What Is AI-Ready Product Data? What PIM Systems Really Need to Deliver
The Bitkom study “Softwarewelt 2036” makes it clear: AI is reshaping what customers pay for. But before AI delivers real value, one prerequisite must be met: structured, consistent product data. The study explicitly identifies inconsistent article master data, fragmented system landscapes, and legacy ERP silos as blockers. Anyone who does not get their data in order cannot deploy AI meaningfully, no matter how good the models become.
What does the Bitkom study “Softwarewelt 2036” say?
The Bitkom study “Softwarewelt 2036” (DOI: 10.64022/2026-softwarewelt-2036) is based on qualitative interviews with twelve executives from the software industry, conducted between calendar weeks 13 and 19 in 2026. The key findings for the software market:
- Hourly billing and license models are under pressure, because AI systems are turning routine work into a commodity.
- Customers will increasingly pay for outcomes, such as an optimized supply chain, a resolved vulnerability, or a passed compliance audit. Outcome- and value-based pricing is gaining importance, particularly in B2B.
- Platforms without interoperability lose relevance. Proprietary silo models and systems without end-to-end digital process chains are under pressure.
- AI levels feature differences between systems within months. Software without its own data intelligence becomes more interchangeable.
By now, almost every PIM and CMS vendor markets itself with AI features. “We have AI too” is no longer a differentiator but a market expectation. The real question is which prerequisites a system brings to make AI actually work.
What makes product data AI-ready?
AI-ready product data fulfills four properties: it is complete, consistent, uniquely identifiable, and accessible via standardized interfaces. The Bitkom study makes clear that what matters is not simply owning data, but its quality, contextualization, governance, and responsible use.
- Completeness: Required attributes are filled for all products; no gaps in core fields.
- Consistency: Same unit, same spelling, same classification across all products.
- Unique identifiers: Products have stable, cross-system IDs with no duplicate records.
- API accessibility: The PIM system delivers data through structured interfaces, not just as export files.
If any of these properties is missing, AI output degrades: incorrect product assignments, inconsistent descriptions, incomplete catalogs. How AI agents work with product data in practice and what data structures they require is covered in our article AI-ready: What product data needs to deliver for AI agents.
Why system architecture is the deciding factor: AI-configurable, not just AI-operable
Most PIM systems are built so that AI fills content: AI sits on top and populates fields. That is useful, but the Bitkom study makes clear that it is not enough. AI levels feature differences between systems within months. Software without its own data intelligence becomes more interchangeable.
The structural difference lies one level deeper: not only the content, but the system configuration itself must exist as data. Only then can an AI not just operate the system but change it: adjust data models, add new attributes, configure workflows. When PIM, CMS, DAM, and MAM share a common model instead of existing as separate tools, there are no integration gaps. That is the difference between an AI-operable and an AI-configurable system.
NovaDB is an AI-configurable PIM system: the entire system configuration exists as structured data, not just the content. An AI can therefore adjust data models, attributes, and workflows autonomously, without manual developer intervention. This fundamentally distinguishes AI-configurable systems from AI-operable systems, where AI exclusively populates content fields.
European data storage as a competitive advantage
As a German vendor, NovaDB stores data exclusively in Europe. The Bitkom study identifies this as a growing competitive advantage: geopolitical fragmentation and the pressure toward digital sovereignty are no longer abstract concepts but operational reality. Vendors who deliver compliance and data sovereignty natively beat the generic alternative.
For companies in regulated industries and the public sector, GDPR compliance and data traceability are not comfort goals but prerequisites for productive deployment. European data centers, clear contractual terms, and independence from US hyperscalers provide a transparency advantage that global platforms cannot structurally offer. Sovereign European cloud infrastructures such as STACKIT provide a reliable hosting option for exactly these requirements, on which NovaDB can be operated on request.
Technical documentation: compliance as a measurable outcome
Beyond PIM, AI also opens new possibilities in technical documentation. Especially in complex, XML-based documentation, AI-supported checks for completeness, standards compliance, and terminological consistency across large document sets can be implemented.
What previously required manual editorial work becomes measurable and automatable. The Bitkom study explicitly cites passing a compliance audit as an example of what customers will pay for in the future: not for the technology deployed, but for measurable value. Noxum Publishing Studio is an example of a solution that delivers exactly that: demonstrable standards compliance instead of effort estimates.
First steps: where to start?
Before starting an AI project, it is worth taking an honest look at your own data foundation. Three questions help: Which product attributes are fully populated, and which have systematic gaps? How consistent are units, classifications, and spellings across product groups? And through which interfaces does the PIM system currently deliver data to other systems?
Those who can answer these questions know where the real effort lies. Those who cannot know where they need to start. Our article AI-ready: What product data needs to deliver for AI agents provides practical guidance.
Conclusion: outcomes over hours
AI is changing what companies pay for. But not what they need to invest in first. The Bitkom study is clear: scalable AI deployment requires data to be systematically structured, quality-assured, and made usable for AI. Anyone who skips this step cannot deploy AI meaningfully, no matter how good the models become.
NovaDB creates that prerequisite: as a PIM that makes product data AI-ready, and as a system that is itself AI-configurable. Add to that European data storage as a structural requirement and deep market expertise that maps cross-category requirements into one system rather than connecting separate tools.
This is not a feature that can be replicated in two releases. It is an architectural decision that matters now.
AI-ready product data is fully populated, consistently structured, equipped with unique identifiers, and accessible via standardized API interfaces. If any of these properties is missing, an AI system cannot produce reliable outputs. Inconsistent article master data, fragmented system landscapes, and ERP silos block any meaningful AI deployment, regardless of which AI tool is used.
AI that merely sits on top of a PIM system can populate content, but it cannot improve the data structure itself. If the underlying data model is inconsistent or incomplete, the AI inherits and amplifies these problems. A truly AI-capable system architecture must make the system configuration itself accessible — data models, attributes, and workflows — not just the content.
An AI-configurable PIM system stores not only product content as data, but also its own system configuration: data models, attributes, workflows. An AI can read and modify this configuration without requiring manual developer intervention. In NovaDB, this is not an add-on but part of the system architecture. This is fundamentally different from systems where AI only populates fields.
For AI projects involving sensitive product data, the geographic location of data storage is legally and contractually relevant. Especially in regulated industries and the public sector, GDPR compliance and data traceability are not comfort goals but prerequisites for productive deployment. A European vendor with EU-based data centers meets these requirements by design, without the need for additional contractual clauses or data transfer approvals.
