It's incredible to look back at how we used to build products just a few years ago. Product engineering was a long, drawn-out process where you'd sketch ideas, build prototypes, test them, identify problems, return to the drawing board and repeat. Sometimes, it took months or even years to get from concept to market. However, the advent of artificial intelligence (AI) has changed the game and something remarkable is happening across the industrial and heavy engineering sectors.

The industries that have long been at the core of global economic development are experiencing a revolution of their own, ever since the rise of computer-aided design. But this isn't just about better software or faster computers. It is about a fundamental reimagination of how products come to life.

For decades, these sectors have accepted certain truths as gospel: design cycles are measured in months, physical prototyping is inevitable, and you discover problems only after you've built something. The iterative, hardware-intensive approach wasn't just how things were done; it was how things had to be done.

But what if that's no longer true?

McKinsey estimates the economic potential of generative AI to contribute from $2.6 trillion to $4.4 trillion globally. Yet, forward-thinking industrial leaders know that efficiency gains alone barely scratch the surface of what's possible when AI transforms the entire product creation paradigm. AI has quietly transitioned from the periphery of industrial operations to the very center. We're not talking about AI as a nice-to-have productivity booster or a back-office automation tool. Now, AI is a strategic imperative, a fundamental shift in how we approach the art and science of engineering itself.

Challenges in product engineering and how AI can help

Product engineering in industrial sectors involves navigating a complex web of connected challenges that have persisted for decades, each creating bottlenecks that compound to slower time-to-market and, as a result, increased costs. These challenges reveal themselves most acutely in the mundane, day-to-day realities that engineers face.

Lengthy design iterations and approval cycles represent perhaps the most visible challenge, with design processes spanning months due to regulatory checks, physical prototyping requirements and multi-stakeholder validation processes. High intangible costs such as time delays, resource inefficiencies and energy wastage, remain hidden in spreadsheets and project reports, creating a false sense of the actual cost of traditional engineering approaches.

Then, there’s the question of data availability and integrity that needs to be addressed. Engineering processes generate vast datasets from CAD models, IoT sensors, simulations and testing protocols, yet this data frequently remains siloed across departments and systems. Conventional wisdom suggests that AI requires perfect, clean datasets to be effective; a belief that has paradoxically become a barrier to AI adoption rather than a driver of better data management practices. Our solution, HCLTech iDoRAN, is an AI-based framework designed for handling unstructured data by extracting technical and engineering specifications from diverse sources such as vendor documents, engineering drawings, ERP descriptions and bills of materials.

For one client, iDoRAN was successfully implemented to extract and verify attributes from CAD drawings of control valves, including dimensions, material specifications and compliance with quality standards. The system processes up to 8,000 valve drawings per month, reducing manual validation efforts by 80%. Additionally, for an oil and gas customer, deployment for technical specification extraction from vendor documents has resulted in up to a 90% reduction in manual effort using ML/NLP techniques. Furthermore, integrating iDoRAN with large language models (LLMs) addresses various accuracy and reliability challenges inherent in language models. For another client, a custom chatbot with iDoRAN and SLM was developed to interact with over 3,000 technical manuals, enabling rapid, accurate and targeted retrieval of product-specific information.

The AI advantage: Beyond efficiency gains

AI addresses these challenges by fundamentally reimagining what's possible when constraints are removed and human creativity is amplified.

Industry leaders can visualize operational efficiency with the ability to automate routine checks like model validation and compliance control, freeing engineers to focus on higher-value, creative and strategic work. However, the real breakthrough comes from AI's capacity to handle complexity that overwhelms human cognitive capabilities, as it can optimize multiple design parameters parallelly with the intelligence to adhere to thousands of constraints and performance requirements. Looking at the costing spectrum, AI moves beyond simple automation to encompass more sophisticated approaches, such as reducing prototyping needs through accurate virtual validation and minimizing engineering change orders through predictive modeling.

These capabilities challenge the traditional assumption that physical testing is always necessary, forcing organizations to confront uncomfortable questions like which validation processes truly add value versus those that exist primarily to manage anticipatory risks. Managing anticipatory risks can be automated with AI. For instance, we provide a custom AI solution, CADIFAI, that improves validation and quality check processes for our clients by automating the testing of prototype CAD models by leveraging AI/ML to simulate realistic industrial scenarios and use cases.

