How AI Engines Decide Which Products to Recommend

Your Product Exists, But AI Doesn't Recommend It

When someone asks ChatGPT "best wireless headphones under $200," why does it recommend specific products while ignoring others that might be equally good or better priced?

We've been working on how different AI systems handle product recommendations, and the process is more systematic than most retailers realize.

It's not random. It's not based on advertising spend. And it's definitely not about brand recognition alone.

AI engines follow specific logic when deciding which products to recommend. Understanding this logic is becoming essential for retail visibility.

The Myth of AI "Preferences"

First, let's clear up a common misconception: AI engines don't have preferences in the way humans do. They don't favor certain brands because they "like" them better.

AI recommendations are based on data quality and accessibility, not subjective preferences.

When an AI engine recommends one product over another, it's because:

• It found better structured information about that product

• The data was more complete and relevant to the query

• The information was easier to extract and verify

• The source appeared more reliable based on data consistency

Think of AI engines as extremely efficient research assistants. They recommend what they can present most confidently and completely.

How AI Systems Process Product Queries

To understand recommendations, we need to understand how AI systems work with product information.

ChatGPT query for "best running shoes for beginners under $150"

When someone asks "best running shoes for beginners under $150," here's what happens:

Step 1: Query Analysis

• AI identifies the product category (running shoes)

• Extracts constraints (beginners, under $150)

• Determines intent level (comparison/recommendation seeking)

• Considers context clues (fitness level, budget consciousness)

Step 2: Information Gathering

• AI searches available data sources for relevant products

• Looks for products matching the specific criteria

• Prioritizes recent, accurate information

• Evaluates data completeness and reliability

Step 3: Relevance Assessment

• Matches product features to user requirements

• Evaluates how well products solve the specific problem

• Considers factors like beginner-friendliness, price accuracy

• Assesses overall fit for the stated need

Step 4: Response Construction

• Selects products with the most complete, relevant information

• Presents recommendations in order of confidence/match quality

• Includes reasoning for why specific products were chosen

• Provides actionable details like pricing and availability

The Six Factors That Influence AI Recommendations

Through our testing, we've identified the key factors that determine which products AI engines recommend:

1. Data Completeness

AI engines prefer products with comprehensive information that helps them provide complete answers.

High-completeness product data:

"Adidas Ultraboost 22 | $150 | Sizes 7-13 | Beginner-friendly | 
Cushioned midsole | Road running | Breathable upper | 
Available at Running Store Downtown"

Low-completeness product data:

"Great running shoes - various styles and prices available"

The first example gives AI everything needed to determine if the product matches a "beginner running shoes under $150" query. The second provides almost nothing useful.

2. Information Structure

AI systems extract data more easily when information is structured clearly rather than buried in marketing copy.

AI-friendly structure:

• Clear product specifications

• Separated attributes (size, price, features)

• Factual descriptions

• Actionable details (availability, location)

AI-unfriendly structure:

• Marketing language without specifics

• Vague descriptions

• Mixed information without clear organization

• Subjective claims without supporting details

3. Query Relevance

AI engines evaluate how well product information matches the specific question asked.

Example Query: "Best coffee maker for small apartments"

High-relevance product info: "Cuisinart DCC-3200 | $89 | 14-cup capacity | 9.75" wide | Programmable | Small counter footprint | Apartment-friendly size"

Low-relevance product info: "Professional-grade coffee equipment for serious enthusiasts"

The first directly addresses apartment constraints with specific dimensions and capacity. The second suggests complexity that doesn't match the query intent.

4. Source Reliability

AI engines learn which sources provide accurate, current information and begin to prefer reliable sources over time.

Reliability indicators:

• Consistent information across time

• Accurate pricing and availability

• Updated inventory levels

• Correct contact and location details

Unreliability indicators:

• Outdated information

• Inconsistent details between sources

• Incorrect pricing or availability

• Missing or wrong location data

5. Information Freshness

AI systems prefer recent information, especially for time-sensitive factors like pricing and availability.

Fresh information advantages:

• Current pricing

• Real-time inventory levels

• Updated store hours

• Recent product specifications

Stale information disadvantages:

• Old pricing that may no longer be accurate

• Outdated availability claims

• Discontinued product information

• Historical details without current context

6. Contextual Completeness

AI engines favor sources that provide complete context for customer decision-making.

Complete context includes:

• Product specifications

• Pricing and availability

• Location and pickup options

• Use case suitability

• Comparative advantages

• Next steps for purchase

Incomplete context might include:

• Product features without availability

• Pricing without location details

• General descriptions without specific applications

• Information without actionable next steps

Why Some Products Get Consistently Recommended

Based on our analysis, products that get recommended frequently by AI engines share common characteristics:

They have structured, complete information that AI can easily extract and present.

