Artificial intelligence (AI) has emerged as a key trending technology. Its rapid advancement is being driven by a shift from traditional machine learning (ML) techniques to large-scale foundation models and the rise of generative AI architectures such as GPT, LLaMA, Gemini and Claude. Enterprises across sectors, including healthcare, retail, banking and manufacturing, are increasingly embedding AI into their operations to improve efficiency, reduce costs and enhance customer experiences. As a result, the global AI market is projected to grow 25-fold, from $189 billion in 2023 to $4.8 trillion by 2033.

AI is also becoming an integral part of the technology stack in telecommunications and network services, enabling automation, predictive maintenance and resource management. It serves a dual function. It is both a key driver of demand for high-speed, low-latency telecom networks, and a foundational enabler of next-generation technologies such as 5G, cloud computing, edge computing and network virtualisation.

To fully leverage its potential, both the government and the industry have launched several initiatives to support AI integration and build future-ready digital infrastructure. However, the growing use of AI in communications has also introduced several challenges. These include a rising energy footprint driven by AI’s computational demand, the emergence of AI-enhanced malware capable of evading conventional detection systems, escalating data privacy and security concerns, the risk of algorithmic bias, and a widening talent gap in AI governance and implementation. Despite these challenges, AI is expected to remain a central force in driving innovation, improving network intelligence and accelerating the digital transformation of the telecom sector in the years ahead.

AI as a catalyst in digital transformation

The conventional methods of managing and maintaining networks are rapidly evolving with the advent of AI. Traditionally, network automation has been facilitated by software-defined networking, which sep­arates the control plane from the data plane. This separation enables centralised network management, simplifying administration and reducing manual configuration needs. In contrast, AI facilitates network management by enabling predictive maintenance and optimisation strategies that surpass the capabilities of conventional network automation methods. AI-driven solutions employ ML algorithms to ana­lyse ­historical­ data and identify patterns that indi­cate potential failures. By enabling enterprises to proactively address issues before they ­escalate, AI-powered networks can minimise downtime, enhance reliability and optimise resource utilisation.

In the Indian market, Reliance Jio has introduced JioBrain, an in-house generative AI and digital twin platform, which assists in network planning, operations and customer engagement. Meanwhile, Bharti­ ­Airtel’s data centre arm Nxtra leverages AI to improve energy efficiency and enable predictive maintenance in its data centres.

AI-powered systems can also process human language, allowing network administrators to interact with the system using natural language commands. This simplifies the management of complex network configurations. Further, AI systems can continuously learn and improve their decision-making capabilities over time as they encounter new data and scenarios. AI can also facilitate the adoption of zero-touch provisioning (ZTP), an approach that enables devices to be automatically configured and provisioned with minimal manual intervention. ZTP is particularly relevant in remote environments, where deploying new equipment poses logistical challenges. Through its intelligent capabilities such as device identification, configuration management and security policy enforcement, AI assumes a critical role in enabling ZTP. It can help autonomously manage the initial set-up and configuration of devices, ensuring a seamless and efficient deployment process without any manual intervention.

Further, the integration of AI with IoT is playing a pivotal role in the connected devices ecosystem by enabling IoT ­devices to autonomously learn from their surroundings and act on that knowledge without human intervention. This is particularly beneficial in predictive maintenance. By analysing data gathered by IoT devices and equipment, AI algorithms can forecast maintenance requirements, thereby reducing downtime and repair expenses. For instance, in manufacturing, sensor data on machine performance can be used by AI algorithms to predict part failures and schedule maintenance.

AI is also enhancing the capabilities of edge computing, a transformative approach that brings computing power and data storage closer to the data source. This proximity reduces latency and bandwidth usage, leading to faster processing and lower transmission costs. By integrating AI and ML models with edge devices, systems can operate continuously and autonomously, even without constant cloud connectivity. Pre-trained models deployed at the edge are capable of making intelligent decisions and executing tasks offline, ensuring uninterrupted functionality. This capability is critical for applications in remote or resource-constrained settings, such as agricultural monitoring across rural and isolated areas.

Growing AI adoption fuels demand for next-­generation networks

AI has emerged as a key demand driver for advanced networking capabilities such as 5G, network slicing and cloud-native core networks. AI-powered applications require high-speed connectivity, extremely low latency and the ability to process large volumes of data efficiently. Traditional network architectures are not designed to meet these demands, prompting telecom oper­ators and technology providers to invest in next-generation networks that can deliver the performance levels required by AI systems. For example, the expansion of 5G services is enabling AI models to function effectively in dynamic environments. With its ultra-low latency, high bandwidth and the ability to support a massive number of connected devices, 5G is ideal for AI-driven applications.

Further, cloud-native core networks are emerging as key enablers of AI integration. These networks rely on microservices-based architecture and containerisation, which simplify the deployment, management and scaling of AI applications across telecom infrastructure. Cloud-native designs offer greater automation and flexibility, both of which are essential for keeping pace with the evolving demands of AI technologies.

Meanwhile, the large-scale adoption of AI has expanded the demand for network slicing. Network slicing enhances the effectiveness of AI deployment by allowing service providers to create multiple virtual networks within a single physical infrastructure. For instance, an AI system supporting emergency healthcare services may require a network slice with guaranteed low latency and high reliability, while a different slice can be configured to support high-bandwidth AI applications such as video surveillance or content streaming. This flexibility allows networks to be optimised for diverse AI workloads without compromising overall performance.

Policy and regulatory impetus for AI adoption

The Indian government has launched several initiatives to promote the development and adoption of AI across sectors. In March 2024, it launched the IndiaAI Mission with an outlay of Rs 103 billion over five years to strengthen the country’s AI capabilities. This mission includes the creation of high-end AI compute infrastructure equipped with thousands of graphical processing units, making it one of the most extensive facilities globally. The government has also established centres of excellence in critical areas such as healthcare, agriculture, education and sustainable cities to advance domain-specific research and innovation.

In addition, a dedicated IndiaAI Dataset Platform is being developed to provide start-ups and researchers with access to anonymised, high quality data sets, enabling the creation of reliable and inclusive AI models.

The government is also encouraging the development of indigenous foundational models and large language models tailored to Indian languages and requirements, supported by initiatives such as BharatGen and Sarvam-1.

On the regulatory front, in July 2023, the Telecom Regulatory Authority of India (TRAI) released its recommendations for leveraging AI and big data in the telecom sector. It called for the urgent enactment of a comprehensive regulatory framework to ensure responsible AI development. The regulator proposed the creation of the ­Artificial Intelligence and Data Authority of India (AIDAI) as an independent statutory body, the establishment of a multi- stakeholder body as an advisory entity to the AIDAI, categorising AI use cases based on risk and outlining the principles of responsible AI. Further, TRAI suggested incorporating a data digitisation and monetisation council as part of the AIDAI, with roles such as framing regulations, recommending policies and overseeing data-related issues.

Outlook

Going forward, as AI tools become more accessible, models more capable and policies more mature, AI is set to become embedded across all facets of digital technology. As per industry estimates, AI’s share in the glo­bal frontier technology market is projected to rise from 7 per cent in 2023 to 29 per cent by 2033. India is well positioned to be among the leading markets for AI adoption, supported by proactive government policies and industry-led initiatives. However, it will be essential to maintain a balance between leveraging AI’s transformative capabilities and addressing critical challenges such as its energy footprint and the limitations of legacy network infrastructure. Strategic investment, regulatory clarity and sustainable innovation will be key to unlocking the potential of AI in the years ahead.