AI Product Recommendation Engines for Ecommerce: Build, Buy or Use Your Platform's?

An AI product recommendation engine helps an ecommerce business decide which products to show each shopper, in which context, and in what order. For most brands, the right approach is not to build one from scratch. Start with your commerce platform’s native capabilities if your needs are straightforward, buy specialist personalization software when recommendations become a serious revenue lever, and build a custom system only when your data, merchandising logic or operating model creates a genuine competitive advantage.

The bigger question is not, “Which recommendation engine has the most AI?”

It is, “Which approach gives us the best balance of relevance, revenue impact, control and operational complexity?”

That distinction matters as ecommerce businesses scale across larger catalogs, multiple storefronts, apps, physical stores and international markets.

What is an AI product recommendation engine?

An AI product recommendation engine is a system that selects and ranks products based on signals such as browsing behaviour, purchase history, product attributes, session activity and merchandising rules. More advanced systems can also consider inventory, location, customer segments, margin, seasonality and real-time intent.

It is the technology behind experiences such as “You may also like”, “Complete the look”, “Frequently bought together”, “Recommended for you” and personalized homepage collections.

But there is an important difference between a recommendation widget and a personalized product recommendation system.

A basic widget might show the same related products to everyone viewing a particular SKU. A mature system can change the recommendation for each shopper based on who they are, what they have done, what is available to buy and what the business wants to optimise.

That is where AI-powered personalization in ecommerce starts becoming commercially meaningful.

Build, buy or use your platform’s recommendation engine?

There are three realistic routes for most ecommerce businesses.

The right answer can also change over time. A brand might begin with native Shopify recommendations, introduce a specialist personalization platform as traffic grows, then develop proprietary decisioning for specific high-value use cases.

The mistake is treating “build versus buy” as a permanent technology decision instead of a maturity decision.

Option 1: Use the recommendation capabilities already in your commerce platform

For many brands, this should be the starting point.

Modern commerce platforms already provide recommendation capabilities that can cover a meaningful percentage of common merchandising requirements without introducing another system.

What do Shopify’s product recommendations actually do?

For teams evaluating AI product recommendations on Shopify, it is important to understand exactly what the platform provides.

Shopify automatically generates related-product recommendations using signals including purchase history, product descriptions and related collections. Merchants can also configure related and complementary products through Shopify Search & Discovery. Complementary recommendations require merchant configuration, while related recommendations can be generated automatically.

Shopify also exposes recommendation functionality through its storefront APIs, allowing developers to retrieve related or complementary products for custom storefront experiences. 

For many stores, this is enough to improve product discovery without adding a separate personalization stack.

It works particularly well when your objective is straightforward: surface alternatives on a product page, suggest accessories, create “pair it with” experiences or support basic cross-selling.

Brands that need more sophisticated Shopify experiences can extend the platform through Shopify development services, rather than immediately introducing a standalone personalization suite.

The limitation appears when the question changes from:

“What goes well with this product?”

to:

“What should this specific customer see next, given their history, session intent, location, inventory availability and likely commercial value?”

That requires a broader decisioning layer.

Adobe Commerce and Salesforce already go further

Enterprise platforms can provide more advanced native options.

Adobe Commerce Product Recommendations uses Adobe AI and machine learning trained on shopper behaviour and catalog data to generate personalized recommendations. Adobe positions the capability as part of its Commerce SaaS services. 

Salesforce B2C Commerce Einstein similarly supports recommenders and strategies designed to predict relevant products for individual shoppers. Implementations depend on catalog and order feeds, recommender configuration and storefront integration.

This matters because “use your platform’s recommendations” can mean very different things depending on whether you operate Shopify, Adobe Commerce, Salesforce Commerce Cloud or a custom stack.

The platform name alone should therefore never determine the decision. Evaluate the actual recommendation capabilities available within your specific architecture and plan.

Option 2: Buy specialist AI personalization software

Buying becomes attractive when personalization is no longer a feature on the product page. It becomes a capability used across the customer journey.

A specialist AI personalization software for ecommerce can potentially coordinate recommendations across the homepage, product pages, category pages, cart, mobile apps, email, campaigns and other customer touchpoints.

