Generative AI for autonomous data analytics
Mattheos Fikardos, Katerina Lepenioti, Alexandros Bousdekis, Dimitris Apostolou, Gregoris N. Mentzas · Intelligent Systems with Applications · 2026
• Wrapping of analytics platform functionalities into tools based on a communication protocol • Definition of a framework to instantiate agentic workflows in the field of analytics • Generative AI analytics agents can utilise the exposed tools and assist the data analyst through natural language • Conducted experiments on three architectures using two different AI models, indicating increased performance, consistency, and model independence of the proposed approach. • Executed a user study collecting quantitative and qualitative data indicating user preference for the proposed approach. Recent advancements in Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) have revolutionised software engineering (SE), augmenting practitioners across the SE lifecycle. In this paper, we focus on the application of GenAI within data analytics—considered a subdomain of SE—to address the growing need for reliable, user-friendly tools that bridge the gap between human expertise and automated analytical processes. In our work, we transform a conventional API-based analytics platform into a set of tools that can be used by AI agents and formulate a process to facilitate the communication between the data analyst, the agents and the platform. The result is a chat-based interface that allows analysts to query and execute analytical workflows using natural language, thereby reducing cognitive overhead and technical barriers. To validate our approach, we instantiated the proposed framework with open-source models and achieved a mean overall score increase of 7.2% compared to other baselines. Complementary user-study data demonstrate that the chat-based analytics interface yielded superior task efficiency and higher user preference scores compared to the traditional form-based baseline.