It is crucial for the Indian construction industry, which accounts for almost 9 per cent of India’s GDP, to embrace digital transformation to unlock its full potential. In fact, given the scale of infrastructure projects in the pipeline, the rapid adoption of advanced and more reliable methods is essential to fast-track construction while maintaining high quality.
Industry stakeholders, including developers and contractors, are now exploring the potential of artificial intelligence (AI) across project delivery.
This shift is being shaped by the rapid evolution of AI tools. What was earlier viewed as a specialist capability is increasingly being treated as a software layer that can be integrated with existing workflows, reducing manual effort and supporting faster responses to time-sensitive decisions. As per industry estimates, AI in the transportation market is valued at around $2.1 billion in 2025 and is expected to nearly double over the next five years to $5.2 billion-$5.3 billion by 2030. The adoption of digital technologies such as AI can also improve productivity by 20-30 per cent. Against this backdrop, AI is emerging as a critical lever influencing outcomes across project planning, design, procurement, construction and operations.
Current level of adoption
The adoption of digital technologies in construction projects is increasingly visible through a combination of established systems and newer AI layers. Tools such as building information modelling (BIM) and geographic information systems are already in use, supported by live cameras for monitoring and satellite imagery. Companies are applying AI across the value chain by combining field-level data capture with analytics to enable faster and better-informed decisions.
In the planning phase, AI is being used for demand analysis and route optimisation. Demand analysis is built on previous data, seasonal patterns and monsoon-related trends to support early project planning. Route optimisation draws on hotspot mapping, traffic flow data and seasonal data sets to help define alignments. From an engineering, procurement and construction perspective, AI is also being applied to customer and client mapping and risk assessment. Robotic process automation, for instance, can aggregate country-level political, economic and geographical inputs, along with competitor-related information, and feed these into analytics tools with an AI layer. This enables continuous risk assessments that earlier took months to compile manually.
In engineering and design, adoption is visible in both optimisation and option generation. Route optimisation is being supported by AI layers that factor in practical constraints such as river crossings, jungle crossings and railway crossings, helping identify more suitable alignments. GenAI is also being explored at the concept stage to generate multiple design options for assets such as bridges and metro alignments, allowing teams to evaluate alternatives faster and shortlist feasible configurations early in the design cycle.
In procurement and execution, AI is being used to bring more structure to cost and logistics management. Analytics layers are being applied to monitor commodity prices on a regular basis and strengthen cost assessment. Logistics is another area where adoption is becoming more tangible. GPS tools fitted on equipment are being used to optimise routes, improve utilisation and reduce fuel costs. Plant and machinery tracking is being managed through GPS trackers and fuel management systems. Some applications are also being explored to make contract management digitally stronger.
Construction and operations are among the most visible areas of AI-led deployment. Drones and internet of things (IoT) are being used for regular monitoring, while drone imagery combined with machine learning is being used to generate automated inspection reports for assets such as transmission towers and railway bridges. Workforce attendance is another active use case, especially for linear projects where biometric systems are hard to deploy across multiple sites. In this model, geofencing is used to detect entry into a defined zone, and facial recognition is used to mark attendance. AI is also being deployed for operations and maintenance, inspection, monitoring and asset performance tracking.
Challenges in AI adoption
Despite the growing number of active use cases, adoption remains uneven and constrained by several factors. The first is resistance to change. Transport projects are delivered in a highly competitive environment and stakeholders often hesitate to introduce new tools if they perceive a risk of delays in design and delivery timelines. Another constraint is the shortage of trained manpower. AI tools require capability building, including training for existing professionals, and skill gaps can slow implementation. In addition, day-to-day realities of construction remain a challenge. Linear projects require frequent drawing and design updates and tracking these changes is difficult.
Data-related constraints are at the root of many of these issues. AI depends on large data sets, but faces data quality and fragmentation issues. There are also questions around ownership and permissions. Under the Digital Personal Data Protection Act (DPDP Act), 2023, security considerations are shaping vendor choices, with organisations avoiding certain partnerships even when products appear strong. External dependencies further complicate adoption. Access to certain data sets, including street-level data that can strengthen alignment design in congested cities, is not always permitted, limiting the extent to which AI tools can support route design and decision-making.
Network constraints add another layer of complexity. Many linear projects pass through remote locations where internet availability is restricted, affecting real-time IoT monitoring. Workarounds include periodic drone or LiDAR surveys, conversion into 3D BIM models and systems that capture data offline and synchronise once connectivity is available, but these shift monitoring from real-time visibility to periodic updates.
Finally, outcomes remain closely linked to user capability and governance. AI can help experts work faster and produce better results, but without domain expertise and control, outputs can introduce new risks. Wider adoption also depends on leadership training, a defined road map over the next three to four years with clear return on investment expectations and a culture of data-driven decision-making.
Future opportunities and the way forward
AI adoption has gained meaningful traction in recent years. However, the most effective pathway to deployment is problem-led rather than technology-led. Instead of deploying AI in silos simply because it is seen as the next trend, stronger and more sustained adoption is needed – beginning with a clearly defined business problem and then deploying AI as a tool to address it. This keeps implementation tied to outcomes, improves user acceptance and helps embed AI into day-to-day project delivery rather than treating it as a standalone initiative.
A practical roll-out approach grounded in how projects actually operate is required. Adoption improves when tools bring down friction for site teams, reduce routine cycles such as review and reporting, and support faster on-ground decision-making. Further, leadership readiness, including basic exposure to AI capabilities, clarity on where AI can deliver measurable value, and a multiyear road map that links investment to expected outcomes will be critical. At the project delivery level, the focus must remain on controlled use, with domain expertise at the centre and AI playing a supporting role. That said, AI-generated outputs still need to be controlled and validated by professionals who understand the underlying engineering context. This balance helps ensure that speed does not come at the cost of reliability, and that AI strengthens project discipline rather than creating new risks through unchecked outputs.
Urban resilience and planning are also emerging as a deployment area where AI can be applied with clear, context-specific objectives. IoT-based approaches are being adopted to forecast rainfall impacts and understand how rainwater moves through urban systems, supporting more targeted responses to flooding. Traffic congestion and pollution are areas where improved data use and targeted analysis can help identify root problems and direct interventions more effectively. In metro operations, energy management is another promising area where real-time monitoring and predictive tools can support better planning and help avoid unexpected cost escalations. w
Based on a panel discussion among key industry experts from AECOM; Dineshchandra R. Agrawal Infracon Private Limited (DRAIPL); Systra India; Kalpataru Projects International; and Bentley at a recent India Infrastructure conference.
