Product data in the mid-market: between complexity and the need for action
How mid-sized companies can make better use of their product data – and how AI solutions like aiuno can support them.
Product data is the backbone of many mid-sized companies – especially in industries such as mechanical engineering, industrial equipment, medical technology or electrical engineering. It forms the basis for sales, marketing, e-commerce, service and support. And yet it is often the least structured, least systematically maintained element in the entire value chain.
Why is that? And what options are there for achieving sustainable, future-oriented improvements – without rethinking everything from scratch?
1. The reality in the mid-market: product data as a permanent construction site
Data islands instead of integrated systems
In many companies, several systems exist in parallel: ERP, CRM, CAD, Excel files, file servers, product catalogs in InDesign, maybe a configurator – but no central system that serves as a “single source of truth”. Information is scattered, often redundant, not synchronized and hard to access.
Lack of standardization and quality
Product information is often incomplete, outdated or inconsistent. Units of measurement vary, product names are not uniform, and technical descriptions differ from sales documents or webshop texts.
Organically grown product logic
Product structures, article numbers and variant logics have grown “organically” over years. There is no clear taxonomy, no consistent system. This works internally up to a certain point – but with increasing digitalization, these structures quickly reach their limits.
High manual effort
Maintaining, preparing and distributing product data is often done manually – via copy-paste processes, table imports or approval loops by email. This is error-prone, time-consuming and barely scalable.
Lack of transparency for new employees or external partners
Anyone joining the company or needing access to external data is often confronted with incomprehensible designations, inconsistent formats and missing documentation. Knowledge transfer is laborious and depends on key persons.
2. Why classic systems are often not enough
Of course, there are established systems for product data management – first and foremost ERP and PIM solutions. But especially in the mid-market, these systems are often only partially in use, incompletely integrated or insufficiently maintained. The reason often lies in scarce resources, lack of capacity for data migration or simply in prioritization.
And even if these systems exist: they offer no answer to a central question of our time – namely, how to make knowledge about products efficiently available, regardless of whether it is structured, semi-structured or completely unstructured.
3. New ways: how AI-based solutions like aiuno can help
Modern AI-powered tools like aiuno do not just extend existing systems – they create entirely new access to product knowledge. Two approaches are at the center:
4. Conversational Product Knowledge: making knowledge tangible
Instead of battling through systems, tables and documentation, users gain access to product knowledge through a dialog-based interface – a chat with the company’s knowledge.
Exemplary use cases:
- “Which of our pumps are approved for use at above 120 degrees Celsius?”
- “How do the X120 and X130 series differ in terms of flow rate and material?”
- “Is there an FDA-compliant sealing ring for the Y540 model?”
With aiuno, these questions can be asked in natural language and answered on the basis of structured and unstructured data – fast, precise and consistent.
Benefits:
- Shorter onboarding times
- Relief for specialist departments
- Faster response to customer inquiries
- Knowledge accessible independently of individuals
5. Document to Data: unlocking unstructured data automatically
Much product-relevant information is not found in databases – but in PDFs, technical data sheets, brochures, CAD documents or internal presentations. Converting this manually into structured form is laborious and expensive.
With aiuno’s Document-to-Data approach, unstructured content can be automatically analyzed, extracted and converted into a usable format – including context, metadata and links.
This enables:
- Faster migration of data into new systems
- Automated filling of PIM or ERP fields
- Efficient maintenance for webshops or catalogs
- Technical extraction for sales or service
6. Strategic benefit: from administration to active knowledge management
With aiuno, product knowledge is no longer just administered, but actively made usable. The added value shows in several dimensions:
- Productivity: less searching time, structured access
- Quality: consistent, reliable information
- Scalability: enabling new markets or partners faster
- Customer experience: competent advice on all channels
7. Not a replacement, but an intelligent add-on
aiuno does not replace existing systems – it complements them. The solution connects to ERP, PIM or DMS systems and creates a semantic, context-related layer that makes knowledge from all sources usable – without laborious migration.
8. A concrete scenario from practice
A mid-sized company with 500 products, 1,200 variants and 25 years of grown product logic wants to improve quality in customer support. Employees work with a mix of ERP extracts, PDFs and CAD drawings.
With aiuno, an interactive knowledge base is built. Documents are analyzed, information extracted and made available via a chat. The result: faster answers, lower error rates, shorter onboarding time.
9. Conclusion: rethinking product data – with the right support
Product data management is often a quiet construction site in the mid-market – until efficiency losses or bottlenecks occur. Those who act early create not just order, but real added value.
aiuno offers a practical way to unlock this potential – with a low entry barrier, strong technology and a focus on accessibility. Whether intelligent extraction or dialog with product knowledge: the future lies in the interplay of structure and intelligence.