AI optimizes knowledge management in companies

The way we handle information has recently changed dramatically thanks to technology, especially artificial intelligence. Although the processing of data has become more efficient in many places, a much larger amount of it is also being produced. Modern knowledge management is therefore faced with the task of harmonizing human and technological access to this valuable asset. And this can only be achieved by finding common ground or building technological bridges where there are significant differences.

This article provides an overview of the possibilities that artificial intelligence offers for knowledge management in companies.

Natural language as an information carrier

Since the Japanese scientists Nonaka and Takeuchi developed the Basics of knowledge management described above, one of the most difficult problems is dealing with different forms of knowledge. From the outset, their transformation - e.g. from implicit to explicit - is considered a particularly profitable process. The transfer of knowledge between employees also often leads to further insights through reinterpretation. However, a common communication channel is a prerequisite; if the two do not speak the same language, communication is very difficult.

This is precisely the problem with the use and transformation of knowledge since the majority of information is processed digitally: Computer systems work primarily with machine and markup languages as well as mathematical coding and relational databases that elude human communication. However, the development of large language models has led to an unprecedented handshake between man and machine. In the end, the most efficient solution was to enable computer systems with neural networks to use natural language.

Large language models bridge the deep divide between human and machine communication. As a result, companies become an informative ecosystem that enables knowledge management without linguistic restrictions.

Areas of application in companies

The further development of LLMs and corresponding chatbots means that the remaining hurdles are also increasingly being overcome: Multimodal LLMs process visual content, speech-to-text and text-to-speech regulate the conversion between spoken and written word. But even before that, AI already offered extensive opportunities for companies to optimize the use of information. This has resulted in a collection of technological possibilities that can be applied to all knowledge management processes.

According to Nonaka and Takeuchi, these would be the four phases of externalization, combination, internalization and socialization. Here, however, is a practical breakdown according to Jarrahi (et al., 2023) which enables a direct reference to technology:

Knowledge generation

New knowledge is often created by recombining and reorganizing existing knowledge by analyzing data from different sources.

• Generative AI - By accessing company-wide knowledge bases, large language models can filter out the searched information on request. The resulting texts can expand previous opinions, make implicit knowledge tangible and reveal previously unknown perspectives.
• data analytics - Machine learning algorithms analyze large amounts of data in companies and uncover previously unknown patterns and correlations. This provides new insights to optimize the company-wide use of resources.

Examples - Optimize inventories based on customer service workload. Chatbots that prevent customer service overload. Identify legal risks in contracts.

Knowledge storage

The main aim of these methods is to store knowledge in a meaningful structure so that employees can easily access it later. In this area intelligent document processing play a particularly important role.

• Categorization and archiving - Using a mixture of classic rule-based algorithms (e.g. RegEx) and modern machine learning models in the field of natural language processing, it is possible to Categorize documents automatically and store them. Users save time, but can quickly access the knowledge they need.
• AI-based data pipelines - Categorization can be integrated into a comprehensive sequence of automated data processing procedures. For example, documents not only move from the input to the appropriate storage location, but also trigger certain subsequent processes.
• Big data and deep learning - Modern neural networks are capable of handling immense amounts of real-time data thanks to their complex model adaptation and close-meshed neuron connections. The information they contain can either be stored based on rules or analyzed directly using stream processing algorithms.

ExamplesAutomate incoming mail, audit-proof archiving, supply chain analysis, create summaries.

Knowledge transfer

The exchange of knowledge between individual employees in companies plays a particularly important role. In principle, cross-departmental knowledge storage already has a significant effect, as it enables more general access. One of the classic technological approaches to knowledge transfer and dissemination is the company's own intranet. With the further development of AI, the range of possibilities is growing considerably:

• Hyper-personalization - Chatbots and enterprise AI software are not only able to identify suitable content, but also prepare it individually for specific roles in companies. For example, new team members automatically receive relevant introductory documents or sales employees receive tailored market analyses.
• Network analysis and knowledge linking - AI-supported systems can recognize patterns in internal communication and connect employees with similar questions or complementary specialist knowledge. Chatbots with a comprehensive knowledge base also allow the Dissolution of knowledge silos in companies.

Examples - Analyze frequent customer inquiries and implement them in sales. Distribute new product information within the company.

Knowledge utilization

For knowledge to be fully effective, information must not only be accessible, but also prepared in a user-friendly way. AI systems support this process by providing knowledge in context and integrating it into existing workflows.

• Intelligent assistance systems - AI-based chatbots and digital assistants help employees to retrieve specific information quickly and integrate it into their work. For example, customer service employees can automatically receive the most relevant case studies and solutions to a problem.
• Data visualizations - Modern AI systems analyze large amounts of data and help with decisions by simulating and evaluating different options. The underlying data can further support the interpretation. This form of transparency is particularly important for strategic business decisions with far-reaching consequences.

Examples - Automatic summary of guidelines for quick retrieval, AI-supported decision-making for purchasing or investment decisions, personalization of training measures.

Knowledge graphs and LLMs

A popular approach for knowledge management and the organization of information is the so-called graph technology and its application as a knowledge graph. Consisting of edges and nodes in a three-dimensional space, it can be used to model relationships between knowledge fragments. For example, a node can represent an object, a person, a department or a customer. The labeled edges show the relationship between the nodes. This creates a complex but easily comprehensible network of data in a semantic representation. 

This principle has long proven its worth in making complex relationships and structures understandable not only for employees in companies, but also for AI systems - think of vector models such as Word2vec. Semantic context is a popular approach to training AI models, especially LLMs. A knowledge graph that maps a company's internal knowledge structures is therefore an ideal basis for training LLMs to become company-specific experts. They learn the roles of employees, where which knowledge is available and can respond to corresponding requests or proactively integrate relevant information into workflows.

Enterprise applications for knowledge management

The need to organize knowledge and be able to find relevant information at any time increases with the amount of content. The use of appropriate tools and technologies such as artificial intelligence is therefore essential, especially in large organizations. The enterprise AI platform Konfuzio supports companies in this respect with various functions:

• Konfuzio IDP - Automated knowledge storage is possible thanks to the AI-based reading of documents and subsequent archiving. Content is individually indexed and can be found at any time.

• Konfuzio Chat - The AI chatbot accesses the company's own data sources and becomes an individual knowledge assistant for employees on this basis. The versatile integration into existing systems breaks down knowledge silos and supports the transfer and use of knowledge.

Modern knowledge management relies on holistic technological approaches that relieve companies in all operational areas and optimize the use of information.

Conclusion

In recent years, knowledge management has evolved from a highly theoretical concept into a wide-ranging field of application for advanced technologies. Artificial intelligence in particular is excelling in this area, as the algorithms and models used are able to find valuable patterns in data and make them usable. In this way, organizations of all sizes can gain insights and connections that would otherwise escape human attention. AI-supported knowledge management enables users to combine their individual knowledge in a meaningful way and make it usable in the long term - even if they leave the company.

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