生成式AI在工程研发中的应用：产品创新与研发效率提升的变革力量

A Game Changer for ProductInnovation and R&D Efficiency July 2025 (revised version) Table ofContents Research Objective and Methodology3 Executive Summary4 Introduction5 Section 1: Strategic Importance of Generative AI7 Section 2a: Use Cases to Improve ER&D Process Efficiency10Section 2b: Use Cases to Improve the Product13 Section 3: Value Creation Drivers15 Section 4: Use Cases in Action22 TCS22Bosch Global Software Technologies26Samsung29 Conclusion33 Appendix34 1.GenAI Use Cases for Improving ER&D Efficiency34 2.GenAI Use Cases for Improving Products or Product35Differentiation Key Contributors36 Research Objective andMethodology technology services firms and engineeringR&D companies, including CXOs representingGCCs and leading tech companies. Byincorporating insights from industry leaders,the report explores the integration ofGenerative AI into existing systems andprocesses, while also analyzing the tangiblebenefits, significant hurdles, and futurepotential of AI-driven innovation in theindustry. This research study delves into the strategicimportance, practical applications, andbarriers towards adoption of AI, moreso Generative AI in the Engineering R&D(ER&D) context, looking at both productand process perspectives. To offer a data-driven perspective on whether GenerativeAI is truly revolutionizing the engineeringsector or still evolving, the study surveyedapproximately 50 senior executives from top Notes: •Diversified group is primarily engineering service provider organizations.•Rest of the audience (vertical-wise distributed) are from the GCC community.•All the participants are at CXO level and a few from the next level or their respective nominations.•This excludes leaders from Nasscom.*Computing & storage, Consumer electronics, Semiconductors, Software products and Telecom and Media& entertainment#Aerospace & defense, Automotive, rail & off-highway mobility, Industrial products, and Mining Executive Summary expectations from customers seeking moreintelligent, automated, and efficient outcomes.Without any doubt, an overwhelming number oforganizations are placing this in their top threepriorities, but the focused end use varies widelyfrom internal process efficiency to productpersonalization. The low hanging fruits todayremain around knowledge assist capabilities,test case generation, synthetic data andsimulations. On the product side, aspectssuch as voice assistance, and self-learning aretaking early root. Interestingly, no use of anykind fell into the low priority quadrant, revealingthe near universal appeal of Gen AI leading toclose clustering of investment preferences. Generative AI is swiftly transforming industriesglobally, and Engineering, and R&D (ER&D)is no exception. As companies strive to driveinnovation, enhance efficiency, and enhancecompetitiveness, understanding the strategicrelevance, applications, and challenges ofGenerative AI has become essential. To assess the current landscape, we conducteda survey among executives from ER&Dorganizations in India, exploring three keydimensions: 1.Strategic Importance of GenerativeAI –How organizations perceive thenecessity of Generative AI to maintain acompetitive edge. Absence of proven methods of TCO assessmentand talent shortage are severely dampeningthe scaled adoption, while concerns on the IPimplications, data quality and data privacyare a pair of unknowns that require publicpolicy interventions. Considering the earlystage of evolution, the investments in buildingown large language models (LLMs) are yet totake root. Public models remain the path toexperimentation. 2.Value-Driven Generative AIApplications –How organizations andtheir customers are currently leveragingor planning to leverage AI in R&D. 3.Value Creation Drivers –The keyenablers and drivers essential forrealizing applications, barriers preventingwidespread adoption, and strategies toovercome them. We do believe that it’s important to keep inmind that all conclusions are at a point of timei.e. today. Given the pace of change, it wouldbe interesting to revisit this survey in say 6-9months, so assess how things have progressed. Most executives recognize Generative AI as acritical investment to maintain competitiveness.Organizations acknowledge that GenAI-driven solutions are not just an internalefficiency driver but also a product andexperience differentiations, driven by emerging Introduction Generative AI, a branch of artificialintelligence that includes machinelearning (ML) and deep learning (DL), isrevolutionizing the technological landscapeby creating new content from existing datapatterns. Its impact is significant, particularlyin Enterprise 2.0, where there’s a paradigm shift from conventional decision-making toAI-based decision-making. This shift allowsfor more informed, efficient, and strategicdecisions, enhancing traditional methods.Research undertaken by TCS (refer chart)indicates a clear shift in decision makingfrom intuition to explora