The State of GEO Readiness 2026: How 100 B2B Brands Are Preparing for the Next Generation of AI Search

Key findings

We analyzed 100 leading B2B brands with GeoScan to measure their GEO Readiness — how well their websites are technically prepared for AI search engines. The findings:

• The average score is 47/100. Most B2B leaders are halfway prepared at best.
• The maximum observed is 77/100 (Braintree). No brand in the dataset is fully optimized.
• 65% of brands scored below 50. GEO Readiness is a problem of the entire market, not individual outliers.
• LATAM brands score 17% below the global average. A measurable disadvantage compounded by less training data heritage.
• OpenAI and Anthropic — the creators of the LLMs themselves — score below average on their own websites (28 and 40 respectively).
• 6 brands block automated crawlers but still appear prominently in ChatGPT responses today. They survive on training data heritage. That advantage erodes with each new model generation.

Full dataset and methodology below. The brands ranking #1 in AI search today are not necessarily the ones that will rank in 2028. This research measures the gap.

The hidden trap of AI search visibility

There's a paradox in AI search that most marketing teams don't see yet.

If you ask ChatGPT today "What are the best online travel platforms in LATAM?", Despegar appears prominently. If you ask "What's the best email marketing platform for creators?", ConvertKit shows up. Both are valid answers. Both are brands with strong AI presence.

But here's what's not visible: both Despegar and ConvertKit block automated crawlers from accessing their websites. The information ChatGPT uses to mention them isn't coming from current crawls — it comes from training data heritage: years of mentions in news articles, blog posts, Wikipedia entries, and third-party reviews that the model absorbed during training.

This heritage is finite. As LLMs evolve toward more real-time retrieval (Perplexity does this aggressively, ChatGPT Search increasingly so, Gemini AI Overviews fully), brands that depend on training data heritage face a slow erosion. The brands that maintain visibility in 2027 and 2028 will be the ones investing in GEO Readiness today.

This research measures that readiness across 100 leading B2B brands.

What we measured

GEO Readiness is the technical preparation a website has for AI search engines. It's not the same as current AI visibility (how often a brand is mentioned in answers today). It's a forward-looking metric that predicts how well a brand will perform as AI search evolves.

We measured 4 dimensions for each brand:

1. AI Visibility (45% of GEO Score) — Brand definition extractable for LLMs: clear identity, semantic markup, AI-readable structure
2. Entity Strength (35% of GEO Score) — How clearly the brand is defined as an entity across schemas, knowledge graphs, and structured signals
3. Citation Readiness (20% of GEO Score) — Whether the site has citable content patterns (factual paragraphs, semantic structure)
4. GEO Score — Composite weighted average of the three above

We used GeoScan (getgeoscan.ai) to scan each brand. The scan combines structural analysis (HTML, schema.org, robots.txt, structured data) with AI-powered evaluation of entity clarity and brand definition.

Methodology

• Dataset: 100 leading B2B brands across 10 categories
• Categories: Payments & Fintech (12), Productivity (15), Developer Tools (12), Design (8), Marketing & SEO (15), GEO/AI Competitors (10), Data & Analytics (10), LATAM Tech (10), Communication & Infra (5), Wildcards (3)
• Scan tool: GeoScan AI Visibility Scan with full AI analysis (gpt-5.4)
• Date: June 2026
• Successful scans: 94 of 100
• Failed scans: 6 (HTTP 403 — bot protection blocked the crawler)
• Full dataset: CSV download at the end of this article

A note on scoring: GEO Score is computed by subtracting deductions from a baseline of 100 (HIGH findings −25, MEDIUM −12, LOW −5), then weighted across the three sub-dimensions. The practical maximum observed is around 75-80, because certain baseline findings (Princeton GEO methods compliance, schema completeness) trigger on nearly every site. Achieving 80+ requires near-perfect semantic architecture. This is consistent with our top observation of 77.

The headline numbers

Score distribution

Among the 94 brands successfully scanned:

• 76–100: 1 brand
• 51–75: 31 brands
• 26–50: 61 brands
• 0–25: 1 brand

65% of brands score below 50/100. The median lands in the 26-50 band. This isn't a problem of a few stragglers — it's the state of the industry.

Top 10 — GEO Readiness leaders

Notable patterns in the top 10:

• Citation Readiness is universally high (92-96). All leaders have the basics right.
• Entity Strength is the differentiator. Brands at the top consistently score 50+ here, while bottom performers often score below 40.
• No category dominates. Top 10 spans Payments, Marketing, Design, Developer Tools, Productivity, Analytics, and Communication. AI readiness is a discipline, not a category.

