From physical prototypes to cloud-first design

India’s manufacturing sector is moving toward a technology-led model where design, testing, and production converge on integrated digital platforms. This “new stack” comprising cloud-native CAD, Artificial Intelligence (AI), and connected lifecycle management tools, is enabling faster product iterations, reduced costs, and improved operational resilience. The change is being driven by both market pressures and the evolution of enterprise technology capabilities.

Cloud-Native Design and Real-Time Collaboration

The shift from desktop CAD to cloud-hosted platforms is redefining collaboration. Distributed engineering teams can now co-create in real time, update designs directly into simulation workflows, and minimise delays caused by version conflicts. According to a report by Mordor Intelligence, the Indian product lifecycle management (PLM) market was worth $391 million in 2024 and is set to grow at a CAGR of 11.5% through 2029.

Rahul Jadhav, Technology Consulting Partner at EY India, notes that cloud deployment is part of a larger change: “Cloud-based AI-enabled tools have transformed design and manufacturing processes with rapid design iterations, real-time collaborations across ecosystem, AI-enhanced simulations, digital twins and AI assisted CAD/CAM/CAE/PLM, making it faster, cost-effective, and integrated digital workflows.”

AI-Driven Product Engineering

AI is increasingly shaping engineering decisions. Generative design tools can propose hundreds of configurations based on constraints like material cost, strength, or weight. Machine learning models embedded in simulation platforms are predicting potential manufacturing defects before a single prototype is built.

Parminder Singh, Country Head — Design & Manufacturing, India & SAARC at Autodesk, said the shift is from a “linear approach, where the product was designed and only then did the complex challenge of manufacturability arise to an integrated, data-driven ecosystem powered by cloud platforms, AI-driven design tools, and advanced simulation capabilities.” He added that “real-time, AI-enabled feedback loops” are now embedding manufacturability into the earliest stages of design.

Digital Twins and Lifecycle Integration

Digital twins, virtual representations of physical products, are being used to simulate real-world operating conditions and enable predictive maintenance. In EV and robotics manufacturing, they help optimise designs for performance and durability. Cloud-based PLM systems are emerging as the central repository for design data, bills of materials, and compliance records.

Rahul Jadhav observes that “generative designs and simulations have reduced cycle times in engineering and operations, while process or asset digital twins have improved throughput and equipment uptime.”

Barriers to Adoption

Technology adoption in manufacturing still faces financial and skills-related hurdles. Ankur Basu, Partner – Digital Operations, Advisory at PwC India, points out that “horizontal and vertically integrated digital workflows require investment in strategy, design and fit-for-purpose solutions. The typical ROI is 24-36 months which becomes a significant deterrent for digital adoption.”

Sanjay Mittal, Senior Partner and Industrial Sector Leader at IBM Consulting India & South Asia, highlighted organisational readiness: “Industry 4.0 is not just about technology, it requires a cultural evolution, talent upskilling, and organisational alignment.” He added that demonstrating ROI and securing stakeholder buy-in remains one of the most persistent challenges.

Policy and Ecosystem Support

Government-led initiatives such as the Design-Linked Incentive scheme and increased funding for manufacturing R&D are intended to lower the risks of adopting advanced design and manufacturing technologies. Public-private collaborations that combine funding with training and infrastructure upgrades could accelerate uptake, particularly among SMEs and early-stage hardware startups.

In parallel, industry experts note that these shifts are as much about mindset as they are about tools. “Product design has become a more agile, data-driven, and collaborative process,” said Poornima Bethmangalkar, Industry Group Head for Industrial, Manufacturing, Energy & Utilities at Happiest Minds Technologies. She points to the adoption of machine learning to rapidly generate design alternatives and the growing use of digital twins to simulate real-world conditions before a physical build. This, she explains, has cut reliance on physical prototypes and accelerated time-to-market, while enabling distributed teams to collaborate in real time through cloud platforms.

How the New Stack is Playing Out in Key Verticals

In electric mobility, robotics, and consumer electronics, India’s manufacturing workflows are undergoing a structural shift. Electric vehicle startups are using cloud-based CAD and AI-driven simulation to model battery architecture, test safety parameters, and optimise designs without the cost of repeated physical prototyping. NITI Aayog’s 2025 EV adoption update shows that such virtual-first workflows have cut prototyping timelines for leading EV players by as much as 30%. Robotics manufacturers are integrating mechanical, control, and AI path-planning designs in parallel, while digital twins allow them to replicate operational environments before deployment. This is proving critical in sectors like warehouse automation, where downtime or design flaws have high operational costs.

The consumer device segment is also moving toward collaborative, cloud-native design. Wearables and IoT product makers are using generative design to minimise material use while maintaining durability, a shift that aligns with both cost and sustainability goals. As adoption spreads, these vertical-specific innovations are reinforcing the broader industry trend toward a unified, AI-augmented product development stack, one that links design, testing, and manufacturing into a single, data-driven pipeline.

Strategic Implications for Leadership

For C-suite leaders, decisions about cloud-based CAD, AI-driven design, and integrated lifecycle systems are now core strategic calls. These platforms affect not just product timelines but also market competitiveness, supply chain agility, and compliance readiness. As Parminder Singh notes, compressing iteration cycles from weeks to hours and aligning all stakeholders on “the same digital canvas” is no longer an experimental capability, it is a requirement for manufacturers that aim to compete globally.

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