A New Paradigm – Gen AI in Engineering R&D

Generative Artificial Intelligence (Gen AI) is emerging as the cornerstone of accelerated and reimagined innovation, reshaping Engineering Research and Development (ER&D). The democratization of Gen AI has prompted industries such as aeronautics, automotive, life sciences, manufacturing, telecom, and more to explore the technology’s potential for gaining a competitive edge.

Recognizing Gen AI’s transformative potential, organizations are transitioning from experimental phases to the deployment of full-scale product solutions. Organizations must establish robust governance frameworks to harness Generative AI’s capability responsibly. This ensures content accuracy, authenticity, data privacy, and the implementation of necessary guardrails to mitigate risks and prevent unintended consequences from Generative AI applications.

Innovation, Design, Processes

Speed is a critical factor in the race to bring new products to market. Generative AI is pivotal in reducing the time from concept to reality. By automating routine tasks, accelerating design iterations, and enhancing predictive modelling, Gen AI helps companies bring products to market much faster compared to the traditional methods. This speed is crucial in industries, such as consumer electronics and automotive, where rapid innovation is a competitive advantage.

While companies primarily focus on achieving efficiencies with Generative AI, its true potential lies in creating industry and domain-specific solutions addressing unique challenges and opportunities. Organizations across industries are implementing diverse use cases to capture Gen AI value creation potential. This also supports the sustainability goals by reducing energy consumption and lowering carbon emissions.

Traditionally, engineers have relied on their expertise and experience to create designs that meet specific criteria. Today, Generative AI has introduced a paradigm shift by enabling a generation of thousands of design alternatives based on predefined constraints and objectives.

For instance, in the aerospace and automotive industries, Generative AI allows engineers to explore innovative designs optimizing weight, strength, and aerodynamics. This reduces material cost and improves performance metrics. 

Foundation models serve as the core of Generative AI, but they often can't meet the unique demands of specific industries. As a result, a new value chain is emerging. The new value chain will refine the foundation models with tailored and industry-specific data to unlock their full potential.

In the medical industry, analysing Intravascular Ultrasound (IVUS) and Optical Frequency Domain Imaging (OFDI) requires expert interpretation and is susceptible to human errors. By fine-tuning Gen AI foundation models with specific imaging data, the process becomes more efficient and accurate. This enhances the speed of analysis while reducing the risk of inaccurate, misleading, or failed results.

Manufacturing Execution System (MES) implementation is iterative and requires Subject Matter Experts (SME) in the manufacturing domain. Generative AI can be leveraged to generate high value MES designs with minimum intervention from SMEs. Manufacturing processes, such as line operation comparisons, recommendations, and optimizations, can be significantly enhanced with Gen AI use.

Generative AI is a powerful tool with disruptive potential across business functions. The technology allows to completely reimagine everything from R&D to marketing, sales, and customer operations. For example, during a customer sales call, a fine-tuned Generative AI model can suggest real-time upselling opportunities to a salesperson by analysing the conversation and integrating internal customer data, external market trends, and social media insights.

A New Blueprint for Innovation

As companies embed Generative AI into their operations, they are leading and redefining innovation. This convergence of AI and engineering is creating a new blueprint for product development, enabling faster, smarter, and more strategic innovation.

Embracing this transformation positions companies to set new industry standards and explore the limitless potential of Gen AI in ER&D.

Companies must evaluate if they have the technical expertise, technology infrastructure, data architecture, operating models, risk management, and governance processes necessary to effectively implement more transformative aspects of Generative AI.

As part of the Generative AI adoption strategy, it is important to assess the related challenges of copyright infringement and quality of output based on the data used to train the models.

Generative AI is not just a tool but a catalyst for a new era in ER&D. By quantifying its impact and adopting a structured approach to its integration, companies can unlock unprecedented levels of innovation, efficiency, and competitiveness.

-By Chandra Reddy, Executive Vice President & Managing Director, Capgemini Engineering, India