# AI-Supported EUD for Data Visualization: An Exploratory Case Study
## 1 Introduction  
Organizations encompass disparate work practices, ranging from specialized microtasks for operational processes (e.g., monitoring of a production device) to long-term strategic plans conceived as part of managerial competencies. All these work practices need a certain level of knowledge (expertise) and information (data) to allow for good decision making at each step of the organizational continuum. Data-driven activities characterize some of these transversal tasks. They represent knowledge-intensive practices based on skills that each person inside an organization should possess and through which they communicate. These skills should receive adequate support from interaction designers. As recently noted in [1], knowledge-intensive work practices can be supported by better end-user development (EUD) tools, but this is not sufficient. What is often overlooked is the need to analyze the domain of practice and to assess the individual computational skills and data literacy in order to determine what is the level of support that EUD tools can effectively bring in organizational activities. This personalization would yield a benefit by improving the quality of work practices [2]. Visual analytics is massively exploited as a key activity related to data-driven decision making in many professional roles and transversal tasks inside organizations. Recent research [3] has identified in data visualizations (data viz from now on) those kinds of \"boundary objects\" capable of becoming common informational capital for knowledge sharing inside and across organizational routines, sectors, and goals. Despite the paramount importance of data analysis and visualization in enterprises, there is a lack of systematization in the analysis of data viz practices, individual literacy, and adequate support from the related technologies [4]. Artificial intelligence (AI), and large language models (LLMs) in particular, bring the promise of supporting users with a more personalized and informal interaction style, often equipped with the ability to hide the complexity of low-level coding and database query formulation. As stated in [5], AI systems are characterized by the paradigm of being _adaptive_, autonomously adapting to users and contexts, whereas EUD systems are characterized by their being _adaptable_, i.e., adjustable from the users who decide their degree of adaptation to the problem at hand. The integration of these two paradigms depends on how they are applied to concrete work practices and customized for individual literacy. Work practices should be attentively investigated and, arguably, those that bring a higher business value and a wider benefit to the company at large should be those that are more carefully considered. We argue that data-driven work practices are gaining momentum as key innovation factors, and visual analytics and data viz are increasingly growing among those practices. Visual analytics is the data viz-based process of using data and processing them properly to investigate business phenomena. Data visualizations are the artifacts yielded for the specific process of visual analytics. This paper aims to investigate how to integrate the adaptive (AI) and adaptable (EUD) paradigms to support visual analytics and data viz design practices, starting from the analysis of those practices in a large Italian company. Therefore, it presents an exploratory case study that aims to answer the following overarching question: _Can we support data analysis and visualization practices in business contexts with EUD enhanced by LLMs?_  
We adopted the exploratory case study research method described in [6] to fill a gap in the research on causal studies of data viz-based practices in organizations. In this way, we aim to pose the initial hypotheses in order to \"define the necessary questions and hypotheses for developing consecutive studies\" (ibidem) in this field. This research fosters an in-depth analysis of an organizational domain and of the potential consequences of technological interventions.