AI in product data management:
The next generation begins now
Artificial intelligence is revolutionizing the management of digital assets and product data. Swiss SMEs have the opportunity to massively increase their efficiency through intelligent automation. Using concrete practical examples, I will explain how AI-supported systems are already transforming processes that used to take weeks of manual hard work, and I will give an outlook on the next generation of AI-based solutions that will become standard in the next one to two years.
Von Marco Nägeli,
Head of Sales & Key Account Management
The integration of artificial intelligence into digital asset management (DAM) and product information management (PIM) marks a turning point for companies in the DACH region. While only 22% of Swiss SMEs consciously integrated AI in 2024, the figure is now 34%. AI promises measurable efficiency gains and decisive competitive advantages for small and medium-sized businesses.
From manual data maintenance to intelligent automation
Today, companies manage exponentially growing amounts of digital assets and product information. What used to involve manual tagging, time-consuming translation processes, and error-prone data imports can now be intelligently automated.
A practical example: Swiss Post manages over 80,000 images for more than 300 campaigns annually. By integrating AI-supported image analysis with OpenAI into the Censhare Content Hub, the tagging process has been completely automated. The AI analyzes image content, recognizes objects, people, and contexts, and assigns relevant tags — in seconds instead of hours.
”Leading DAM and PIM system solutions have made massive progress in AI integration.
AI features that should already be standard today
Leading DAM and PIM system solutions have made massive progress in AI integration.
Automatic image tagging
Modern DAM and PIM systems offer AI-powered facial recognition and smart tagging, which can save marketing teams 10,000 media files over three working weeks per year. AI not only recognizes faces, but also analyzes image composition, colors, and content. In addition, the integration of AI services enables automated tagging that goes far beyond simple tagging, significantly simplifying searchability in the ever-growing amount of data and, in turn, saving a lot of time in the search process.
Intelligent translation processes
AI-supported translation services such as DeepL and OpenAI can be seamlessly integrated into DAM and PIM systems. A Swiss trading company was able to reduce translation costs by 80% and processing time by 90% through DeepL integration. The system translates automatically, uses central glossaries for consistent wording, and enables quality gates.
Image prompt: 3D lucid artwork showing a perfect floating sphere in pink and white, encircled by thin, curved lines. The background is a soft gradient, symbolizing perfection and infinity.
Automated product data classification
Modern PIM systems rely on AI-supported classification. The systems analyze unstructured data from PDFs or supplier feeds, recognize product categories, and assign attributes. What used to take weeks now takes minutes. AI-supported import of PDF product data sheets enables them to be automatically categorized and assigned to the correct data fields.
Content generation
The latest generation of DAM and PIM systems integrates generative AI for the automatic creation of product descriptions in order to generate product texts from structured data and images, optimize them for different channels, and adapt them for over 1,000 marketplaces and social media platforms. These features enable products to be brought to market up to 70% faster and increase conversion rates through channel-optimized content.
”Eine multimodale KI kann ein Produktfoto analysieren, die Bildkomposition bewerten, passende Texte generieren und automatisch für verschiedene Ausgabekanäle optimieren – alles in einem Workflow.
Next generation: What will become standard in 2026
AI development is not standing still. Several trends will shape the next generation of DAM and PIM solutions in 2026. What is not possible today may be possible two months later. The pace of change in this area is immense.
Autonomous AI agents
From my perspective, autonomous AI agents that proactively take on tasks are a very important trend. The AI independently recognizes missing or incorrect data, suggests corrections, or implements them automatically. It monitors data quality in real time, identifies duplicates, and optimizes product information for different channels — without human intervention. This proactive approach changes the role of employees: instead of performing administrative tasks, they monitor processes and make strategic decisions.
Multimodal AI
The next generation of AI not only understands text or images, but also intelligently combines different types of data. Multimodal AI can analyze a product photo, evaluate the image composition, generate suitable text, and automatically optimize it for different output channels – all in a single workflow. This holistic processing is particularly relevant for companies that communicate complex product worlds across many channels.
Intelligent syndication
The solutions push product information to over 1,000 channels in real time and automatically adapt content to the respective requirements. Combined with digital shelf analytics, AI continuously optimizes for better rankings and higher conversion rates.
This rapid development will become standard in the next 12 to 24 months: systems that not only manage and play out data, but also measure and continuously optimize its performance. The cycle of syndication, analysis, and optimization runs fully automatically.
The new colleague, AI-generated.
The most important AI functions at a glance
- Automatic image analysis: AI recognizes objects, people, and contexts in images and assigns relevant tags fully automatically.
- Intelligent translation: Integration of DeepL or other AI translation services reduces translation costs by up to 80%
- Smart classification: AI automatically assigns products to categories and validates data quality in real time.
- Content generation: automatic creation of channel-optimized product texts from structured data or images
- Autonomous agents: proactive AI systems take on tasks independently and continuously optimize them.
The Swiss approach: pragmatic and secure
Swiss SMEs are characterized by a pragmatic approach to AI implementation. Instead of focusing on large, complex transformation projects, successful companies start with concrete and tangible use cases and scale gradually. A proven approach involves three steps: First: Process analysis and identification of automation potential. Which tasks currently tie up a lot of resources and require a huge amount of time? Where do errors arise due to manual processes? Second: Pilot project with measurable goals. Start with a manageable area – such as automatic tagging of new product images or AI-supported translation for a market. Third: gradual scaling based on learnings.
A particularly important issue for Swiss companies is, of course, data protection and governance. The integration of AI must be GDPR-compliant, and data sovereignty must be guaranteed.
An investment that pays off
The figures speak for themselves: companies that integrate AI into their data management report measurable successes. Customers have been able to reduce asset search times by 75%, lower the workload for product content development by 20%, and cut the time needed to create product listings by 98%.
Investing in AI-supported systems pays for itself quickly. Studies show that the return on investment is measurable after just a few months. It’s not just about cost savings: faster time-to-market, better data quality, and channel-optimized product information also significantly increase sales and customer satisfaction.
An investment that pays off
The integration of AI into DAM and PIM systems is no longer a vision of the future, but a practical reality. Companies that act now will gain decisive advantages.
Specifically, decision-makers should take three steps. First, check existing systems for AI readiness. Modern platforms such as censhare cloud, Centric PXM, ATAMYA, and pixx.io already offer comprehensive AI functions. An evaluation of the current system landscape shows where there is potential for optimization. Second: start with quick wins. Automatic image tagging, AI-supported translations, and intelligent product data classification can often be implemented with manageable effort and deliver quickly measurable results. Third: plan strategically. The next generation of AI functions – autonomous agents, multimodal processing, intelligent syndication – should be incorporated into medium-term planning.
The competitive advantage lies not in waiting for the perfect solution, but in taking concrete steps today. The companies that will be leaders in the next two years are those that have laid the groundwork for this now. The next generation starts now.
This article appeared in Magazin KI-Kompass für Führungskräfte, Issue 02 – February 2026
Marco Nägeli
For 25 years, I have been working in the areas of marketing, sales, and management, and am responsible for the successful management of corporate development projects, the transformation of market strategies, and the development of new business areas.
As Head of Sales & Key Account Management, I advise and support our customers from the conception and implementation to the further development of their solutions, combining content expertise with partnership-based cooperation.