RESEARCHJanuary 30, 20265 min read

Why Most Companies Score Below 20% on AI Visibility

After auditing dozens of brands, the same gaps appear every time. Here's what's going wrong and the fix order that matters.

Mia Cheraghian, PhD

Mia Cheraghian, PhD

Founder, Miaren AI · Researcher · AI visibility methodology creator

The audit results are consistent

After auditing dozens of brands across industries, from B2B tech to hospitality to professional services. The same gaps appear almost every time. Most companies score below 20% on our AI Visibility Score. The problems aren't random. They're systematic.

Gap #1: Entity inconsistency

The single most common failure. Brand name spelled differently on LinkedIn vs. the website. Description on Crunchbase that doesn't match Google Business Profile. Product names that vary across platforms. AI engines lose confidence when they can't confirm basic facts about your brand from multiple sources.

Gap #2: Zero structured data

The vast majority of websites we've analyzed have no Schema.org markup at all. Without it, AI engines must infer what your content is about from unstructured text. With it, they can directly parse your brand information, products, FAQs, and expertise. This is often the single highest-impact change.

Gap #3: No third-party validation

If the only source saying your brand is great is your own website, AI engines aren't convinced. Earned media, review site presence, expert citations, and backlinks from high-authority domains all serve as trust signals. This is the hardest gap to close but has the biggest compounding impact.

The fix order matters

Entity consistency first (2-4 week impact). Structured data second (4-8 week impact). Content restructuring third (ongoing). Authority building fourth (3-6 month play). This is exactly the prioritization the AI Visibility Audit delivers: highest-impact, lowest-effort fixes first, building toward long-term advantage.

Mia Cheraghian, PhD

Mia Cheraghian, PhD

Founder of Miaren AI · PhD Researcher · Creator of the AI visibility methodology

Mia is a PhD researcher studying how AI reshapes discovery for underserved communities. She founded Miaren AI to help businesses become visible, citable, and recommendable in AI-powered search engines.

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