Platforms in this category can offer capabilities such as affinity-based recommendations, real-time behavioural personalization, experimentation, audience segmentation, custom merchandising logic and APIs for headless environments.

For example, Mastercard’s Dynamic Yield supports product recommendations alongside segmentation, targeting, experimentation and cross-channel personalization. Its ecommerce offering includes affinity-based and real-time recommendation approaches rather than limiting recommendations to product-to-product similarity.

This broader capability becomes useful when your commercial problem is bigger than “improve related products”.

You may want to personalize an entire homepage for a returning shopper, rank products differently for loyalty members, recommend replenishment products after a previous purchase, prioritize items available in a shopper’s region, or test different recommendation strategies against revenue and margin.

What should you evaluate before buying?

Do not compare vendors primarily on the number of algorithms in the demo. Compare them on what happens after the demo.

The last question gets overlooked surprisingly often.

A sophisticated platform that only your technical team understands can create more operational drag than a simpler system the ecommerce team actively uses.

Option 3: Build a custom AI product recommendation engine

Building can create the most differentiated system, but it also creates the most responsibility.

A custom engine makes sense when the recommendation decision depends on proprietary information or business rules that generic software cannot model effectively.

Consider a multi-brand retailer operating across several countries. The “best” recommendation might need to consider customer behaviour, product compatibility, warehouse availability, delivery region, gross margin, markdown risk, brand rules and local assortment.

That is no longer simply a recommendation model. It is a commerce decisioning system.

Building does not necessarily mean creating every machine-learning component from scratch either. Services such as Amazon Personalize allow companies to generate personalized ecommerce recommendations using their own interaction and product data, including real-time recommendations.

Google’s AI Commerce Search similarly provides recommendation models trained using catalog and user-event data, with capabilities for personalized and real-time recommendations. 

These services can reduce the machine-learning infrastructure burden, but they do not eliminate the larger implementation challenge.

You still need event tracking, catalog pipelines, identity logic, inventory integration, storefront components, business rules, monitoring, experimentation and ongoing optimisation.

If your requirements genuinely justify that level of control, custom commerce app development can become part of the wider commerce architecture rather than another isolated ecommerce plugin.

What data does a product recommendation engine for ecommerce need?

A recommendation engine becomes useful when it understands more than products.

The strongest implementations bring several types of commerce data together.

More data does not automatically create better recommendations.

Clean and connected data does.

An advanced algorithm running against incomplete inventory feeds, inconsistent product attributes and broken event tracking can produce worse experiences than simple merchandising rules backed by reliable data.

Recommendation engines should understand inventory, not just intent

This is where recommendation projects frequently become commerce transformation projects.

Suppose the engine predicts that a shopper is highly likely to buy a particular handbag. That recommendation is commercially useless if the product is unavailable in the shopper’s region.

The same applies to size availability, delivery eligibility, regional assortment, restricted products and products about to be discontinued.

Platform-level systems already demonstrate why this matters. Shopify excludes certain unavailable products from recommendation scenarios, while Google’s commerce recommendation tooling provides product-attribute filtering and treats availability as an important input to recommendation delivery.

At enterprise scale, recommendation quality therefore depends on systems behind the storefront: ERP, PIM, OMS, warehouse systems, customer platforms and catalog governance.

Personalization cannot compensate for disconnected commerce operations.

The best recommendation is different on every part of the customer journey

One recommendation strategy should not be copied across every page.

On a homepage, the job may be discovery. For a first-time visitor, popularity, category affinity or current context can provide useful starting points. For a returning shopper, recent browsing or individual affinities may become more relevant.

On a product detail page, the decision is different. Similar products can reduce dead ends when the current item is not quite right, while complementary products can expand the basket.

Inside the cart, the recommendation should usually become tighter. Accessories or genuinely complementary products can increase basket value, but irrelevant discovery recommendations can introduce friction just before checkout.

Post-purchase recommendations have another objective. Depending on the product category, the engine might suggest replenishment, accessories, complementary products or a logical next purchase.

The engine should therefore answer a business question for each placement before an algorithm is selected.

Build versus buy often comes down to business rules

Imagine two recommendation engines predicting the same five products.

