AI recommendations: The complete guide to getting your products found and chosen

McKinsey projects that agentic commerce could represent a $3 to $5 trillion global opportunity by 2030, and the engine driving much of that value is AI recommendations. Already, 44% of consumers who have used AI-powered search say it has become their preferred way to find products online — a behavioral shift that is changing how brands get discovered and chosen. 

Whether you sell direct-to-consumer or through B2B channels, that shift has a direct commercial consequence: products that AI recommendation engines cannot read, match, or surface do not get bought. 

This guide covers how recommendation systems work, what affects their accuracy, and what your team needs to do to show up in the right results.

What are the different types of AI recommendation systems? 

Recommendation engines do not all interpret your product data the same way, and the method a platform uses determines which attributes it prioritizes, how it ranks results, and where gaps in your catalog hurt you most. Understanding the main approaches tells you exactly where to focus your data preparation.

1. Personalized recommendations

Personalized recommendation systems are driven by an individual buyer’s behavior, including past purchases, browsing history, and items added to the cart but not purchased. 

The more complete your product attributes are, the more accurately the engine can match a buyer’s pattern to a relevant product. 

Where product data enrichment is sparse or inconsistent, the algorithm has less to work with, and that directly affects which products get recommended and which get skipped.

2. Collaborative filtering

Collaborative filtering identifies patterns across users with similar behavior and uses those patterns to recommend products a buyer may not have found independently. 

In practice, a B2B procurement manager researching industrial components might see recommendations based on what similar buyers across comparable organizations have purchased, not just what they have browsed themselves. 

The more behavioral overlap exists across your buyer base, the more accurately this method connects the right products to the right buyers.

3. Content-based recommendations

Content-based systems analyze product attributes directly: category, material, specification, and brand, to recommend items similar to what a buyer is currently viewing. The quality of this approach depends almost entirely on how well your product attributes are structured and enriched. 

A product with incomplete specifications or missing category tags is far less likely to surface in these recommendations, regardless of how relevant it actually is to the buyer’s need. This is where AI-driven product data enrichment makes a measurable difference to recommendation performance.

4. Real-time contextual recommendations

These systems factor in live signals from the current session: device type, location, time of day, and what a buyer has clicked in the last few minutes. 

Real-time recommendations respond to intent as it forms, which makes them particularly effective for high-consideration purchases where buyers spend time comparing options before deciding. 

The challenge is that real-time systems require clean, current product data to serve accurate results under fast-moving conditions.

5. Hybrid recommendation systems

Most enterprise platforms now combine multiple approaches: using collaborative filtering to establish patterns, content-based matching to validate relevance, and real-time signals to refine the final output. 

Hybrid systems tend to outperform single-method engines, but they also place greater demands on product data quality because they draw on more attributes simultaneously. 

According to McKinsey’s agentic commerce research, the shift toward AI-mediated commerce is accelerating adoption of these hybrid models across both B2C and B2B platforms, making data completeness a more urgent priority than ever.

AI product recommendations rely on complete, structured product information to surface relevant products. See how stronger listings improve the visibility of recommendations across digital channels.

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How to get your products recommended by AI

Getting your products surfaced in AI-driven recommendations is not purely an algorithmic outcome. It is a data and content strategy problem, and the actions you need to take differ depending on whether you are selling directly to consumers or operating in a B2B environment. Understanding the difference between B2B and B2C e-commerce is the starting point for knowing which levers to pull.

For B2C brands and retailers

Consumer platforms such as Amazon, Google Shopping, and retail marketplaces run recommendation engines that heavily weight product data. Incomplete titles, missing images, vague descriptions, or absent size and color attributes will push your products down in recommendations regardless of how competitive your pricing or reviews are. Getting consistently surfaced starts with product listings that are complete, structured, and optimized for each channel your products appear on.

Your e-commerce content strategy also needs to account for how AI discovery tools interpret product information. AI-powered search interfaces and chat-based shopping tools rank results based on semantic relevance rather than keyword density, which means your product descriptions need to answer the specific questions your buyers are asking, not just repeat search terms. Structuring content around buyer intent, covering use cases, compatibility, and key decision criteria, directly improves how frequently your products get surfaced in these environments.

