Understanding AI Behavior and Realistic Use Cases

Summary

To help you get the most value from AI Configurations in the PIM, we strongly recommend following the guides below:

 

Overview

Akeneo’s AI capabilities are designed to assist your team in creating, translating, and improving product information - not to replace your product experts.
By understanding what the AI can do well (and what it cannot), you’ll be able to write better prompts, interpret outputs correctly, and use the system confidently.

This page outlines:

  • The types of tasks AI handles effectively
  • Its technical and contextual limitations
  • Best practices for maintaining quality
  • Realistic examples of good versus poor prompts
     

What AI Can Do

Akeneo AI performs best when it works with your existing data and structure. It does not invent facts, and in some cases it can hallucinate (like any AI engine) . It uses the data you provide in your catalog.

The AI can:

  • Generate new product content such as short descriptions, marketing blurbs, or bullet points.
  • Translate localized content between supported languages while maintaining tone and brand style.
  • Summarize or reformat data to improve readability or meet basic marketplace requirements.
  • Analyze data quality and identify missing or inconsistent information.
  • Interpret assets (images or PDFs) included in your configuration to extract relevant details.
  • Follow structured instructions that clearly define the role, tone, and output format.

Example:
If your configuration includes Product Name, Material, and Short Description, the AI can write a consistent product paragraph highlighting features, benefits, and material composition.

 

What AI Cannot Do

Akeneo AI works entirely within the data and context available inside your PIM.

AI cannot access external sources, browse the internet, or “know” things that aren’t in your catalog.

 
Limitation Description
Web browsing or research The AI does not access live internet data or external websites. It can only use product information stored in Akeneo. ChatGPT's general knowledge is current up to June 2024 - it will not be aware of events, product, changes that occurred after that date.
Real-time product validation It cannot verify information against online stores or supplier databases.
Cross-variant access

When generating content with AI, the data that can be used depends on which level you are working on.

 

At the product model (parent) level:

  • AI can only use attributes available on the product model itself. 
  • It cannot look down and use attributes that exist only on its variants.

At the variant level, AI can use:

  • Attributes defined on the variant
  • Attributes inherited from the parent product model (if they are part of the selected data for the AI configuration).
Category and reference entity data Product categories, labels, and reference entity attributes are not accessible in current AI configurations.
State memory Each generation is stateless. The AI does not “remember” previous requests or learn from past generations.
Post-processing logic Akeneo does not automatically modify or optimize AI output. Any validation or transformation must be handled through rules or workflows.

In short, Akeneo AI is not a search engine or a fact-checker. It’s a controlled assistant that creates new content only from the data you choose to share with it.

 

Setting Realistic Expectations

AI should be viewed as a smart assistant, not an autonomous decision-maker.
It accelerates repetitive tasks, scales content creation, and maintains consistency - but still benefits from human oversight.

Key Principles

  1. Clarity beats complexity
    A concise, specific prompt produces far better results than a long, vague one. 
  2. AI cannot invent missing data
    If your attributes are empty or inconsistent, the AI can’t fill the gaps with factual information.
  3. Be specific about role and tone
    Clear role definitions like “Act as a technical writer” or “Write as a playful lifestyle brand” dramatically improve results.
  4. Test before automating
    Always use Preview to review outputs for several products before enabling a rule for mass generation.
  5. Review before publishing
    Treat AI-generated content like a first draft. Review it through Workflows or manual checks to ensure accuracy and compliance.
     

Example: Good vs Poor Prompts

Poor Prompt

“Generate engaging product descriptions using my website data and current market trends. Make sure it sounds nice.”

Why it fails:

  • The AI cannot access your website or “market trends.”
  • “Sounds nice” is too subjective and unclear.
  • No role or format defined.

Improved Prompt

“Act as an SEO copywriter. Using the product name, short description, and key features provided, write one 120-word paragraph optimized for search engines. Maintain a friendly and professional tone.”

Why it works:

  • Clear role and action
  • Structured output request
  • Uses data available in the PIM
  • Sets tone expectations
     

Understanding Stateless AI

Each AI generation is independent of the last.

This means that if you ask the same configuration to run twice, the content may vary slightly - even with identical data.

This variability isn’t a bug. It’s a characteristic of natural language generation: multiple valid outputs can exist for the same task.

Tip:
If you need fully standardized phrasing (for example, compliance labels or product safety lines), handle those through fixed templates or rules, not AI generation.
 

How to Get the Best Results

Strategy Why It Helps
Start with high-quality data AI accuracy depends entirely on the clarity and completeness of your attributes.
Use structured prompts Following the C.R.A.F.T.+R framework ensures every instruction is clear and relevant.
Leverage the Optimizer Analyze and rewrite your prompt until scores reach a consistent high level.
Iterate with Preview Adjust your prompt wording gradually and compare results.
Validate before automation Once confident in output quality, deploy through the Rules Engine for scale.

 

Example Use Cases That Work Well

  • Product Descriptions: Generate brand-consistent copy based on product attributes.
  • SEO Metadata: Automate meta titles and descriptions.
  • Feature Summaries: Create short lists of key benefits for marketplace listings.
  • Tone-Aligned Translations: Translate while keeping your brand voice intact.
  • Data Audits: Automatically detect missing or conflicting information.

These use cases all rely on structured data within Akeneo and produce measurable time savings for enrichment teams.

 

Use Cases That May Not Work Well

  • Generating content based on external data (e.g., supplier websites).
  • Creating content for empty products with no attributes filled.
  • Producing statistical claims or measurements not present in the PIM.
  • Performing real-time comparisons between similar products.

For these, manual enrichment or non-AI rules are more suitable.

 

Key Takeaways

  • Akeneo AI is most powerful when used with clear prompts and quality data.
  • It cannot research or verify external information.
  • Always review and validate AI output before publishing.
  • Treat the system as a skilled assistant - it enhances human productivity rather than replacing it.

 

Next Steps

Now that you understand AI behavior and realistic expectations, you can:

Learn how your data is protected and processed in Security and Data Privacy.

Explore Prompting Guidelines to refine your own prompt-writing skills and get consistent, high-quality results.

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