LLM-Enhanced Accessibility Optimization for Digital Experience Platforms

Abstract

Digital experience platforms (DXPs) aim to offer smooth, personalized interactions, but a lot of them are still unable to provide the right kind of inclusive experiences due to persistent accessibility problems such as unstandardized semantic structure, content lacking descriptions, and compliance workflows that are fragmented. Some of the issues can be identified with the help of conventional rule-based scanners and manual reviews, but these methods miss contextual nuances, take a lot of human time and effort, and are incapable of scaling at the same rate as new digital content is generated. The difference has repositioned Large Language Models (LLMs) as a new generation of accessibility optimizers who can grasp the intent, figure out complicated layouts, and come up with practical human-centered remediation suggestions that can be implemented. The paper presents an LLM-facilitated method of addressing accessibility problems in DXPs via automated content understanding, adaptive accessibility checks, and guided recommendations that change depending on platform behavior. The method uses LLM-powered analysis alongside the current DXP pipelines to allow for the on-demand examination of text, metadata, navigation flows, and interactive components. The example traces how the tool was employed on a high-traffic enterprise portal to make it serve as proof of concept. Measurable improvements were reported in the clarity of the content, alt-text accuracy, the coherence of the heading structure, color-contrast compliance, and the overall user experience especially for users who rely on assistive technologies. The results show that the addition of LLMs to the workflow not only cuts the time needed for manual reviews but also enhances the rate at which issues are detected beyond what is achievable with conventional tooling and that more consistency can be obtained across content teams that are distributed.