Methodology for Establishing Digital Expert Authority of Web Resources in a Generative Search Environment
DOI:
https://doi.org/10.57125/FEL.2026.09.25.02Keywords:
AI-mediated retrieval, content governance, corporate blogging, E-E-A-T Framework, knowledge management, semantic structuring, source credibility.Abstract
Generative search is reshaping how web sources gain visibility and credibility within AI-driven digital ecosystems. Unlike traditional search engines that present web pages, generative search systems provide direct responses and cite selected sources, posing new challenges for optimising online content, recognising it, and establishing its reliability. AI-mediated visibility affects customer acquisition, market access, enterprise competitiveness, source attribution, and digital trust. The study designs a sequential method for building the digital expert authority of a web resource through the systematic expert blogs and the structured organisation of knowledge. It draws on methodological design, system analysis, comparative analysis, content analysis, thematic synthesis, and structural-functional modelling. As a conceptual and methodological study, the paper develops the GKAI Method through documentary synthesis and structural-functional modelling rather than through empirical implementation. GKAI Method links expert knowledge production with digital visibility, economic value creation, institutional trust, and compliance-oriented communication in generative search environments. The method begins by defining the authority domain, aligning expertise with services, and analysing the authority landscape. It then advances to expert position development, expert semantic architecture, expert content architecture, verified expert attribution, and the alignment of cognitive demand with knowledge voice. The concluding stages comprise generative citation optimisation, knowledge graph alignment, authority signal reinforcement, and artificial intelligence authority recognition. The study shows that a corporate blog can be a dynamic expert knowledge system as well as a communication tool. The method demonstrates how expert knowledge can be acquired, assigned to verified experts, structured around services and user needs, prepared for interpretation by generative search systems, and evaluated through economic, institutional, and regulatory indicators. GKAI methodology, a reproducible twelve-stage process for establishing digital expert authority, supports enterprise competitiveness, digital market governance, source transparency, accountability, and generative artificial intelligence regulation.
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