Thesis Eemil Häikiö


AI-powered dashboards are business intelligence tools that collect and present data in a comprehendible manner to enhance decision-making. The integration of artificial intelligence (AI) in data-driven decision-making (DDDM) processes is continuously increasing in modern organizations, however, current research examining AI-powered dashboards does not address the deployment of the technology. To complement prior research, this thesis aims to provide a foundation for the implementation of AI-powered dashboards by investigating the current dashboard best practices, as well as the adoption and governance of AI-powered dashboards in the context of executive decision-making. The concepts of DDDM and AI function as a foundation regarding the purpose and functionality of AI-powered dashboards, whereas IT governance provides a basis for their adoption and governance. The research methodology encompasses a review of scientific literature addressing these concepts to enable the proposal of two distinct theoretical frameworks addressing the adoption and governance of AI-powered dashboards.

The results of this study highlight three critical areas regarding AI-powered dashboards: current best practices, adoption processes, and governance. Best practices show that AI-enabled dashboards are dynamic and versatile BI tools that enhance decision-making and operational efficiency through detailed visualizations and data analysis. Concerning adoption, the study emphasizes the importance of selecting a suitable framework tailored to organizational needs, suggesting that a combination of existing models might often be necessary to integrate AI and BI within organizational environments effectively. Results addressing the governance of AI-powered dashboards emphasize the importance of BI and AI governance. Although the development of AI governance frameworks is still in its early stages compared to BI and IT governance, the findings suggest that adopting flexible and diverse governance structures enables organizations to manage the security, transparency, and accuracy risks associated with AI technologies.

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