The key PXM trends 2026: How to future-proof your product data

While recent years have been marked by economic uncertainties, sharp cost increases, restrained innovation, and the first cautious steps with AI tools, the question now is how companies can align their PXM landscapes in a way that measurably increases product innovation.

In this blog post, we present the five key PXM trends for the new year and place particular emphasis on the best practices that can be decisive for success in tackling the challenges ahead.

1.     The AI journey continues

AI technologies are advancing at an unstoppable pace. No longer limited to individual tools that fulfil specific tasks, this trend is increasingly evolving into a holistic organisation of data processes with the aim of achieving far-reaching autonomy. Agentic AI frameworks, i.e., platforms for autonomous AI agents, are currently emerging in many places and are pursuing the concept of earlier platform solutions in a practical way that acknowledges the advantages of best-of-breed architectures. Maximum flexibility in equipping the information supply chain is combined with perfectly integrated data processes and the highest degree of automation for optimal business value. What this means in terms of system architecture is discussed in section 4 – ‘Composable architectures shift the focus to integration’.

For companies, the question is how they want to approach the topic of AI in the coming years. The fact is that every company needs a dedicated AI strategy with clear objectives and priorities. The analyst firm Gartner assumes that 40 per cent of all agentic AI projects will fail by 2027, with the reasons lying particularly in unclear business value, escalating costs, and insufficient risk assessment.

At the same time, increasing cost pressure and ever-intensifying competition are forcing companies to pursue greater automation, ever shorter time to market, more product innovation – and all of this with fewer resources. Therefore, the question is no longer whether AI will play a role in the future, but when.

Our experts have compiled the following best practices from numerous AI projects:

• Initial analysis: At the beginning, there must always be an in-depth analysis of data, processes, and organisation in order to identify risks and potential and to take these into account in subsequent planning. This includes, for example, overall data quality, media disruptions in processes, as well as defined roles and responsibilities.
• Strategy: For AI projects more than ever, a holistic perspective is essential. Every company needs an individual AI vision that considers the purpose, economic viability, and potential of AI. For some, this may lie in the area of product data onboarding, and for others, in the automation of content creation and syndication.
• Objectives: Clear objectives aligned with overarching AI strategy are crucial for every project. Whether revenue growth, product innovation, greater efficiency, or cost-effective expansion into new markets, the objectives define the framework of the project and also provide guidance on the right KPIs by which project success can ultimately be measured.
• Roadmap: Based on the objectives, prioritisation, and the current situation, a detailed roadmap can be derived that takes into account quick initial successes as well as possible synergies, risks, and further developments.

2.     Analytics continues to expand its central importance

Decision-makers rely more than ever on robust information about the current state of affairs. Whether revenue figures, conversion rates, usage data of deployed tools and solutions, or the quality of data and product content, everything that can be measured can also be optimised. As a result, more and more companies are adopting comprehensive analytics solutions in individual areas as well as broad-based visualisation solutions to gain a clear, holistic view of their business and its framework conditions.

The following tools in particular are enjoying growing popularity:

• System monitoring: Especially for central solutions such as those in the PXM environment, it is essential to closely monitor both system performance and actual usage. Outages, performance issues, and inadequate data handling can quickly lead to far-reaching problems.
• Shop analytics: Key KPIs such as conversion rates, basket abandonment, and click-through rates provide insight into how successfully products are presented and promoted in the online shop. These insights can be used to optimise customer journeys, increase conversion rates, and effectively reduce return rates.
• Digital shelf analytics: Most companies not only offer their products in their own online shop but also use additional channels such as online marketplaces. Digital shelf analytics tools collect data on the performance of their own products as well as those of key competitors, enabling targeted optimisation of channel-specific content.

Here, too, a clear objective for analytics initiatives is essential. Companies often measure a multitude of KPIs without questioning the actual business benefit or taking effective measures to improve the performance of their products or deployed solutions. An intelligent business needs both: the right tools and a comprehensive strategy.

3.     More effective customer engagement with real-time data

Customer and consumer expectations are also continuously increasing – in both B2C and B2B environments. Potential customers expect personalised product content in real time, regardless of the channel they are using. This requires a seamless data supply chain, comprehensive and targeted product messaging, and automated processes, particularly for the syndication of product content.

