AI features to look for when choosing a PIM solution

In only a matter of a few years, Artificial intelligence has expanded from boardroom speculation to providing significant operational muscle. If you’re in the process of selecting a Product Information Management (PIM) platform today, you’re also making choices about how quickly your data improves, how safely and securely it can be scaled, and how far you can reduce the manual grind you’ve suffered from when processing, enriching, syndicating, and updating your product information.

Below, we dig down into what AI truly offers you – the AI-powered features in a PIM you need to look for if you want a tangible ROI on your commercial outcomes.

In one respect, the core role of a PIM solution hasn’t changed. It provides the single, centralised source of product data truth – trustworthy by any user. What’s changing is the volume, speed, and granular detail demanded from that single source of truth. Many businesses have deployed PIM platforms, but frequently not to best effect. AI is now a core part of a Product Information Management (PIM) system’s “job description” because it’s the power behind unprecedented automation, efficiency, and personalisation at scale, simply unachievable using manual methods. Our perspective is that when used mindfully, AI tools are capable of transforming your PIM into a genuine multiplier of productivity rather than just being a glorified storage unit.

Having said that, beware of being seduced by vague “AI-powered” claims which never actually land on specific, measurable tasks. Conversely, a big green flag is being assured of AI automation which will reduce time-to-market, improve the accuracy of your channel-targeted content, guarantee up to date information 124/7/365, and provide solid evidence of enhanced product experiences and lower overall commercial risk.

Using AI automation tasks and tools: specific benefits

1. Intelligent supplier data onboarding and cleansing

Poor-quality supplier data is where catalogue content withers on the vine. But if you have automation tools to ease speed, standardisation, and normalisation of incoming data, it’s one of the fast tracks to real value.

For onboarding and cleansing, AI capabilities can:

• Map attributes automatically, even when the suppliers’ field names don’t match neatly with your product taxonomy.

• Standardise values across units, formats, and naming conventions.

• Recommend or fill key missing details for product attributes based on patterns across similar products.

If the PIM also supports an AI-assisted supplier portal, that can push quality improvements upstream, where they’re cheapest and easiest to fix.
While plenty of PIMs claim to support AI-driven enrichment, far fewer can handle messy, real-world supplier data at the point it actually enters your ecosystem. That work is often pushed back onto spreadsheets, email chains, or internal teams long before the data ever reaches PIM.

This gap is exactly why we created SKULaunch.

SKULaunch sits upstream of PIM and commerce platforms to deal with the hardest part first: supplier onboarding at scale. It is designed for situations where data arrives incomplete, inconsistent, or in formats that do not resemble your taxonomy at all.

In practice, this means:

• Accepting supplier data in whatever form it arrives, including spreadsheets, PDFs, portals, or raw exports
• Using AI to map supplier fields to your attribute model before data enters PIM
• Normalising units, formats, and naming conventions consistently across suppliers
• Enriching missing attributes using product patterns and reference models
• Validating data against your rules before it contaminates downstream systems

By fixing data quality before it lands in PIM, teams avoid expensive rework, fragile automation, and ongoing governance debt.

Whether you use SKULaunch or another upstream solution, this capability matters. If a PIM cannot control data quality at the point of entry, it will always struggle to deliver clean outputs downstream, no matter how advanced its AI features look in a demo.

2. Generated content with brand and channel controls

Customers are increasingly demanding when it comes to product pages (descriptions, titles, visual assets, reviews, and so on) and better search performance (because your SEO has hit the nail on the head). They want better. And you can give them the quality they demand when you operate with AI which is capable of generating high-quality and effective content. That’s the case, even if you’re dealing with thousands of near-identical product blurbs. Up to now, many businesses have been doing all this by hand – simply unfeasible as they vainly attempt to grow, scale, and simply keep up with their more PIM-savvy rivals.

