Key Takeaways
- Knowledge management tools fall into five categories: collaboration tools, document management systems, knowledge bases, search engines, and specialized AI knowledge platforms
- According to a study by Fraunhofer Austria (2025), 68% of companies use collaboration tools like Microsoft Teams – but only for storage, not as genuine knowledge management
- The most common problem: knowledge exists but isn't usable – 78% of respondents report regular knowledge loss
- For industrial companies with technical knowledge spread across distributed systems, generic tools often fall short – specialized platforms are better suited
Which tool is right for knowledge management in your company? The choice is vast: from Microsoft Teams and Confluence to knowledge bases like Notion, all the way to specialized AI knowledge platforms. The right tool depends on which problem needs solving – and most companies face very similar problems.
A study by Fraunhofer Austria Research GmbH (2025) of 56 companies in the DACH region shows: 78% say that essential knowledge in their company is frequently or occasionally lost. 61% have no clear knowledge management strategy. And yet 68% rely on collaboration tools – even though these don't solve the actual problem.
This article explains the key tool categories, their differences, and how to choose the right tool for your company.
Table of Contents
- Why classic tools fall short for knowledge management
- The five categories of knowledge management tools
- Comparison table: which tool is right for what?
- What features should a knowledge management tool have?
- Selection criteria: what really matters?
- Knowledge management tools for industry: special requirements
- FAQ: Frequently asked questions about knowledge management tools
Why classic tools fall short for knowledge management
Many companies believe they're already practicing knowledge management because they use Microsoft Teams, SharePoint, or Confluence. But according to the Fraunhofer Austria study (2025), 35% of respondents rate their knowledge management as inefficient despite having tools in place. The reason lies in a fundamental distinction: storage is not the same as knowledge management.
Collaboration tools store documents. Knowledge management makes information usable in the right context – for the field service technician on-site, the new employee getting up to speed, or the manager who needs to make a well-informed decision quickly.
According to the study, the most common causes of knowledge loss are:
- Frequent employee turnover (more than 65% cite this as a major factor)
- Time pressure and insufficient documentation (rated as challenging by more than 70%)
- Communication problems between departments (rated as significant by more than 60%)
The five categories of knowledge management tools
1. Collaboration tools (Microsoft Teams, Slack, Google Workspace)
What they do well: communication, file storage, simple team collaboration.
What they can't do: structure knowledge, connect it, or provide it in the right context. A document sitting in a Teams folder isn't accessible knowledge – it's just storage.
Usage according to the Fraunhofer study: 68% of companies use them company-wide – making them the most widely used tool of all. But high adoption doesn't correlate with effective knowledge management.
Best suited for: daily communication, quick file sharing, project-based collaboration.
2. Document management systems (Microsoft SharePoint, DocuWare)
What they do well: structured storage, version control, access permissions, workflows for document approvals.
What they can't do: bring together knowledge from different sources, search semantically, or establish connections between content.
Usage according to the Fraunhofer study: 55% of companies use them company-wide.
Best suited for: document administration, compliance, audit-proof archiving.
3. Knowledge bases / wiki systems (Confluence, Notion, BlueSpice, MediaWiki)
What they do well: structured knowledge documentation, internal wikis, linking articles together, search within the platform.
What they can't do: incorporate knowledge from other systems (PLM, ERP, SharePoint) – they're just another island, not an integration.
Usage according to the Fraunhofer study: 34% of companies use knowledge bases company-wide.
Best suited for: companies that want to actively document and structure knowledge – either as their sole knowledge source or as part of a larger system.
4. Enterprise search engines (Google Workspace Search, Elasticsearch, Sinequa)
What they do well: company-wide search across multiple systems, full-text search, indexing of various sources.
What they can't do: structure knowledge, contextualize it, or deliver it at the right step in a workflow. They find documents – but whether what's found is actually the right thing is up to the user to judge.
Best suited for: large companies with many document sources that need fast access to existing content.
5. AI knowledge platforms (JUNOfy, Guru, Shelf, Zive)
What they do well: connect knowledge from different systems, structure it with AI support, deliver it in context, and make it retrievable through natural-language search or chat. Metadata is added automatically, and connections between content are established.