Social commerce platforms like TikTok Shop and Instagram Shopping also run their own recommendation logic, where product attribute completeness and visual content quality directly affect how frequently your products get surfaced in feeds. As buyers increasingly discover products through social channels, keeping your product data optimized for these surfaces is as important as maintaining your marketplace listings. 

Behavioral signals also feed the recommendation engine over time. Strong imagery drives click-throughs, detailed descriptions reduce bounce rates, and review volume reinforces rankings. Each of those signals compounds, so products that are well-presented from launch build recommendation momentum faster than those optimized reactively. Consumer behavior research consistently shows that buyers engage more with listings that feel complete and trustworthy, and recommendation engines reflect that engagement in how they rank and surface products.

McKinsey projects that the US B2C retail market alone could represent up to $1 trillion in AI-orchestrated revenue by 2030. Brands preparing their product data for agentic commerce will have a structural advantage as AI agents become a primary discovery surface. The psychology of the digital shelf determines whether your products capture that opportunity: where your product appears, how it is presented relative to competing options, and whether the information it surfaces matches what the buyer needs at that moment all affect whether a recommendation converts or gets ignored. 

For B2B manufacturers and distributors

In B2B, recommendation engines operate across procurement platforms, distributor portals, and industrial marketplaces. The challenge is that B2B product catalogs are often large, technically complex, and managed across multiple systems, which creates inconsistencies that degrade recommendation performance at scale.

Manufacturers and distributors that want their products recommended more frequently need to standardize product taxonomy, ensure technical specifications are complete and machine-readable, and maintain consistency across every channel where their catalog appears. AI for B2B recommendation systems also relies on relational data, so connecting compatible parts, accessories, and replacement components within your product data directly increases the number of relevant recommendation slots your products can fill.

According to McKinsey’s State of AI report, 62% of organizations are already experimenting with AI agents, and 23% are actively scaling them, with procurement and knowledge management among the fastest-moving functions. 

B2B recommendation logic factors in account-level behavior, contract pricing, and procurement history rather than individual browsing patterns, so your product data needs to support more complex matching criteria than a typical B2C listing. Richer attribute structures, more precise categorization, and tighter data governance across your catalog are the baseline. 

Distributors managing thousands of SKUs across multiple manufacturers face the added challenge of maintaining that consistency at volume, where manual processes consistently break down.

What factors affect AI recommendation quality?

Most underperforming recommendation engines are not failing because of poor algorithms. They are failing due to poor input. Four factors consistently determine whether an AI recommendation system produces results that are relevant enough to convert.

• Data completeness
  A recommendation engine can only work with the attributes it can read. Products missing key fields, whether that is material composition, compatibility data, target audience, or technical specification, are effectively invisible to large portions of the recommendation logic. 
  
  Every missing attribute is a missed match, and in catalogs with thousands of SKUs, those gaps compound quickly. AI tools for product content creation help teams close those gaps at scale without the manual overhead that typically slows catalog enrichment down.
• Product attribute structure
  AI systems do not interpret free text the way a human editor would. Attributes need to be tagged, categorized, and consistent across your catalog. A product described as “blue” in one listing and “navy” in another will not be treated as the same attribute, which fragments recommendation coherence across your range. 
  
  The same problem appears in B2B catalogs where technical specifications are formatted differently across product families or supplier feeds, making it harder for recommendation engines to draw accurate connections between related items.
• Behavioral signals
  Click-through rates, time on page, add-to-cart rates, and purchase completion all feed back into the ranking logic of recommendation engines. Products with strong engagement signals are recommended more frequently, creating a compounding advantage for well-presented listings. 
  
  Behavioral performance is not just a marketing metric. It is a direct input into how recommendation systems weight and prioritize your products over time, meaning that getting your foundational content right drives the signals that reinforce your product rankings.
• Contextual accuracy
  Whether your product data reflects current availability, pricing, and specifications affects whether recommendations convert after they are served. A recommendation that leads to an out-of-stock page or an inaccurate specification damages both conversion and platform trust, and platforms track that signal.
  
