The growing adoption of AI/ML and data analytics pipelines in industrial and research settings has increased the demand for accessible, user-centred tools forpipeline management. While existing orchestration platforms provide mechanisms for execution tracking, they often expose complex execution information in ways that are difficult to interpret, limiting their usability. This paper presents the Data Analytics and Visualization Environment (DAVE) Execution Environment, a user-centred interface designed to support the execution, monitoring, and debugging AI/ML and ETL pipelines within the DAVE framework. The proposed approachis grounded in principles of visual clarity, task-level observability, and progressive information disclosure, enabling users to interpret pipeline behaviour and diagnose issues more effectively. The solution has been validated across multiple pilots in different sectors, demonstrating its effectiveness in improving the accessibility and interpretability of pipeline execution for non-expert users. These results highlight the potential of combining visual representations with task-levelinspection to bridge the gap between pipeline complexity and usability in no-code/low-code MLOps environments.