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Generative AI in ER&D: A Game Changer for Product Innovation and R&D Efficiency

This survey 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 process perspectives. Based on the survey of 50+ senior executives from top technology service firms and ER&D companies, including CXOs representing GCCs and leading tech companies, the research is a culmination of overarching insights into the potential of GenAI in ER&D covering the three key aspects – Strategic Importance of Generative AI (GenAI) in ER&D, Value Driven GenAI Applications, and Value Creation Drivers.
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Key Findings

01

  • About 63% of ER&D senior executives identify GenAI as a high priority spend area crucial for maintaining a competitive edge.
  • Nearly 72% of the companies use/ planning to use GenAI for enhancing customer experience followed by 64% for improving business process efficiency, and 59% for augmenting product features and personalization.
02

  • Test case generation, Knowledge assistants, Data insight generation, Code generation, and Synthetic data emerged as the top identified use cases for improving ER&D process efficiency.
  • Generative AI for enhancing products is not as popular yet as for improving process efficiency. Generative AI-based voice assistance and self-learning product features seeing an upward trend.
03

  • The top three barriers to Generative AI adoption in organizations are high investment costs , data privacy concerns, and system integration challenges.
  • Ownership disputes and IP leakage are the top concerns when co-developing GenAI applications in open innovation ecosystems.

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