Accordingly, a system landscape is needed that fulfils all these conditions while ensuring clean data quality:

• Product information management: PIM is the heart of PXM – this is where all relevant product data and information are managed, connected, and provided.
• Digital asset management: Together with PIM, DAM forms product content management for companies. DAM complements product data, information, and descriptions with visual and audio content such as images, photos, graphics, and documents, as well as logos and corporate design colours. Through close integration of PIM and DAM, suitable product messages can be assembled for each channel, target group, and country.
• Multi-domain master data management: While perfectly aligned product content management ensures impactful customer engagement, multi-domain master data management provides clean, comprehensive data organisation. Product, supplier, customer, and location master data are linked and placed in meaningful contexts. This enables supply chains to be optimised, insights into product performance in brick-and-mortar retail to be gained, and marketing and sales activities to be defined based on clear information.
• Syndication: Through the syndication of product content, product messages are delivered in real time to all relevant channels – from the company’s own online shop and social media to online marketplaces, comparison portals, and search engine listings.

It is important to understand that every company has very individual requirements in each of these areas. Therefore, it is essential to build a system landscape that covers these requirements – including future ones – as comprehensively as possible. This includes ensuring that individual solutions follow modern technological standards. Only in this way is it ultimately possible to link the individual components into a powerful value chain.

4.     Composable architectures shift the focus to integration

In addition to the nature of the individual solutions along the PXM value chain, system implementation also plays an essential role in achieving success at every customer touchpoint. The more tools and central solutions that need to be interconnected, the more important seamless integration and sustainable interface maintenance become. Data processes must be aligned and optimally support overall value creation. Requirements and feedback from communication channels must be fed back into the information supply chain as quickly as possible and result in optimisations of product content.

A successful composable architecture therefore relies on the following prerequisites:

• Modern technologies: As mentioned above, a modern tech stack is required to build a scalable and flexible system landscape. Cloud-native, API-first solutions are the basic prerequisite for effective interoperability between systems. They also make it easier to replace and expand components, ensuring that the overall architecture remains future-proof.
• End-to-end processes: The days of monolithic thinking are definitively over – this applies not only to individual systems but also to data processes. Anyone implementing systems today needs a comprehensive data concept, from its origin – for example through supplier data onboarding – through its transformations to all relevant usage scenarios such as publication on online marketplaces.
• Automation: Identifying automation potential is also a key task of system integration. Workarounds and manual data maintenance clearly belie the principles of operational efficiency and business agility.

5.     Flexibility and agility become the game changer

Flexibility and business agility have already guided successful companies safely and reliably through crises, business risks, and times of economic uncertainty. They not only enable rapid responsiveness to changing conditions but also support the early identification and assessment of risks and market potential.

Flexibility and agility are therefore core characteristics of data-driven companies and are enabled by the following building blocks:

• Optimal data pipelines: To ensure that data is available everywhere and at all times for all authorised users, an integrated and optimised data infrastructure is required. This includes standardised interfaces, harmonised data models, and overarching workflows.
• Clear data governance: Data quality is a top priority for most companies. However, it is only one part of comprehensive data governance that ensures the proper handling of central data overall.
• Growing data culture: Rules alone are not enough if employees do not develop intrinsic motivation to actively use data in their day-to-day work and proactively improve data quality. Therefore, it is becoming increasingly important to build a data culture and develop data literacy among employees for a future-oriented corporate strategy.
• State-of-the-art data & analytics: Data is generated everywhere and provides valuable product insights that help decision-makers steer day-to-day operations in the right direction. Intelligent analytics solutions create the prerequisite by enabling a clear and wide-ranging view of all areas and visualising information in such a way that insights are easy to understand and directly actionable.

2026 – a year that counts

In 2026, no established company in the PXM environment is starting from scratch. Many are facing major projects such as ERP modernisation and are now confronted with the challenge of keeping pace with rapid developments in the PXM environment while gradually transforming established structures and monolithic system components into a future-proof system landscape.

The greatest challenge is not to lose sight of the overall perspective and vision. It is crucial to maintain clarity about which objectives have priority and in which direction the organisation should evolve. In an increasingly interconnected corporate world, the potential and risks in all areas must be considered for every issue.

Our mission is to join companies on their journey into the future and to support them in every challenge – whether in selecting suitable system solutions and technologies, evaluating AI potential, or implementing scalable PXM and integration architectures.

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