Integrating Explainable AI into Model-Driven and Low-Code Enterprise Applications

Abstract

Explainable artificial intelligence has become increasingly important in enterprise settings, as organisations require transparent and trustworthy decision-support tools. At the same time, low-code and model-driven platforms are widely adopted for building business applications, yet their high level of abstraction often hides the reasoning behind automated recommendations. This study examines how explainability can be systematically incorporated into such environments by introducing a modular approach that separates predictive functions from the generation of human-interpretable explanations. The proposed concept builds on an external reasoning layer that provides both predictive outputs and concise, user-oriented justifications through a unified interface, allowing enterprise systems to present explanations without modifying existing development workflows. To demonstrate the feasibility of the approach, the study applies it to a representative enterprise scenario involving personalised recommendations. The proof-of-concept implementation shows that explanations can be delivered in real time and integrated seamlessly into standard business user interfaces. The results highlight that the proposed solution can enhance transparency, support user trust, and increase the adoption of data-driven features in low-code and model-driven applications. The study contributes a practical architectural pattern that can serve as a foundation for future explainable enterprise systems and provides initial evidence that explanation services can operate effectively alongside contemporary development paradigms.