AI excels at hyper-efficient exploration of design spaces, generating optimised variants

Ravikiran Pothukuchi, INDIA BRAND Sales DirectorBrands, DASSAULT SYSTÈMES.

How is Generative AI fundamentally changing the way engineers’ approach early-stage design, particularly in exploring vast design spaces that were previously impractical to evaluate manually?

Traditionally, engineers relied on iterative trial-and-error, constrained by time, compute power, and human intuition often missing optimal solutions hidden in millions of possibilities.

With our 3DEXPERIENCE platform integrated with generative AI capabilities, engineers can define high-level goals (e.g., lightweight structures meeting strength and manufacturability constraints), and AI rapidly generates, simulates, and ranks thousands of design variants in minutes.

In industries like aerospace and automotive in India, this accelerates innovation cutting design cycles by up to 70% while uncovering sustainable, high-performance outcomes impossible before. Dassault Systèmes India is empowering engineers to design the future faster, smarter, and at scale.

With AI increasingly co-creating alongside engineers in CAD environments, how do you see the balance evolving between human intuition, domain expertise, and machine-generated design intelligence?

AI excels at hyper-efficient exploration of design spaces, generating optimised variants from vast data in seconds, grounded in physics-based simulations and real-world constraints.

Humans bring irreplaceable qualities: contextual judgment, creative leaps, ethical considerations, and deep domain knowledge like anticipating regulatory nuances or cultural manufacturing preferences in India. Engineers now oversee AI outputs, applying intuition to select, hybridise, and refine designs, while upskilling to orchestrate these tools.

At Dassault Systèmes, we envision engineers as ‘design conductors’ leveraging AI for scale and speed, amplified by human insight for breakthrough innovation. This partnership boosts productivity by 50-70%, fosters creativity, and drives sustainable outcomes in aerospace, automotive, and beyond.

How is Generative AI transforming engineering software development especially for control systems, embedded systems, and industrial automation and what risks or validation challenges remain?

On our 3DEXPERIENCE platform, AI analyses system requirements such as real-time constraints or IoT integration and auto-generates verified code snippets, control logic, and embedded firmware, slashing development time from months to days.

In India's booming automation sector, this accelerates digital twins for predictive control, fault-tolerant embedded designs, and scalable industrial AI, enhancing efficiency in manufacturing and smart factories.

Yet challenges persist: AI-generated code risks subtle errors in edge cases, demands rigorous validation through simulation and hardware-in-the-loop testing, and requires human oversight for safety-critical certifications (e.g., ISO 26262). Bias in training data and explainability gaps also necessitate hybrid workflows.

Dassault Systèmes addresses these with AI-trust frameworks combining generative speed with model-based validation to ensure reliable, certifiable intelligence at scale.

Design optimisation for lightweighting is a major advantage with reduced wastage. How meaningful are these gains in real industrial settings for achieving sustainability and net-zero goals?

Design optimisation for lightweighting, powered by generative AI on our 3DEXPERIENCE platform, delivers profound real-world gains by minimising material use, waste, and emissions directly advancing net-zero goals. In industrial settings like aerospace and automotive, it achieves 40-60% material reductions while preserving strength, as seen in optimised structures that cut scrap rates by 40% and avoid thousands of tonnes of CO2e per project.

These benefits scale meaningfully in India: manufacturers using our tools embed sustainability from design, slashing up to 80% of emissions at the concept stage and supporting net-zero by 2050 ambitions ahead of national targets. Reduced fuel consumption up to 3-6% per 10% weight cut further amplifies lifecycle savings in energy-intensive sectors.

Dassault Systèmes proves these aren't theoretical: validated via virtual twins, lightweighting drives circular economy practices, regulatory compliance, and competitive edge for sustainable manufacturing.

How much has Generative AI realistically reduced design-to-prototype timelines, and what organisational or workflow changes are needed to fully realise these gains?

Generative AI on Dassault Systèmes' 3DEXPERIENCE platform realistically reduces design-to-prototype timelines by 30-70%, often slashing weeks or months from traditional iterative processes through automated exploration, simulation, and optimisation of design variants. In real-world industrial applications like aerospace and automotive manufacturing in India, this acceleration enables rapid prototyping of complex components, with customers achieving up to 300% efficiency gains in early-stage validation via integrated virtual twins that predict performance before physical builds.

To fully realise these benefits, organisations must adopt key workflow changes: upskilling engineers in AI-human hybrid decision-making, integrating cloud-based platforms for seamless data flow across design, simulation, and manufacturing teams, establishing robust data governance to address AI biases, and investing in validation protocols like hardware-in-the-loop testing for safety-critical outputs. Dassault Systèmes facilitates this transition with customised training and adoption frameworks, empowering manufacturers to scale innovation while meeting net-zero sustainability targets.

As engineers increasingly rely on AI-generated designs and recommendations, how should organisations approach validation, certification, and accountability, especially in safety-critical industries?

Generative AI accelerates engineering innovation on Dassault Systèmes' 3DEXPERIENCE platform, but in safety-critical industries like aerospace, automotive, and defense, organisations must prioritise rigorous validation, certification, and accountability through hybrid human-AI workflows.

For certification, leverage pre-qualified tools like our ISO 26262 TCL2-certified CATIA MBSE solutions, which automate compliance documentation while ensuring audit-ready traceability, eliminating manual qualification burdens and aligning with standards like DO-178C or ASPICE. Accountability is maintained by human oversight: engineers govern AI decisions, apply domain expertise for edge-case reviews, and use platforms with immutable audit trails to assign responsibility clearly.

Dassault Systèmes partners with clients via reskilling programs and tools like LDRA integrations to embed these practices, enabling safe AI adoption that builds regulatory trust and accelerates certified innovation in high-stakes sectors.

(The views expressed in interviews are personal, not necessarily of the organisations represented)