For a few years, "AI" in product management software meant a chatbot that could answer basic questions or a suggestion engine that recommended categories. It was impressive in demos and underwhelming in daily use.
That's changed. Today's AI capabilities in product information management are genuinely useful in ways that affect the economics of running a catalog operation. This article is a practical look at what's real, what's overhyped, and how to think about AI-powered PIM in 2025.
What AI in a PIM actually does
Content generation and rewriting
The most mature AI capability in modern PIMs is text generation. Given a product name, SKU, type, and a few attributes, a well-configured AI can generate a compelling product title and description that matches your brand voice.
The key is configurability. Generic AI text sounds generic. The best implementations let you define tone-of-voice instructions, brand guidelines, and output constraints - and apply them consistently across your entire catalog.
This isn't about replacing copywriters for hero products. It's about generating acceptable-quality descriptions for the long tail of your catalog: the hundreds or thousands of products that would otherwise have blank or copy-pasted descriptions.
Field enrichment
A step beyond text generation is field enrichment: filling in structured attributes, not just prose.
Given a product image and a product name, AI can reliably infer:
- Product category and taxonomy classification
- Relevant attributes (material type, care instructions, target audience)
- Missing structured data that the supplier didn't provide
This matters most when you're importing supplier feeds. Suppliers often provide basic data but leave dozens of your custom attributes empty. AI enrichment can fill in the gaps automatically, turning a 40% complete product record into an 80% complete one without manual work.
AI translation
Product localization used to mean hiring translators or managing a DeepL integration. Modern PIMs integrate both.
The distinction matters: DeepL is better for structured content where literal accuracy is critical (legal disclaimers, technical specs). AI models are better for marketing copy where tone and naturalness matter more than exact equivalence.
A good PIM lets you configure which approach applies to which attribute types, and run translation across all localizable fields in bulk.
Natural language catalog queries
One of the more surprising capabilities is natural language queries against your product catalog. Instead of building complex filters in a UI, you ask a question:
"Show me all active products in the Jackets family that are missing an EAN and have a selling price above €100."
"Which products were updated in the last 7 days but haven't been published to the Shopify channel?"
This sounds like a novelty but becomes genuinely useful for catalog audits, QA workflows, and ad-hoc analysis that would otherwise require custom exports and spreadsheet manipulation.
Import formula assistance
When you're mapping a supplier's column to your schema, you often need a transformation: extract the first word, parse a combined size/color string, convert from the supplier's category codes to your taxonomy.
AI can look at a sample of column values and suggest the right transformation formula. What used to require a developer or a careful lookup table can now be done in seconds.
What AI doesn't do well (yet)
Accuracy on specialized data. For commodity products (clothing, electronics, home goods), AI enrichment is reliable. For highly specialized industrial, chemical, or medical products, the accuracy drops and human review is still necessary.
Replacing human judgment on hero products. AI-generated content is fine for the long tail. For your top-selling products, you still want a human copywriter.
Managing the exceptions. AI is excellent at the average case. Products that fall outside normal patterns - unusual categories, highly custom attributes, conflicting source data - still need human attention. The job of the PIM is to surface these exceptions clearly so humans can focus on them.
How to evaluate AI in a PIM
When evaluating whether a PIM's AI capabilities are real, ask these questions:
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Is AI native or bolted on? Systems that added an AI layer on top of an existing product have different data access than systems built AI-first. Native integration means AI can see all your attributes, images, and context - not just the fields it was trained on.
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Can you configure the model? Different tasks benefit from different models. Generating marketing copy is a different job than classifying a taxonomy. Look for PIMs that let you configure which AI provider and model handles each task.
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Can you run operations in bulk without blocking your work? AI operations on large catalogs are slow. A well-designed PIM runs these as background jobs in a queue, so a bulk enrichment of 10,000 products doesn't lock anyone out of the system.
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Does translation support both AI and dedicated translation services? For markets where text quality is critical, dedicated translation services like DeepL produce better results than general-purpose AI models. You want both options available.
The bottom line
AI in product data management has crossed the threshold from interesting to economically compelling. The teams benefiting most are those with large catalogs and limited data teams - where the ratio of SKUs to people who touch them makes manual enrichment genuinely impractical.
If your catalog has more than a few hundred products and you're still manually writing descriptions or reformatting supplier data, the AI capabilities in modern PIMs represent real leverage. The question isn't whether to use AI - it's which implementation is actually mature enough to trust with your production catalog.
Applosive's AI features are built into the core product, not a premium add-on. Content generation, enrichment, translation, and bulk operations are all available from day one.