AI for product content: enrichment, attribute extraction and human review

AI for product content uses generative AI to write, enrich and translate product content and to extract attributes from images and documents, working from approved product data in your PIM with people reviewing the results before they are published. It works best on repetitive, high-volume work such as channel-specific descriptions, attribute extraction from supplier PDFs, translations and seasonal refreshes. It needs guardrails wherever an error matters, such as specifications, safety and regulatory information, fitment and claims.

For how AI search and shopping assistants read product content, see How AI “thinks” about your products. This guide covers the workflow that produces the content.

What AI can do for product content today

• Channel-specific descriptions: copy written from your approved attributes and brand rules for each retailer, marketplace, language, audience or season, instead of one description used everywhere.
• Attribute extraction: reading attributes such as color, material, dimensions and shipping requirements from product images and PDF specifications, and proposing values for empty fields.
• Classification and gap detection: suggesting categories and attributes, and flagging products that are missing what a channel requires.
• Translation and localization: producing first drafts of translated content for new markets, which reviewers then check.
• Image text: writing alt text and classifying images, for example as lifestyle or close-up shots.
• Data clean-up: normalizing inconsistent values, units and naming across large catalogs. Our article on using LLMs for data cleanup covers what to expect.

Where AI needs guardrails

Generative AI writes fluent text whether or not the facts behind it are right. Treat these areas as high risk and keep a person, or a check against the source data, between the AI and publication:

• Specifications and measurements, such as dimensions, weights and capacities.
• Safety, regulatory and compliance information, such as warnings, allergens and certifications.
• Fitment and compatibility data, where an error means a wrong part.
• Claims about performance, health, sustainability or comparisons with competitors.
• Prices, promotions and anything with legal terms.

A useful rule: AI may phrase facts, but the facts must come from the product record. If the model adds a detail that is not in the source data, reviewers should reject it.

A workflow that works at catalog scale

1. Start from approved data. The PIM remains the source of truth. The AI reads attributes, assets and documents from product records rather than inventing content from a product name.
2. Set the rules for each channel. Brand voice, required and forbidden terms, length limits and each retailer’s content requirements become part of the prompt or configuration.
3. Generate or extract. Run the task for one product to test it, then for a batch, then for the catalog.
4. Check automatically. Apply the PIM’s validation and completeness rules, length limits and banned-claim checks before any person reviews the output.
5. Review by risk. Approve high-risk content item by item, and review lower-risk content by sampling (see below).
6. Write back and publish. Save approved results to the PIM, then syndicate from there, so every channel receives the same approved content.
7. Measure and tune. Track rejection reasons and adjust prompts, rules and source data over time.

Store approved output in the PIM instead of regenerating it each time. Large language models can give different answers to the same prompt, as our guide to non-determinism in LLM output explains, so the approved version should be the one every channel receives.

Human review: match the effort to the risk

Reviewing every AI output in full removes most of the time saved. Reviewing nothing puts errors in front of customers and retailers. Most teams settle on a tiered approach:

Reviewers should record why they reject an output, such as a wrong fact, off-brand tone or a missed retailer rule. Those reasons show where to fix the prompt, the rules or the source data.

Labeling AI-generated content for channels

Some channels ask sellers to identify AI-generated content. Google Merchant Center, for example, requires AI-generated product titles and descriptions to be submitted in its structured title and structured description attributes, marked as created with generative AI, and asks that AI-generated images keep the IPTC metadata that identifies them as AI-generated (Google Merchant Center Help: AI-generated content, checked September 2026). Record which content was AI-generated in your PIM, so feeds can mark it correctly as channel rules change.

Build, buy or both

There are three common ways to add AI to product content work, and many teams combine them:

• AI features in your PIM. PIM vendors are adding AI features to their platforms. Check what your platform includes and whether it covers your channels and languages. Our summary of what’s new in Salsify is one example.
• A dedicated product content tool. Tools built for product content add prompt libraries, channel rules and bulk workflows. Sitation makes one, Plezio Draft, which generates product copy and extracts attributes from images and PDFs inside Akeneo or Salsify or from its own web app, with your team approving results before they are saved to the PIM.
• Custom workflows and agents. For work that spans systems, such as enrichment that combines PIM, ERP and supplier data, custom workflows or managed AI agents connected to your PIM or MDM can run defined tasks with permissions and human review points.

