challenges such as human error and retention into the equation.

The introduction of the cloud transformed the Telecom landscape, enabling the transition from fixed hardware to dynamic, open networks with abstraction layers, simplifying deployments and maintenance. Major players such as Google, Microsoft, and AWS also introduced their specific tools, catering to Telecom network deployment and management. These tools are tailored to the unique demands of Telecom networks.

The primary goal of network operations is to ensure high availability. DevOps partially addresses traditional operational challenges. Beyond scalability and high availability, operations bring unique challenges, including guaranteeing that business Service Level Agreements (SLAs) are met, generating accurate call data records, detecting anomalies, managing network congestions, data migrations, and providing customer support.

The operations team also receives requests to address issues related to dependencies among multiple application services, rest API calls, and interconnected services. These issues affect a minuscule percentage of users but demand high human involvement. Solving these operational challenges within specific timeframes is critical, as they directly impact Return on Investment (ROI).

Telecom teams constantly face the need to reskill and upskill themselves. Telecom predominantly dealt with circuit switching in the past, and Telecom teams were trained accordingly. Evolving technologies require Telecom teams to undergo periodic reskilling.

Using AI for QA Automation

The world of AI is making remarkable advancements and recent applications like ChatGPT have demonstrated that AI is not just hype and can be leveraged for practical, real-world applications.

In Telecom, AI has the potential to address two key objectives: minimizing disruptions and maximizing service availability. AI applications need to understand complex network and Telecom service meshes, along with network topology, monitor health, Service Level Agreements (SLAs), and perform deep packet inspection to detect hard-to-simulate anomalies. Predicting future failures is another goal, although it remains a significant challenge due to the vast amount of operational data.

However, current AI engines are not yet mature enough to provide end-to-end solutions. One strategy for bridging this gap is the development of intermediary tools. Recent advancements, like GPT-3, have demonstrated the potential of AI, particularly in Natural Language Processing (NLP). Mature NLP models, when properly trained, can understand context along with keywords. This paves the way for creating abstraction layers that simplify using AI-enabled engines.

Tools such as Octopus assist in QA, DevOps, and operational challenges. Octopus is an interpreter, translating between different languages to facilitate AI understanding and adoption.

Future of AI in Telecommunication

Looking toward the future, there are some exciting prospects to consider:
  • Open SourceTelecom AI: With the increasing adoption of open source Telecom technologies, there is a possibility to have open source Telecom-specific AI. Users won’t need to develop AI engines from scratch, as AI will learn from traffic patterns and be available out of the box with open source Telecom cores. This autonomous system will continuously train itself, reducing human effort and enabling customization when needed.
  • Explainable AI:This concept ensures that AI developers provide reasoning for AI decisions. This move can enhance trust in AI, especially in critical applications, where the reasoning behind AI decisions can be stored in non-alterable systems like hyper-ledgers.
  • Distributed AI:AI could be integrated at multiple levels within Telecom components, allowing them to work independently and collaboratively. For instance, a user’s pattern and application context could lead to dynamic bandwidth allocation for seamless user experiences.

These advancements hold promise for the future of Telecom, driving efficiency, minimizing human effort, and enhancing overall performance and trust in AI-driven systems.

GS Lab | GAVS plays a pivotal role in enabling key players in the telecom ecosystem to transform their infrastructure in tandem with the dynamic changes that this transformation demands. Equipped with about two decades of experience in the telecom, networking, cloud, and enterprise space, GS Lab | GAVS is a trusted partner in end-to-end system integration for global telecom leaders. For more on our telecom offerings, please visit

This blog is a gist of the webinar, but you can watch the entire discussion here. GS Lab | GAVS periodically organizes insightful webinars with our own tech leaders, the leadership team, and industry thought leaders to explore current and emerging trends. To watch all our webinar recordings, please visit and