Ashish Khushu, Chief Technology Officer, L&T Technology Services Limited

Ashish, with over three decades of experience in technology-led industries, drives LTTS’s innovation roadmap, focusing on intellectual property, products, platforms, and digital engineering solutions. Under his leadership, the company has built a strong culture of innovation among thousands of engineers and significantly expanded its patent portfolio. He is also a prominent voice on AI, edge computing, digital twins, and future-ready engineering.

In an interaction with Sejal Singh B G, Correspondent at siliconindia. Ashish Khushu, Chief Technology Officer at L&T Technology Services Limited (LTTS) shared his insights on ‘How AI-Led Engineering Is the Defining Frontier for Engineering Services’.

Engineering services are entering a new era. For years, cloud and digital transformation helped firms modernize infrastructure, improve efficiency, and scale operations. But AI-led engineering is different. It is not simply another layer of technology sitting on top of existing workflows. It is reshaping how products are imagined, designed, tested, manufactured, and improved.

That distinction matters because the product development landscape itself has changed. Across mobility, medical devices, consumer electronics, and industrial systems, companies are under intense pressure to launch faster, innovate continuously, and deliver better user experiences at lower cost. A product cycle that once had 18 months of breathing room may now need to be compressed into half that time. In some industries, even two years can feel too slow.

So the real question is not whether AI will influence engineering services. The real question is; can engineering firms remain competitive without making AI central to their operating model?

Also Read: Cloud Ecosystems Transforming AI, Big Data, and IoT into Unified Intelligence

Why AI-Led Engineering Goes Beyond Cloud & Digital

Cloud and earlier digital initiatives were largely about optimization. They helped enterprises move workloads, standardize systems, and improve operating efficiency. AI, by contrast, reaches much deeper into the engineering lifecycle.

It supports ideation by helping teams explore concepts faster. It accelerates design by enabling rapid reuse of components and smarter alternatives. It improves simulation by reducing dependence on physical prototypes. It strengthens testing through digital twins and virtual environments. It even influences manufacturing and supply chain planning by improving prediction, visibility, and responsiveness.

Think about what that means in practical terms. If a company previously built four physical prototypes before launch, AI may allow three of those cycles to be simulated digitally. That can save time, reduce cost, and speed up learning dramatically. The value is not just incremental. It is structural transformation.

This is why AI-led engineering is not comparable to the cloud shift. Cloud made delivery more efficient. AI changes the engineering equation itself. It creates a new model where knowledge, data, and domain expertise are continuously embedded into the development process.

There is another important difference. AI is not one technology. It is a family of technologies where machine learning, computer vision, natural language processing, embedded intelligence, digital twins, and more. Each use case demands a different combination. That makes AI-led engineering more nuanced, but also more powerful. The opportunity is not simply to ‘adopt AI’. The opportunity is to decide where AI can fundamentally improve outcomes and where traditional methods still make more sense.

So ask yourself; is AI acting as a support tool in your organization, or is it becoming part of the engineering core? That answer separates experimentation from transformation.

AI is not just another technology layer; it is a tool that intervenes at every stage of engineering, from ideation to manufacturing, fundamentally shrinking design cycles and redefining how products are built.

From Services to IP and Platform-Led Growth

For engineering services firms, AI is also forcing a business model rethink. Traditional services models are built around talent, process execution, and problem-solving capacity. Clients bring a need, and the service provider brings expertise and manpower to solve it. Product and platform models work differently. They package capability into reusable assets that can be scaled, standardized, and monetized repeatedly.

AI makes this shift more urgent. Why? Because the firms that can turn AI into reusable engineering assets will move faster, deliver more value, and create stronger client lock-in. In other words, AI is not just helping firms do the same work more efficiently. It is enabling them to create new forms of intellectual property, new service offerings, and new recurring revenue streams.

But this transition is not easy. Many firms are still stuck in legacy thinking. They may be investing in AI, but they are treating it like a bolt-on tool instead of a redesign of their engineering operating model. Others are chasing every new technology without asking a more important question how does this improve reliability, safety, speed, or economics?

That question matters especially in safety-critical industries such as automotive, healthcare, batteries, and industrial equipment. In those environments, speed cannot come at the expense of reliability. Governance, ethics, IP protection, responsible AI, and technology maturity all become non-negotiable. The best AI-led engineering organizations are not the ones moving fastest blindly. They are the ones building systems that are faster and safer at the same time.

And that is the real frontier. AI-led engineering is not merely a technology trend. It is becoming the strategic foundation for how engineering services firms compete, how they deepen client relationships, and how they create future revenue.

The firms that win will be the ones that move beyond task execution and start building intelligent engineering systems, reusable platforms, and domain-specific AI capabilities. Those that wait may still survive for a while. But in a market where product cycles are shrinking and customer expectations are rising, waiting is already a competitive decision.

Conclusion

AI-led engineering is defining the future of engineering services because it changes both how value is created and how it is delivered. It compresses design cycles, reduces prototyping costs, improves simulation, and opens the door to smarter, more adaptive products. At the same time, it pushes firms to rethink their delivery model, their IP strategy, and their role in the client ecosystem.