Choose based on where the content lives, how many channels and languages you serve, and who will maintain prompts and rules after the pilot.

What to measure

Agree the measures before a pilot starts, and take a baseline from current work:

• Time from new product record to published, channel-ready content.
• Completeness of required attributes by channel.
• Reviewer rejection rate and the reasons for rejection.
• Retailer or marketplace rejections of submitted content.
• Translation turnaround for new markets.
• Effort per product, in hours of writing and review.

Published results from Sitation clients show the kind of change to look for:

• BIC reduced copy generation from weeks to hours and cut translated copy completion time by 75% using Plezio Draft (case study).
• BIA Cordon Bleu, with no dedicated marketing staff, shortened its content creation timeline by 90% (case study).
• ShelterLogic moved from agency-dependent translation to an AI-assisted workflow in Salsify that its own team manages, with on-demand French-Canadian and Spanish translations (case study).
• Giant Tiger connected Akeneo, automation and Plezio Draft and reduced average online time-to-market by 50% (case study).

How to get started

Pick one workflow that is repetitive, measurable and important, such as descriptions for one retailer or attribute extraction for one supplier’s catalog. Test it on a few hundred of your own products, compare the results with your baseline and your reviewers’ judgment, then decide whether to scale it. If your product data is incomplete, fix the source data first: AI works from what is in the PIM, and it cannot supply facts that are missing. Our PIM implementation and taxonomy work often comes before, or alongside, an AI rollout.

AI for product content checklist

• The PIM is the source of truth, and the AI reads from approved product records.
• Channel rules, brand voice and banned claims are written down and built into prompts or configuration.
• High-risk fields are identified and always verified against source data.
• Automated checks run before human review.
• Review effort is tiered by risk, and rejection reasons are recorded.
• Approved output is saved to the PIM, and channels receive it from there.
• AI-generated content is recorded, so feeds can label it where channels require.
• A baseline and success measures are agreed before the pilot.
• Someone owns prompts and rules after the pilot.

How Sitation helps

Sitation helps retailers, distributors, brands and manufacturers put their product data to work, with PIM, MDM and DAM implementation, managed services, AI and Plezio software. More than 400 of them have chosen Sitation as their partner. Our AI for product content work starts with a use-case workshop that tests one workflow on your own data, then moves to a pilot with agreed success measures and human review points. We use Plezio Draft and custom workflows inside your PIM, and our AI for Product Content specialists can keep the workflows accurate as part of managed services.

Frequently asked questions

Can AI write product descriptions that meet retailer requirements?

Yes, when it works from complete, approved product data and each retailer’s rules are part of the prompt or configuration, such as length limits, required terms and formatting. Automated checks and human review should confirm the output before it is published.

What is AI attribute extraction?

AI attribute extraction reads product images and documents, such as supplier PDFs and specification sheets, and proposes values for product attributes like color, material, dimensions and shipping requirements. People or validation rules confirm the values before they are saved to the PIM.

Should AI-generated product content be reviewed by a person?

Yes, with the level of review matched to the risk. Specifications, safety information, fitment and claims should be approved item by item or verified against the source record. Lower-risk content, such as alt text or seasonal copy variations, can be reviewed by sampling.

Do we need to label AI-generated product content?

Some channels require it. Google Merchant Center, for example, asks for AI-generated titles and descriptions to be submitted in dedicated structured attributes marked as AI-generated, and for AI-generated images to keep their identifying metadata. Record which content was AI-generated in your PIM so feeds can follow each channel’s rules.

Does AI replace a PIM?

No. AI tools generate and enrich content, but a PIM stores, governs and distributes the approved product data that the AI works from and that every channel receives. AI makes a well-run PIM more productive; it does not replace the need for one.

How do we measure the value of AI for product content?

Take a baseline before the pilot, then track time to publish, attribute completeness by channel, reviewer and retailer rejection rates, translation turnaround and effort per product. Compare the pilot with the baseline before deciding to scale.