Bottom 10 — Most work to do

We list the bottom 10 to show the gap, not to single out brands. Most are household names with strong businesses — they simply haven't optimized for AI search yet.

Important caveat: AI Visibility scores of "0" should be read as ≥100 points of accumulated deductions, not as literally zero AI visibility. Our scoring clamps to 0 at the floor, so brands at this level are essentially indistinguishable in that dimension. Several of these brands (Mercado Libre, OpenAI, Asana) are absolutely mentioned by current LLMs. The score reflects readiness for future AI search, not current mention rate.

Category averages

Two surprises here.

First, Marketing & SEO leads at 52.1. This makes intuitive sense — brands whose product is content (Ahrefs, Semrush, etc.) invest heavily in content infrastructure. They eat their own cooking.

Second, Payments & Fintech is below average at 47.4. Despite being one of the most-discussed B2B categories in AI conversations, the actual websites of payment companies are not particularly optimized for AI retrieval. Stripe (a top performer in current LLM mentions) is not in the top 10 here. Its GEO Readiness lags its current AI visibility — meaning it's relying on training data heritage, just like the crawler-blocked brands below.

The crawler block paradox

Six brands returned HTTP 403 (bot protection) to our scanner: Canva, ConvertKit, Otterly AI, Globant, Despegar, and Cornershop.

Conventional reading: these brands are invisible to AI.

We tested all six manually in ChatGPT with category-appropriate queries. All six appeared prominently in current answers. Despegar shows up first for "online travel platforms in LATAM." Canva is recommended for accessible design tools. ConvertKit appears for "email marketing for creators."

So what's actually happening?

These brands have training data heritage. Years of mentions in news, blog posts, Wikipedia, reviews, third-party comparisons — all of which the LLMs absorbed during training. The information about them in the model's weights isn't coming from their own websites.

This is sustainable in the short term and unsustainable in the long term, for three reasons:

1. Models retrain. Each new generation refreshes its knowledge. Brands that were heavily covered in 2024 may be less covered in 2027. The heritage decays.

2. Retrieval-heavy search is growing. Perplexity is pure retrieval. ChatGPT Search increasingly so. Gemini's AI Overviews combine both. Retrieval-heavy models can't access brands that block crawlers.

3. Updated information requires updated crawls. Even if the brand stays known, its features, pricing, products, and positioning are frozen at the training cutoff. A brand that ships new features but blocks crawlers becomes increasingly out of date in AI answers.

This isn't a hypothesis we can test today. It's a trajectory. Six well-known brands are betting that their heritage will outlast the shift to retrieval-heavy AI search. We're not sure that's a winning bet.

What "403 to scanner" actually tests

To be precise about what we measured: each of the six brands returned HTTP 403 Forbidden to GeoScan's automated request. This is the same response a generic crawler would receive — including any LLM crawler that isn't explicitly whitelisted in the brand's bot management configuration (Cloudflare WAF or equivalent).

Whether GPTBot, ClaudeBot, or PerplexityBot would receive the same 403 depends on the brand's per-bot whitelist. We didn't test per-bot behavior in this round. But a generic 403 is a strong signal that the brand has chosen a restrictive bot policy by default.

The OpenAI/Anthropic paradox

The creators of the LLMs themselves score below average on their own websites.

• OpenAI: GEO Score 28, AI Visibility 0, Entity Strength 37
• Anthropic: GEO Score 40, AI Visibility 19, Entity Strength 38

Why does this matter?

OpenAI and Anthropic are obviously visible in AI search — they're the most-mentioned brands in queries about AI. But that's not because their own sites are optimized. It's because the entire internet talks about them. Every news article, every tech blog, every academic paper. The training data heritage is overwhelming.

What's interesting is the gap. Both companies invested billions in building the LLMs. Neither invested significantly in optimizing how those LLMs perceive their own websites. The result: their AI presence is driven entirely by external coverage, not by their own digital infrastructure.

For comparison: Persiscal (our parent company) scores 43/100 — outperforming both OpenAI and Anthropic on GEO Readiness, despite being a fraction of the size. Entity Strength of 73 is particularly strong. This is what optimization looks like.

The LATAM gap

LATAM Tech is the second-lowest category average at 40.3. The gap to global average (47.6) is 17%. This compounds with a second disadvantage: less training data heritage in English.

Mercado Libre, the largest tech company in LATAM, scores 28 — bottom 5 overall. Globant, the most-recognized LATAM IT services brand, blocks crawlers entirely. Despegar, the iconic travel platform, also blocks crawlers.