What they can't do: replace people – knowledge still has to be created and maintained. AI improves access, not the quality of the source.
Best suited for: companies that want to make knowledge from distributed systems usable without documenting everything from scratch.
Comparison table: which tool is right for what?
✅ = Well suited | ⚠️ = Limited | ❌ = Not suitable
What features should a knowledge management tool have?
The Fraunhofer study shows: it isn't the mere presence of tools that determines how efficient knowledge management is, but how well those tools capture, distribute, and make knowledge usable.
Essential features:
System integration The tool must be able to incorporate knowledge from existing systems – SharePoint, Teams, ERP, PLM, CRM. Building yet another island instead of integrating existing sources creates more complexity, not less.
Intelligent search Full-text search isn't enough. Semantic search understands the meaning behind a query and delivers relevant results even when the exact terms vary. According to the study, more than 60% of respondents say inadequate use of IT tools is a major factor in knowledge loss – a sign that tools which aren't intuitive enough simply don't get used.
Contextualization Knowledge shouldn't be stored in isolation but delivered in connection with products, processes, or roles, so the right person finds the right information at the right time.
Access rights and data protection Especially in industrial companies with confidential design knowledge, manufacturing processes, or customer data, GDPR compliance and granular permission management are a basic requirement.
AI support 89% of respondents in the Fraunhofer study believe AI will have a positive impact on knowledge management. Among companies already using AI, 54% are already applying it productively to knowledge use. AI integration should therefore be factored into tool selection from the start, even if the initial rollout doesn't include it.
Selection criteria: what really matters?
1. Problem first, tool second The most common bad investment: buying a tool before it's clear which problem needs solving. Someone trying to combat knowledge loss from staff turnover needs different features than someone trying to keep field service technicians supplied with information.
2. Integration before rebuilding Companies usually have more knowledge than they realize – it's just not accessible. A good knowledge management tool integrates existing sources instead of rebuilding everything from scratch.
3. Adoption is what counts The best tool is worthless if nobody uses it. Usability, intuitive search, and fast answers are decisive for everyday adoption. According to the Fraunhofer study, lack of adoption is one of the main reasons knowledge management initiatives fail.
4. Scalability Knowledge management isn't a project – it's an ongoing process. The tool should be able to grow with the company: more users, more sources, more languages.
5. Assess costs realistically License fees are only part of the total cost. Implementation effort, training, ongoing maintenance, and the internal time needed for rollout and change management should be factored in from the start.
Knowledge management tools for industry: special requirements
Industrial companies in mechanical and plant engineering, medical technology, or the automotive sector face special requirements that generic tools often fail to meet:
Technical knowledge from structured sources Operating manuals, maintenance handbooks, bills of materials from PLM systems, and service documentation exist in specific formats – XML, PDF, CAD data. A good tool must be able to process these formats and make them searchable.
Knowledge loss from demographic change According to the Fraunhofer study, knowledge loss occurs 13 percentage points more often in manufacturing than in other industries. The knowledge held by departing specialists – maintenance technicians, design engineers, service technicians – must be systematically captured.
Field service and mobile use Service technicians need access to knowledge on-site – at the equipment, via QR code, or on mobile devices. Long search paths or poor mobile usability mean the tool won't get used when it matters most.
GDPR and data security Design data, customer knowledge, and manufacturing processes are confidential. Cloud solutions must be GDPR-compliant and run on European infrastructure.
JUNOfy by DOCUFY was built specifically for these requirements: it connects structured content from COSIMA, unstructured documents, supplier information, and product data – and delivers them with AI support, in context, and in full GDPR compliance.
Frequently Asked Questions About Knowledge Management Tools
What are knowledge management tools?
What is the difference between knowledge management software and a DMS?
Which knowledge management tools are used most often?
How do I choose the right knowledge management tool?
How much does knowledge management software cost?
Do I need AI for knowledge management?
Are there knowledge management tools built specifically for industry?
Source
Fraunhofer Austria Research GmbH (2025): “Knowledge Management in Transition – A Study on the Current Handling of Knowledge in German-Speaking Companies in the Age of Artificial Intelligence.” Ed.: Prof. Dr.-Ing. Sebastian Schlund. Vienna, March 2025.
