09.04.2026 | BJÖRN BAYARD
How companies increase revenue, scale processes, and prepare for new regulatory requirements with intelligent Product Experience Management
Product Experience Management (PXM) is evolving rapidly – driven by rising customer expectations, new regulatory requirements, and the increasing use of AI. In 2026, the focus is less on pure system functionality and more on the tangible value that PXM solutions deliver for companies. These five trends will shape the PXM landscape particularly strongly in the coming year:
30.03.2026 | BJÖRN BAYARD
How companies increase revenue, scale processes, and prepare for new regulatory requirements with intelligent Product Experience Management
Product Experience Management (PXM) is evolving rapidly – driven by rising customer expectations, new regulatory requirements, and the increasing use of AI. In 2026, the focus is less on pure system functionality and more on the tangible value that PXM solutions deliver for companies. These five trends will shape the PXM landscape particularly strongly in the coming year:
1. Business value becomes the most important KPI
Companies are increasingly evaluating software solutions based on their actual business value. Attractive promises and marketing buzzwords are no longer sufficient – manufacturers and implementation partners must deliver measurable results.
The focus is clearly shifting from pure tool selection to successful implementation and individual system configurationi. Only when PXM solutions are precisely tailored to industry- and company-specific requirements can they realise their full potential.
A holistic perspective is becoming particularly important: clean data modelling, well-designed processes, and a clear strategy with defined goals and KPIs. Companies expect answers to questions such as: What potential can be unlocked? Where do synergies arise? And how does PXM concretely contribute to revenue, efficiency, and time-to-market?
At the same time, the measurability of AI initiatives is coming increasingly into focus. Companies expect clear ROI evidence for the use of GenAI in PXM – for example, in the form of reduced maintenance effort, higher conversion rates, or faster product launches.
1. Business value becomes the most important KPI
Companies are increasingly evaluating software solutions based on their actual business value. Attractive promises and marketing buzzwords are no longer sufficient – manufacturers and implementation partners must deliver measurable results.
The focus is clearly shifting from pure tool selection to successful implementation and individual system configurationi. Only when PXM solutions are precisely tailored to industry- and company-specific requirements can they realise their full potential.
A holistic perspective is becoming particularly important: clean data modelling, well-designed processes, and a clear strategy with defined goals and KPIs. Companies expect answers to questions such as: What potential can be unlocked? Where do synergies arise? And how does PXM concretely contribute to revenue, efficiency, and time-to-market?
At the same time, the measurability of AI initiatives is coming increasingly into focus. Companies expect clear ROI evidence for the use of GenAI in PXM – for example, in the form of reduced maintenance effort, higher conversion rates, or faster product launches.
2. Data quality is becoming increasingly important
Data quality is developing into a central success factor for PXM. Key drivers include AI applications, retail partners, business intelligence applications, and growing regulatory requirements. Poor data leads not only to inefficient processes but also to faulty analyses, legal risks, and loss of trust.
For brands and retailers, trust in their own product data is becoming increasingly important. Only those who understand, measure, and actively manage their data can truly use it as a strategic asset.
Accordingly, demand is rising for PXM tools with integrated data quality management functions. DQM metrics, scorecards, and dashboards enable transparent evaluation of data quality and make optimisation potential intuitively visible – even for business departments without deep data expertise.
With the increasing use of AI, data quality is becoming a prerequisite for reliable AI results (“garbage in, garbage out”) and effective AI governance. Companies are therefore increasingly establishing data governance frameworks, including responsibilities, quality rules, and auditability.
In addition, regulatory requirements such as the Digital Product Passport (DPP) and sustainability data (e.g., ESG) are gaining significant importance and increasing the pressure on structured, complete, and verifiable product data.
3. No communication without images
Visual content is no longer a nice-to-have but rather an essential part of product communication. High-quality images, videos, certificates, or nutritional tables not only increase conversion rates but – particularly in the grocery retail sector – also determine whether products are listed at all.
As a result, the importance of close integration between PIM and DAM is growing. Companies are faced with a choice: composable architectures with specialised systems – which entail significant integration and governance requirements – or platform solutions that seamlessly combine PIM and DAM within a single system.
Additional complexity arises from legal requirements and standards such as GDSN, digital product passports, QR codes, or the provision of certificates. Media assets are thus becoming not only marketing elements but also carriers of regulatory-relevant information.
At the same time, generative AI for visual content (e.g., automated image variants, backgrounds, localisation) is gaining considerable importance. DAM systems are increasingly evolving into AI-powered content hubs that not only manage media but actively generate and optimise it.
The challenge is therefore shifting from pure asset management to the governance of AI-generated content (brand compliance, legal certainty, consistency).
2. Data quality is becoming increasingly important
Data quality is developing into a central success factor for PXM. Key drivers include AI applications, retail partners, business intelligence applications, and growing regulatory requirements. Poor data leads not only to inefficient processes but also to faulty analyses, legal risks, and loss of trust.
For brands and retailers, trust in their own product data is becoming increasingly important. Only those who understand, measure, and actively manage their data can truly use it as a strategic asset.
Accordingly, demand is rising for PXM tools with integrated data quality management functions. DQM metrics, scorecards, and dashboards enable transparent evaluation of data quality and make optimisation potential intuitively visible – even for business departments without deep data expertise.
With the increasing use of AI, data quality is becoming a prerequisite for reliable AI results (“garbage in, garbage out”) and effective AI governance. Companies are therefore increasingly establishing data governance frameworks, including responsibilities, quality rules, and auditability.
In addition, regulatory requirements such as the Digital Product Passport (DPP) and sustainability data (e.g., ESG) are gaining significant importance and increasing the pressure on structured, complete, and verifiable product data.
