From Data to Impact: Scaling AI and Digital Twins for Efficient Projects and Reliable Assets
Shubham Rajvanshi, Senior Director& Client Partner, L & T Technology Services
In this insight-packed thought leadership session, Shubham Rajvanshi of L&T Technology Services shared a transformative roadmap for scaling AI and digital twin technologies across capital projects and operational assets. Drawing on extensive global project experience and real-world deployment at scale, Shubham Rajvanshi highlighted how trusted, high-quality data is the critical enabler for successful digital transformation in energy and industrial sectors.
He addressed key challenges plaguing digital programs—ranging from legacy infrastructure and siloed data to organisational resistance—and demonstrated how L&T Technology Services is solving them through a suite of AI-powered, modular solutions. These include tools for automated information extraction, anomaly detection, generative design, predictive maintenance, asset health frameworks, and real-time monitoring, all seamlessly integrated into leading digital twin platforms.
The presentation featured flagship deployments, such as a multi-site digital twin rollout for a major chemical company and an integrated operations center for a $40B energy investment in the Middle East. Shubham Rajvanshi illustrated how AI-driven digital twins have reduced maintenance costs by up to 30%, improved productivity, enhanced compliance, and supported seamless project-to-asset handovers.
Crucially, L&T Technology Services’ hybrid AI frameworks combine domain expertise with data science, allowing for scalable, cloud-native platforms that follow data mesh and MLOps principles. These enable stakeholders—from plant operators to executive leaders—to make informed, real-time decisions across the entire lifecycle of assets and infrastructure.
Shubham Rajvanshi closed with a forward-looking view of the energy sector’s digital trajectory, touching on the role of clean energy investments, cybersecurity readiness, and operating model redesign as essential components of a future-proof digital strategy.
