The New B2B Standards Powered by AI and Smart Product Data

Digital commerce finds itself before a thrilling phase. Whereas some may speak of a disruption, what is currently transpiring does in fact feel more like an upgrade to the next-higher e-commerce version: more intelligent, more visual, more modular, and faster than ever.

In B2B, in particular, a noticeable dynamic is emerging: Complex products as well as an increase in both requirements and digital sales paths, like B2B marketplaces, make it so that modern commerce trends can have an extraordinarily significant impact here. One minor spoiler: There is simply no way around AI anymore.

Here are the five trends that will call the tune in 2026, and all of them have one thing in common: Without clean product data, they are not going to work.

 

1. Immersive Commerce: 3D & AR will Become the New Quality Standard for Complex Products

Trend analyses like Quid Report (The State of AI in E-Commerce: 2025 Quid Trend Report) indicate: Immersive technologies such as 3D models, virtual product views, and augmented reality are gaining more and more ground in e-commerce. Increasingly more retailers and manufacturers work on making digital products livelier and more inspiring, in order to make the purchase decision easier for customers.

While it is often times AR/VR that makes for the inspiration in B2C, they take on a much different significance in B2B: They help you present complex products in an exact manner. Before they even place their order, construction teams and purchasers want to examine if a component will fit, what measurements are of relevance, or how a specific module will run in the existing infrastructure.

A typical example: A complex product is provided as a rotatable 3D model. This allows you to evaluate measurements, ranges of motion, or junction points first-hand in your browser. And it is precisely this that makes immersive commerce approaches so valuable: They reduce further inquiries, accelerate decisions, and bring transparency into the technical product landscape.

If you want to dive deeper into the topic of product experience and learn how product experience can be optimized in a targeted way for B2B, we recommend to you our expert interview: Product Experience in B2B: Why the Turning Point is Now.

 

2. AI-Generated Items & “Sell-Before-Make”: Test Variants Prior to their Creation

AI is increasingly being used to design product variants before they go into physical production. This enables companies to define faster which variations are truly relevant and which are not. Instead of creating every single variant manually or validating it only after production, digital concepts are designed first in order to test them internally or in small sample groups.

A typical example: A technical component is generated in multiple digital variants, including different measures, materials, and geometries. Constitution, development, and sales can evaluate even at early stages which version is meaningful and which variant requires further adjustments. The benefits are obvious: Less maldevelopment, faster feedback loops, and decisions firmly grounded upon real requirements.

 

3. Predictive & Automated Commerce: When Commerce becomes Predictive rather than Reactive

In e-commerce, everything that renders processes faster, more precise, and less error-prone is gaining significance. And it is exactly here where AI unfolds its intrinsic strengths. More and more companies trust in automated recommendations, intelligent price logics, and predictions for availability and demand.

This is of particular value especially in B2B: Wearing parts, components, or consumption materials can be ordered on the basis of past data or appropriate software predictively. A practical example: A system identifies that a specific component ought to be replaced after a certain usage period – and automatically recommends the order at just the right time.

This reduces non-payment risks, alleviates a burden from teams, and makes commerce less reactive and more predictive.

 

4. Circular Commerce & Resale 2.0: Intelligent Reuse instead of Resource Loss

Circular commerce becomes more prominent as a supplement to classic order processes. In B2B, in particular, reparability and reuse are substantial economic advantages because the product can be used over the long term.

Digital tools support you in evaluating the current status of components, identifying usable spare parts, and documenting data about runtime or materials. A typical scenario: Some spare parts are digitally analyzed after intensive use, and the system recognizes which elements can be reused. This creates an efficient lifecycle with minimized friction, more transparency, and lower costs.

 

5. Headless & Composable Commerce: Flexibility for Complex System Environments

Modern B2B commerce architectures increasingly trend towards more flexible and modular approaches. Headless and composable models offer the option to operate and combine frontend, backend, and individual services freely and independently – the ideal basis for companies working with multiple systems while having to fulfill diverse requirements.

Be it ERP, PIM, CAD, PLM, configurator or marketplace: A modular system environment guarantees that each and every system can play to its own strengths without setting limits to the whole infrastructure. This way, interfaces and sales channels can be connected without the need to make foundational changes to running systems.

For this method to function like a well-oiled machine, however, it also takes a consistent, well-structured data basis. Only in doing so will all modules be connected cleanly while also allowing the environment as a whole to deliver consistently high performance.

 

Conclusion: 2026 Belongs to the Companies that Control their Data and Apply AI Meaningfully

AI, 3D, automation, and modular commerce architectures visibly change digital commerce. This becomes particularly clear in B2B: The more complex the product, the more important a solid data foundation is. At the same time, a further trend is emerging: Bring Your Own LLM. More and more companies trust not merely in external AI services but self-hosted AI models enriched with company-specific knowledge and made to measure their processes. This means: AI is running in your own environment in a controlled manner, processes sensitive data in a confidential manner, and delivers results that are more precise and technically relevant than in the case of generic models.

The benefits are apparent:

• Data Sovereignty: Full control of data and AI usage
• Security: Sensitive information remains within your own systems
• Expert Knowledge: Models learn what really makes a company
• Made-to-Measure Processes: AI supports workflows instead of bending them

ATAMYA supports this approach: The platform is designed in such a way that companies can seamlessly integrate LLMs and use it for catalog maintenance, text generation, variant logic, quality checks, or process automation.

In short: Data sovereignty meets data quality and, on top of that, real AI performance. For B2B companies, this is a great step towards a future of sustainability.

How Companies with Well-Structured Product Data and PIM Systems Master the New Challenges

Products with digital components have long been an integral part of our everyday lives – from smart household devices to industrial IoT applications. With the growing number of interconnected systems, however, you also paint a bigger bullseye on yourself for cyber attacks. Security holes do not only put individual users into danger nowadays but even entire companies, critical infrastructures, and, at the end of the day, trust in technology.

Up until now, there have been no consistent regulations by the EU to make the cyber security of products mandatory. The Cyber Resilience Act (CRA) of the European Union changes this fundamentally. Unprecedentedly, it creates an obligatory legal framework for products with digital elements while also turning cyber security into a quality factor.

The Cyber Resilience Act will regulate for all of Europe how secure hardware and software products must be when they are launched on the market. For manufacturers, retailers, and importers, this means: Cyber security turns into an obligation, and it does so throughout the entire lifecycle of a product.

But what exactly is behind the regulation? And why does data management play such a central role therein? This is what you will learn in this blogpost.

 

What is the Cyber Resilience Act?

The Cyber Resilience Act is an EU regulation that defines the minimal standards for the cyber security of products with digital elements. The aim is to identify security gaps well ahead of time, resolve them, and, consequently, make products more resilient against cyber attacks.

Affected are all products that are connected to the internet either directly or indirectly, ranging from routers to smart home devices. Software solutions that interact with such products or are part of the product’s functionality do also fall under this legal category.

