Artificial intelligence is changing India’s engineering landscape by connecting product design, simulation, manufacturing, and factory operations. Dassault Systèmes is advancing this transformation through industrial AI, combining engineering knowledge, scientific models, and virtual twins to improve product development and decision-making, tells The Indian Express.
Sudarshan Mogasale, CEO of Dassault Systèmes Solutions Lab, emphasizes that industrial AI must deliver measurable business outcomes rather than simply generate information. The company is embedding decades of engineering expertise into AI models, enabling systems to recognize previous design failures, identify potential problems, and provide context-specific recommendations.
Ramakrishnan Venkataraman, director of SolidWorks and 3DEXPERIENCE Works at Dassault Systèmes India, explains that AI is becoming integral to the company’s engineering environment. Unlike conventional generative AI, these systems combine computational intelligence with physics-based models to support technically informed decisions.
For example, engineers designing vehicles can use AI-supported simulations to evaluate multiple responses to road conditions. AI can also retrieve historical project information to accelerate requests for quotations, reducing repetitive work while preserving human oversight.
Another priority is eliminating information silos. By connecting design, materials, supplier availability, production schedules, and manufacturing costs, AI can help engineers understand the broader consequences of their decisions.
Virtual twins extend these capabilities by allowing organizations to evaluate products, processes, and factory configurations digitally before committing physical resources. This approach can identify potential problems earlier and reduce costly modifications.
India’s expanding automotive, aerospace, electronics, and industrial machinery sectors present significant opportunities. Electric vehicles, robotics, autonomous manufacturing, and domestic aerospace development are increasing demand for integrated engineering solutions.
Startups could also benefit from software-driven approaches that lower traditional development barriers.
However, Dassault Systèmes stresses that organizations must identify specific applications and expected returns before investing in AI infrastructure. Consumption-based models could make these capabilities more accessible to smaller companies.
The emerging engineering model combines AI-driven exploration, virtual validation, and human judgment to develop products more efficiently while maintaining technical accountability.