The best kind of generative AI tool in PIM will operate within a rigorous set of parameters. With human oversight still in the loop, It’s a governed and data-led content generation engine which can:

• Produce channel-ready variants (different marketplace-compliant formats, web copy generation, B2B-focused attribute/spec-first descriptions).
• Follow brand rules, preferred written style and tone, and comprehensive compliance constraints.

• Support translation and localisation, adapting tone and granular details to specific market (and cultural) variables.

Try requesting one simple test in demos: ask how the system manages approvals, versioning, and audit trails. After all, speed without control is just chaos speeded up!

3. Proactive data quality and governance

Traditional validation rules catch the more obvious gaps, but AI’s forensic capacity helps spot and flag ‘hidden’ discrepancies and inaccuracies (often overlooked in manual processing).

Look for:

• Anomaly detection which flags suspicious outliers (such as weight or price units which don’t fit a category).

• Cross-checks between images and attributes, so obvious mismatches are caught early.

• Channel-specific completeness scoring, so you don’t fail to fulfil the content and format requirements which marketplaces like Amazon, or your retailer partners, or regional regulations stipulate.

All in all, your teams can now shift from constant firefighting and reworking to surety and confidence when pushing information to channels.

4. Asset intelligence and visual search

Many PIMs now sit close to or incorporate Digital Asset Management. Essential AI features here include:

• Granular auto-tagging of images and videos (colour, finish, style, material).
• Visual similarity search, useful for large ranges and frequent changes in assortments.
• Assistive alt text for accessibility and discoverability.

5. Smarter search, categorisation, and taxonomy support

Your PIM should make product information easy to find internally, not just externally.

AI supports this by:

• Auto-classifying new SKUs based on attributes and descriptions.

• Suggesting better category placements as your hierarchy evolves.

• Enabling semantic search so users can find products by intent, not just exact keywords.

Working consistently in the background, this saves you massive amounts of people hours each week and minimises the risk of inconsistency across your catalogue.

6. Personalisation and digital shelf feedback loops

This area is where a PIM with AI earns its keep as a product experience engine.

Most advanced platforms can:

• Serve different attribute sets and content variants for different audiences (B2B vs B2C, expert vs casual buyer).

• Pull in performance signals from marketplaces and your own sites.

• Flag underperforming listings and suggest specific enrichment improvements.

Think of it as closing the loop between “we published it” and “it’s really performing.”

7. The maturity question: does AI act or only suggest?

A fast-emerging trend is “agentic” AI. It’s not hype, but you need to understand the direction of travel for this area of AI. In practical terms, ask whether the AI can:

• Only highlight issues, or
• Trigger workflows and apply approved fixes within clear parameters.

As your range and channel mix expand, even limited action can unlock big productivity gains, if well-governed.

A concise PIM buyer checklist

When you’re comparing vendors, keep it simple with key queries:

• Where exactly will AI reduce manual effort in our current process?
• How do we review, approve, and roll back AI outputs?
• Are we locked into one model or roadmap?
• What safeguards exist for data privacy and regional compliance?

Prick up your ears: If the answers sound like hot air, the functionality probably is too.

Closing comments

You do not need every advanced AI capability on day one. What you do need is clarity on where automation will remove friction from your current product data process, not add another layer of complexity.

A good PIM uses AI to reduce manual effort, and support consistent, channel-ready outputs at scale. A great one does this within clear governance rules, so speed never comes at the cost of accuracy, compliance, or trust.

When you are comparing platforms, focus less on how impressive the AI sounds and more on where it applies real pressure to your biggest bottlenecks. Supplier data intake. Attribute completeness. Content consistency. Ongoing validation as ranges change.

If the answers stay vague, the value usually does too.

If you want an independent view on which AI features will genuinely move the needle for your catalogue and channels, Start with Data can help. We support teams through PIM selection and delivery with a practical, vendor-agnostic approach grounded in real data challenges, not feature checklists. The goal is simple: cleaner data, scalable processes, and AI that earns its place in your stack. 

Get in touch with us today