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    During PIMpoint Amsterdam on 10 June 2025, Yentl Bosma, Christ Klinkenberg and Eline Schoonen from Ctac took part in an energetic fireside chat led by moderator Joakim. The theme: how product experience management (PXM) helps companies prepare for the future. What became clear above all else is that in a world of AI, sustainability and omnichannel commerce, there is one constant: product data. And PIM is at the heart of it. The discussion started with the question: what are the essential enablers for reliable, scalable commerce? Yentl kicked off:

    ‘If the basic data is incorrect, you cannot offer a reliable experience. You not only lose conversion, you lose trust. So always start there.’

    Four success factors emerged:

    1. Data quality – There’s no question about it: if your data is incomplete or incorrect, your conversion rate and brand value will plummet.
    2. A scalable PIM platform – A single source of truth prevents errors, speeds up time to market and enables consistency.
    3. Smart integrations – For example, with SAP, e-commerce platforms and DAM. Only when systems talk to each other can organisations move forward.
    4. AI as a lever – From generation to translation and prediction. AI makes work smarter, faster and more scalable.

     

    Eline, Modern Work Expert, emphasised that AI adds value above all when it truly serves people:

    ‘AI helps us keep product information up to date, accurate and relevant. And when you use it right, it doesn’t feel like technology—it feels like a colleague.’

    ‘Without good data, everything you build is unstable.’

    Yentl Bosma - PIM and DAM consultant at Ctac

    Practical examples from Ctac customer cases

    Christ shared an impressive case study from Atlas Copco:

    “They manage 90 webshops worldwide with millions of product variants. Managing that manually is impossible. By cleverly linking InRiver to external data sources and a noSQL solution (CosmosDB), we can automatically link the right content to the right webshop. Without human intervention.”

    Kafkas and Tectum were also discussed, where AI is used for quality control, plausibility checks and even production control. According to Christ:

    “Smart PIM solutions combined with AI make it possible to keep the scale and complexity of modern product ranges manageable. That’s not a luxury, it’s a necessity.”

    Want to know more about the Kafkas customer case?
    Sustainability data

    Van compliance naar concurrentievoordeel

    De opkomst van de Digital Product Passport (DPP) en nieuwe wetgeving zoals ESPR (Ecodesign for Sustainable Products Regulation) zorgen voor druk bij veel organisaties. Maar juist hier ligt een kans.

    Yentl benoemde het belang van een flexibele, iteratieve aanpak en de juiste tooling.

    Christ gaf concreet advies over hoe te starten:

    1. Maak een transparante roadmap met heldere mijlpalen.
    2. Verdeel eigenaarschap in de keten – laat leveranciers hun eigen data beheren.
    3. Vermijd dubbele data-opslag: PIM is belangrijk, maar niet de plek voor alles.
    4. Zet in op traceerbare, fraudebestendige technologie zoals blockchain.

     

    Download the DPP white paper

    AI in PIM: here, there, everywhere

    “AI comes in many forms within modern PIM solutions,” said Eline, sharing a summary of several levels:

    • Generative AI automatically generates product descriptions, translates texts and fills in missing content. Ideal for organisations with large product ranges that operate globally.
    • Predictive AI analyses historical sales data, seasonal influences and external trends (such as weather, search behaviour or social media buzz) to predict which products will become relevant in the market.
    • Machine learning recognises patterns in incorrect data, duplicate records or missing fields—and uses this information to make automatic suggestions for improvement. This supports data teams in their quality control without manual detective work.
    • Conversational AI makes it possible to communicate with your PIM system in natural language. Think of: ‘Show me all products that do not yet have CE certification’ or ‘Adjust the description of product line X in five languages.’
    • Agentic AI goes one step further: these systems independently perform actions based on objectives. For example, generating, checking and publishing product information for a new collection, fully automated and tailored to the requirements of each channel.

    Eline emphasised that these forms of AI not only help to speed up processes or reduce costs. Above all, they increase the quality, relevance and scalability of your product information—and with it, your reliability towards your customer.

    But perhaps most importantly, AI should enhance the human touch, not replace it. Every product description, every data point, every interaction—online or offline—is an opportunity to earn trust. AI helps with that, but it’s the people behind the data who determine whether the experience is right, feels right and convinces.

    “AI doesn't belong in one place in your PIM landscape. It should be everywhere there is friction. Everywhere processes can be smarter. Everywhere data deserves more value. But it must always have one goal: a customer experience that is right, at every touchpoint.” Eline Schonen - Modern Work Expert at Ctac

    What should you do (differently) tomorrow?

    As a final question, Joakim asked what priorities organisations should set now. The panel gave four clear messages:

    1. Make sure your PIM landscape is scalable and flexible.
    2. Invest in data quality before you automate.
    3. Use AI where it really adds value—not everywhere.
    4. Build trust—in your data, your tools and your people.

    KPIs that matter

    Yentl, Christ and Eline concluded with measurable insights: Conversion, return rate and NPS remain valuable.

    But so do data quality and data speed within your e-commerce chain. That’s why Yentl advocated KPIs that provide insight into the substantive strength of your PIM processes and the quality of your e-commerce infrastructure:

    • Data completeness and consistency per product category
    • How complete and up-to-date is your product information, including features, media and translations?
    • Time-to-Publish
    • How quickly can you get a new product from source data to live, including local adjustments? This is crucial for seasonal products, promotions or international rollouts.
    • Data delays in the chain
    • How much time elapses between the moment data becomes available (e.g. from a supplier) and the moment it is visible in the channel? Friction here means lost revenue.
    • Number of data changes per product
    • Too many changes can indicate poor source data. Too few can indicate outdated information. Balance is essential.

    But also:

    • Adoption of AI tools by employees,
    • Time savings on data processes,
    • Speed with which new products go live,
    • And above all: how employees feel about using these tools.

    ‘Good KPIs are not just about performance, but about trust, creativity and progress,’ said Eline. Yentl concluded by saying that without healthy source data, there can be no healthy commerce.

    Conclusion

    PIMpoint showed that the future of commerce is not just about technology—it’s about organising your product data intelligently, building lasting relationships and using AI to enhance human work.

    At Ctac, we believe that you can only be successful if your PIM architecture, your processes and your people work together in harmony.

    Want to know more about our vision on PIM, AI and sustainability?

    Please contact Yentl Bosma as soon as possible.

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