TelecomDrive.com | Spotlight on AI
At a time when AI is fundamentally transforming networks from static, hardware-centric infrastructures into dynamic, self-optimizing systems – global telecoms major Nokia is using AI as a powerful accelerator for transforming networks into intelligent and automated systems. The company’s unique approach brings together traditional ML, causal AI, and Multi-Agent Reinforcement Learning (MARL) to enable real-time sensing and decisioning.
Arvind Khurana – Regional VP & Country Head for Cloud and Network Services, Nokia India speaks with Zia Askari from TelecomDrive.com about the fast emergence of AI and how Nokia is helping its customers with AI-driven tools in terms of transforming networks and elevating experience.
What does a truly AI-native network look like in practice, and how close is the industry to achieving autonomous network operations at scale?
A truly AI-native network reflects the industry’s shift toward the era of AI-native mobility, where AI-driven network designs, advanced algorithms, and trusted security frameworks are built into the network from the ground up. This approach enables networks to become more adaptive, autonomous, and seamless to the user.
At Nokia, one of our focuses is on autonomous networking, supported by a programmable, cloud-native core, zero-touch automation, and API-led monetization to help telecommunications service providers scale innovation efficiently. From a readiness perspective, many of the foundational elements, such as cloud-native core network functions, orchestration systems, AI models, and observability frameworks, are already production-ready or being trialled at scale.
While progressing toward TM Forum Levels 3 – 4 autonomy remains a journey that depends on richer data, stronger standards, and wider ecosystem adoption, the critical technical building blocks required to achieve autonomous network operations are firmly in place.
How is Nokia using AI and Generative AI to move beyond traditional automation and improve performance, operational efficiency, and customer experience?
At Nokia, we view AI, including Generative AI (GenAI), as a powerful accelerator for transforming networks into intelligent and automated systems. Our approach brings together traditional ML, causal AI, and Multi-Agent Reinforcement Learning (MARL) to enable real-time sensing and decisioning, while GenAI is applied to manage unstructured data, summarise insights, support telco assistants, and accelerate complex, non-real-time automation tasks.
In practical terms, this delivers faster fault detection and resolution, more accurate capacity forecasts, automated service orchestration, and AI-assisted customer and security workflows. Together, these capabilities help reduce mean-time-to-repair, enhance operational efficiency, and improve the overall customer experience.
As 5G deployments mature globally, where do you see the most compelling revenue opportunities for telecom service providers beyond basic connectivity?
As 5G deployments continue to mature, the most compelling revenue opportunities beyond basic connectivity are emerging around network-exposed platform services, including APIs / Network as Code, that allow developers to directly consume network capabilities. Additional upside comes from enterprise slices tailored to vertical needs, such as industrial automation and logistics, as well as edge-native applications spanning AR/VR and real-time analytics. Cybersecurity services also represent significant monetization potential.
The differentiator lies in packaging network capabilities as modular digital services that developers can easily consume. This shift enables telecommunications service providers to move from infrastructure-led models to recurring, higher-value digital services.
The API economy is increasingly seen as a catalyst for telecom innovation. How do network APIs change the role of CSPs in the digital services ecosystem? What role do open, programmable network architectures play in accelerating innovation, and how can operators safely expose network capabilities through APIs?
Network APIs simplify how advanced network functions, such as QoS adjustments, slice creation, location, edge placement, and telemetry, are consumed by abstracting them into straightforward software calls.
This allows application developers and system integrators to innovate quickly without requiring deep telco expertise. When programmable network capabilities are exposed through developer portals and marketplaces, networks evolve into platforms where third parties can create, deploy, and monetize digital services, shifting telecommunications service providers from connectivity pipe providers to platform providers within the broader digital services ecosystem.
To support this transition, we have developed a Network as Code approach through a developer portal and platform that presents network capabilities as easily consumable resources. This accelerates developer onboarding, aligns with industry standards such as GSMA Open Gateway / CAMARA, and enables telecommunications service providers to launch APIs and service marketplaces efficiently. Built on a cloud-native architecture, with service meshes and API-enabled automation tools, the platform allows service providers to safely expose network capabilities while maintaining control, security, and assured performance.
With the expansion of AI-driven and cloud-native networks, how are cybersecurity priorities evolving for telecom operators today? How is Nokia embedding AI-driven threat detection and cyber resilience within its network offerings?
As networks become increasingly AI-driven and cloud-native, cybersecurity priorities for telecommunications service providers are expanding beyond traditional perimeter defenses. Today, the emphasis is on end-to-end visibility and telemetry, threat detection tailored to telco protocols, and strong identity and access controls across cloud-native components. These service providers are also focusing on supply-chain and OSS/BSS hardening, along with readiness for more advanced threat scenarios, including AI-assisted attacks. In parallel, resilience planning, segmented architectures, and robust incident response capabilities are critical to maintaining network integrity.
Within our network offerings, we embed AI and XDR capabilities directly into the security portfolio, including solutions such as NetGuard Cybersecurity Dome integrated with telco-centric GenAI assistants. These capabilities help aggregate signals, reduce false positives, and accelerate triage and remediation. By using AI to correlate cross-domain telemetry, prioritize incidents, and recommend or automate containment actions, we significantly enhance both threat detection quality and operational response times, strengthening overall cyber resilience.
Looking ahead, how will the convergence of AI, 5G, and cloud technologies reshape the telecom ecosystem and business models over the next few years?
Over the next few years, as AI extends into autonomous systems, robotics, industrial automation, and augmented reality, networks will continue to evolve toward delivering ubiquitous, trusted, and adaptive connectivity that increasingly becomes invisible to the user. This implies connectivity that is automatically provisioned, optimized in real time, and able to support new workloads with the required bandwidth, latency, and security.
The growing convergence of AI, 5G, and cloud will also reshape the telecom ecosystem by moving it away from connectivity-centric models toward platform and data-centric ecosystems. 5G and distributed cloud will provide highly reliable connectivity and local compute, while AI converts network and application telemetry into actionable insights and automation. Through open APIs, developers, enterprises, and partners will be linked into new value chains, enabling real-time industries such as autonomous logistics, remote healthcare, and advanced AR, and redefining how telecommunications service providers monetize networks.
This interview is published inside the January 2026 edition of Disruptive Telecoms
