Abstract
In recent years, the availability and accessibility of geospatial data have greatly increased, providing valuable information for various industries such as transportation, real estate, and retail. However, the great volume and complexity of this data can make it difficult for businesses to extract meaningful insights. By incorporating AI algorithms such as machine learning or deep learning techniques, businesses can more effectively manage, analyze and interpret the data. This paper provides an overview of the current challenges and opportunities in using geospatial data for business decision-making and explores how artificial intelligence (AI) techniques can be applied to better in-form strategic decisions and improve operational efficiency. The study aims to identify best practices and design principles for creating effective automated decision-making systems using AI and geospatial data. A systematic review of the literature was conducted to address the research questions, which revealed that despite challenges, geospatial data presents numerous opportunities for business decision-making. The results suggest that integrating AI techniques can enhance the efficiency and accuracy of geospatial data analysis, enabling organizations to make more informed strategic decisions. This study highlights the importance of adopting best practices and design principles to maximize the benefits of geospatial data and AI for business decision-making. Overall, this paper argues that the integration of AI and geospatial data analysis has the potential to revolutionize the way businesses make decisions and gain insights from their data
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