One simply ranks them by expected likelihood of purchase.

The second understands that one product has almost no inventory remaining, another cannot ship to Saudi Arabia, another has unusually high returns and a fourth is already in the shopper’s cart.

Technically, both produced relevant recommendations.

Commercially, only one produced a useful answer.

This is why enterprise ecommerce teams should evaluate the rule and decisioning layer as carefully as the underlying AI model.

The goal is not maximum personalization. It is a commercially intelligent personalization.

What changes for UAE, Saudi Arabia and GCC ecommerce?

Recommendation systems become more complex when a brand expands across GCC markets because customer behaviour is only one part of relevance.

A UAE shopper and Saudi shopper may visit the same brand while encountering different inventory, fulfilment networks, campaigns, currencies, seasonal demand and assortment strategies. English and Arabic experiences may also influence how product attributes, search behaviour and customer intent are interpreted.

For multi-region commerce, a recommendation engine therefore needs to work with regional catalog and inventory architecture rather than operating independently from it.

There is also a data-governance layer. The UAE’s federal Personal Data Protection Law regulates the processing of personal data and includes requirements relating to processing, individual rights and cross-border transfers. Saudi Arabia has its own Personal Data Protection Law and implementing regulations overseen by SDAIA. UAE

Any personalization architecture using identifiable customer behaviour should therefore be designed alongside the organisation’s privacy, consent and data-governance requirements. The technology vendor should not be treated as the compliance strategy.

A real commerce example: personalization depends on the wider stack

Autumn’s work with Blueage in Saudi Arabia shows why recommendation technology cannot be separated from commerce infrastructure.

The project combined a headless commerce architecture connected to SAP ERP, customer-data integration with live behavioural segmentation, an AI recommendation engine and automated inventory synchronization. Autumn’s company deck reports a 22% increase in storefront AOV and a 20% increase in targeted customer engagement, alongside improvements to inventory synchronization. Autumn Company Deck - GCC

The important point is not simply that recommendations were introduced.

The recommendation layer was connected to customer behaviour and commerce operations. That is the architecture that allows personalization to influence both experience and commercial performance.

How should you decide between platform-native, buy and build?

A simple way to make the decision is to examine where personalization sits in your growth strategy.

Use your platform’s capabilities first when your main requirements are related products, complementary products, basic cross-selling and straightforward merchandising. You will usually get to market faster with less operational overhead.

Buy specialist software when personalization needs to operate across multiple pages or channels, customer-level behaviour materially changes what should be shown, experimentation matters, and your ecommerce team needs sophisticated controls without maintaining its own machine-learning stack.

Build when recommendation logic itself is strategically differentiated, your requirements depend heavily on proprietary data or unusual business rules, your scale justifies the investment, and you have the technical capability to operate the system continuously.

There is also a fourth answer: hybrid.

An enterprise might use platform-native recommendations for simple PDP use cases, a personalization platform for customer-facing experiences and custom decisioning for specific high-value workflows.

Architecture does not have to be ideological.

Measure incremental revenue, not recommendation clicks

One of the easiest ways to overestimate a recommendation engine is to look only at engagement with recommendation widgets.

Customers who click recommendations may already be your most engaged visitors.

The more important question is what changed because the recommendation existed.

That requires controlled measurement.

For most brands, the commercial evaluation should include recommendation-assisted conversion, average order value, revenue per session, units per transaction, gross margin impact, product discovery, recommendation coverage and downstream return or cancellation behaviour.

Shopify itself provides recommendation reporting around metrics such as click and purchase rates. More sophisticated implementations should go further and test whether a recommendation strategy creates incremental commercial value compared with an appropriate control experience. Shopify Help Center

This is also why personalization should sit inside a wider ecommerce growth optimization programme rather than becoming a standalone technology project.

What a practical implementation should look like

The first stage should define the commercial use case. Decide whether the objective is better product discovery, higher basket size, more cross-sell revenue, repeat purchases, margin optimisation or another measurable outcome.

Next, audit the data required to make that decision. Catalog attributes, behavioral events and real-time inventory deserve particular attention because gaps here will limit every engine you evaluate.

