Visualization dashboards adapt with generative AI but keep human roles
The Future of Visualization Dashboards in the Age of Generative AI
Human-Computer Interaction
Summary
Creating dashboards will get easier with generative AI, but dashboards will still be important for regular questions and monitoring. The experts interviewed say that people will still guide how dashboards work and check their results, while using new ways like language and gestures to interact. They also warn that easier creation might cause challenges like too many dashboards or users not fully understanding them. The paper suggests looking into how to keep dashboards reliable and useful as AI helps make them.
What this means in practice
- •For business intelligence teams: Design new dashboard workflows that combine AI-generated views with human curation and evaluation to support ongoing monitoring and reporting needs.
- •For data governance officers: Develop organizational policies and training to ensure AI-created dashboards maintain shared understanding, personalization, and validation standards.
A position paper. It proposes an approach and reports no results.
Authors
Vaishali Dhanoa, Duosi Dai, Gabriela Molina Léon, Eduard Gröller, Niklas Elmqvist
Abstract
Generative AI promises easier dashboard creation, raising questions about the future of dashboards and the people who create and use them. We interviewed 16 experts based in 14 countries about their practices and expectations. Almost all expected dashboards to persist for recurring questions, monitoring, and reporting. They anticipated adaptive views and combinations of language, graphical controls, and gestures, while emphasizing interaction as part of human exploration and understanding. Participants expected authors' responsibilities to shift toward specifying requirements, curating generated work, and evaluating outputs, with design knowledge and communication remaining important. Easier creation also raised concerns about validation effort, users' understanding, maintenance, and personalization weakening shared understanding. We discuss seven opportunities for research and practice concerning validation, end-user education, dashboard proliferation and rot, organizational guidance, adaptation, novel visualizations, and accountability for AI-generated content. Our findings connect dashboard evolution with the human and organizational work needed to sustain their use.