3. No communication without images
Visual content is no longer a nice-to-have but rather an essential part of product communication. High-quality images, videos, certificates, or nutritional tables not only increase conversion rates but – particularly in the grocery retail sector – also determine whether products are listed at all.
As a result, the importance of close integration between PIM and DAM is growing. Companies are faced with a choice: composable architectures with specialised systems – which entail significant integration and governance requirements – or platform solutions that seamlessly combine PIM and DAM within a single system.
Additional complexity arises from legal requirements and standards such as GDSN, digital product passports, QR codes, or the provision of certificates. Media assets are thus becoming not only marketing elements but also carriers of regulatory-relevant information.
At the same time, generative AI for visual content (e.g., automated image variants, backgrounds, localisation) is gaining considerable importance. DAM systems are increasingly evolving into AI-powered content hubs that not only manage media but actively generate and optimise it.
The challenge is therefore shifting from pure asset management to the governance of AI-generated content (brand compliance, legal certainty, consistency).
4. AI and automation are revolutionising product data management
Artificial intelligence is fundamentally transforming product data management – from classification and enrichment to continuous optimisation. Modern AI systems are now taking over tasks that were previously performed manually and required significant time, thereby creating speed, consistency, and scalability.
A central use case is the automated classification of product content: AI models analyse existing product information and independently assign it to the correct category.
This is particularly important for large catalogues or when data from different sources needs to be consolidated. Instead of manual tagging, AI performs semantic pattern recognition, understands context, and assigns appropriate categories and attributes – even when different terms or languages are involved.
The enrichment of product data is also being automated step by step. AI models can intelligently fill missing data fields by deriving insights from existing attributes, texts, or metadata and generating suggestions for product descriptions, technical specifications, or classification labels. This automated enrichment function saves considerable resources while simultaneously improving data consistency across all product groups.
AI and automation are becoming true game changers for product data management – particularly through the use of generative AI (GenAI) and increasingly also agent-based systems (Agentic AI), which can independently orchestrate complex workflows.
New use cases are emerging, particularly in the following areas:
- automated creation of channel- and target group-specific product texts
- real-time optimisation of product data based on performance data
- intelligent translations and localisation
- AI-supported compliance checks (e.g., regulatory requirements)
At the same time, the importance of human-in-the-loop concepts is increasing in order to ensure quality, control, and traceability.
4. AI and automation are revolutionising product data management
Artificial intelligence is fundamentally transforming product data management – from classification and enrichment to continuous optimisation. Modern AI systems are now taking over tasks that were previously performed manually and required significant time, thereby creating speed, consistency, and scalability.
A central use case is the automated classification of product content: AI models analyse existing product information and independently assign it to the correct category.
This is particularly important for large catalogues or when data from different sources needs to be consolidated. Instead of manual tagging, AI performs semantic pattern recognition, understands context, and assigns appropriate categories and attributes – even when different terms or languages are involved.
The enrichment of product data is also being automated step by step. AI models can intelligently fill missing data fields by deriving insights from existing attributes, texts, or metadata and generating suggestions for product descriptions, technical specifications, or classification labels. This automated enrichment function saves considerable resources while simultaneously improving data consistency across all product groups.
AI and automation are becoming true game changers for product data management – particularly through the use of generative AI (GenAI) and increasingly also agent-based systems (Agentic AI), which can independently orchestrate complex workflows.
New use cases are emerging, particularly in the following areas:
- automated creation of channel- and target group-specific product texts
- real-time optimisation of product data based on performance data
- intelligent translations and localisation
- AI-supported compliance checks (e.g., regulatory requirements)
At the same time, the importance of human-in-the-loop concepts is increasing in order to ensure quality, control, and traceability.
5. Data sharing: high investments in onboarding and syndication expected
Product data no longer ends in a company’s own shop. Industry, retail, online marketplaces, and numerous other touchpoints must be reliably and consistently supplied with data.
Accordingly, investments in structured partner onboarding and powerful syndication processes are increasing. Companies require flexible models to efficiently handle different data requirements, formats, and quality levels.
PXM is thus increasingly becoming the central hub for cross-company data processes – and a decisive factor for reach, speed, and consistency along the entire supply chain and customer journey.
Particularly relevant is the ability to support data-driven ecosystems, including marketplaces, retail media, platform economies, and regulatory data spaces.
In addition, the standardisation and automation of data flows (APIs, data spaces, EU initiatives such as Gaia-X) are gaining importance in order to ensure scalability and interoperability.
5. Data sharing: high investments in onboarding and syndication expected
Product data no longer ends in a company’s own shop. Industry, retail, online marketplaces, and numerous other touchpoints must be reliably and consistently supplied with data.
Accordingly, investments in structured partner onboarding and powerful syndication processes are increasing. Companies require flexible models to efficiently handle different data requirements, formats, and quality levels.
PXM is thus increasingly becoming the central hub for cross-company data processes – and a decisive factor for reach, speed, and consistency along the entire supply chain and customer journey.
Particularly relevant is the ability to support data-driven ecosystems, including marketplaces, retail media, platform economies, and regulatory data spaces.
In addition, the standardisation and automation of data flows (APIs, data spaces, EU initiatives such as Gaia-X) are gaining importance in order to ensure scalability and interoperability.
Conclusion
In 2026, PXM stands for measurable business value, trust in data, and intelligent automation.
Companies that invest early in the right strategy, data quality, and scalable software solutions will secure clear competitive advantages.
Successful PXM strategies are increasingly characterised by the integrated use of AI, clearly defined governance structures, and the ability to scale across ecosystems.