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At the Manufacturing Innovation Conclave during ARC Advisory Group’s 24th India Forum in Bengaluru, held July 9–10, 2026, DS Latha, Senior General Manager and Discipline Head at Tata Consulting Engineers (TCE), presented “Digital + AI: Powering Industrial Futures.” She described how digital technologies, artificial intelligence (AI), digital twins, building information modeling (BIM), and intelligent automation can support engineering, construction, and industrial operations across the asset lifecycle.
Latha’s presentation, “Digital + AI: Powering Industrial Futures,” can be viewed on YouTube or here:
AI Across the Project Lifecycle
According to Latha, AI is increasingly supporting decision-making from feasibility studies through operations. By integrating procurement, engineering, and construction data, AI can provide a unified project view and improve planning, forecasting, and schedule management. AI-driven design tools can also generate and evaluate multiple design alternatives, reducing engineering effort and minimizing rework when project requirements change.
TCE also highlighted AI’s potential role in construction safety, including the identification of unsafe patterns and potential risks before incidents occur. Beyond construction, AI is being incorporated into commissioning, predictive maintenance, digital twins, and operational optimization initiatives.
Building Digital Foundations
TCE described digital transformation as a phased journey. Early stages focus on digitizing information through cloud infrastructure, digital workflows, BIM, drones, and image-processing tools. The next stage integrates previously isolated systems into common data environments, enabling better collaboration among engineering, procurement, construction, and operations teams.
As organizations mature, they can incorporate predictive analytics, optimization tools, and AI-enabled project intelligence. This evolution allows companies to move beyond data collection toward generating operational insights that support better business decisions.
Plant Digitization Through Scanning and Drones
A key focus of the presentation was the digitization of existing industrial facilities. TCE uses 3D laser-scanning technologies to capture detailed representations of plants, creating point-cloud datasets that form the basis for accurate 3D models and digital asset records. These models can be validated on-site and developed into as-built documentation and BIM environments that support maintenance and lifecycle management.
The company also uses drones equipped with high-resolution cameras, thermal sensors, and LiDAR for inspections in inaccessible or hazardous locations. While inspections traditionally required manual review of collected imagery, TCE is applying AI-powered analytics to identify defects and anomalies automatically.
Applications include steel plant bunker inspections, pipeline leak detection, boiler and turbine inspections, structural assessments, and solar-panel defect detection.
BIM as a Collaboration Platform
BIM remains a central component of TCE’s digital engineering strategy. The company develops BIM models progressively through the design process, bringing together architecture, civil, mechanical, electrical, HVAC, and piping information into a single federated model.
TCE’s BIM implementation roadmap, progressing from coordinated models and collaboration to digital project monitoring and plant digital twins
One of the major benefits is clash detection, which identifies conflicts between systems before construction begins. This can reduce rework, improve project coordination, and minimize delays.
TCE also integrates BIM into field operations, allowing site teams to access current project information through mobile devices, document progress, and communicate updates through cloud-connected platforms. The company extends these capabilities through 4D BIM, which combines project schedules with 3D models for digital project monitoring, and mixed-reality technologies such as Microsoft HoloLens, which overlays digital models onto physical construction environments.
Advancing Toward Digital Twins
TCE views digital twins as a natural progression from BIM. These digital representations of physical assets enable simulation, monitoring, and operational analysis throughout the asset lifecycle.
One example presented was an in-house digital twin developed on the NVIDIA Omniverse platform for an aluminum sorting plant. The system provides real-time synchronization, simulation capabilities, and 3D visualization to support conveyor monitoring, material-sorting optimization, downtime prediction, safety management, and energy optimization.
The company has also developed a digital twin platform for high-speed rail infrastructure. The solution combines 3D station visualization, crowd analytics, integrated monitoring, and asset visibility to support operational readiness, passenger-flow management, and safety oversight.
Applying AI to Operations and Maintenance
Beyond engineering and construction, TCE is deploying AI to improve industrial operations. The company highlighted its Advanced Process Control (APC) solution for thermal power plants, which combines model predictive control with AI-based algorithms that continuously learn from operating conditions.
According to TCE, the system supports plant performance through heat-rate optimization, combustion control, emissions reduction, temperature management, and pressure stability. These improvements can contribute to lower operating costs, improved efficiency, and reduced carbon emissions.
TCE has also implemented Dynamic Line Loading technology for 400-kV transmission networks. By combining sensors, communications infrastructure, and analytics software, the system adjusts transmission-line capacity based on real-time weather and operating conditions. This enables better use of existing grid assets while reducing overheating risks and potentially delaying new infrastructure investments.
Expanding Industrial AI Use Cases
The presentation also showcased several specialized AI applications. These include an AI-powered construction safety-monitoring platform that uses video analytics and NVIDIA’s accelerated computing ecosystem to monitor worker safety, verify personal protective equipment compliance, detect restricted-area violations, and track multiple objects in real time.
Other applications include AI-based quality-control systems that use computer-vision technologies, such as convolutional neural networks and YOLO models, to identify cracks, fractures, and material defects. TCE has also developed predictive maintenance tools for pumps and motors, an AI-powered engineering knowledge assistant, a tool that converts PDF-based P&IDs into AutoCAD drawings, and an intelligent single-line-diagram generation application for electrical systems.
Human Oversight Remains Critical
Despite rapid advances in AI, Latha emphasized that industrial AI deployment remains a structured process involving pilot programs, validation, risk management, and organizational learning. Decision-making, she noted, remains firmly in human hands, with AI currently serving as an advisory technology rather than an autonomous decision-maker.
The presentation showed how AI is moving beyond standalone productivity tools into integrated engineering and operational environments. As digital twins, predictive analytics, intelligent automation, and AI-assisted design continue to mature, industrial organizations can gain new opportunities to improve efficiency, safety, asset performance, and decision-making across the lifecycle of industrial facilities.