Enhanced quality and safety actually represent areas where AI's impact is both transformative and controversial. When AI systems can detect defects that human inspectors miss or predict failures before they occur, they challenge established quality control processes and the expertise of seasoned professionals.

Finally, it would be safe to say that we are moving to a more environmentally sustainable approach as we benefit from AI's ability to optimize material efficiency and lower emissions, although this is still in the works. True sustainability requires systemic thinking about entire product lifecycles, not just algorithmic optimization of individual components; a nuance that often gets lost in discussions of AI's environmental benefits.

Strategic enablers for AI adoption in product engineering

Implementing AI in product engineering requires CTOs to embrace changes across technology infrastructure, organizational processes and human capabilities spectrums. The strategic enablers that determine AI success can be found in mundane implementation details rather than exciting technological possibilities.

The foundation begins with cloud-native platforms that provide scalability and real-time collaboration capabilities for AI-heavy workloads. Yet this transition challenges traditional approaches to data security, IPR protection and regulatory compliance built around on-premises systems. Companies must focus on developing cloud strategies that align with engineering workflows rather than forcing processes to adapt to generic architectures.

Complementing cloud capabilities, GenAI, edge AI and IoT integration enable real-time data processing close to machinery and field assets, reducing latency and accelerating deployment in ways centralized processing cannot match. For example, in one of our recent AI implementation projects, we accelerated the software development process by 20-30% across four code layers (Frontend, Backend, Middleware and Network Computing System).

We also leveraged GenAI to reduce testing efforts by up to 50% through automated test case and script generation. This improved decision-making and enabled faster turnarounds across the client organization. Such setups are particularly valuable in industrial settings where split-second decisions can prevent equipment failures and enhance plant-wide production efficiency.

Underlying both cloud and edge deployments, robust data governance and AI model training pipelines represent the true foundation of successful implementation. Building effective frameworks means confronting uncomfortable realities about data consistency and accessibility and then balancing the need for standardization with the flexibility required for engineering innovation. This often means accepting some data imperfection while developing AI systems that work effectively within real-world constraints.

The final hurdle to AI adoption in product engineering is change management and workforce upskilling. Enterprises must cultivate skills that go beyond technical training, encouraging teams to approach industrial engineering problems with new perspectives. Success is only possible when leadership, technology and talent are aligned with a deep understanding of AI’s potential. In many cases we’ve observed, this cultural transformation is more challenging than the technical implementation itself, as it requires reshaping established organizational cultures and long-held professional identities.

Embarking on introducing AI in the product engineering lifecycle

Organizations beginning their AI journey must navigate between technological possibilities and practical implementation constraints. The most successful approaches start with carefully selecting proofs-of-concept that demonstrate clear value while building organizational confidence and capabilities.

While AI isn't simply replacing traditional engineering methods, it's augmenting human capabilities in previously unimaginable ways, creating new pathways for innovation while challenging established approaches to the existing engineering practice.

• Augmentation, not replacement: Balance here is the key. The future lies in identifying new forms of human-AI collaboration that capitalize on the unique strengths of both AI and human design. AI excels at pattern recognition and optimization across multiple variables, while humans provide creative insight, strategic thinking and contextual understanding that AI cannot replicate
• Competitive advantage through AI integration: As product complexity increases and customer expectations evolve, engineering agility becomes the key difference between market leaders and followers. Organizations successfully integrating AI into product development processes not only achieve a faster time-to-market but also achieve superior product performance and better resource utilization

The organizations that will lead the next decade of industrial innovation are those that embrace AI not as an add-on to existing processes, but as a fundamental reimagining of what's possible in product engineering.

At HCLTech, our AI-integrated engineering solutions are helping global industrial leaders transform their development lifecycles with unprecedented speed, precision and intelligence. The smart industrial revolution is happening now, and the question isn't whether AI will transform product engineering, but whether your organization will lead this transformation or be transformed by it.

To find more about our engineering capabilities and solutions, please visit: /engineering