GenAI is rapidly transforming the engineering research and development (ER&D) landscape, ushering in a new era of intelligent automation, accelerated innovation and cross-functional collaboration. This ISG-Nasscom Thought Leadership Paper explores the strategic deployment of GenAI across seven key ER&D verticals — automotive, semiconductors, industrials, energy and utilities, telecom, healthcare and life sciences, and consumer electronics — highlighting high-impact use cases, ROI metrics and scalable adoption frameworks.
GenAI's role in ER&D: A strategic inflection point
GenAI is no longer a peripheral innovation, it is central to engineering transformation. In ER&D, GenAI is driving:
- Software modernization through automated code generation and testing
- Manufacturing optimization via predictive maintenance and defect detection
- Product innovation through adaptive design, simulation and personalization
These capabilities deliver measurable outcomes in speed, quality, cost and innovation throughput. For example, GenAI-enabled electronic control unit (ECU) code synthesis in automotive R&D has reduced development cycles by up to 50 percent, while wafer yield prediction in semiconductors has improved accuracy by over 21 percent.
Current adoption and investment trends
According to ISG Market Lens™ data:
- Just over 10 percent of enterprise applications are GenAI-enabled today, projected to reach 25 percent by the end of 2025.
- GenAI's share of IT budgets is expected to grow from 4 percent in 2024 to over 6 percent in 2025.
- The average GenAI initiative investment is $2.6M, rising to $4M in full production.
Spending is distributed across applications/SaaS (36 percent), personnel (25 percent), infrastructure (21 percent) and managed services (18 percent). Enterprises are balancing off-the-shelf solutions with custom builds, often relying on external service providers to bridge talent and capability gaps.
Key use case themes across ER&D:
- Software modernization and code automation: GenAI is automating legacy code translation, unit test generation and performance testing, accelerating migration to microservices and enhancing engineering productivity.
- Edge AI and embedded intelligence: Lightweight GenAI models are deployed on constrained devices (for example, Raspberry Pi) for offline inference, enabling real-time decision-making in surveillance and diagnostics.
- Visual AI for inspection and monitoring: AI-powered image and video analytics are improving safety and accuracy in field operations, from rust detection to equipment monitoring.
- Knowledge discovery and management: Retrieval-augmented generation (RAG) and knowledge graphs enable intuitive access to domain-specific insights, enhancing research and innovation.
- Intelligent automation and workflow optimization: GenAI agents are automating complex workflows, such as compliance validation, assessment generation and customer support, boosting scalability and user experience.
- Responsible AI and evaluation: GenAI is being used to test AI systems for fairness, robustness and ethical compliance, supporting trustworthy and regulation-aligned deployments.
- Training and simulation: Conversational bots and role-play simulations enhance employee training, offering scalable and personalized learning experiences.
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About the authors
Srinivasan PN
Srinivasan PN brings over 11 years of market research experience. He is currently a Senior Lead Analyst with ISG Research, focusing on emerging technologies and their influence on the industry. He is also responsible for authoring Provider Lens quadrant reports for Digital Engineering Services, AWS Ecosystem and Agentic AI. Srini is also an author of research articles and thought leadership papers in the above-mentioned areas. In his role, he collaborates with advisors to support enterprise clients by fulfilling ad-hoc research requests related to his expertise areas across various industries.
Swadhin Pradhan