Example: The AI-Favorite Product Profile

Product: Nike Air Pegasus 39
Price: $130 (within budget)
Features: Beginner-friendly cushioning, versatile for road running
Sizes: 6-14 available
Availability: In stock at 3 nearby locations
Context: Good for new runners, comfortable for daily training
Purchase path: Available pickup or 2-day delivery

This gives AI everything needed to confidently recommend the product for relevant queries.

Compare to: The AI-Ignored Product Profile

Product: "Premium athletic footwear collection"
Price: "Competitive pricing available"
Features: "High-performance design for active lifestyles"
Availability: "Contact store for current inventory"

This provides almost nothing AI can use to determine relevance or help customers make decisions.

The Local Recommendation Factor

For local searches, AI engines apply additional logic focused on geographic relevance and accessibility.

When someone asks "running shoes near me available today," AI prioritizes:

Geographic proximity: How close is the store to the user? Availability confidence: Is the product actually in stock? Pickup accessibility: Can the customer get it today? Store accessibility: Hours, contact, easy directions?

Local recommendation example: "Nike Pegasus 39 | $130 | Size 9 in stock | Fleet Feet Downtown | 0.8 miles away | Open until 8pm | (555) 123-4567"

AI can confidently recommend this because it provides complete local context.

The Competitive Recommendation Landscape

Understanding AI recommendation logic reveals why some retailers consistently appear in AI responses while others remain invisible.

Consistently recommended retailers typically have:

• Product data structured for AI extraction

• Real-time inventory and pricing integration

• Location-specific information for each store

• Complete product specifications and context

• Reliable, frequently updated information

Rarely recommended retailers often have:

• Generic product descriptions

• Vague availability information

• Missing location or context details

• Inconsistent or outdated data

• Information buried in unstructured content

The gap isn't usually in product quality—it's in information structure.

Testing Your AI Recommendation Potential

Want to understand how AI systems see your products? Try these simple tests:

Test 1: Direct Product Query Ask ChatGPT about your specific products. See what information it can find and how accurately it presents your offerings.

Test 2: Category Comparison Ask AI for recommendations in your product category. See if your products appear and how they're described compared to competitors.

Test 3: Local Availability Try "near me" searches for your products. Check if AI can connect your inventory to local availability.

Test 4: Specific Use Cases Ask about products for specific situations your items solve. See if AI can match your products to customer needs.

Most retailers discover their products are much less visible in AI recommendations than they expected.

Building for AI Recommendation Logic

Understanding how AI engines make recommendations reveals what retailers need to focus on:

Priority 1: Structure Information for AI Extraction Organize product data in clear, extractable formats rather than marketing copy that AI struggles to parse.

Priority 2: Provide Complete Context Include all information customers need to make decisions: specifications, pricing, availability, location, and use cases.

Priority 3: Maintain Information Accuracy Ensure data stays current and reliable so AI engines learn to trust and prefer your information.

Priority 4: Connect Products to Intent Structure information around customer problems and questions, not just product features.

Priority 5: Enable Local Discovery Make location-specific availability and accessibility information available to AI systems.

Building the infrastructure to consistently appear in AI recommendations requires systematic approach to data organization and management.

Most retailers discover they need:

• Product information restructured for AI compatibility

• Real-time inventory integration with discovery systems

• Location-specific data management across multiple stores

• Ongoing optimization based on AI platform changes

The technical complexity often exceeds internal capabilities, which is why many retailers partner with infrastructure specialists.

We focus on building systems that make AI engines consistently recommend our retail partners by providing the structured, complete information AI systems prefer.

What We're Seeing in AI Recommendation Evolution

AI recommendation logic continues evolving as systems become more sophisticated:

Current trends:

• Increasing preference for real-time, accurate data

• Better understanding of location-specific customer intent

• Growing ability to match products to specific use cases

• More sophisticated evaluation of source reliability

Emerging patterns:

• Voice search driving more conversational product queries

• AI engines getting better at understanding customer context

• Increased emphasis on actionable, complete information

• Growing importance of local inventory and availability data

Future direction: AI engines will likely become even more selective about which sources they trust and recommend, making high-quality infrastructure increasingly valuable.

The Competitive Advantage

Retailers who understand and build for AI recommendation logic are creating sustainable competitive advantages.

While competitors focus on traditional marketing approaches, these retailers are becoming the default recommendations for their product categories.

The advantage compounds over time as AI engines learn to trust and prefer sources with consistently high-quality information.

This isn't just about technology—it's about serving customers better by being discoverable and recommendable when they ask AI systems for help with purchasing decisions.

The retailers building this capability now will own AI-driven discovery as it becomes the dominant customer behavior.

We're continuously studying AI recommendation patterns and happy to share insights with retailers exploring these challenges.