What is AI product enrichment and how to use it in eCommerce

AI product enrichment could help you improve product data quality at scale by creating AI product attributes and content.

But it’s not worth the investment for all eCommerce teams. AI product data enrichment is most relevant when catalogs grow faster than manual processes can keep up: when missing details and inconsistent values limit product performance across Shopping ads and marketplaces.

If you're managing thousands of SKUs on multiple channels, this guide is for you. It breaks down what AI product enrichment looks like in real life and outlines best practices for using it safely, combining AI with rules, validation, and monitoring to keep product data accurate and channel-ready.

Key Takeaways

• AI product enrichment works best as part of a workflow, not as a standalone shortcut.
• Use AI for repeatable gaps (missing attributes, standardization), and Rules + validation to stay in control.
• Better enrichment leads to faster launches, cleaner feeds, and more efficient ad spend.

What is AI product enrichment?

AI product enrichment is a practical way to generate and improve product data at scale. Instead of fixing missing attributes or rewriting titles across never-ending SKU listings by hand, teams use AI to handle repetitive enrichment tasks, such as:

• Filling in missing attributes using existing product information
• Standardizing inconsistent values (for example, colors, materials, or sizing formats)
• Supporting product categorization at scale
• Improving product titles or descriptions so key attributes are clear and usable across channels

AI product data enrichment is often misunderstood as a replacement for manual feed management. But it’s not.

AI product enrichment vs. manual enrichment

Here’s how the two approaches compare in real-world feed management:

Manual product data enrichment	AI product enrichment
Teams update attributes and content by hand	AI generates or enhances attributes and content
Becomes slow and error-prone at scale	Scales more easily across complex catalogs
Inconsistencies creep in over time	Rules enforce consistency across products and channels
Hard to maintain across markets	Easier to adapt as products and requirements change

AI product data enrichment works best as a support layer, helping teams generate and standardize product attributes and content while rules and validation stay at the helm.

Without structured workflows, AI output can introduce new inconsistencies instead of fixing existing ones. That’s why you should think of AI product enrichment as part of a broader feed management workflow, not as a standalone solution.

What AI does not do is understand your commercial priorities, channel constraints, or business logic on its own. It does not know which attributes are mandatory for a specific marketplace, which values are allowed, or how different channels interpret the same field. Those decisions still need to be defined by humans, through rules, mappings, and validations.

When applied with the right guardrails, AI product enrichment supports several high-impact use cases:

• Generating values for required fields without manual cleanup
• Normalizing inconsistent values so products can be matched and approved correctly
• Reducing manual category mapping by suggesting classifications based on existing data
• Enhancing product descriptions and titles so key attributes are clear to both shoppers and channels
  In marketplace-heavy setups, AI marketplace product data enrichment helps teams generate and standardize attributes required by platforms like Amazon, Bol, or Zalando, where strict category rules and attribute completeness directly affect visibility.

Why AI product data enrichment matters: Key benefits for eCommerce

The benefits of AI product enrichment go beyond abstract efficiency gains and show up in how teams operate, launch products, and scale campaigns and sales.

More accurate product data with less manual effort for eCommerce teams

Instead of manually filling missing values SKU by SKU, teams that use AI product enrichment typically see:

• Fewer manual edits
• Fewer human errors and faster approval cycles across channels
• Uniform outputs across all SKUs

This matters because detailed, high-quality product information is one of the primary factors customers use when deciding whether to buy online. When attributes are missing or unclear, products are harder to find and less likely to convert, placing a burden on teams to manually identify and fix issues across multiple channels.

Faster time-to-market for new products

AI product enrichment does for launches what a runway does for takeoff. Products get airborne sooner, campaigns start earlier, and performance data begins accumulating without unnecessary delays.

Before a product can go live, channels require specific attributes, formats, and categories. Doing this manually while prioritizing data accuracy is difficult at scale. Google notes that missing or incorrect attributes are a common reason products fail validation or remain limited in Merchant Center. Until those issues are fixed, products do not show.

With AI, required attributes can be completed and standardized before products are pushed live, so launches are not followed by days of retroactive fixes.

Better ad conversion rates and ROAS

Google’s own documentation consistently shows that ads with more relevant product data enter more auctions and match a broader set of high-intent queries, increasing the share of impressions that have genuine purchase intent. Products with incomplete attributes, by contrast, are either excluded from auctions or matched to less relevant searches.

Google’s research shows that high-quality data improves relevance signals and helps advertising systems allocate spend more efficiently, supporting stronger returns from the same budget.

