Year: 2024
Type: dissertation
Abstract: The potential of artificial intelligence (AI) in strategic management and operations remains unexplored mainly due to the novelty of the technology. This study examined how generative artificial intelligence (GAI) can enhance leading by objectives in scalable agile enterprises to improve productivity efficiency. Using a design science research (DSR) approach, the study developed a leading by objectives framework over four iterative cycles. In the first cycle, the themes of high-level leading by objectives were identified and evaluated for technical suitability and potential benefits. The second cycle mapped the AI features of most used project management tools and refined the framework into more specific use cases. In the third cycle, the impacts of increasing context in prompts were analysed, and the framework’s use cases were tested with ChatGPT using simulated data to evaluate their feasibility. The fourth cycle observed Microsoft Copilot experiments conducted by a business team in the partner company to understand the real-life potential of the leading by objective use cases. The findings show that GAI can already support nearly all identified leading by objective use cases, despite current technical and reliability challenges. Furthermore, the growing integration of GAI features into the most used project management tools supports the long-term investment in these technologies for productivity gains. To maximize GAI’s benefits in leading by objectives, integrating the technology into business systems, identifying valuable use cases, and mastering effective prompting techniques are crucial to creating a positive and impactful AI experience.
Source: LUTPub (LUT University)
Author: Tuunainen, Henna
Institution: Lappeenranta-Lahti University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7093723762