The CRA follows the aim of establishing a consistently high cyber security level for products with digital elements – throughout the entire product lifecycle. This concerns nearly all products that contain a piece of software or feature network connectivity, like networked hand devices, operating systems, industrial control systems, or embedded software such as cloud-connected apps and platforms. Excluded are only specific sectors such as medical devices, automobiles, and aircraft technology, since they are already subject to more specialized regulations. Even open-source software is only excluded if it is provided non-commercially. As soon as an open-source component is utilized within a commercial product, the CRA requirements apply.

 

What will Change for Manufacturers and Retailers in Detail?

The CRA comes with far-reaching obligations for manufacturers, importers, and retailers. It dictates that cyber security is now no longer an optional measure secondary to the product launch but a central quality requirement in its own right as early as in product development. In short: Cyber security advances to the position of an integral product requirement, it is no longer something that comes after the fact as an add-on.

• Security-by-Design and Security-by-Default
  Security requirements must be integrated throughout all phases of development from the very beginning. Products must be delivered in a default configuration that can be restored at any time.

• Lifecycle-Overarching Responsibility
  Manufacturers are obligated to observe vulnerabilities across the entire product lifecycle, provide updates, and define support periods.

• Documentation and Verification Requirements
  Companies must provide both technical documentation and a software bill of materials (SBOM) consisting of a list of all utilized software components including open-source libraries. The documentation must be handed over to national authorities such as the BSI in Germany or the ANSSI in France.

• Risk Management and Incident Handling
  Security leaks must be identified, evaluated, and reported. Companies must establish processes for vulnerability management, incident response, and patch management.

• Conformity Assessment and CE Marking
  CRA conformity is the feature requirement for CE marking. Without proof of this requirement, a product can no longer be brought onto the European market.

 

Deadlines and Transitory Regulations

The regulation (EU) 2024/2847 has been enacted on 23rd October 2024 and came into force in December 2024 with a transition period until 2027. The transition period phases were defined as follows:

• December 2024: CRA comes into force.
• Starting in June 2026: Conformity assessment bodies can comply with requirements (auditability).
• Starting in September 2026: Obligation to inform in case of security gaps and leaks.
• December 2027: End of the three-year transition period, from here on all requirements are obligatory: manufacturers, retails, and importers must have established complete CRA conformity, including risk analysis, documentation, update processes, and the procedure of furnishing proof.
• Starting in December 2027: Only CRA-conform products with CE marking can be brought onto the European market.

For many companies this means: The time for strategic preparations is now. Without preparation, the CRA will turn into a business risk. Since, without CRA conformity, there will be no CE marking in the future – and, without CE marking, no sales in the EU.

 

Why Well-Structured Product Data is Decisive

Cyber security makes its beginning not with technology but with clear, consistent, and transparent processes and methodologies. The CRA demands complete technical documentation, risk evaluation, as well as proofs and validations. All this hinges upon a clean data architecture. Wherever central information is missing gaps, duplicates, and errors loom large. This is a compliance risk with potentially high fines.

For manufacturers, retailers, and e-commerce companies in particular: Only those who manage their product data centrally can efficiently handle the effort revolving around documentation requirements, audits, and update tracking.

 

From Cyber Resilience to Data Transparency: The Digital Product Pass as a Logical Expansion

The Cyber Resilience Act does not stand in isolation. Starting from 2027, the Digital Product Pass (DPP) will become obligatory as a further central EU instrument, initially for batteries, textile, and electronics. Additionally, the regulations for product liability will be revised. All regulations follow the same agenda: Trustworthy, secure, and transparent products on the European market and clear rules for responsibilities.

The CRA focusses on security and verifiability. The DDP, on the other hand, lays its focus on transparency and digital accessibility. Product liability governs all consequences caused by damage. The decisive middle term between the two is the underlying data.

One and the same set of data processed and documented for the CRA does also form the very foundation of the digital product pass. Technical descriptions, software versions, security, and lifecycle information must, in the future, not only be well-maintained but also made digitally accessible. Such information must be interoperable, machine-readable, and provided via standardized interfaces. With this, things grow into one another that were previously separate. Security and sustainability become two sides of the same product responsibility.

 

PIM as the Bridge Between CRA Compliance and Digital Product Transparency

In the context of all this, a Product Information Management (PIM) system becomes the central link between technical security and organizational compliance. A modern PIM supports the central management of all product-relevant information, versioning and audit capacities, integrations of SBOM, CE documentation and risk analyses, as well as interface capabilities for the automated processing of data to product passes or government portals. This way, a consistent data foundation comes into being that enables companies to fulfill both the CRA requirements and the DPP regulations with minimal effort.

Those who build on structured data management today will realize two obligatory regulation areas at the same time: security and transparency.

 

Why Data Management is Decisive for Your Business Success

Without uniform data structures, implementing the new requirements is nigh impossible. In many companies, product information is scattered across departments, tools, and even physical locations. This comes with risks concerning compliance, efficiency, and security.

A modern PIM system does away with aforementioned issues:

• Central Data Warehousing: All product-related information in one and the same space.
• Faster Audit Capacities: Proofs and documentation are immediately available.
• Higher Data Quality: Less redundancy, more consistency.
• Automated Reports: Compliance data and product data can be directly exported into the digital product pass.

This saves time, costs, and reduces the margin for error. In the face of possible fines of up to 15 million euros or 2.5 percent of the annual global revenue, this is a decisive competitive advantage.

 

Conclusion: CRA is More Than a Mandatory Necessity

The Cyber Resilience Act is no bureaucratic obstacle but a chance to establish security as a quality factor. Companies that connect cyber security with data management now, secure not only compliance benefits but also the competitive edge and the customers’ trust.

Those who manage product information centrally, document their updates, and design processes in a transparent way, lay the cornerstones for real cyber resilience and will face the upcoming EU requirements with confidence.

PXM as the Game Changer? An Interview with communicode & ATAMYA

We all know the force of habit. When we go shopping today, we expect smooth processes, intuitive navigation, and recommendations that fit perfectly – an experience major platforms have long since accustomed us to. Signing in, browsing the offers, comparing, making a decision: fast, clear, uncomplicated. The customers of today expect clarity, speed, and immediately accessible information – in B2C just as they do in B2B. It is in B2B, however, where our expectations clash with complex products, multi-variant assortments, and different target groups. Those who sell machines, components, or technical systems, require precise and well-structured product information that all relevant target groups can find quickly – be it specialist planners, engineers, or decisionmakers.

Back in the days, purchase decisions were dominated primarily by technical factors. Today, the quality of the digital product experience decides whether a company even makes it onto the shortlist. How can one go about making even complex assortments intuitively experienceable? And what role does PIM, AI, and automation play? This is what Tiffany Wiener, ATAMYA Senior Manager Demand Generation & Partner Marketing, discusses with three experts who have been working for decades in the intersection field of data, technologies, and customer experience.

Our Interview Partner

Michael Ochtrop – communicode AG
Co-founder of communicode and expert for information management, systems evaluation, and PIM implementations for more than 20 years. He develops operative and processual concepts and knows: software is only effective when humans and processes go along with it.

Stephanie Ebbert – communicode AG
UX designer with focus on complex B2B and B2C platforms. For her, good product experience does not come to be through “beautiful design,” but through authentic user understanding, tests, and psychology.

