AI-Augmented Executive Dashboards: Integrating Generative Artificial Intelligence and Data Visualization for Project Decision-Making in Saudi Arabia

Executive project dashboards are no longer expected to do merely than display historical key performance indicators. In the case of complex portfolios, managers need to interpret weak signals, connect both structured and unstructured evidence, evaluate various courses of action, and have to justify their decisions all while under time pressure. This study examines how generative artificial intelligence can be combined with data visualisation in order to create AI-augmented executive dashboards for project decision-making in Saudi Arabia. A structured integrative review was conducted in order to gather thirty peer-reviewed studies published between 2020 and 2025 in the fields of project management, decision support, visual analytics, human-AI collaboration, generative AI, and Saudi digital transformation. The results indicate that the most value is not derived from making autonomous decisions, but rather from having a multi-layered interface in which validated project data, predictive models, visual representations, and a conversational generative element support executive judgement. The quality of the dashboard is assessed according to the currency of the information, its completeness, the suitability of the visual design, the degree of cognitive load, semantic consistency, and the clear communication of uncertainty. Generative AI can improve dashboards by allowing natural-language queries, giving narrative explanations, developing scenarios, carrying out cross-source synthesis, and providing interactive visualisations; nevertheless, these features involve risks such as hallucination, automation bias, opaque reasoning, and the diffusion of accountability. The level of adoption in Saudi Arabia is also affected by organisational readiness, data governance, the amount of attention given by leadership, the digital infrastructure, and the existing project delivery practices. The review proposes a human-governed framework which divides the stages of evidence, analytics, generation, presentation, and decision-making, while at the same time ensuring that traceability is maintained for each generated statement back to the source data. This framework provides a design approach for executive project intelligence in Saudi organisations and establishes a research agenda focused on decision quality, calibrated trust, multilingual interaction, portfolio-scale validation, and governance-by-design.