Only then should you select the technology. Compare the native platform capability against specialist software and custom architecture using the same business requirements.

Before launch, add merchandising and operational guardrails. Recommendations should respect availability, geographic eligibility, business exclusions and other rules that the model itself may not understand.

Finally, test against a control and scale only what demonstrates measurable value.

The model is one component. The operating system around it determines whether personalization actually performs.

Where Autumn fits

Autumn is an AI-first commerce transformation company.

Autumn works with growth-stage and enterprise brands to modernise commerce ecosystems, improve revenue performance, increase operational efficiency and support expansion across GCC and global markets. Its positioning covers commerce transformation, AI transformation, scalability, international expansion, localization, omnichannel operations and conversion optimisation rather than treating ecommerce as a storefront-development exercise. WeAreAutumn Brand Voice

For recommendation-engine projects, that broader perspective matters.

Sometimes the answer is a Shopify capability. Sometimes it is an enterprise personalization platform. Sometimes it is custom development. And sometimes the real constraint is not the recommendation technology at all. It is fragmented customer data, poor catalog structure, delayed inventory synchronization or an architecture that cannot act on the recommendation in real time.

The right solution begins by identifying which of those problems you actually have.

FAQs

What is the best AI product recommendation engine for ecommerce?

There is no single best engine for every ecommerce business. A Shopify merchant with straightforward cross-selling requirements may get more value from native recommendations than from an enterprise personalization platform. A multi-market retailer with millions of interactions may need specialist or custom technology. Evaluate solutions based on data integration, personalization depth, inventory awareness, merchandising control, experimentation, channels, scalability and total operating effort rather than comparing AI features alone.

Does Shopify have AI product recommendations?

Shopify provides automatically generated related-product recommendations and merchant-controlled complementary recommendations through Shopify Search & Discovery and its storefront recommendation APIs. Automatically generated related recommendations can use purchase history, product-description similarity and related collections. Shopify Help Center For deeper customer-level personalization across multiple touchpoints, brands may need additional personalization technology or custom logic depending on their requirements.

How much data is needed for a personalized product recommendation system?

There is no universal minimum because different recommendation approaches require different information. Product similarity can work from catalog attributes even when customer history is limited, while collaborative or individual behavioural personalization becomes stronger as interaction and transaction data accumulates. A practical system should also include cold-start strategies for new shoppers and newly launched products instead of assuming every visitor and SKU has extensive historical data.

Should an ecommerce business build or buy its recommendation engine?

Most businesses should buy or use existing platform capabilities unless custom recommendations create a meaningful strategic advantage. Building makes more sense when proprietary business rules, unusual catalog relationships, multiple data sources or very large scale justify owning the decisioning layer. Managed machine-learning services such as Amazon Personalize and Google AI Commerce Search can reduce the modelling burden, but businesses still need data pipelines, integrations, experimentation, monitoring and merchandising governance.

Can AI recommendations work for anonymous shoppers?

Yes. An anonymous visitor might not have a long-term customer profile, but recommendations can still use current-session activity, product context, popularity, category relationships or contextual signals. As more interaction occurs during the session, the experience can become more relevant. Businesses should design explicit fallbacks for new visitors rather than showing an empty recommendation unit or forcing personalization that the available data cannot support.

How do you measure ROI from an AI product recommendation engine?

Measure incremental commercial impact, not only clicks on recommendation widgets. Compare a recommendation experience against an appropriate control and track metrics such as revenue per session, average order value, units per transaction, conversion, margin and repeat behaviour. Also watch operational metrics such as recommendation coverage, unavailable-product exposure and latency. A recommendation engine is valuable when the overall customer and commercial outcome improves, not simply when shoppers interact with the carousel.

Are AI product recommendations compliant with UAE and Saudi privacy laws?

They can be, but compliance depends on how customer data is collected, processed, stored, transferred and used. The UAE has a federal Personal Data Protection Law, while Saudi Arabia operates under its PDPL and associated regulations. UAE Brands using behavioural or customer-level personalization should assess consent, legal basis, data minimisation, vendor relationships, retention and cross-border data transfers with qualified privacy advisers as part of the implementation.