David Klein – ATAMYA
Senior consultant for ATAMYA. He accompanies companies from all industries on their path to better product data – for a customer journey that holds from the first click to the final decision.

 

Why Is Good Product Experience Indispensable in Today’s B2B?

Stephanie Ebbert: Customers in B2B expect the very same usability as they do in B2C. They want to understand products quickly, compare them, and conduct a save evaluation. Good product experience means: complete and correct information, clear-cut structures, expressive images, as well as functions such as filters or comparison tables. Those who do not offer convincing product experience are often times do not even qualify for the shortlist.

David Klein: And B2B content is becoming ever more target-group-oriented. Specialist stores, installers, technical salespersons – all of them present different requirements. One and the same product must work in entirely different contexts.

 

How Can Companies Design Complex Products and Variants in a Way that Provides Convincing Product Experience?

Michael Ochtrop: The truth is as follows: Many B2B companies have not structured their variant landscape cleanly because their sales team has handled things manually on site. Once they make the digital turn, they suddenly come to realize that they are lacking both systems and data structures.

Stephanie Ebbert: Complexity must not be visible. Intuitive searches, filters, and complete product pages with all relevant information – everything must come together to help the customer in finding suitable solutions faster.

David Klein: Creating comparisons is essential. Be it smartphone or complex industry goods: humans seek orientation. And product experience also means factoring in everything revolving around the product: accessories, care instructions, service contracts. Our customer Christ Juweliere  shows how it’s done: not only do they sell high-quality necklaces but also teach you how to maintain them. This strengthens purchase decisions and trust alike.

 

What Preconditions Must Be Met by Companies to Distribute Continuous Product Experience Throughout All Channels?

Michael Ochtrop: It may sound trivial, but it makes up the very core: one common data basis! The product information must be centralized, up-to-date, high-quality, and as granular as possible. And it must be accessible and available: through intuitive interfaces without the need to launch a new integration project every time. Simply introducing a PIM system, on the other hand, is insufficient. What is commonly underestimated in practice is organization. In many companies, product areas are organized in many small units. When it then comes down to forming a consistent presentation throughout all areas, the teams must come together and agree upon shared processes. This is real change management – it usually constitutes the greatest challenges.

 

Does B2B even have Room for Emotions?

Stephanie Ebbert: Yes, absolutely. Even in B2B, people have to make decisions – and they do react emotionally. Good product experience creates not only functionality but also trust and security. Once a customer notices ‘I can find all product information quickly, I can rely on them being correct,’ then trust is established. And without such trust, you will usually not even make it into the second round in B2B selection procedures – no matter how good the product is.

 

How do AI and Automation Help Without Losing the Human Side?

David Klein: It’s impossible to get by without automation these days. The time-to-market is decisive, while manual processes simply take much too long. AI is very helpful to this end. It helps, for example, when it comes to translating, generating text recommendations, or data checks. We do, however, also need the human in the loop: only people can make judgments about whether the content is truly meaningful.

 

Which AI and Automation Features Are of Particular Relevance in B2B?

David Klein: In B2B, automation mainly reduces the workload. A good example is smart imports in ATAMYA Product Cloud: it identifies which products are being imported, automatically assigns the data, and delivers an initial draft – all that’s left is to briefly double-check it and make adjustments where necessary. AI also helps serving different target groups. Engineers require installation instructions and tools – the specialist store, on the other hand, needs prices, quantity, and weight. These contexts can be generated automatically by us. And AI spots errors that people are quick to overlook by accident in daily business. Recently, it recognized that a supposedly 60-inch monitor is, in fact, a 60-centimeter product. Checks like this are worth gold.

 

Quick Wins: What Measures Can Companies Take to Improve Their Product Experience?

Michael Ochtrop: We usually launch our projects with quick wins – small measures that quickly return results. In many cases, a compact analysis of product pages already translates into small UX improvements. And many companies do not tap into available potential: when images or assets are delivered with a clear naming convention, you can use this for automatic assignment. This saves enormous amounts of time and quickly generates success. An example: A customer had 40,000 products but only roughly 100 of them had rich content since everything was done manually. With a PIM, we could equip more than 1,000 products with rich content automatically and, hereby, generate measurably more profit.

David Klein: And, of course: A quick onboarding is a quick win, too. With ATAMYA, companies can get themselves started within 30 minutes – standardized interfaces to other systems, workflows, and a clean UI. This guarantees that users will feel the value immediately.

 

What Are the Most Frequent Mistakes Companies Ought to Avoid When Optimizing Their Product Experience?

David Klein: A popular mistake is to build data models that strictly abide to a classification. This works only until something needs to be multi-classified. The entire data model will be quick to collapse. On top of this, many try to manage their product data in Excel, shop backends, or even enterprise resource planning systems. This eventually hits a limit at certain data quantities, is prone to error, and tough to distribute.

Michael Ochtrop: Often times, companies underestimate just how important an understanding of the target group and organization is. Many think: ‘We can handle that one on our own’ – without first defining what users truly need. Equally as often, one tries to solve everything at once instead of prioritizing quick wins with clear business value. One further typical mistake: Reconfiguring systems beyond recognition instead of optimizing processes. This leads to technological debts and prevents updates.

 

How Can Companies Design the Product Experience So That They Can React to New Requirements, Markets, And Channels Flexibly?

Michael Ochtrop: With clear guidelines: technological, organizational, data-driven. A central API, a central design system, service-oriented architecture – all this guarantees that modifications scale and no department is just doing their own thing.

David Klein: Cloud, microservices, headless and APIs according to the MACH principles – this is future. No retrofitting of servers, no outdated plugins. Modern platforms scale automatically.

 

What Role Does Data Quality Play for Product Experience?

David Klein: It’s everything. Without quality, no experience. Users compare in various ways, so they need many technical facts, neatly structured, and target-oriented. No walls of text but chunked data, lists and relations such as recommended products, spare parts, service contracts, and much more.

Michael Ochtrop: Once they spot an error, all trust is gone. Consistency, up-to-dateness, and technical accuracy are not just data requirements but business requirements.

 

What Is the Most Important Switch for Sustainably Improving Product Experience?

Michael Ochtrop: For me, there are two central switches: first, a clean PIM process with a central, high-quality data foundation. Second, the target-group-oriented processing of data for each and every channel.
What companies commonly underestimate is accumulating leftover issues. We had projects where about 7 million assets piled up in the old system – and, after data migration, it came to light that they only really needed roughly 10% thereof. 90% of the assets were files that accumulated over the years that nobody needed anymore. Such legacy issues hinder any PIM or PXM project, both technologically and organizationally. Those who continuously work on reducing such issues create the basis for efficiency and scaling.

David Klein: And to this end, everything needs to be managed centrally. A single system that pools product data centrally and prepares it for all channels – it is precisely this that makes the difference.

 

Conclusion: Product Experience in B2B is a Strategic Factor Today.

Product experience in B2B is no nice-to-have any more – it decides over whether companies become visible at all. Those who master their product data can tap into new markets, improve sales processes, and establish trust. And for precisely this reason, the combination of good technology, good organisation, and authentic user orientation is required.

