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AI product recommendations are not automatically better than rules-based recommendations. AI can personalize suggestions from product attributes and shopper behavior, while rules give merchants direct control over what appears. The right choice depends on your catalog and data, the placement you want to improve, and whether measured results justify the added setup and oversight.
How AI and rules-based recommendations work
Rules-based recommendations
A merchant specifies conditions for what to show, such as compatible accessories for a product, items from a chosen category, or products that are in stock. The logic is visible and can be edited directly, but someone needs to keep it current as products, inventory, and merchandising priorities change.
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AI-based recommendations
An AI system infers which products may be relevant from item information, shopper activity, or both. Content-based filtering uses product characteristics; collaborative filtering learns from patterns in browsing, ratings, or purchases among users; hybrid methods combine these signals. Shopify describes these approaches and their common uses in its AI recommendation systems guide.
A hybrid approach
A practical middle ground is to let a model rank candidate products while explicit merchant rules exclude unavailable items, enforce compatibility, or set category boundaries. This separates personalization from constraints, though it still requires decisions about setup, monitoring, and who owns each rule.
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Which approach fits your store?
| Decision area | Rules-based | AI-based |
|---|---|---|
| Recommendation logic | Merchant-defined conditions | Patterns inferred from product attributes, shopper behavior, or both |
| Data needs | Can work with basic catalog information and business logic | Content-based methods need useful product attributes; collaborative methods rely more on interaction history |
| Merchant control | Conditions are generally easier to inspect and edit | Control and auditability depend on the model and tools; constraints may be needed |
| Adaptation | Changes when a merchant edits the rules | Can adapt to learned patterns, depending on data quality and update process |
| Setup and upkeep | Often simpler for a limited number of placements, but rules need maintenance | May require more data preparation, integration, tuning, and monitoring |
| Typical risks | Stale, conflicting, or overly broad rules | Cold starts, weak signals, narrow suggestions, privacy concerns, or unstable outputs |
These are tendencies, not guarantees; details vary by platform and catalog. If your store has little behavioral data, rules are often a sensible starting point. Shopify recommends that smaller stores begin with clean product data and straightforward rules, then improve them based on observed performance. AI becomes more attractive when the catalog and signals are strong enough to support personalization across many products or shoppers. Content-based recommendations can use product attributes when customer behavior is sparse, while collaborative methods generally need more activity to learn useful patterns.
Neither approach is maintenance-free. An old rule can promote irrelevant products or ignore current stock. AI can struggle with new products or stores, incomplete catalog data, privacy constraints, and recommendations that become too narrow. Shopify discusses cold-start and privacy concerns, as well as the risk of overly narrow content-based results, in its guide.
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Does AI reliably increase sales?
No general sales uplift is established by the evidence cited here. It would be misleading to assume AI always beats rules or guarantees higher profit: results depend on the system, the placement, the audience, and the quality of the underlying data.
A theoretical marketplace analysis by Bo Zhou and Tianxin Zou found that, under the competitive conditions in their model, more accurate predictions of consumer preferences could be associated with lower marketplace and seller profits. This is a model-based finding about marketplace incentives, not a measured outcome for ordinary online stores. See their Marketing Science paper.
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Other recent studies address adjacent questions rather than testing on-site recommendation engines against merchant rules. A Wharton Generative AI Labs report published August 27, 2026, describes roughly 26,000 simulated fitness-watch shopping-agent tests. In that task, choices were more consistent when agents saw only a product grid; added context, including a review, competing sources, their order, injected user memory, or retrieval setup, could shift the selected product. The result is specific to the simulated task and models tested, but illustrates that AI shopping suggestions can depend on which information a system sees and in what order. Read the Wharton report.
A separate observational study by Maximilian Kaiser and Christian Schulze analyzed ChatGPT referrals to e-commerce websites, not recommendations shown inside those retailers. It used 12 months of first-party data from 973 websites, covering August 2024 through July 2025, and examined more than 50,000 ChatGPT-referred transactions alongside 164 million transactions from other channels. The authors report that results varied with product complexity and characterize organic LLM referrals as low-volume and modest in revenue per session during the period observed. Those figures describe an external traffic channel; they do not compare AI recommendations with rules on a store site. See the Marketing Science study.
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How to choose and test recommendations
- Pick one placement and a specific job. Related products on a product page, cart cross-sells, bundles, and homepage personalization serve different purposes. Start with one placement rather than changing recommendations across the whole store at once. Shopify advises testing a placement and documents product-page recommendations in its recommendation guide.
- Audit the data used there. Check that product attributes, categories, prices, compatibility, and availability are accurate and current. If you are considering collaborative filtering, also check whether the store has enough useful browsing, rating, or purchase history.
- Write down constraints. Decide which products must be excluded or prioritized, including unavailable items, incompatible variants, or restricted categories. Make those conditions explicit even if a model chooses the ranking.
- Compare on a defined audience and time period. Keep the placement and audience conditions steady as you compare approaches. Track click-through rate, conversion rate, and average order value; Shopify recommends these measures when testing a placement. Also review relevance, diversity, coverage across the catalog, and the operational work required to keep suggestions useful.
- Interpret results beyond clicks. A rise in click-through rate alone does not establish a customer or profit benefit. Consider the business outcome you set for the placement and whether recommendation quality and merchandising constraints remain acceptable.
- Include operational and privacy costs. Factor in integration, ongoing tuning, data governance, explainability, and the consequences of stale or unsuitable suggestions. If data or controls are not ready, begin with rules or a hybrid setup and reassess after collecting evidence.
A practical decision
Choose rules when direct control, transparency, and a quick start matter more than personalization from behavior data. Consider AI when product information and shopper signals are strong enough to support it, and test it against a clear baseline at one placement. A hybrid design can preserve merchant constraints while allowing personalized ranking. In every case, let measured results—not the label “AI”—decide whether the approach is earning its place.
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