ARTIFICIAL INTELLIGENCE IN DATA VISUALIZATION: REVIEWING DASHBOARD DESIGN AND INTERACTIVE ANALYTICS FOR ENTERPRISE DECISION-MAKING

Abstract

This study conducts a comprehensive systematic review to examine how artificial intelligence (AI) is transforming data visualization, with a particular focus on dashboard design and interactive analytics within enterprise decision-making environments. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a total of 146 peer-reviewed articles published across major academic databases were screened, analysed, and synthesized to ensure methodological rigor and transparency. The review explores how AI-driven modules—such as automated chart recommendation systems, real-time anomaly detection, adaptive visual interfaces, and predictive modelling engines—are reshaping dashboards from static reporting tools into dynamic cognitive environments that actively support strategic and operational decision processes. A key finding is the convergence of AI techniques with cognitive and perceptual design principles, where visual hierarchies, pretensive attributes, and minimalistic layouts are increasingly operationalized within algorithmic generation engines to reduce cognitive load, enhance clarity, and ensure design consistency at scale. The review also highlights the emergence of interactive analytics features, including coordinated views, brushing, and mixed-initiative exploration, which enable users to collaborate with AI systems to accelerate sensemaking, improve analytical depth, and strengthen cross-team consensus formation. Evidence from the reviewed studies indicates that these AI-enhanced dashboards deliver measurable improvements in decision speed, accuracy, and organizational alignment by consolidating complex data streams into actionable, context-rich insights. However, the analysis reveals significant gaps in governance, explain ability, and ethical oversight, with relatively few studies addressing issues such as reproducibility, transparency, or long-term institutional integration of AI-driven visualization systems. Overall, this review underscores that the convergence of AI, cognitive design, and interactive analytics is reshaping the role of data visualization from a presentation layer into a central decision-support infrastructure, while also identifying critical areas where governance and organizational frameworks must evolve to support sustainable enterprise adoption.