How Clean Data and Smart Workflows Make the Difference on Black Friday

Black Friday is the highlight of the year for many companies. The offers are being defined, marketing campaigns are set up, discounts are planned, etc. More often than not, however, there is something else entirely operative in the background that decides over success: the quality of product data.

What does this mean in detail? This is best illustrated by taking a glance at the day-to-day business of two roles that are absolutely inseparable during Black Friday: e-commerce and product data management. To this end, let us observe two fictional examples: e-commerce manager Emma and product manager Daniel. Their stories will make clear what challenges teams will have to tackle again and again on the markets – and how they can be easily resolved with the right processes and tools.

 

Emma’s Challenge: Speed and Visibility

For Emma, there is one thing before anything else when it comes to Black Friday: an increase in sales. For weeks upon weeks, she was busy planning campaigns, defining offers, and preparing channels from the company’s own shop to Amazon and social media. Now that we enter the hot phase, however, problems begin to surface: The prices have not been correctly distributed everywhere, some products are not even available in some marketplaces, and some translations are missing for international markets. It is here where Emma will hit a limit. It is precisely in this moment that she comes to realize just how much of the success depends on clean, reliable product data.

 

This will Help Emma

With a PIM system as the central platform for product data, Emma gains the much required speed. Instead of maintaining information across multiple systems, she updates data only once – and all connected channels check and update automatically. AI-controlled functions support in creating texts and translations so that even short-term promotion runs can be realized internationally. Thanks to automated workflows, campaign content is distributed error-free and just-in-time.

• Consistent data source for all channels
• Automated exports to shops, marketplaces, and social media
• AI assistance for texts and translations
• Flexible adjustment of content in real time

 

Daniel’s Challenge: Quality and Consistency

Our product manager Danial thinks less in campaigns and more in structures and processes. For him, Black Friday is a real stress test for data quality: inconsistent descriptions make for unfindable products; outdated records translate directly into canceled purchases; and excel lists with errors delay release. Even small improvements made to product pages, such as better images, clearer descriptions, or complete information can significantly increase conversion as proven by a study of Ecommerce Bridge. Daniel now knows for sure: Without a consistent, reliable data basis, Black Friday will quickly turn into a risk.

 

This will Help Daniel

Modern data management grants Daniel with the overview he needs. The automated data validation displays directly where specifications are missing or where they contradict each other. The workflow-based release assures that only approved data goes live. With the help of AI, missing attributes such as sizes, materials, or colors can be completed so that the catalog stays consistent even with growing assortments.

• Automated data validation for completeness and consistency
• Clear release processes with traceable workflows
• AI-assisted auto-completion for product attributes
• Stable data foundation for all subsequent commerce processes

 

Conclusion: When Processes and Quality Come Together

Black Friday shows just how tightly data quality and ecommerce performance are intertwined. While Emma trusts in swift campaigns and outreach, Daniel establishes the foundation: consistent, complete, and reliable data. Only this interplay creates real competitiveness.

Companies that unify both perspectives profit not only on promotion days such as Black Friday but secure long-term success. Modern solutions like ATAMYA Product Cloud guarantee that product data is available, up-to-date, and consistent on all channels. Every team working with product information in the company will act more efficiently with this. Thanks to AI-assisted functions, translations, and automated PIM workflows, companies run like a well-oiled machine even in the face of highest sales pressure.

Key Takeaways:

• Product data quality is a decisive success factor on Black Friday.
• Ecommerce teams profit from speed, automation, and AI.
• Data managers create the stabile foundation with quality assurance and workflows.
• Even small optimizations in product presentation or image quality improve conversion rates.
• The combination of speed, efficiency, and data quality does not only pay dividends on Black Friday but throughout the entire business year.

The New Google AI Mode – Paradigm Shift in Product Search

The internet search as we know it is on the brink of a radical change. With the new AI Mode, Google introduces a system which no longer banks on classic link lists but generative answers – created by Large Language Models (LLMs). Users directly receive comprehensive, context-sensitive information directly on the search results page without the need to visit a website.

This paradigm shift does not only change user behavior but also poses new challenges to both e-commerce companies and brands. This is because the product search, too, is undergoing massive changes. Those who want to stay afloat in the light of AI-assisted product searches must grapple with structured data provision and hyper-personalization. Google AI Mode makes one thing clear: Only high-quality, machine-readable product data stays visible.

In this article, you learn what impact Google AI Mode has on the topic of product search and why product data management advances to the strategic key discipline.

 

Google AI Mode vs. Google AI Overviews – what’s the Difference?

Even if the concepts are often used interchangeably, there is a clear-cut difference between Google AI Mode and Google AI Overview:

• Google AI Mode is the overarching search mode that users can activate (currently only in specific regions such as the US and India). With this mode, you get the full AI-based search experience – with interactive answers, context-related follow-up questions, and extended functions like product comparison, travel and tour planer, or even shopping promotions.
• Google AI Overviews, on the other hand, stands for the visible, AI-generated answer fields displayed directly in the regular Google search results – even without AI Mode being activated. They deliver compact summaries, are based on generative models, and supplement the classic search results.

And how do LLMs such as Gemini and ChatGPT differ from this?

While Google AI Mode and Google AI Overview represent concrete functions within Google Search itself, Gemini (Google) and ChatGPT (OpenAI) are so-called large language models (LLMs) – that is to say, they constitute the technological foundation on which all such functions are based.

• Gemini is Google’s own LLM utilized for a great many applications – among others Google Search, Workspace (Gmail, Docs, etc.), and even AI Mode itself. It delivers the intelligence for generative answers and interactions within the Google ecosystem.
• ChatGPT is the LLM-based dialog system by OpenAI which is also based on its own language model (GPT-4). With the ChatGPT Search Extension, you can also access real-time data from the web and integrate it in answers.

Therefore, the difference lies in the application:
Gemini and ChatGPT are “motors” (models), while the likes of AI Mode, AI Overview, or ChatGPT Chatbot are “automobiles” (user interfaces / functions) into which this motor is built.

 

Google AI Mode vs Classic Search Engines: What Will Change?

The classic times of users inserting term-based search queries into Google, click on results, and tediously work through displayed websites is a thing of the past. Over the last few years, Google has already found a competitor in Amazon when it comes to product searches. In particular when it comes to product searches for making a concrete purchase decisions. At the same time, TikTok has revolutionized the search behavior of generation Z: here, people search for experience reviews, inspirations, and trends – fast, visual, and emotional.

While classic SERPs (Search Engine Result Pages) are a combination of ads and organic links, Google now – that is, since May 2025 regionwide in all of Germany – provides direct contextualized answers (source: Sistrix). This changes the customer journey drastically: it is shorter and much more shaped by AI.

 

Google AI Mode, the New Generation of Search

The actual paradigm change, however, starts now: With Google AI Mode, a new generation of Google Search appears on the scene. Product recommendations are no longer provided by classic SEO rankings or paid advertisement on page 1, but through a smart, AI-generated answer with only a single reply. There no longer are any classic link lists. Instead some well-selected links pop up as part of the answer text – why click on them, though, if you have already been provided with what you have been looking for?

