Luxury skincare brand Elemis is using Agentic Ziggy within the Akeneo Product Cloud to turn complex, manual product data tasks into faster, governed workflows. By bringing AI into the same environment where product information is structured and managed, Elemis reduced a critical launch-day analysis from approximately three hours to just 30 minutes while creating a more scalable foundation for AI-powered product operations
To be honest, [Agentic Ziggy] has been a massive change, a lifesaver for some of these more complex things that I would have had to do manually otherwise. And it saved me a great deal of a lot of time.
Elemis is a globally recognized luxury skincare brand known for innovative formulas, high-performance products, and premium customer experiences.
Behind those experiences sits a highly complex product data operation. Skincare products require detailed ingredient information, allergen data, localized descriptions, regional requirements, digital assets, and multiple variants of core products (such as the iconic Pro-Collagen Marine Cream SPF 30).
For Elemis, maintaining accurate and trusted product information is essential not only for creating premium digital experiences, but also for helping products move efficiently from enrichment to activation across markets. Akeneo PIM provides the centralized and governed product data foundation supporting those operations.
Agentic Ziggy has been extremely helpful in making enrichment status and product information easier to understand and act on
As Elemis prepared to launch its new UK website, its product team entered a fast-paced hypercare period where product information needed to be accurate, complete, and ready for activation.
Complex tasks such as comparing attributes across locales, identifying inconsistencies, investigating publishing issues, and preparing updates could require multiple manual steps. A seemingly simple investigation could involve extracting data, filtering information, comparing spreadsheets, reviewing differences, and preparing updates for upload.
During a major launch, those additional steps matter. An incorrect or inconsistent attribute can prevent a product from publishing correctly, creating delays when teams can least afford them. At the same time, Elemis was beginning to explore a broader opportunity with AI.
There are many individual areas where AI can support product operations, from enrichment and localization to quality checks, compliance, and activation. But adding more standalone AI tools can also introduce more complexity if each one operates independently from the trusted product information and business rules already managed within the PIM.
I was able to ask it to do the comparison between the attributes across the ones that I knew, and it basically did the whole analysis for me, which again, if I did that manually, would have taken me probably three to four times longer. I knew the result was correct and was able to quickly do some updates for launch day within half an hour instead of three hours, essentially.
Elemis turned to Agentic Ziggy, the AI operational layer within Akeneo Product Cloud, to support complex product data tasks directly within the environment where its product information is already structured and governed.
Rather than moving product information into separate tools or relying on AI that operates outside the PIM, the team at Elemis can use natural language to investigate product information, compare attributes, identify differences, and prepare complex updates.
This distinction becomes increasingly important as AI use cases grow. Individual agents can be useful for specific tasks, but long-term scalability depends on those capabilities having access to trusted product information, shared context, common business rules, and appropriate governance. By utilizing agentic AI directly inside Akeneo PIM, Elemis can build on the product foundation it already has rather than creating isolated automation workflows around it.
Human oversight remains central to the process. Agentic Ziggy can analyze product data, identify issues, and propose a plan of action, while giving the Elemis team visibility into the expected impact before changes are made. The team can then review and approve the proposed actions, keeping people in control while allowing AI to take on more of the operational work.
For a luxury skincare brand managing ingredients, allergens, localization, and customer-facing information, that combination of speed and control is critical.
During the new UK website launch, the Elemis team identified an attribute discrepancy that needed to be resolved quickly. Instead of manually extracting and comparing the affected information, Tiaan Heystek, Product Data Strategy Manager at Elemis, asked Agentic Ziggy to perform the comparison across the relevant attributes.
Agentic Ziggy performed the comparison and surfaced the discrepancies, giving the Elemis team the information needed to validate the results and move forward with confidence. The process allowed them to resolve the issue in approximately 30 minutes. A task that would have taken around three hours manually.
For Elemis, this experience demonstrated how AI working directly with trusted product context could remove many of the manual steps between identifying a problem and taking action.
Approximately 70% of Elemis’s Agentic Ziggy use cases center on enrichment-related tasks. These include comparing regional locales, identifying discrepancies, synchronizing approved information, and supporting complex updates across the catalog.
Tasks that would previously have required multiple rounds of extraction, comparison, review, and upload preparation can now be handled much more efficiently. This is particularly valuable for a lean product data operation. Instead of spending time preparing information for AI or moving data between tools, the Elemis team can focus on evaluating the result and deciding what action to take.
Agentic AI is also helping Elemis make product information more accessible to teams outside of product operations. When the customer care team needed reliable information about products containing specific allergens such as soy, the team used Agentic Ziggy to analyze the relevant product information and produce a clear, structured report.
The use case turned a complex product data question into an actionable resource for another business team without requiring another manual reporting workflow.
As organizations adopt AI for enrichment, localization, compliance, content, activation, and other product workflows, the challenge will increasingly be how those capabilities work together. When AI operates separately from the primary source of product information, teams risk creating new automation silos, each with its own context, rules, and version of product data.
Elemis is taking a different approach. By bringing agentic capabilities into the same environment where product information is centralized and governed, Elemis is creating a foundation where AI can work from trusted product context while humans remain in control of the decisions that matter.
Today, that means resolving a launch issue in 30 minutes instead of three hours, accelerating enrichment workflows, and helping customer care access product information faster.
As Elemis expands its use of AI, that shared foundation provides a path toward more coordinated and scalable product operations without sacrificing the quality, governance, and control expected from a premium global brand.
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