From Innovation to Implementation: The Role of Generative AI in Optimizing NPD Performance
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2024-07-08
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en
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The aim of this thesis is to get in depth insights in the drivers by which the use of Generative AI in the New Product Development (NPD) process impacts NPD performance within businesses. Additionally, it explores the interaction between the NPD performance measures (cost, speed, mistakes, and knowledge sharing). By exploring these drivers through seven mini case study and eight semi-structured interviews, this study seeks to provide a comprehensive understanding of how generative AI could enhance NPD outcomes. Several drivers were identified through which genAI usage in NPD impacts the NPD performance measures, such as an increase on efficiency leading to less NPD cost, increasing task execution speed enhancing NPD speed, improving knowledge capture, and finding through a genAI based knowledge repository enhancing knowledge sharing, and less (repetitive) work prone for human error decreasing NPD mistakes. However, genAI tools also introduce a new way of errors, since itself is prone to making mistakes. Furthermore, it brings risk in privacy for sensitive data and poses challenges in integrating into the process. Despite, it shows a lot of potential for enhancing NPD performance. However, implementing genAI tools should not be the goal in itself; it should be used when it adds value.
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Faculteit der Managementwetenschappen