AI Mode has been in use in the US since May and has recently also been rolled out in India. When it will be introduced in Europe or Germany is currently unknown. Possible candidates include a release within this year still or perhaps even an implementation at a later date.

Parallel to the changes in search results, AI assistants such as ChatGPT by OpenAI or Google’s Gemini do also gain in significance. Both technologies are based on Large Language Models (LLMs) that do not only answer search queries but also interpret and contextualize relevant content in order to deliver direct, personalized results – without users having to click actively.

For e-commerce specialists, this means the following: Those who do not follow the trend will lose visibility – not only in Google applications but everywhere where AI systems dominate access to information.

 

This is How Generative AI Changes Google Search – and what this Means for Companies

The integration of generative AI in search systems revolutionizes digital retail – this is not a vision of a distant future but the here and now. The more affinity your target group has for technology, the more you can feel the first changes as early as today. Especially large language models (LLMs) such as ChatGPT and Gemini deeply influence the customer journey. The following examples show in more concrete terms what this means:

Informational Content Loses in Reach

LLMs answer simply questions directly – often times before a user even gets to take a look at the listed websites.

Example: An online shop for household devices which previously benefitted largely from organic traffic thanks to tips-and-tricks articles like ‘5 household solutions against stains’ are now experiencing a collapse in page visits. The reason: Google AI presents the answers directly in the search.

Keyword Data Loses in Meaning

Thanks to AI, search requests become more individual, context-related, and dialog-oriented. Classic keyword tools are currently hitting a wall. In the future, content ought to be centered more around search intentions and thematic relations.

For example, a sporting goods retailer who previously banked on keywords such as ‘running shoes woman’ must now come to the realization: new search requests go like ‘Which running shoes are suitable for marathon training despite knee problems?’ – a highly specific longtail question answered directly by the AI system with personalized recommendations.

AI as the Purchaser: Who will Make the Purchase Decision in the Future?

With the release of AI Mode in the US, Google advances more and more to the position of an active shopping assistant. While users used to do their own research, AI now takes care of this proactively – including the processing of the purchase for the vendor directly via Google Pay.

When AI systems prepare or even make purchase decisions, the supply chain changes down to its very core. Here, however, many questions are still left open!

What will happen with classic cross-selling measures? Today, users browsing online shops see notifications such as ‘Customers also bought…’ or ‘Buy in bulk to save money.’ Will an AI to which the task is given to procure a specific product, however, also take these additional offers into consideration?

Or maybe the development will take the opposite turn – and AI systems may actively ask whether you need something else? Be it Google, OpenAI, or another provider: with increasing integration into various end devices, AI assistants may very well soon be able to make hyper-personalized purchase recommendations that transcend anything today’s product searches are capable of.

And how will all this be affected by legal regulations? The legal perspective – in particular from the point of view of European courts – is still open. One thing, however, is clear: providers such as Google have a strong economic interest. With ad revenues of 265 billion US dollars in the year of 2024 (source: Statista) the stakes are high.

What about monetization of AI searches in the future? Will classic ads continue to be relevant – and if so, where will they be displayed? And how will retailers pay so that the Google AI assistant will make purchases in their shop given that there will be no more costs per click or target CPAs (cost per acquisition)?

The logical answer would be the following: An AI assistant should be geared towards maximizing sales, from Google’s viewpoint. While it takes five clicks to buy a product today, AI can do so in a single click – this is efficient but potentially less profitable for Google. Could a transaction-based payment model be more practical for AI? If so, then AI would turn from a pure buyer to a strategic seller – with the goal of generating more purchases and more sales.

What courts will say about this remains to be seen – this also counts for the question of whether AI systems will even be allowed to make autonomous purchases in the first place and who will be held accountable should legal conflicts arise. Here, one thing is for sure: these questions will come to determine both the legal and ethical frame of AI in e-commerce.

 

Product Data as the Foundation of AI Search

As we have seen: Many questions revolving around the implementation of AI in product searches remain unanswered for the time being. There is one point, however, that is certain as early as today – and it is of utmost importance:

Only those who deliver machine-readable, up-to-date, and high-quality product information will be taken into consideration by AI systems. The quality of product data will decide whether a product can be found, parsed, and consequently recommended.

This is relevant because AI systems such as Gemini or ChatGPT do not function like classic search engines that merely link to websites. They analyze and interpret content semantically – they must understand what the product is about, which properties it has to offer, how it differentiates itself from the competition, and into which use case context it fits.

This can only be successful when product data is…

• complete (e.g., technical details, measures, materials, use case applications),
• structured (e.g., in the form of well-defined attributes, hierarchies, and categories),
• consistent (across all channels),
• and updated regularly (such as for prices and availability).

Without this data foundation, an AI cannot build a valid product understanding – so that the product will simply not be displayed or recommended. On top of that: The higher the data quality, the better the AI system’s hyper-personalization, product recommendations, and automatic content generation will function.

Last but not least, the following applies: Product data is not only the information source but also a strategic asset. Those who neglect it in the world of AI search will not only lose visibility but also their competitive edge in the markets in the long run.

 

How Companies Should Prepare Right Now

We stand before the beginning of a foundational change – and it will soon become reality, sooner than most may expect. With its shopping assistant, Google has delivered a first foretaste, more functions by ChatGPT, Gemini, and others will soon follow. The development is proceeding at a rapid pace.

Now is the time to act. Companies ought to prepare as well as possible for the upcoming changes:

• Observe the current developments revolving around AI and Search – in particular Google, OpenAI, and other relevant players.
• Check your existing product information for completeness, structure, and up-to-date-ness.
• Invest into a modern PIM system, like ATAMYA, to manage data in a central, efficient, and sustainable manner.
• Optimize your product data specifically for AI processing, including semantic structures and relevant attributes.
• Enrich your information as much as possible – from technical specifications to context-relevant descriptions.
• Keep taps on the legal developments on how to handle AI in retail in order to prevent risks well ahead of time and take legal precautions.

Only those who act at an early stage will be visible and stay both competitive and relevant in the AI-driven commerce world.

Why Product Experience is the Key to E-Commerce Success – and How You Can Set it Up Strategically

In a world full of exchangeable products, experience determines success. Customers are no longer simply buying a product – they purchase the use, emotion, and effect it unfolds. A shirt is more than a piece of clothing: it changes the appearance, the feeling, and the impression you leave on others. This transformation must be consistently experienceable across all channels – from the first touchpoint to the actual usage. And it is precisely here where product experience management (PXM) comes into play: It guarantees that products are not only available – but that they inspire.

How then can one go about designing a consistent product experience – in a world where expectations for content and services are ever-growing? And why is a PIM system indispensable to this end?

 

What Does Product Experience Actually Mean?

Imagine, for a moment, a potential customer stumbling upon your product – be it in an online shop, marketplace, or via a social media post. What keeps sticking? A technical datasheet? Or rather a feeling?

Product experience is just this: the sum of all impressions that a product has on the customer – in a visual, emotional, or informational manner. It’s about how a product appeals, not only what it is.

