On the other hand, knowledge management is often confronted with the opposite problem. The large amount of data, including knowledge articles, templates, metadata, access data, etc., makes it difficult to keep track of it all and to fully exploit the value of the available information.
That is exactly why AI and KM are made for each other. The strengths of one are the solution to the problems of the other. This article presents some use cases where the synergy of KM and AI opens up new opportunities, increases efficiency and improves quality.
Find knowledge: Search and navigation
The larger the knowledge base, the more difficult it is to find the information you are looking for. AI can support this and thus minimise time expenditure and increase efficiency. Here are some examples:
A frequently used method, which shows how AI can provide support, is recommendations in the form of “Other users were also interested in”. These recommendations are mostly based on which articles users have viewed before or after the current article. Articles that come up particularly often are recommended. The prediction can be even more accurate if the users are divided into groups. Then the interests of the group the respective user is in can be taken into account to an even greater degree.
The same mechanism can also influence the ranking of search results. If a page is used more frequently by a particular user group, it appears further up in the results list.
Identifying user groups
User groups can be derived, for example, from the user’s job description, department, or rights. AI systems are also able to form groups automatically and assign new users to a group. The basic concept is that users with similar user behaviour form a group.
If the knowledge base uses a hierarchical menu for navigation, this can also benefit from the findings about the user groups. The system can suggest which pages should be next to each other in the menu because they are often used together. A separate menu structure can even be created for each user group, adapted to their respective needs.
The timing of the search
In addition to the question of who is searching, AI can also consider the question of when. If a user enters several search queries in a row without thoroughly looking at any of the results, it may be worth proactively suggesting another communication channel.
Calendar information can also be included. Certain information is only needed at the end of the month or year, or at certain time intervals. AI recognises these regular patterns and makes it possible to offer the respective information at the right time, without the user having to search for it first.
KM processes: Monitoring and moderation
Information becomes outdated. Even the requirements for a knowledge article can change over time. That’s why the quality of the content must be constantly monitored. Manual monitoring would be too inefficient, so it makes sense to use AI here as well. A whole range of KPIs are conceivable and can be collected automatically.
To take one example: Many knowledge bases offer a comment function. AI can use sentiment analysis to determine whether comments are broadly positive, broadly critical, or generally asking questions. In combination with other information, such as the frequency of access and the number of likes, this can be used to determine how well the article is rated from the user’s point of view. The system automatically places articles with poor or rapidly declining ratings on the list of articles that need to be revised.
(More details on how to monitor the quality of a knowledge base can be found in the article “Slimming Cure for Confluence Wikis“.)
Wherever there is a comment function, there must also be a certain amount of moderation. Again, it would be inefficient to have content experts regularly scan the comments for questions and suggestions. However, if an AI system assesses the comments according to their urgency and only asks the person responsible to react if necessary, the best possible result is achieved with the least possible effort.
Training: Identifying and eliminating knowledge gaps
Knowledge management does not only include the administration of written information. The knowledge in the minds of customers and employees can and should also be managed. This is done through targeted training. Personalised training is more effective than a standardised plan that is based on the average rather than the individual’s needs. AI can identify the needs of the individual and suggest appropriate refresher courses and in-depth training. If a user often looks for information on a certain topic or makes similar mistakes again and again, a training course on this topic will be suggested to that user.
Training courses are difficult to generate automatically due to their complexity. Tests to check the level of knowledge, on the other hand, can be generated automatically, at least in cases where a query about factual knowledge is sufficient. Modern methods for knowledge extraction can generate a question including the corresponding answer from a text. After editorial review, these tests can then also be used to determine individual training needs.
Without AI, only a small percentage of the data generated in and around knowledge management can be used. AI, in turn, needs this data and can generate real added value on the basis of this. This helps to improve knowledge management. The result: high-quality content, efficient use and, in the end, better results and fewer errors. AI and KM: a perfect team.