AI Integration in Indian Public Infrastructure

The AI Action Summit 2025, held in Paris and co-chaired by the Prime Minister of India, marked a significant moment in the evolving discourse on state-led technological futures. A central theme of the Summit was the integration of artificial intelligence into public infrastructure, reflecting a broader global trend wherein many governments are increasingly exploring the ability of artificial intelligence as a tool for enhancing governance and service delivery. This trend is undergirded by a predominantly optimistic vision, one that positions AI as a tool for accelerating efficiency. However, as AI tools are progressively embedded into the infrastructural and bureaucratic apparatuses of everyday life, it becomes imperative to approach such integration with attention to dimensions of ethics and access.

The concept of Public AI, which has gained renewed salience in policy discourse, refers to a state-owned or a publicly accountable AI system developed for the collective welfare rather than profit maximisation. At the Summit, the Prime Minister made a strong case for collaborative innovation through the pooling of technological expertise, publicly sourced data, and open-source developmental frameworks. He articulated a vision for user-centric AI that is locally grounded and culturally sensitive, built upon the Indigenous Knowledge Systems. The emphasis was not merely on building intelligent systems but on building democratised, ethical, and inclusive technologies that respond to the socio-cultural diversity of the Indian populace.

In line with this policy orientation, the Indian Railways, a historically significant yet often beleaguered pillar of national infrastructure, has become a critical site of AI-led transformation. Established in 1850 during the British colonial period, the Indian Railways has long been associated with inefficiency and delays, so much so that metaphoric allusions to 'running like Indian trains' became commonplace in some Indian languages. Today, however, it stands at the forefront of digital reinvention. As the third-largest rail network in the world by size, Indian Railways has, in recent years, witnessed significant infrastructural and technological interventions designed to modernise its services and enhance user experience.

One of the most significant recent interventions within the Indian Railways pertains to the optimisation of the Tatkal or the Waiting List Ticketing System, which has long been susceptible to inefficiencies and malpractices. Particularly, the widespread misuse of automated bots and fraudulent ticketing mechanisms has emerged as a challenge in recent times, undermining access to passengers and compromising the integrity of the booking process. In response, the Ministry of Railways undertook a data-driven initiative that leveraged artificial intelligence to enhance system transparency and operational efficiency. Central to this initiative was the development of the ‘Ideal Train Profile’, a machine learning-based decision support tool designed to optimise seat capacity allocation. Initiated as a pilot programme and then refined over the course of two years, the ‘Ideal Train Profile’ was trained on the data of ticket booking of the previous years to detect recurring demand patterns, booking anomalies, and other inefficiencies. The complexity of this task is underscored by the vast number of ticket class combinations available on a single train, often exceeding five thousand permutations. It thus serves as a critical component of AI-enabled infrastructure, which is not only efficient but also curbs fraud and improves user access.

On 9 June 2025, the Digital India Bhashini Division (DIBD) and the Centre for Railway Information Systems (CRIS) formalised a collaborative initiative through the signing of a Memorandum of Understanding (MoU) aimed at the co-development of multilingual artificial intelligence (AI) tools for the Indian Railways. This initiative seeks to address linguistic diversity and accessibility by providing AI-driven support across twenty-two recognised Indian languages in the Eighth Schedule of the Indian Constitution. As part of this collaboration, the project will integrate BHASHINI’s advanced language technology stack into existing CRIS-managed digital infrastructures, primarily the National Train Enquiry System (NTES) and RailMadad. The language technology suite encompasses a range of Natural Language Processing and speech technologies, including Automatic Speech Recognition, Text-to-Text Machine Translation, Text-to-Speech synthesis, and Optical Character Recognition. These tools are intended to enable real-time, seamless, and inclusive multimodal interactions for users engaging with railway services. The partnership also envisions the co-development of multilingual chatbots and voice assistants capable of delivering passenger support and resolving queries in the twenty-two languages.

In March 2025, the Indian Railways launched an advanced Artificial Intelligence-enabled Intrusion Detection System (IDS) aimed at enhancing rail safety through real-time wildlife monitoring. This system utilises Distributed Acoustic Sensors (DAS) to detect the presence of wildlife on or near railway tracks, particularly in ecologically sensitive zones. Upon identifying acoustic signatures indicative of animal movement, the system promptly alerts loco pilots, enabling timely interventions and minimising the risk of wildlife-train collisions. This innovation was designed to, particularly, prevent elephant deaths on tracks and was named AI-based ‘Gajraj’. Earlier, in December 2024, the Indian Railways introduced LISA (Linen Inspection and Sorting Assistant), an AI-powered system specifically developed to automate the quality assessment of linens distributed in air-conditioned coaches. Built to ensure hygiene and service consistency, LISA employs computer vision algorithms and machine learning models to detect defects, stains, and other quality issues with a commendable level of precision.

Despite these progressive steps, a significant structural limitation persists. A substantial portion of India’s AI computational needs continues to be met by foreign cloud service providers, raising critical concerns related to data sovereignty, latency, and technological dependency. While India’s AI policy frameworks are ambitious and largely future-oriented, the absence of robust domestic AI infrastructure poses risks to national autonomy in the digital domain. For instance, overreliance on external servers and proprietary models undermines local innovation ecosystems and exposes sensitive data flows to extra-jurisdictional scrutiny.

Recognising these challenges, the Indian government launched the IndiaAI Mission in 2024 with an initial budget of ₹10 crore. The mission aims to catalyse national artificial intelligence technological development, support startups, and establish public computational resources that are scalable, accessible, and rooted in ethical AI practices. Importantly, this mission acknowledges that India’s human resource capital constitutes a significant comparative advantage. Leveraging this demographic dividend requires not only investment in hardware and software but also a clear regulatory and governance framework that ensures transparency, accountability, and citizen trust. Rather than remaining passive consumers of international AI systems, there is an increasing push to become producers of AI, which can then be integrated into public infrastructure. However, the realisation of all these advancements depends on the state's capacity to balance innovation with equity and efficiency, and it can be witnessed in the coming years, and it ought to be subject to critique and reinvention.