AI product enrichment supports this by improving the inputs that ad platforms rely on:

• More complete attributes that enable accurate matching
• Consistent values that prevent products from drifting into irrelevant auctions
• Clearer titles that reflect how users actually search

Scalable data management for large and changing catalogs

AI product enrichment allows teams to grow their catalogs without rewriting enrichment logic every time something updates. With this, the risk of inconsistent or outdated data decreases as changes are handled systematically.

Higher product visibility across channels and better customer experience (CX)

Products with complete, structured data are easier for platforms to surface and easier for shoppers to evaluate. This also matters as discovery increasingly happens through AI-powered and conversational interfaces, where missing attributes make products harder to interpret and match. For eCommerce teams, cleaner discovery signals translate into less irrelevant traffic and fewer mismatches between what shoppers see and what they receive, improving the buying journey.

If you’re seeing these benefits in theory, the next question is how to apply AI product enrichment in your own workflows. The next section breaks down six simple steps to get started.

6 Simple steps to get started with AI product enrichment

Here’s a simple and structured approach to getting started with eCommerce product data enrichment using Channable, a product feed management and PPC optimization tool.

Key moments include:

Step 1: Import your product feed into a Channable project and map fields

Start by importing your product data into a Channable project. You can connect Channable directly to Shopify and other eCommerce sites, like Magento and WooCommerce, or import raw data via XML, CSV, Google Sheets, or APIs.

You can also add additional Imports, like margins, inventory data, or custom labels, to provide more context before enrichment logic is applied.

Once imported, map your fields. Mapping is what turns raw source data into a clean, consistent structure, so attributes like title, brand, category, size, or color mean the same thing across products and sources. This step is critical when you work with multiple inputs or plan to enrich data later.

Step 2: Define which attributes you want to enrich

Once your product data is imported and mapped, the next step is deciding what needs enrichment.

Not every field (i.e., technical specs) should be touched by AI. Focus first on attributes that directly affect eligibility, matching, and filtering across channels, such as:

• Required or recommended attributes that are missing or inconsistently filled
• Attributes used heavily in filters (for example: size, color, material, compatibility)
• Product titles or descriptions that lack key qualifiers shoppers use to evaluate relevance

In Channable, this selection happens before any automation runs. Defining which fields are in scope helps you avoid over-enriching already reliable data and keep AI focused on filling gaps. This makes validation easier later and reduces the risk of introducing unnecessary changes across your entire catalog.

Step 3: Configure input fields and filters

In Channable, you can use Rules and IF/THEN conditions to target only the products that need enrichment, for example, products with missing attributes, specific categories, brands, or markets. You can also exclude products where existing data is already complete or should remain untouched.

Next, select the input fields AI should reference, such as brand, category, existing titles, descriptions, or key specifications, to ensure enrichment results stay aligned with your product catalog structure and channel requirements.

Step 4: Run AI enrichment and validate outputs

Channable applies AI-generated values within your existing project structure, alongside your mappings and Rules, so enrichment stays aligned with how data is prepared for each channel.

Validation is what makes this step safe. Use Rules before you enrich product data
to enforce allowed values, formats, and length limits, and rely on error checks and previews to review outputs before anything is exported.

Spot-check high-volume categories first and confirm results against channel requirements, especially for ads and marketplaces.

Step 5: Export enriched data to Channels and monitor results

Once enrichment and validation are complete, export your product data to Channels in Channable, such as:
Google Shopping ads
Marketplaces
Comparison sites

Each Channel applies its own requirements, so exports reflect the correct formats, attributes, and Rules for that destination.

After going live, monitoring becomes essential. Use Channable’s Insights & Analytics to track feed errors, warnings, and item statuses at the Channel level, and keep an eye on performance signals like impressions, clicks, and approvals. This feedback loop helps you spot issues early and refine enrichment logic where needed.

Step 6: Scale and standardize enrichment across projects and markets

Once your enrichment logic works for one catalog, scale it across others. In Channable, you can reuse enrichment setups by duplicating projects and applying Master Rules to standardize logic across brands, countries, and languages.

This approach keeps enrichment consistent as you expand into new markets or add new channels. The result is predictable output at scale.

Bring AI product content enrichment into your eCommerce workflow with Channable

AI product content enrichment works best when it’s embedded in a structured workflow. Used this way, it helps teams improve product data quality at scale.
Channable is designed for this exact balance. Imports, field mapping, AI enrichment, Rules, validation, and Channel exports all live in one place, so enrichment supports how you already manage feeds, ads, and marketplaces.
If you’re getting started, keep it simple. Start with one category or market, validate results early, and standardize what works before scaling across projects and regions.