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Neither AI nor rules-based product recommendations are automatically better for an online store. Rules give merchants explicit control and are often a practical starting point when behavioral data is limited. AI can tailor suggestions using product attributes, shopper activity, or both, but its value depends on the quality and quantity of those signals. Choose for a specific shopping placement, keep essential merchandising constraints visible, and compare results against a suitable baseline.
How the two approaches work
Rules-based recommendations
A rules-based system follows conditions set by the merchant. For example, it can show accessories compatible with the product being viewed, feature items from a chosen category, or suppress products that are out of stock. The logic is generally straightforward to inspect and change, but someone must maintain it as inventory and merchandising priorities shift.
AI-based recommendations
An AI recommendation system uses product information, shopper interactions, or both to infer which items may be relevant. Content-based filtering draws on product characteristics. Collaborative filtering identifies patterns in activity among users or products. Hybrid methods combine these signals. Shopify describes these approaches and their common use cases in its AI recommendation systems guide.
Hybrid systems
A store can use AI to rank candidate products while applying merchant-defined rules to exclude unavailable items, enforce compatibility, or prioritize a business objective. This can combine personalization with control, though it adds design and tuning decisions; there is no single architecture that fits every store.
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AI product recommendations vs. rules-based: what differs?
| Decision factor | Rules-based | AI-based |
|---|---|---|
| How suggestions are chosen | Explicit conditions written by the merchant | 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 shopper activity |
| Merchant control | Conditions are usually easier to inspect and edit directly | Control and explainability depend on the model and tools; constraints may be needed |
| Adapting to change | Changes when someone updates the rules | Can reflect learned patterns, depending on data quality and the system’s update process |
| Setup and upkeep | Often simpler for a limited number of placements; rules still need maintenance | May require data preparation, integration, monitoring, and tuning |
| Common failure modes | Stale, conflicting, or overly broad conditions | Cold starts, weak signals, narrow suggestions, privacy concerns, or unstable results |
These are tendencies, not guarantees. The platform, catalog, implementation, and placement all affect what a system can do.
When each approach makes sense
Start with rules when control and simplicity matter most
Rules can be a sensible choice when a store has little behavioral history, needs transparent merchandising decisions, or wants to launch a straightforward recommendation placement. Shopify advises smaller stores to begin with clean product data and simple rules, then improve based on observed performance.
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Rules are also useful when the recommendation must obey clear constraints, such as product compatibility or availability. They are not maintenance-free: an outdated condition can keep showing an irrelevant item or fail to reflect a change in stock.
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Consider AI when the available signals can support it
AI becomes more attractive when product information is complete, useful shopper activity is available, and suggestions need to adapt across many items or shoppers. Content-based methods can use product attributes when behavioral history is limited. Collaborative methods generally need more browsing, purchase, or other interaction history to identify reliable patterns.
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AI is not a guarantee of relevance or higher profit. New stores and products can face cold-start problems; incomplete catalog data or privacy constraints can limit useful signals. Content-based suggestions can also become too narrow. The approach needs monitoring, and the amount of setup and tuning varies.
What the available evidence can—and cannot—tell you
The evidence does not establish that AI recommendations always outperform rules or reliably increase sales. A theoretical marketplace study by Bo Zhou and Tianxin Zou, published online in 2022, found that more accurate predictions of consumer preferences could be associated with lower marketplace and seller profits under the competitive conditions in its model. That is a result about modeled marketplace incentives, not a measured general effect for ordinary online stores. Read the study in Marketing Science.
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A Wharton Generative AI Labs report published August 27, 2026, examined AI shopping agents rather than recommendation engines embedded in retailer websites. Across roughly 26,000 tests in a simulated fitness-watch selection task, agents made more consistent choices when shown only a product grid; adding context, changing the order of sources, injecting user memory, or changing retrieval setup could shift the selected product. This finding is specific to the task and models tested, but it illustrates that an AI shopping agent’s recommendation can depend on the information it sees and its order. Read the Wharton report.
A separate observational study by Maximilian Kaiser and Christian Schulze, published online in Marketing Science on April 21, 2026, analyzed ChatGPT referrals to e-commerce websites. 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 outcomes varied with product complexity and describe organic LLM referrals as low-volume and modest in revenue per session during the observed period. This concerns an external traffic channel, not an on-site comparison between AI and rules-based recommendations. Read the study in Marketing Science.
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How to choose and test a recommendation system
- Choose one placement and task. Decide whether you are addressing product-page related items, cart cross-sells, bundles, or homepage personalization. These placements have different contexts and goals. Shopify recommends starting with one placement and tracking its performance.
- Check the data that placement needs. Review whether product attributes, categories, prices, compatibility, availability, and interaction history are complete and current. Attribute-based recommendations depend on useful item information; collaborative methods depend more heavily on shopper activity.
- Write down constraints. Identify products or categories to exclude, compatibility requirements, and items or business priorities to favor. Keep those rules visible even if AI supplies the ranking.
- Compare under controlled conditions. Where practical, define an audience and time period and compare the new approach with a suitable baseline while keeping placement and audience conditions steady. Shopify recommends tracking click-through rate, conversion rate, and average order value. Also assess relevance, diversity, coverage, and operating effort. A click increase alone does not prove a customer or profit benefit.
- Include the cost of operating the system. Account for setup and integration, ongoing tuning, data governance, explainability, and the impact of stale or poor suggestions. If data or controls are not ready, rules or a hybrid arrangement can provide a starting point while you gather evidence.
How do product recommendation engines work in practice?
Think of a recommendation as a candidate-selection and ranking task. A rules-based engine applies the conditions a merchant has defined. An AI-based engine estimates relevance from available product or shopper signals. In either case, the result is only as useful as the candidate products, information, constraints, and placement allow.
For example, a product-page placement might first exclude unavailable or incompatible items, then show complementary products. The store could begin with explicit rules and later test whether behavioral signals improve ranking. The important comparison is not “AI versus no AI” in the abstract; it is whether a particular implementation improves the chosen placement’s outcomes without creating unacceptable operating or customer-experience trade-offs.
Should your online store use AI recommendations?
Use AI when your catalog and available signals can support useful personalization, and when you can monitor outcomes and enforce necessary constraints. Use rules when you need direct control, have limited behavioral data, or want a manageable first placement. A hybrid approach is worth considering when personalization is useful but certain merchandising conditions must always be respected. Decide with a controlled comparison at the placement you intend to improve—not with a general claim that one technology is always superior.
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