Beyond Accessibility Reports: AI-Driven WCAG Remediation at Scale

Abstract

The Web Content Accessibility Guidelines (WCAG) establish the global benchmark for digital accessibility, yet existing compliance practices remain disproportionately focused on detection rather than remediation. Automated analyzers routinely identify extensive violations across websites, documents, and multimedia resources, but without effective remediation, these outputs neither enhance user experience nor mitigate legal and reputational risk. This study underscores the centrality of remediation in accessibility workflows, analyzes the technical and organizational challenges of implementing large-scale fixes, and proposes an AI-augmented remediation architecture. The framework integrates deterministic rule engines, machine learning (ML), and large language models (LLMs) to enable scalable, context-aware remediation supported by human oversight. Furthermore, the paper discusses strategies for embedding remediation into enterprise operations and outlines future directions, including real-time correction, multimodal accessibility support, and adaptive AI-driven personalization.