Optimizing Websites for Accessibility and Visibility in AI-Driven Search: Applying WCAG, SEO, and Structured Data Standards to Public Sector Websites

This bachelor’s thesis addresses the optimization of public sector websites with respect to accessibility, search engine visibility, and readiness for AI-driven search. The theoretical section establishes a conceptual framework across four interrelated domains: web accessibility and its legislative context (EU Directive 2016/2102, EN 301 549, Act No. 99/2019 Coll.), the WCAG 2.2 standard, Search Engine Optimization (SEO), and JSON-LD/Schema.org structured data; the final part of the theoretical section examines the emerging disciplines of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) as responses to the proliferation of AI-driven search. The practical section applies these findings to two anonymized public sector entities operating WordPress-based websites: a primary school (Subject A) and a municipal office (Subject B). An initial audit combining Google Lighthouse, WAVE, axe DevTools, and Screaming Frog revealed serious deficiencies across all dimensions – both websites exhibited WCAG 2.2 AA violations, lacked meta descriptions and structured data, and failed to publish the legally required accessibility statement. Post-optimization measurements demonstrated a 65–78 % reduction in WCAG violations, a 6–22-point increase in Lighthouse SEO scores, and successful implementation of JSON-LD schemas (School, GovernmentOrganization, FAQPage), whose practical effect was that Perplexity AI stopped confusing the municipal office with another municipality. The thesis formulates prioritized recommendations transferable to thousands of comparable public sector websites across the Czech Republic, and demonstrates that comprehensive optimization is achievable without financial investment or specialized technical expertise.