  Generative AI in e-commerce carries real risk when product data is not kept current, because AI systems will confidently surface and describe products based on whatever information they have access to, accurate or not. Keeping your data current is not just a hygiene task. It is a quality control measure.

How to set guardrails for AI recommendations that actually convert 

Most AI recommendation strategies fail at the implementation level, where teams skip the foundational checks that determine whether recommendations help buyers or frustrate them. These guardrails give your strategy the operational discipline it needs before and after go-live.

1. Audit your data first

Before activating any recommendation logic, run a full audit of your product catalog against a minimum attribute threshold. Recommendation engines surface what your data tells them to surface, so products with missing attributes, outdated pricing, or low-quality imagery will generate suggestions that feel irrelevant to buyers. 

Establishing a data readiness benchmark before go-live means your recommendations start from a position of accuracy rather than spending their first weeks eroding buyer trust. AI enrichment tools can accelerate that audit process and help teams identify exactly where catalog gaps are concentrated.

2. Test before you assume

Every recommendation module you deploy should be treated as a hypothesis with a defined success metric and a review timeline, not a permanent feature. A/B testing recommendation widgets against clean product pages gives you actual conversion data rather than assumed lift, and that data should drive decisions about which modules stay, which get refined, and which get removed. If you skip this step, you may end up carrying underperforming modules indefinitely because there is no mechanism to identify them.

3. Build on first-party data

Personalization strategies built on first-party behavioral signals — data collected directly from your own channels with buyer consent — are more accurate, more sustainable, and less exposed to regulatory and platform risks than those that rely on third-party data. As AI chatbots and discovery tools become the primary surface for product recommendations, the brands with the richest first-party data foundations will have a structural advantage in accurately matching their products to buyer intent.

4. Retire what isn’t working

A recommendation module that performed well at launch can become noise over time as your catalog, audience, and buyer behavior shift. Schedule regular reviews of every active recommendation surface and retire modules that are not generating measurable engagement. 

Keeping underperforming widgets live is not a neutral decision. On mobile in particular, recommendation carousels that don’t convert add friction to the experience and can interrupt purchases already in progress.

5. Match recommendations to intent

A buyer comparing options on a product detail page needs different guidance than a buyer who has already added an item to their cart and is moving toward checkout. Serving the wrong recommendation at the wrong stage not only misses the upsell opportunity; it also creates friction that can cost you the original sale. 

Map each recommendation surface to the specific purchase stage it serves, and audit whether the underlying logic actually reflects what buyers need at that moment in their journey.

How does Inriver support AI recommendation engines?

AI recommendations are only as good as the product data feeding them. Inriver’s product information management platform provides commercial teams with the data foundation that recommendation engines need to consistently surface accurate, relevant results across every channel.

Product data in Inriver is structured, enriched, and distributed from a single source, which means the attributes recommendation engines read: specifications, categories, compatibility data, and channel-specific content, stay consistent whether a product appears on your own storefront, a distributor portal, or a third-party marketplace. 

Inconsistencies across those touchpoints are among the most common reasons recommendation performance degrades at scale, and a centralized product information management system removes those inconsistencies at the source. 

Inriver also uses AI to accelerate the enrichment process, helping teams identify catalog gaps and push complete, channel-ready content at the speed modern commerce demands. For manufacturers and distributors managing complex catalogs, PIM-powered AI enrichment makes the difference between a catalog that recommendation engines work with and one they largely ignore.

As PIM market trends shift toward real-time enrichment and AI-native workflows, the gap between teams with a governed product data foundation and those without it will only widen.

Start getting your products found and chosen

If you want to be successful in AI-driven commerce, you need a product data foundation that recommendation engines can actually work with. Incomplete catalogs, inconsistent attributes, and outdated content all push products down in recommendations regardless of how strong the underlying offer is. 

Every section of this guide points back to that same reality. If your catalog is not ready, your products will not be recommended. Contact us today to see how Inriver can help you build the data foundation that keeps your products visible, relevant, and converting.