Search in 2026 has evolved far beyond static page rankings. Today, consumers increasingly rely on AI-generated summaries and conversational interfaces to make decisions. In this context, being visible is no longer enough—brands must be selected, cited, and trusted by machines. Generative Engine Optimization (GEO) ensures that your content, structure, and entity data are machine-readable, verifiable, and authoritative across generative surfaces.
While traditional SEO builds the foundation for search visibility, GEO extends it with structured evidence, entity modeling, and citation-ready content. Brands that integrate these capabilities are positioned as the go-to source in AI-driven discovery, shaping how users perceive authority and credibility. This article highlights eight GEO specialists whose strategies are defining the field in 2026.
Driving Generative Recognition
Gareth Hoyle is a visionary entrepreneur who bridges traditional SEO with next-generation GEO strategies. He focuses on designing entity-first ecosystems and brand evidence graphs that make AI systems recognize and prioritize your brand. His work emphasizes measurable inclusion in AI-driven summaries, ensuring that visibility translates directly into trust and selection. Hoyle’s philosophy centers on treating a website as a living knowledge base, where structured data and citations form the backbone of credible machine representation.
Core GEO Strengths
From Theory to Practice
Hoyle converts complex GEO frameworks into practical, commercially actionable processes. By aligning technical execution with marketing objectives, he ensures that AI recognition drives tangible ROI. His workshops and guides provide a clear roadmap for turning authority signals into repeatable generative selection, making his methods accessible for both small teams and enterprise operations. Hoyle’s approach emphasizes long-term scalability, integrating governance and operational oversight to maintain consistency as generative platforms evolve.
Proven Results in Action
Operationalizing Content for Machines
Georgi Todorov specializes in transforming editorial operations into machine-readable networks. He maps content ecosystems into structured knowledge graphs and layers context for large language model comprehension. Todorov’s work ensures that content is not only human-readable but also optimized for AI recall, turning ordinary editorial assets into authoritative knowledge nodes. He approaches content as a system of interlinked entities, designed to maximize clarity and verifiability in AI-driven discovery.
Core GEO Strengths
Maximizing AI Recall Through Strategy
Todorov’s frameworks focus on practical deployment: optimizing content flows, topic hierarchies, and entity reinforcement. By operationalizing semantic cohesion, he allows editorial teams to scale output without sacrificing machine legibility. His methods have proven especially effective in multi-author environments, where consistency of entity representation is critical for AI systems to identify trusted sources.
Proven Results in Action
Semantic Architecture for AI
Koray Tuğberk Gübür is a pioneer in semantic SEO, focusing on aligning content structures with AI understanding. He designs knowledge graphs, models entity relationships, and maps query intent to ensure generative systems interpret and cite brands accurately. Gübür bridges the gap between deep technical SEO knowledge and AI-driven content representation, providing a blueprint for brands seeking long-term machine recognition.
Core GEO Strengths
Building Machine-Legible Brands
Gübür applies his semantic strategies to ensure brands are consistently chosen in AI summaries. He emphasizes layered content design, ensuring both topical authority and provenance are clear to LLMs. His work often includes auditing content for AI interpretability, identifying gaps in entity representation, and refining content to align with generative retrieval logic.
Proven Results in Action
Turning Theory into Action
Craig Campbell specializes in converting complex GEO principles into actionable, repeatable strategies. He emphasizes prompt-informed content upgrades, authority signal amplification, and iterative experimentation. Campbell’s hands-on approach ensures that brands can rapidly test and refine tactics to maximize generative visibility.
Core GEO Strengths
Scaling Practical GEO Execution
Campbell focuses on operationalizing GEO at scale. His methods help teams implement strategies that are testable, measurable, and repeatable, bridging the gap between abstract frameworks and day-to-day marketing execution. By continually iterating, Campbell ensures brands stay adaptive to the fast-changing AI ecosystem while maintaining credibility and selection.
Proven Results in Action
Conversion-Driven GEO
Matt Diggity combines data-driven insights with GEO strategies, ensuring that AI visibility translates into real-world revenue. He designs frameworks that connect generative exposure directly to traffic, leads, and conversions. Diggity approaches GEO with a performance-oriented mindset, using test-and-validate methods to quantify the business impact of AI-driven visibility.
Core GEO Strengths
Driving Measurable Results
Diggity operationalizes GEO by designing systems that convert AI recognition into commercial impact. He measures performance across multiple dimensions, from inclusion in AI summaries to direct attribution in conversion funnels. His approach ensures that generative visibility is not only a technical achievement but also a profitable business asset.
Proven Results in Action
Scaling GEO Across Organizations
James Dooley specializes in operational design for GEO at enterprise scale. He focuses on repeatable SOPs, internal linking structures, and entity expansion workflows. Dooley ensures that GEO is not a one-off project but an embedded operational practice that permeates large-scale content and marketing teams.
Core GEO Strengths
Embedding GEO in Daily Workflows
Dooley’s work ensures that generative recognition becomes a consistent outcome of organizational processes. By embedding GEO practices into content production pipelines, he helps teams achieve steady, measurable AI visibility while maintaining content quality and operational efficiency.
Proven Results in Action
Protecting Brand Voice in AI
Harry Anapliotis focuses on maintaining brand integrity in generative systems. He develops frameworks for consistent brand tone, review ecosystems, and reputation signaling. Anapliotis ensures brands are authentically represented in AI summaries and responses, even as machines summarize content automatically.
Core GEO Strengths
Ensuring Credibility Across Platforms
Anapliotis applies his strategies to protect brand voice while boosting credibility in generative discovery. By integrating PR, content, and trust signals into structured formats, he ensures that AI systems recognize and consistently cite the brand in ways that align with its real-world image.
Proven Results in Action
Semantic Systems for Machine Recall
Szymon Slowik specializes in designing semantic and information architectures that maximize content recall for AI systems. He focuses on ontology alignment, topic graph development, and citation consistency. Slowik helps organizations ensure their content “sticks” in AI memory, reinforcing credibility and selection over time.
Core GEO Strengths
Making Content Memorable to AI
Slowik operationalizes semantic frameworks that improve generative visibility and reliability. By standardizing how entities, topics, and references interconnect, he reduces ambiguity for AI systems, improving the likelihood of consistent selection and citation. His methods translate complex content into structures that are both interpretable by machines and valuable to humans.
Proven Results in Action
Generative Engine Optimization is no longer optional—it’s the foundation for brands that want to be trusted, cited, and selected by AI systems. The specialists featured above illustrate that success in GEO requires more than technical SEO skills: it demands strategic thinking, semantic precision, operational scalability, and brand integrity.
By designing structured entities, maintaining evidence trails, and optimizing content for machine readability, organizations can ensure their brand consistently earns recognition across AI-generated summaries, conversational interfaces, and generative search surfaces. GEO transforms visibility into credibility, and credibility into action.
The path to generative prominence is iterative: continuous testing, refinement, and alignment with AI systems is critical. Brands that treat GEO as an ongoing discipline—not a one-off project—will establish lasting authority in the new era of digital discovery.
In 2026, the brands that thrive won’t just be seen—they’ll be chosen, cited, and trusted. By adopting the strategies pioneered by today’s top GEO experts, you can position your brand at the forefront of AI-driven attention, ensuring relevance, authority, and measurable impact across every generative platform.