In 2026, AI search is no longer a trend – it’s reality. What was once considered a supplement to traditional search is now visibly reshaping the rules of the game.
AI overviews, chatbots, and virtual assistants are increasingly delivering the first answer – and with it, capturing attention. Traditional top-10 rankings are steadily losing relevance.
Organic traffic is declining
Gartner previously predicted that brands could face significant losses in organic traffic due to the growing use of generative AI search (2023 study: up to 25% decline by 2026 and up to 50% by 2028).
Much suggests this development is happening faster than expected.
For marketing departments, waiting is no longer an option. Those who want to secure visibility must strategically align their websites for AI readability, structure, and authority – not someday, but now.
What can brands do now to remain discoverable?
As AI systems increasingly provide the first answer, the question shifts from “How do we rank?” to “How are we understood and cited?”
We examined the biggest gaps currently preventing many brands from appearing in AI overviews. We also looked at best practices: What are the winners doing differently?
Our work reveals a clear pattern: Most brands have high-quality content and strong products – yet lose visibility due to structural, semantic, or technical gaps. At the same time, we see early market examples where exactly these levers are already being strategically applied.
Common GEO gaps – Where brands lose AI visibility
The following patterns occur across industries and are based on insights from readiness checks, in-depth audits, and competitive analyses.
1. Topic dilution instead of topical authority
Problem
Brands publish on too many topics simultaneously without systematically owning a clearly defined core domain.
Typical symptoms
No recognizable topic hierarchy (missing entity structure in content)
Many isolated articles without strategic classification
Content produced for traffic motivation rather than positioning logic
Unclear target audience: Who is this content actually for – and why?
Impact in AI systems
Lack of thematic authority
No clear subject-matter assignment
Competitors with stronger focus are cited more often
Best practice: Concrete steps for more AI visibility
In practice, the key lever is clear content positioning. Strategic topic clusters must be defined, target groups sharpened, and content structured along a coherent entity and information architecture.
This requires close alignment between content strategy, overall digital strategy, and UX design – turning individual content pieces into a consistent, AI-readable knowledge system.
2. Content fluff & internal corporate language
Problem
Texts contain a lot of corporate wording but few verifiable statements, data points, or clear key messages.
Typical symptoms
Prose instead of precise statements
No clearly quotable semantic units
Hardly any facts, studies, numbers, or sources
Internal language instead of customer language
Missing entity mapping in content
Impact in AI systems
Content is difficult to verify
Low adoption in generative responses
Brand rarely cited as a reference
Best practices: How to become AI-ready
Companies should consistently sharpen content for their target audiences: formulate clear key messages, support them with data or sources, explain technical terms from a customer perspective, and structure content into concise, quotable units.
Only when content is technically precise, linguistically clear, and structurally sound can AI systems reliably interpret and reuse it.
We support this with expertise in content marketing, SEO, and targeted GAIO services to systematically align content with AI visibility.
3. Technical barriers for AI bots
Problem
Technical obstacles prevent or hinder machine readability.
Typical symptoms
JavaScript-heavy websites
Non-machine-readable accordions
Interactive one-pagers without crawlable structure
Problematic parameter URLs from filter logic
Unintentional AI bot blocking
Faulty or missing H-structure
Impact in AI systems
Content captured incompletely
Relevant passages remain invisible
Competitors with clean structures are preferred
Best practices: Removing technical barriers
Ensure full crawlability and machine readability: make hidden content accessible, test JavaScript rendering, implement clean heading structures, and prevent parameter logic or bot blocking from excluding relevant content.
The goal is a technically clear and logically structured information architecture that AI systems can process without friction.
4. Missing or incorrect Schema.org implementation
Problem
Important entities are not properly marked in the code, leading AI systems to misinterpret content.
Typical symptoms
No author markup
Missing FAQ, product, or review markups
Inconsistent or faulty implementation
No modeling of entity relationships
Impact in AI systems
Misinterpretations
Entities not recognized
Content ignored or incorrectly classified
Best practices: Structuring entities correctly
Systematically enrich key content with structured data: clearly identify authors, integrate FAQs, mark up products and reviews correctly, and technically model entities and their relationships.
This ensures AI systems clearly understand who is speaking, about what topic, and in which context.
5. Performance issues (Mobile First)
Problem
Slow loading times – especially on mobile devices – cause AI bots to switch to faster alternatives.
Typical symptoms
Poor PageSpeed scores
Delayed rendering times
Heavy script load
Impact in AI systems
Faster sources preferred
Reduced crawling efficiency
Best practices: Making performance AI-ready
Adopt a consistent mobile-first optimization approach: reduce load times, eliminate unnecessary scripts, accelerate rendering, and identify technical legacy issues within the CMS.
A performant website not only improves user satisfaction but also increases AI crawling efficiency.
Tips & Tricks: Brand examples where AI visibility already works
Some companies already demonstrate how GEO/GAIO can be successfully implemented:
Apple relies on clearly structured, data-rich product pages with tabular specifications and precise image descriptions, making information interpretable for both users and multimodal AI systems.
Stripe provides a dedicated “llms.txt” file – similar to robots.txt but designed for large language models – making relevant content more discoverable and prioritizable.
IBM transparently showcases expertise: specialist articles are assigned to clearly identifiable authors with profile pages detailing background, role, and publications – strengthening E-E-A-T signals.
NVIDIA consistently separates technical documentation from marketing content and presents complex information in a structured, logical format, facilitating semantic interpretation.
Conclusion
Most brands lose AI visibility not because of poor content, but because of missing structure, technical barriers, unclear authority signals, and limited citability.
GEO/GAIO is therefore not just an SEO topic – it requires the interplay of:
In a world where AI search is redefining the rules, visibility is no longer determined by volume and noise, but by structure and clarity. The clearer topics, entities, and expertise are organized, the higher the likelihood of being considered a source in AI-generated answers.
Our diva-e conclusion experts are happy to support you with strategic, technical, and content-related setup.
