1、July 2025(revised version)Generative AI in Engineering R&DA Game Changer for Product Innovation and R&D EfficiencyIndustry Partners Table ofContentsResearch Objective and Methodology 3Executive Summary 4Introduction 5Section 1:Strategic Importance of Generative AI 7Section 2:Value Driven GenAI Appli

2、cations 9Section 2a:Use Cases to Improve ER&D Process Efficiency 10Section 2b:Use Cases to Improve the Product 13Section 3:Value Creation Drivers 15Section 4:Use Cases in Action 22TCS 22Bosch Global Software Technologies 26Samsung 29Conclusion 33Appendix 341.GenAI Use Cases for Improving ER&D Effici

3、ency 342.GenAI Use Cases for Improving Products or Product 35 DifferentiationKey Contributors 36Executive SummarySection 4 Use Cases in ActionIntroductionSection 1 Strategic Importance of Generative AISection 2 Value driven GenAI applicationsSection 3 Value Creation Drivers3Generative AI in Engineer

4、ing R&DA Game Changer for Product Innovation and R&D EfficiencyResearch Objective and MethodologyThis research study delves into the strategic importance,practical applications,and barriers towards adoption of AI,more so Generative AI in the Engineering R&D(ER&D)context,looking at both product and p

5、rocess perspectives.To offer a data-driven perspective on whether Generative AI is truly revolutionizing the engineering sector or still evolving,the study surveyed approximately 50 senior executives from top Sector%of ParticipantsTechnology*15%Manufacturing#42%Energy,Utilities,Oil and Gas6%Healthca

6、re and Life Sciences4%Diversified 33%Total100%Below is the executive representation from each sector for the survey.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 CX