The LATAM situation is structurally different from the US/EU one:

• Less English content referencing LATAM brands in AI training data
• More crawler blocking as a security default (we observed 3 of 7 LATAM brands blocking — 43% vs ~5% global)
• Lower technical GEO Readiness on average (40.3 vs 47.6)

LATAM brands face a triple disadvantage: less heritage, more blocking, lower readiness. The opportunity is also bigger: a LATAM brand that fixes GEO Readiness today has clearer differentiation than a US brand doing the same, because the bar is lower.

Practical takeaways

Based on the dataset, we observe consistent patterns. If you scan your own brand with GeoScan, your action depends on your current score:

If your score is under 30

Entity Strength is your priority. Your site doesn't communicate clearly enough what the brand is, who it serves, and what it does. Practical fixes:

• Add explicit Organization schema with all available properties
• Use clear, declarative paragraphs on the homepage stating what your brand does (not marketing copy — explicit definitions)
• Audit the home page for ambiguity about who you serve and what you offer

If your score is 30-50 (most brands)

Add semantic structure. The basics are there but signals are weak. Practical fixes:

• Implement BreadcrumbList, FAQPage, HowTo, and Article schemas where appropriate
• Add a Knowledge Panel-style "About" section with clear entity definitions
• Audit your robots.txt — make sure you're not accidentally blocking AI crawlers
• Add an llms.txt file with summary, key links, and brand definition

If your score is 51-75

Citation Readiness is your differentiator. Most brands at this level have the basics right but lack genuinely citable content. Practical fixes:

• Add short factual paragraphs that LLMs can extract directly
• Use the Princeton GEO methods: statistics, quotes, citations to authoritative sources
• Add explicit comparison content (you vs. competitors)
• Audit your content for "snippetability" — does each section answer one question well?

If your score is above 75

You're among the leaders. The remaining 25 points are about polish:

• Knowledge graph entity completeness
• Cross-language consistency
• Citation-worthy content density
• Active monitoring of how LLMs describe you over time

The next 12 months

Our hypothesis: GEO Readiness will become a standard metric for B2B marketing teams by mid-2027.

The drivers:

1. Real-time retrieval is becoming default. As more LLMs default to retrieval-augmented generation, brands that depend on training data heritage will see slow decay.

2. B2B buyers are using AI search at scale. Sales conversations increasingly start with "I asked ChatGPT and..." If your brand isn't mentioned, you're not in consideration set.

3. Measurement is becoming accessible. Tools like GeoScan, Profound, and Otterly AI make it possible to measure GEO Readiness in minutes. Once it's measurable, it gets managed.

The brands that invest in GEO Readiness in 2026 will have a measurable advantage by 2027. The brands that wait for AI search to "settle" will find themselves catching up.

Limitations of this research

We aim for honesty about what GeoScan measures and what it doesn't.

• GeoScan measures readiness, not current visibility. A brand can have low GEO Readiness and still be mentioned by current LLMs due to training data heritage. We don't measure that heritage directly.
• AI Visibility scores clamp to 0. Brands that accumulate 100+ deduction points all show as 0. We can't differentiate between "weak" and "catastrophic" at that level.
• Citation Readiness is structurally generous. Few rules attack this dimension, so most sites score 85+ easily. Treat this as a baseline check, not a differentiator.
• The dataset is 100 brands, not exhaustive. We selected leading B2B brands across categories. Smaller brands and consumer brands may show different patterns.
• One scan per brand. GEO Readiness can fluctuate. A second scan in a different timeframe might yield different results within ±5 points.
• AI-enhanced sub-analysis used gpt-5.4. Results may vary slightly with different LLMs.

Full dataset

We're publishing the complete dataset as CSV for anyone who wants to verify findings, run their own analysis, or extend the research.

Download the full dataset (CSV)

Columns: brand name, domain, category, status, GEO Score, AI Visibility, Entity Strength, Citation Readiness, top 3 findings, scan timestamp.

Want to measure your own brand?

GeoScan offers a free first scan at getgeoscan.ai. You get your GEO Score plus a prioritized Action Plan with specific fixes for your site.

If you want to track how AI mentions your brand over time (mention rate, citation rate, share of voice across ChatGPT, Claude, Gemini, and Perplexity), our Pro plan adds Prompt Tracking — a complementary view of current AI visibility.

GeoScan is a product by Persiscal.

Research conducted June 2026. Methodology and full dataset available for verification. Questions or want to extend the analysis? Reach out via getgeoscan.ai.