When it comes to a pair of running shoes, customers do not only want to know the material it is made out of. They want to see how it feels as they walk, how it makes them go faster – perhaps even how it motivates them to stand up in the morning and go for a run.

For a shirt, it is not only the material and the fit that counts, but also the impression it conveys. Confident, stylish, attractive. This is the actual sales argument.

It is this effect that must be experienceable throughout all touchpoints – from the first image to the last product evaluation. This is what product experience is about.

 

How Does One Create Convincing Product Experience?

Strong product experience is not a matter of happenstance – it is the result of a well-thought-out strategy and precise execution. The most important steps:

1. Understand Target Groups: What information is relevant for your customer? What emotions should be evoked?
2. Design Consistent Content: Uniform product information across all channels is essential.
3. Assure Visual Quality: High-quality images, videos, and interactive content boost attractivity.
4. Utilize Emotional Story Telling: Tell a story revolving around your products – this creates proximity and trust.
5. Capitalize On Personalization: Relevant content at the right time and the right place increases the conversion rate.

 

What to Look Out for During Implementation?

The greatest challenge lies in assuring that product information is consistent and up-to-date. Various systems, manual processes, and decentralized data management are quick to lead to inconsistencies – and, consequently, to a glaring hole in the product experience.

Important Success Factors:

• Central Data Management: All product data is managed and enriched in and through a single space.
• Automated Processes: Minimize manual error sources and shorten the time-to-market.
• Channel-Overarching Distribution: Content must be optimized for and exported to all touchpoints.

 

Transformation as the Aim of Product Experience

Convincing product experience does not only communicate properties but demonstrates how the product fulfills wishes for changes. Customers do not only want to know what a product can do – they want to feel how it improves life. This emotional dimension is decisive for purchase decisions – and it is precisely what comes to life when product data, visual elements, and stories harmonize together.

 

What Tools do You Need for Product Experience?

A central element for the implementation is a product information management system (PIM). It enables the following:

• Central administration of all product data
• Enrichment of media, texts, and translations
• Distribution to various channels (web shops, marketplaces, print, POS, etc.)
• Integration with other systems like DAM, CMS, or e-commerce platforms

A PIM system is, therefore, the central basis for an impactful product experience strategy.

 

Conclusion

Product experience is more than a short-term trend – it is decisive for securing the competitive advantage in digital retail. Those who manage to keep their products consistent, emotional, and well-staged across all touchpoints win both the customers’ attention and trust. With a high-performance PIM system, you lay the foundation for top-notch product experience – and, consequently, for your long-term success.

For what Purpose do I need MAM?

With a market that is more and more driven by visuals, companies gather increasingly more image and video material. Keeping stock of all that and developing a good file system for it can pose a significant challenge. A Media Asset Management system (MAM) relieves the pressure and optimally complements your PIM.

Many companies place increasing focus on content and social media marketing, resulting in higher need for media assets internally. On top of this, customers have come to acquire a higher demand for a diverse range of visual means of representation for supporting their purchasing decision. Especially when it comes to e-commerce, good product images and videos play a crucial factor in the customer’s decision-making process. With great output, however, the number of files and, consequently, the potential for chaos increases.

To avoid this issue before it even comes up in the first place, you should consider the integration of a Media Asset Management or a Digital Asset Management (DAM). In such a system, you manage your digital content in an efficient and organized manner – which, in turn, enables you to optimize your product communication.

 

MAM as an Indispensable Foundation for Efficient Content Management

A MAM or DAM is a central storage system for all your digital media assets. Every user who has relevant permissions can access, view, download, any perhaps even upload assets. In contrast to conventional Cloud storage directories, you also benefit from many other functions.

Before anything else, the management and organization of your images grants you a better overview. The possibilities to tag images with keywords facilitates searching and finding content in a quick and easy manner. All important meta-data can be displayed at the click of a button so that you know about the usage and copyright of the individual asset. This way, for example, you know which product image is up to date and legally permissible for your use case.

If you want to share media with others, you can easily do so with dynamic links. In the same way, you can collect images by other users. And if you want to use or further edit files in another tool, you can usually do so without manually downloading them by directly using the respective integration or interface.

A MAM brings order into your media chaos and saves you not only time but also valuable storage space.

Benefits of DAM/MAM at a glance:

• Clear-cut overview in organization and management of media assets
• Simple and fast search thanks to keywords
• All important information at the click of a button
• Simple sharing and collecting of large files without a tedious compression process
• Integrations of other software for faster and easier access to relevant content and functions
• Numerous possibilities to flexibly and individually adjust your own system to your own needs
• And much more

The exact functions differ from system to system so that you will have to evaluate ahead of time which features matter to you before making your choice. As a company that works with a lot of visual content, however, it will be nigh impossible to make do without MAM. Even if the classic file directories “somehow” do what they are supposed to, you will still end up giving away a lot of time for handling your media assets.

 

Best Practices for Utilizing Your MAM

To successfully employ your MAM throughout the entire customer journey, we have compiled some tips for you that you should keep in mind:

• The Planning: Once you have decided that MAM is the right approach, you ought to think about how to organize your content in it. Consider which structures and folders you want to create for images or videos together with which information.
• Define Rules: As soon as the structure of your MAM is mapped out, define which rules users have to follow when uploading media. This way, you maintain order. Such rules may include, for example, the types and number of keywords or meta-data, where in your structures the respective asset is best stored, etc.
• Install Integrations: To always benefit from quick access to all media files, integrate other software tools into your DAM. Edit your images and videos by integrating, for example, Canva or Adobe Creative Suite into your MAM. You can then put the edited material directly to use, e.g. for your social media marketing in Hootsuite, your blog in WordPress, your online shop on Shopify, or your sales pitch deck in PowerPoint. And you can do so without downloading and uploading images for each and every system individually. A direct connection to PIM is possible, too, so that you can assign suitable product images to your respective product information.
• Create Portals: If you are to provide image material for PR or retailers on a regular basis, you can create a portal in your MAM. Here, you make selected images available that can then by easily used by the respective shareholders. Now, you do no longer need to process every single request individually via email and always gather matching images manually.

These tips will help you in bringing all your media in order and making sure that it stays so. At the same time, you always have everything available at your fingertips so that you can always provide during every phase of the customer journey and at every point of sale just the right visual material.

 

How MAM and PIM Complement Each Other

As you can see, MAM offers a great many benefits. You will only be able to draw from its full potential, however, if you combine it with your PIM. This way, you store both your product images and videos as well as your product data centrally and in a manner that grants you the optimal overview. File storage and file management are easy to handle and, thanks to integrations and interfaces, you are quick to access all required content.

One possible combination of PIM and DAM would be, for example, the use of ATAMYA with pixx.io. Both solutions are scalable and can be adjusted to match your requirements so that they grow together with your company. In pixx.io, you store your current images and videos – and, in ATAMYA, you link them with your product data. As a result, everything is always well organized.

 

Conclusion

A MAM or DAM brings both structure and overview into your file chaos and, at the same time, makes searching, sharing, and editing media easy and efficient. In the increasingly more visually oriented industries, it is indispensible and fully replaces your dusty folder structures on your local desktop. In combination with a PIM, it constitutes the ideal foundation for smooth processes, guaranteeing that data and files are always up to date and easy to manage.

