AI is reshaping engineering as India moves towards intelligent product design

Artificial intelligence is beginning to change the way engineering organisations approach product development, moving beyond isolated design automation towards a more connected model spanning product design, simulation, manufacturing and the factory.

For Dassault Systèmes, the shift is particularly relevant as enterprises move from digitising individual processes to using digital platforms and virtual twins to understand how products and production environments will perform before they are built.

Sudarshan Mogasale, CEO, Dassault Systèmes Solutions Lab, describes this as the emerging space of industrial AI, where the value of AI is measured not simply by its ability to generate information but by its impact on engineering and business outcomes.

“Industrial AI is directly impacting the business outcome,” says Mogasale.

The company is applying AI across manufacturing, life sciences and healthcare, as well as cities and territories. In engineering, its AI capabilities are being developed to assist engineers across the product lifecycle — from conceptualisation and design to simulation and manufacturing.

The approach is also distinct from the consumer-orientated generative AI experience that has shaped much of the current AI conversation. In engineering, the focus is increasingly on combining AI with accumulated engineering knowledge and know-how.

From data to engineering knowledge

For industrial AI, having large volumes of data is only the starting point. Mogasale argues that data becomes valuable when it can be converted into knowledge that can inform future engineering decisions.

“If you have numbers all over the place, it has no meaning, unless you convert it into knowledge,” he says.

An engineering system, for example, can contain historical information about designs that failed under particular conditions. Converting those experiences into reusable engineering knowledge allows AI to identify potential problems when an engineer attempts a similar approach in the future.

Dassault Systèmes has decades of accumulated engineering software experience across industries, which Mogasale sees as an important foundation for its AI efforts. The company is embedding industry-specific knowledge into AI models and converting it into skills that can be made available to engineers.

The result is intended to be AI that understands the engineering context rather than simply responding to a natural-language prompt.

Ramakrishnan Venkataraman, Director – SolidWorks & 3DEXPERIENCE Works, Dassault Systèmes India, explains that this is also influencing how SolidWorks is evolving.

SolidWorks began primarily as a three-dimensional design tool but has expanded into areas including simulation, manufacturing and virtual representations of factories and products. AI is now becoming another layer within this engineering environment.

Venkataraman says the company’s approach combines AI with scientific and physics-based models rather than relying only on the natural-language models associated with mainstream consumer AI.

AI as an engineering companion

The emergence of AI is also raising questions about whether engineering jobs will eventually be replaced by intelligent systems. Dassault Systèmes’ view is that AI should instead act as a virtual companion that gives engineers greater computational capability and access to knowledge.

“The decision will be done by the human,” Venkataraman says.

A car-design example illustrates the approach. An engineer could define conditions such as vehicle speed, direction and the impact of a pothole, and AI-supported simulation can generate multiple iterations showing how the vehicle could respond under different scenarios.

Rather than replacing the engineer, the system can reduce the amount of time and computational effort needed to explore alternatives. The engineer can then evaluate the outputs and select the appropriate design.

A similar principle can apply to requests for quotations. An engineering organisation may have undertaken comparable projects previously, but finding and assembling the relevant information manually can take considerable time. An AI assistant can retrieve and synthesise that accumulated knowledge more quickly, helping the engineer prepare an RFQ.

In both cases, the value comes from making organisational knowledge more accessible and usable.

Breaking engineering silos

AI can also extend beyond individual engineering tasks to connect decisions across different functions.

Historically, engineering, simulation, manufacturing and supply-chain teams have often worked within their own domains. With AI increasingly able to draw on knowledge across these areas, an engineer can potentially receive information about material selection, supplier availability, cost, quality and production schedules while making a design decision.

Production planning provides another example. A company may need to introduce an urgent project while existing manufacturing lines are already running. AI can help assess production timelines, identify lines that could be prioritised and evaluate whether supplier lead times are compatible with the required production schedule.

This points towards a broader change in engineering software: the system becomes less of a tool for performing one specific task and more of an intelligence layer connecting decisions across the product lifecycle.

