| dc.description.abstract | This master's thesis explores the interaction between research fields such as artificial intelligence (AI), innovation, and project management. The purpose of the study was to investigate how AI can be used as a tool to optimize the innovation process in innovation projects. There were identified knowledge gaps in the literature on the topic, which provided a basis for the research along with the rapid development of the technology. Firstly, challenges that can hinder innovation were identified with qualitative interviews. These interviews contributed in combination with relevant literature to explore how AI can be used to solve or simplify the challenges identified. Lastly, the thesis concluded its empirical findings with a proposed conceptual framework that integrates the use of AI into an optimized version of the innovation process.
The interviews contributed to mapping a set of challenges including generating ideas, idea conversion, resource constraints, market and technology knowledge, learning, and exiting. The findings presented shows that different AI models could be effective for aiding the different challenges. Statistical models can be effective regarding challenges such as resource constraints and exiting. Deep Learning models can contribute to aiding idea generation, exiting, providing market and technical knowledge, and aiding learning difficulties within an organization. Lastly, Large Language Models (LLM) can contribute to solving challenges such as idea generation, idea conversion, communication, and market and technical knowledge.
The main contribution of this thesis to the literature is exploring the potential of AI in work with innovation in projects. This contribution is conceptualized in a proposed framework of an AI integrated optimized innovation process. This innovation process consists of five phases, including an added phase of post-launch evaluation. Statistical models, Deep learning models, and LLMs can all contribute to effectiveness in the innovation process and have been added in each of the phases. In further research, it would be interesting to test this conceptualized framework with independent testing. Testing the framework with quantitative evaluation metrics would provide a basis for evaluating the framework, and further developing it. There are still unexplored gaps in the interaction between AI, innovation, and project management. Thus, further research on the related fields is highly recommended. | |