The eCommerce Markt Continues to Grow

The worldwide profits generated in ecommerce increases year by year. According to Statista, the total profit will amount to 2,905.00 billion euro in the year of 2024. Until 2029, it is predicted to rise to 4,582.00 billion euro, which would equal an expected annual growth rate of 9.54 percent. (Source: Statista)

The expert predictions do not only indicate continuous growth on the global scale, but also for countries such as Germany, too. While there were 30.34 million people in Germany who made online purchases, this number is expected to increase to 43.95 million users in the ecommerce market. By 2029, it is predicted to have reached 53.85 million users. This equals an increase by 9.9 million users over the next five years (i.e., by 22.52 percent).

These numbers clearly prove that the growth of ecommerce will continue and that its full potential is far from exhausted. This alone is already reason enough to have a closer look at the up-and-coming ecommerce trends in order to be well prepared against the competition in the market.

 

Personalization: Driver of Digital Marketing

An important trend in ecommerce is personalization since it is a pivotal driving force in the evolution of online retail and since it has advanced to become a decisive factor for both customer acquisition and retention. This is emphasized by the new “Marketing Monitor Retail 2023–2026” by the EHI Retail Institute, according to which 76.5 percent of the people surveyed see in personalization and relevance a great driver of digital marketing.

In the face of both the gigantic product variety and the ever-increasing customer expectations, ecommerce platforms are striving to provide each and every customer a shopping experience tailor-made to suit their individual demands. From personalized product recommendations and targeted discount events, all the way up to the individualized design of websites and interactive purchasing assistants, personalization is put to use in order to forge a deeper and more meaningful relationship with the customer. This strategy is based on the analysis of customer data and the users’ behavior patterns, so that the best possible user experience can be guaranteed. As a result, customers nowadays expect a purchasing experience that is both comfortable and individualized to a high degree. With this, personalization is no longer a luxury but an indispensable must-have for success in the digital retail environment.

 

Examples for Personalization in eCommerce:

• Recommendation Algorithms: Online shops use AI to analyze their customers’ behavior and recommend products to them which match their past search request and the activities of similar user profiles.
• Personalized Emails: Customers receive emails based on their purchasing behavior, including reminders for incomplete purchases (“shopping cart abandoners”), product recommendations matching previously purchased articles, or offers on birthdays.
• Individual Discounts and Offers: Based on the purchasing behavior, customer groups can be segmented and addressed with strategic discounts, hereby increasing the probability of more sales.
• Individualized Mobile Apps: Mobile shopping apps save preferences and offer alerts for new products and deals which are based on the user’s specific interests.
• Dynamic Website Design: eCommerce platforms adjust the information displayed in real time by, for example, automatically redesigning the homepage to match the user’s preferences and displaying so-called “dynamic code blocks,” which vary from visitor to visitor.
• Virtual Try-On: Augmented Reality (AR) applications enable customers to try out products such as clothing or glasses virtually, hereby creating a personalized experience and supporting the purchasing decision.
• “Shop the Look” Features: Some online fashion retailers allow their customers to view and purchase complete outfits or room decorations based on a selected style of preference for specific articles.
• Chatbots for Customer-Specific Support: Intelligent chatbots provide help by accessing personal customer data and strategically answering questions on the basis of prior interactions and purchases.

 

The Zukunft in eCommerce

The topic of personalization is trending, and so does AI. Artificial intelligence is achieving revolutionizing progress by continuously refining the personalization of the customer experience through data-driven recommendations and automated customer-service options, among many other feats.

Composable Commerce, a method that emphasizes modular and exchangeable software components, is gaining momentum. This is what enables online retail to react to changes on the market in a quick and flexible manner by composing ecommerce platforms out of selected services and solutions which best suit the respective demand.

Spatial Commerce, the next big step in the evolution of online shopping, integrates Augmented Reality (AR) and Virtual Reality (VR) in order to provide immersive purchasing experiences. This blurs the hitherto rigid separation between physical and digital shopping.

Social Commerce, which enhances conventional ecommerce with social media, transforms networks and platforms such as Instagram and Facebook into dynamic marketplaces. Here, users can make direct purchases in a comfortable fashion.

And, last but not least, the sustainability of ecommerce, too, is growing increasingly more important since customers have come to make increasingly more eco-conscious purchase decisions. This is why retailers must integrate sustainable practices such as environment-friendly packaging and climate-neutral shipping as part of their business model if they want to stay competitive.

Google Merchant Center is now Google Merchant Center Next

Developments in ecommerce are increasingly shaped by Artificial Intelligence. Google now places great value in the individual presentation of products and launched Merchant Center Next at the end of July, 2024. It is a tool that spells significant changes for many online shops.

New Google accounts were already directly transferred to the new Merchant Center Next. Now, the migration of existing accounts to this new platform boasting with high-performance machine-learning algorithms to manage shopping feeds efficiently is also complete.

Google Merchant Center is a digital platform on which online retailers can upload their product data in order to sell them on Google Shopping and other services. Merchant Center Next is a simpler version of the platform to facilitate the process of getting started for smaller retailers. All functions on which the big online retailers rely are also still in place.

 

What’s New?

Direct Access to and Comparison of Product Information from Your Website

With Merchant Center Next, Google can directly retrieve product information from your website such as titles, prices, and images. Consequently, changes made on the product website will also be transferred over to the platform. Your reliance on a product data feed is hereby reduced, even though you still have the option of using it. What’s key here is that this increases the relevance of carefully managed product information and complete, well-structured data on product details pages so that you can guarantee that all data is always consistent and up to date.

 

More Efficient Product Data Management

The management of products is significantly facilitated since you can directly edit them in the Merchant Center. This way, errors in the feed or the structured data can be fixed quickly. Previously, you only had the option of uploading and deleting products.

 

Implementation of the Product Studio for More Appealing Product Images

Google has implemented Product Studio, providing you with a plethora of small AI tools. These tools can be used for improving the quality of existing product images, among other things. Currently, this function is only available in the US, Great Britain, Canada, Australia, Japan, and India. More regions are scheduled to follow.

 

Updates to Diagnostics and Analysis Functions

The analytics functions are still available but received a visual overhaul. The former “Diagnostics tab” has been removed and related diagnostics functions have been integrated in “Products,” now with focus on the current state of the products. As part of this, Google automatically prioritizes products which require the most attention.

 

New Metric Click Potential

The new metric, “click potential,” utilizes historical data to evaluate the probability of users clicking your product ads. This analysis compares your product’s performance with others in your account and supports you in optimizing less successful products so that you can improve your overall advertisement strategy.

 

Precise Product Information, Optimized Results: The Influence your PIM System has on Google Merchant Center Next

Product information is of significant importance concerning most of the new features. This means that the optimal use of Google Merchant Center Next hinges upon good management of your product information. A Product Information Management system (PIM) can play a central role here. Thanks to precise and continuously maintained product data, you can make sure that that everything is always up to date and of highest quality.

