Research

What we found after scanning
thousands of real stores

We run automated scans on Shopify and DTC stores to understand how AI shopping agents actually read, evaluate, and recommend them. The data is live, continuously updated, and ours alone.

1,783
Stores analyzed
45/100
Avg AI score
52%
AI Invisible Risk
0%
Highly Recommendable
Score distribution across 500 scanned stores — May 2026
52% AI Invisible Risk, 38% low confidence, 8% moderate, 2% highly recommendable.
52%
AI Invisible Risk — score below 50
46%
Low or moderate confidence — score 50 to 84
0 stores
Reached Highly Recommendable — no store cleared 85
AI Invisible Risk (0 to 49) Low confidence (50 to 69) Moderate (70 to 84) Highly Recommendable (85+)
From the founder
D
Daniel, Founder of Atom Foundry
Scanning DTC stores daily since January 2026
What I keep seeing — personal observations
Observation 01
The most surprising thing I keep seeing is that big brands score worse than small niche stores. A well-known fashion brand with millions in revenue can score 12/100. A small skincare startup with 500 Instagram followers scores 68. Brand recognition has zero correlation with AI readiness. The signals AI needs are completely different from what built those brands.
Observation 02
Most founders have no idea this is happening. When I reach out after scanning a store, the response is almost always the same: "We didn't know AI couldn't read our products." The invisibility is silent. There is no error message. The store just gets skipped, and revenue attributed to "other channels" quietly declines.
Observation 03
The stores that score highest are not the ones with the best design or the most traffic. They are the ones where someone once added proper schema markup, wrote real product descriptions, and made the return policy readable without JavaScript. Small technical decisions from years ago are now worth thousands per month in AI-driven revenue.
Observation 04
Trust is the hardest signal to fix fast. Missing schema markup can be patched in an afternoon. Low trust scores take longer because trust requires reviews, real policies, contact information, and SSL all working together in a way that AI can parse without rendering JavaScript. Most stores have the trust but AI cannot see it.
Observation 05
I genuinely believe that the stores optimizing for AI in 2026 will have a compounding advantage through 2027 and beyond. AI agents learn which stores to trust. Early movers build recommendation history. The longer you wait, the harder it gets to catch up. This is not a future problem. It is already costing stores money right now.
Featured Research
May 2026 · 500 stores
2026 AI Recommendation
Readiness Report
By Daniel Pokorny, Founder at Atom Foundry.
500 US and CA Shopify stores analyzed across 8 AI recommendation factors.
Download PDF Browse store data
500
Stores analyzed
45/100
Avg AI score
52%
AI Invisible Risk
0%
Highly Recommendable
🏗️
Schema markup is the biggest gap. Average score 9.0 out of 20. Most stores have no structured data that AI can reliably parse.
🏢
Large retailers scored as low as 4 out of 100. Brand recognition does not equal AI readiness. The signals Google rewards are not the signals AI needs.
🤖
Shopify Agentic Dashboard is live. Every store now has llms.txt. But file existence does not equal recommendation confidence. Content quality determines outcomes.
Average score per factor — how stores fail AI evaluation
Factor averages: Schema 9 of 20, Semantic 9.7 of 18, Trust 10.9 of 18, Interpretability 6.4 of 12, Commerce accuracy 6.2 of 10, Brand authority 2.5 of 3.
Average score Maximum possible
Key Findings

What the data actually shows

Average AI score by niche — all categories fall below 50
Niche averages: Pets 47, Beauty 43, Food 43, Fashion 42, Home 40, Kids 40, Health 39, Sports 37, Tech 36, Jewelry 36. All below 50.
The dashed line marks 50, the minimum threshold for Low Recommendation Confidence. Every niche falls below it.
52% of analyzed stores score below 50 out of 100 and are classified as AI Invisible Risk. When buyers ask ChatGPT or Alexa for Shopping for product recommendations, these stores are systematically excluded from the answer.
2026 AI Readiness Report · 500 stores · May 2026
Not one store out of 500 reached Highly Recommendable status (85 or above). The bar for full AI recommendation confidence is high, and no store in our dataset has cleared it yet.
2026 AI Readiness Report · 500 stores · May 2026
Schema markup averages 9.0 out of 20 across all stores. Most stores have no structured JSON-LD data that AI can reliably parse. Product extraction, pricing, and availability are invisible to AI agents as a result.
2026 AI Readiness Report · 500 stores · May 2026
Major retailers scored as low as 4 out of 100. Brand recognition and organic traffic do not translate to AI recommendation readiness. The signals AI needs are completely different from what Google rewards.
2026 AI Readiness Report · 500 stores · May 2026
Fashion stores average 42 out of 100 and 53% are AI Invisible Risk. Despite high brand recognition across the category, fashion is one of the weakest verticals for AI recommendation readiness.
Fashion Niche Analysis · 1,541 stores · May 2026
Shopify launched its Agentic Dashboard in May 2026, generating llms.txt, llms-full.txt, and agents.md for every store. The infrastructure is now universal. Content quality is the only remaining differentiator.
Shopify Agentic Analysis · May 2026
Research Topics

What we are studying

These are the areas where we are actively collecting data and publishing findings. Each topic feeds directly into our scoring model and audit methodology.

🔍
AI Visibility
How AI shopping agents crawl, parse, and evaluate ecommerce stores. Which signals matter most for getting included in AI-generated recommendations.
Active research
⭐
Recommendation Readiness
What separates stores that get recommended by ChatGPT, Alexa for Shopping, and Perplexity from those that get skipped entirely in buyer queries.
Active research
🛡️
Machine-Readable Trust
How reviews, return policies, SSL, and brand signals are interpreted by AI systems, and which formats AI agents can actually extract without JavaScript rendering.
Active research
🤖
Agentic Commerce
Shopify Agentic Dashboard adoption, llms.txt quality, agents.md checkout flows, and how AI agent infrastructure evolves across the ecommerce ecosystem.
Active research
📊
Ecommerce Benchmarks
Industry benchmarks by niche including fashion, beauty, health, food, and home goods. How does your category compare on AI recommendation readiness?
Coming soon
💰
Commerce Accuracy
How pricing visibility, stock availability, variant structure, and product data quality affect AI confidence when presenting products to buyers.
Coming soon
Our Methodology

How we score stores

We evaluate stores across 8 factors that determine how confidently AI shopping agents can understand, trust, and recommend them. Our analysis is automated, systematic, and updated continuously.

Read the full methodology
AI Structured Signals
Schema markup, JSON-LD, structured data quality. Maximum 20 points.
Semantic Clarity
Content clarity, product descriptions, positioning signals. Maximum 18 points.
AI Trust Confidence
Reviews, return policy, SSL, trust badges. Maximum 18 points.
User Intent Match
How well store messaging matches AI query patterns. Maximum 14 points.
AI Interpretability
Technical signals, meta tags, crawlability. Maximum 12 points.
Commerce Accuracy
Pricing visibility, availability, variant structure. Maximum 10 points.
Recommendation Confidence
Overall AI recommendation signal strength. Maximum 5 points.
External Authority
Brand entity, external signals, authority markers. Maximum 3 points.
See where your store stands
Free AI Recommendation Score across all 8 factors. Find out exactly what AI agents see when they evaluate your store.
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