The AI era is changing what manufacturers need from product information
21.10.2026
For years, manufacturers have invested in making product information easier for people to find and understand. Websites, catalogs, datasheets and distributor portals have shaped how that information is organized and presented.
AI introduces another audience for the same knowledge. Software is increasingly being asked to interpret specifications, compare products and help employees respond to technical questions. The usefulness of those interactions depends on more than having content available. It depends on whether the information carries enough context to support a reliable answer.
For manufacturers, this changes the business value of product information. Its structure, relationships and governance become part of the foundation for how knowledge is used across the organization.
Product knowledge lives in the relationships
A machine’s specifications describe only part of what a manufacturer needs to know about it.
Its components determine performance. Its configuration affects compatibility. Its revision determines which documentation applies, while its service history may influence the replacement parts it needs.
These relationships matter whenever someone asks a practical question: Can this component be used in that machine? Does this operating limit apply to every variant? Which instructions belong to the equipment the customer actually owns?
People often resolve those questions through experience. They know which document is current, which spreadsheet contains an exception and which colleague can explain a technical difference.
As manufacturers introduce AI into sales, service and information workflows, more of that context needs to be represented explicitly. Knowledge that exists only in someone’s head is difficult to reuse consistently, whether the person asking is a new employee or an AI assistant.
Faster answers depend on better foundations
Consider a manufacturer using AI to help sales compare two industrial pumps.
A useful comparison needs accurate flow rates, materials and operating limits. It also needs to distinguish variants, recognize relevant compatibility information and use the specifications approved for the intended application.
If those details are scattered across spreadsheets, supplier documents and outdated datasheets, the business has an information problem alongside its AI opportunity.
The same problem already affects everyday work. Sales waits for engineering to confirm a specification. Marketing checks which document can be published. Service searches for information about an earlier revision.
AI creates a further reason to address these weaknesses because it gives the business more ways to reuse its knowledge. The benefits of a stronger information foundation can begin well before any AI workflow is introduced.
A product change is also an information change
The challenge becomes especially visible when products evolve.
Imagine that engineering replaces a component and changes a machine’s rated capacity. Approving the engineering change is one step. Updating the related datasheets, sales information and distributor content requires coordination across several teams and systems.
Earlier revisions may still need their original documentation for customers operating existing equipment. The business must preserve those distinctions while making the latest approved information available for new products.
This is where product information management becomes an operational concern. Teams need to understand what is connected, which information needs attention and who is responsible for approving it.
Without that structure, every change creates another round of searching, checking and copying. As information reaches more channels and applications, maintaining it through manual coordination becomes harder to sustain.
The information lifecycle begins before the ERP item
Manufacturers also create valuable knowledge long before a product is ready for release.
Proposed specifications, supplier evidence, images and market requirements emerge while teams are evaluating an idea. That information develops alongside the product, often across folders, spreadsheets and individual conversations.
When the business waits until an ERP item exists before organizing this knowledge, teams may need to reconstruct work already completed during development.
Capturing information earlier creates continuity. Knowledge gathered during the idea process can develop into approved product information, support launch preparation and remain useful through sales and service.
It also gives the business a clearer basis for understanding how a product and its supporting information have evolved.
Why the PIM decision matters now
AI is one reason to reconsider how product information is managed. Growing portfolios, new markets and sustainability requirements add further pressure.
Digital Product Passports and packaging requirements such as PPWR bring attention to the relationships between products, materials, packaging and supporting evidence. The information required depends on the applicable obligations, but the underlying challenge is familiar: knowing what applies to a product and keeping that knowledge current.
For manufacturers evaluating PIM, this calls for a broader assessment than catalog preparation alone.
The platform needs to support the complexity of the business: shared components, product variants, revisions, technical documents and the relationships between them. It should help teams maintain information efficiently today while allowing the model to evolve as requirements change.
The investment case rests on practical outcomes, including less repeated work, faster preparation for launch and more reliable information for customers and employees. AI strengthens that case by expanding how the same knowledge can be used.
Where Perfion fits
Perfion is designed for businesses whose product information extends beyond individual product records.
Its adaptable data model allows manufacturers to connect products with components, spare parts, services, documents and other related entities. Teams can maintain shared information, work efficiently across large volumes and reuse approved content across relevant systems and outputs.
Perfion can also support information work from early product ideas through launch, sales and service. Through supported configuration, manufacturers can adapt information structures, relationships and workflows as their needs evolve while preserving the core platform.
Alongside the core PIM capabilities needed to enrich, approve and distribute product information, this gives manufacturers a way to represent the wider knowledge surrounding their products. That connected, governed information can also provide a more dependable foundation for AI applications.