A PIM system enables you to manage and adjust all product information across all sales channels in a consistent manner. In particular, it is crucial that the data is personalized and adjusted to fit the needs of your target groups. This assures that Google has access to the best available information to meet the specific interests and requirements of your customers.

Through tailored and up-to-date product information provided by your PIM system, you boost your products’ visibility and relevance substantially. This, in turn, translates into addressing your target group more precisely, a higher click potential, and better conversions. With high-quality and target-group-oriented product information, your communication with your customer is always effective while your findability on Google as well as other search engines is greatly enhanced.

Sustainability and Customer Retention: The Digital Product Pass in Focus

One Further Step Towards the Future

In April 2024, the European Parliament gave their go for the so-called “Ecodesign for Sustainable Products Regulation” (ESPR) with focus on the introduction of the Digital Product Pass, DPP in short. It comes with fundamental changes which open up an entirely new paradigm of how products are presented and managed. This innovative solution promises not only improvements in the circular economy and an increase in both transparency and sustainability, but also strengthens the relation between company and customer. However, what exactly does this concept mean for companies in detail and how can you make the most out of the benefits provided by the Digital Product Pass?

 

What is a Digital Product Pass?

At first glance, the concept of a “Digital Product Pass” appears to be something like the passport you use for travelling – a metaphor which is, in fact, more than apt.

This is because the DPP documents the “journey” of your product. It’s the central space collecting all information about the lifecycle of all your products: from the first industrial blueprints to the finishing touches – in the near future, your customers will have the possibility to browse the journey of their desired product at the click of a button. Instead of hunting for relevant information for hours, they can, for example, simply scan the CR code via smartphone app in a quick and easy manner.

 

Establishing the Conditions for the Digital Product Pass

Even though the specific requirements for the content of product passes are still in development as far as individual industry sectors are concerned, companies can already act as early as today. In the process, however, you’re to tackle the following challenges:

1. Data Integration and Management: In the future, retailers must gather and integrate a comprehensive set of data about their products. This doesn’t only include fundamental product information such as dimensions and technical properties but also details such as materials, manufacturing methods, environmental pollution rate, recycling options, and much more. Here, high data quality, consistency, and validity are decisive when it comes to fulfilling all these requirements.
2. Compliance: Companies now face the challenge to take a wide range of new regulations into consideration – and implement them accordingly. This includes, for example, the REACH regulation, the RohS directive, CE marking, different ISO norms, data protection regulations (GDPR), as well as strict IT security guidelines.
3. Investments and Resources: A successful implementation of the Digital Product Pass requires investments in technologies and professional training, perhaps even additional personnel.
4. Change Management: To make optimal use of the Digital Product Pass, you must accelerate your data processes. At the same time, it’s imperative to adjust your corporate structure in order to promote the internal acceptance rate and use of the DPP. This entails that employees are open for new technologies and ready to adjust their working style accordingly.
5. Technologies and Infrastructure: Companies need state-of-the-art systems and applications which can handle the processes revolving around data processing and organizing following the most recent methods and best practices – on top of that, they should also be easy to integrate into your existing system environment and structures.

 

Benefits of the Digital Product Pass

The Digital Product Pass may only be an EU regulation as of now, however it also offers several advantages for companies:

1. Increased Customer Satisfaction and Retention: In the times of ever-increasing customer expectations, DPP enables companies to inform customers about their products in a holistic manner. It offers detailed insights into the origin, composition, and production processes of all your products. This transparency strengthens the trust and increases the satisfaction rate of customers significantly.
2. Boosting Sustainability and Efficiency: With DPP, companies have the possibility to design their processes in a more efficient and sustainable manner. The high transparency of data allows you to discover errors quickly and, consequently, lower costs, improve production chains, and use resources more effectively. This, in turn, attracts new customers and boosts the trust received by existing customers drastically, since sustainability has advanced to become in increasingly more important sales argument to consumers. In a survey conducted by IBM in the year of 2022, 51 percent of all interviewed participants have stated that sustainability is even more important to them than it was 12 month before.
3. Transparency as a Competitive Edge: By providing transparent and detailed product information, companies can be streets ahead of the competition. When customers know exactly what they’re paying for and when the product matches their expectations, they gain a sense of security and are more likely to remain loyal to your brand and opt for products made by the same company in the future.
4. Environmental Protection and Trust: By conforming to the DPP, you don’t only conform with legal environmental protection regulations but you also demonstrate to your customers that your company proactively contributes to a green environment. Ecosensitive companies can strengthen their brand and win the trust of green-minded customers.
5. Understanding Customers Better: By analyzing the consumer behavior and expectations, companies gain deeper insights into the customers’ wishes. Equipped with this, more strategic adjustments can be made to products, information, and services.
6. Collaboration in the Supply Chain: In the future, companies will collaborate more tightly with suppliers across the entire supply chain. This opens up new changes for well-synced partners and efficient collaboration.

 

The Role of a PIM System for the Digital Product Pass

The pros of a Digital Product Pass demonstrate that well-organized product data is the core ingredient for fulfilling all requirements and securing the decisive competitive edge. In order to provide, maintain, and optimize the entire spectrum of the required data in a sustainable manner, it’s best practice to make use of an innovative software solution. PIM systems offer just the right functions to this end, allowing you to enrich data objects with DPP-conform information such as material regulations, recycling options, as well as data governance information, on top of environment standards. Not least, all this also contributes to improving transparency and sustainability.

1. Data Centralization and Consolidation: One of the greatest challenges is the collection and integration of all relevant product data from various internal and external sources. PIM systems offer a central platform on which all data can be consolidated and maintained. This secures both data quality and up-to-dateness.
2. Improving Efficiency through Automation: Managing large quantities of data manually is time-consuming and prone to errors. PIM systems enable the automation of processes through which you can design workflows more efficiently. This reduces the manual effort required for data management and improves the overall allocation of resources.
3. Controlling the Data Quality: With functions revolving around the validation and cleaning of data, both the correctness and exactness of product information can be guaranteed.
4. Conforming to Norms and Compliance: A PIM system offers an overview of all relevant norms and makes sure that the necessary information is provided pursuant to legal requirements.
5. Smooth Data Exchange: Through the smooth integration of a PIM system into the existing IT system landscape, all connected systems gain direct access to up-to-date information around the clock. This allows for an efficient collaboration.

The data transmission between a PIM system and the Digital Product Pass depends on your preferred method of implementing product passes. What matters the most are the processes: should your product pass include live data or be updated by data validation workflows? You can transmit data either in a scheduled manner or directly upon update. A PIM solution can flexibly adapt to your processes in order to supply the Digital Product Pass with up-to-date and relevant information.

 

Chances Provided by the DPP

The implementation of the Digital Product Pass constitutes a crucial step towards an environment-friendly and transparent circular economy as well as long-term customer retention. Companies should seize the possibilities provided and already start preparing for this novelty in order to exhaust the full potential of DPP. The introduction of a PIM system forms a valuable foundation which can facilitate the transition to the Digital Product Pass and support you in living up to your customer’s expectations when it comes to the ever-increasing data quality standards.