Improving Knowledge Management with Generative AI

Organisations often have the information employees need, but finding and applying it can be difficult. Policies sit across different systems, product documents change, and useful knowledge remains buried in files or individual teams.

Generative AI can make this information easier to access through natural-language questions and concise summaries. Its value depends on the quality of the underlying content, appropriate access controls and the ability to check answers against reliable sources.

Make existing knowledge easier to use

Traditional search often requires users to know the right keywords and read several documents before finding an answer. Some chatbot systems also depend on predefined questions and responses, which can limit their usefulness when a request falls outside familiar patterns.

Generative AI offers another way to interact with organisational knowledge. It can help users ask questions in their own words, summarise relevant material and explore a topic through follow-up questions.

For example, a support employee could ask about a product’s features or a documented troubleshooting procedure. The system could retrieve relevant material and present a summary, helping the employee locate the information needed to respond.

The goal is to make approved knowledge more accessible while keeping the original sources available for verification.

Connect answers to trusted information

A language model’s general knowledge is not a substitute for an organisation’s own records.

One approach is retrieval-augmented generation, or RAG. The system retrieves relevant content from a designated knowledge base and uses that material to help generate a response.

This allows organisations to maintain source documents separately from the model. Content changes can be reflected through updates to the knowledge base and retrieval process, rather than necessarily requiring model retraining.

However, retrieval does not guarantee a correct answer. A system may select an outdated document, miss important context or produce an unsupported interpretation. Responses should therefore make relevant sources accessible and indicate when the available information is insufficient.

Keep knowledge current

A useful AI knowledge solution needs clear content ownership.

Before implementation, organisations should identify which documents are authoritative, who maintains them and how outdated versions will be retired. Conflicting policies, duplicated files and unclear terminology can undermine answers regardless of the model used.

Maintenance should include:

• Assigning owners to important knowledge sources.
• Reviewing content for accuracy and completeness.
• Managing document versions and effective dates.
• Updating retrieval indexes when source material changes.
• Providing a way for users to flag missing or incorrect information.

Making content easier to update helps subject-matter experts contribute without taking on responsibility for the underlying AI system.

Design access and oversight into the workflow

An AI interface should respect the permissions that govern the information it retrieves. Users should receive answers based on content they are authorised to access.

Implementation decisions should address data handling, storage, access permissions and monitoring. Sensitive use cases may also require human review before an answer informs a decision or reaches a customer.

Multilingual use introduces further considerations. Organisations should test retrieval and response quality in the languages they intend to support, particularly where terminology or document coverage differs.

These requirements should be established during design and evaluated throughout operation.

Start with a focused use case

A practical starting point is a defined knowledge collection with a clear audience, such as an internal support knowledge base or product-documentation library.

Test the solution using representative questions, including ambiguous requests, questions with no documented answer and attempts to retrieve restricted information.

Measure more than response speed. Useful indicators include answer accuracy, source relevance, permission compliance and whether employees can complete their tasks more effectively.

User feedback can also reveal gaps in the underlying knowledge. Repeated unanswered questions may indicate missing documentation rather than a problem that a different model alone would solve.

Beyondsoft’s approach

Beyondsoft’s work in knowledge management has developed from chatbot solutions and tools designed to simplify AI maintenance.

That experience informs an approach that connects organisational knowledge with the workflows in which people need to use it. Generative AI can support this process, but effective delivery also requires content preparation, integration, evaluation and ongoing maintenance.

The objective is practical: help people find relevant information, understand its source and use it with appropriate oversight.

Talk to Beyondsoft about applying generative AI to your organisation’s knowledge-management needs.