Start with the use case, not the AI

Despite the enthusiasm around AI, both executives stress the importance of starting with a clear business problem.

Venkataraman says one of the most common mistakes enterprises make is beginning an AI programme without first identifying the use case and the potential return on investment.

“AI as a buzzword, people talk about it, but for your organisation, what is the low-hanging fruit? What is the maximum impact you can derive out of AI?” he says.

Larger organisations, he says, are increasingly identifying a small number of priority areas where the relationship between investment and potential return is clearest.

This is important because industrial AI can require significant computing infrastructure. Deploying technology without a clear understanding of the business problem can lead to substantial expenditure without corresponding value.

Dassault Systèmes is also looking at consumption-based models that can make AI capabilities more accessible to smaller and mid-sized organisations by reducing the initial capital investment required.

The broader principle is that AI adoption in engineering needs to be driven by measurable outcomes rather than the technology itself.

The rise of the virtual twin

Looking ahead, Dassault Systèmes expects AI to become increasingly integrated with virtual twins across three broad areas: the product, the process and the factory or facility.

Mogasale says AI will be deployed around the virtual twin of a product, the virtual twin of a factory or shop floor, and the virtual twin of a process.

This could allow organisations to assess different designs, production approaches and facility configurations before making expensive physical changes. For a new product or greenfield project, the ability to simulate alternatives digitally can help organisations identify potential problems and optimise decisions earlier in the lifecycle.

The combination of AI and virtual twins therefore moves engineering further towards a model in which organisations can test, learn and optimise digitally before committing resources in the physical world.

India’s engineering opportunity

The evolution is taking place against a backdrop of growing engineering activity in India.

According to Venkataraman, electronics and original design manufacturers are among the sectors with significant growth potential over the next five to ten years. Aerospace, transportation and mobility are also expected to benefit from greater engineering and design activity.

The automotive sector is simultaneously moving towards electric vehicles and new energy technologies, creating demand for new approaches to motor design and materials.

Aerospace and defence is another area where localisation is creating opportunities. As India increases domestic design and manufacturing capabilities, engineering software becomes an important part of developing products and components locally.

Industrial machinery is also moving towards greater automation and robotics. The emergence of dark factories and increasingly automated production environments is creating demand for robotics, autonomous systems and the engineering capabilities needed to design them.

Interestingly, Venkataraman sees startups playing an important role in this transition. Their relative agility allows them to experiment with autonomous robotics and alternative designs, while lower-cost and software-driven approaches can help reduce traditional barriers to entry.

Electronics and the expanding manufacturing ecosystem

The growth of electronics manufacturing illustrates how one industry can create opportunities across an entire ecosystem.

As more complex products are manufactured in India, the requirement extends beyond the final product itself. Components, printed circuit boards, antennas, fixtures, jigs, production lines and automation systems all need to be designed and manufactured.

This creates opportunities for engineering companies across the supply chain and increases the importance of digital product-development platforms.

For Dassault Systèmes, the evolution of SolidWorks and the broader 3DEXPERIENCE environment is therefore taking place alongside a larger transformation in Indian engineering — one where product design is increasingly connected to simulation, manufacturing, supply chains and factory operations.

Making industrial AI deliver value

For Mogasale, the immediate priority is not simply to accelerate AI adoption but to make industrial AI deliver measurable value.

The company is accelerating its products with AI capabilities that are intended to be industrial, reliable and capable of producing a tangible return on investment.

The shift marks an important distinction in the enterprise AI conversation. While consumer AI has demonstrated the possibilities of conversational and generative systems, industrial AI has to operate within the constraints of engineering knowledge, physics, manufacturing processes and business economics.

As AI becomes embedded into engineering platforms, the opportunity is to bring together accumulated knowledge, computational power and human judgement.

The eventual result could be an engineering environment in which AI does much of the exploration and analysis, virtual twins provide a digital representation of products and production environments, and engineers remain responsible for deciding which outcomes should move from the virtual world into the physical one.