By Nagendra Nath Sinha, Managing Director, Rodic Digital & Advisory
India’s infrastructure sector is undergoing a metamorphosis. As of early 2026, the nation is no longer just building “physical assets”; it is architecting an artificial intelligence (AI)-driven ecosystem. With the India AI Mission deploying over 38,000 graphics processing units (targeting 100,000) and the PM Gati Shakti National Master Plan evaluating projects worth over Rs 16.10 trillion, the integration of AI and analytics is moving from experimental pilots to national development.
For industry leaders, this shift presents an unprecedented opportunity to leapfrog legacy inefficiencies and a complex set of challenges rooted in data governance and human capital. The emergence of AI has triggered a wave of modernisation that is redefining the very DNA of global development.
AI and advanced analytics offer a practical response, not by replacing engineering judgement, but by improving the quality, speed and transparency of decisions. The most significant shift is simple: planning is moving away from “single forecast, single plan” toward probability-based decisions, where multiple scenarios are tested, risks are quantified and designs are optimised with continuous data.
Predictive modelling
Predictive modelling marks a major change in infrastructure planning. Planners can use machine learning to analyse different datasets. It helps planners spot demand trends, rising costs, capacity issues and environmental risks before starting projects.
AI-driven systems adapt with new data. In contrast, fixed models depend on set assumptions. This improves accuracy throughout the project. The outcome is better capital allocation, fewer cost overruns and enhanced asset use. Predictive modelling makes planning in transportation, utilities and urban development flexible.
Digital twins as strategic decision
AI-powered digital twins are changing how we design, test and improve infrastructure. They allow planners to simulate real-world conditions with precision before spending money. Sensors, simulation engines and analytics work together to demonstrate how assets behave in different situations.
This approach helps infrastructure leaders explore complex “what-if” situations. They can better understand extreme weather, changing mobility trends and peak utility demands. Planners should not stick to fixed assumptions. They can test connections, find weaknesses and improve performance in a controlled digital space and design/plan better.
Digital twins also boost resilience. They model long-term challenges, such as climate change, population growth and interconnected networks. This helps reduce risks during the design phase.
Capital and risk optimisation through analytics
Infrastructure is capital-intensive and exposed to uncertainty macroeconomic swings, commodity volatility, contract risk and schedule disruption. Advanced analytics can strengthen both investment decisions and delivery discipline.
On the capital side, portfolio analytics can compare project options across cost, benefit, risk and resilience, helping leadership answer the hard question: what should we build first and what should we defer? On the delivery side, analytics can identify early warning signals in procurement timelines, change-order patterns, contractor performance and critical path risks before delays become irreversible.
Analytics also enables predictive maintenance forecasting asset deterioration and scheduling interventions before failures occur.
Why is adoption uneven?
The adoption of AI and analytics is uneven, primarily due to the AI skill gap and the persistence of legacy systems. Many agencies lack sufficient in-house expertise in data science, AI engineering and systems integration. A 2023 NITI Aayog study found that only 18 per cent of state-level infrastructure agencies have centralised, digitised data systems. The rest rely on manual records, siloed databases and formats that do not interoperate.
Effective integration requires operations or project management digitalisation, digital workforce upskilling, cross-disciplinary collaboration and new institutional structures that support analytics fluency within planning teams. As per World Bank, India needs 150,000 data and AI professionals specifically for infrastructure and urban planning by 2030.
Besides, ethical considerations are equally important; AI must augment rather than replace human oversight and decision-making in infrastructure governance, while policymakers remain mindful of potential job-market disruptions.
Moving from pilots to performance
The question is not whether AI will reshape infrastructure planning. It is whether we will deploy it thoughtfully with governance, accountability and human judgment intact.
Three shifts are essential. First, invest in data infrastructure. Second, build AI literacy and not just AI tools. Third, establish governance-first AI adoption before deploying any AI system in public infrastructure. The next phase is not about more experimentation. It is about operationalising AI in a way that improves outcomes. If deployed responsibly, with the right governance and human judgment, AI can help us build smarter, faster and more sustainably than ever before.