With the recent change in the landscape of how organisations approach work, knowledge management (KM) is more crucial than ever. Remote working has received a real boost and the digital transformation of business models has come to the fore. Consequently, the demands on companies and employees, as well as on the tools they use, must adapt accordingly. To prevent knowledge loss, innovative forms of KM are needed.
KM is all about optimising the flow of knowledge and creating the conditions for its flow in a way that supports organisations. KM affects productivity, engagement and inclusion. It also deals with the "life cycle" of knowledge, starting from knowledge creation, to capturing, sharing and applying knowledge to updating outdated knowledge. KM aims at leveraging knowledge to make better decisions, and to collaborate and spark innovation in order to accelerate organisations towards better performance. The classic definition of KM combines people, processes and technology for improved knowledge-sharing and proficiency.
How AI can help manage knowledge management
Due to the overflow of information within organisations, the ways in which this information is stored and archived must change according to the need, time and purpose, but also according to the technical standards. Information can only be valuable if it is available at the right time and in the right context. However, there are often different sources of knowledge that are not connected to each other, such as employees' e-mail inboxes, filing systems, archives and so on.
In combination with the familiar enterprise search-solutions, artificial intelligence (AI) based methods (such as machine learning and deep learning) are used to link data from different sources. Those "insight engines" become better with AI and help to apply relevancy methods to describe, discover, organise and analyse data. When processing all data from a database (cloud, archives, networks, etc.) connected to an insight engine product, the content is semantically analysed and existing relationships are identified. AI works in the background and ensures that the insight engine continuously evolves by analysing search queries, new data sets and user behaviour. Based on interactions with the results, relevance models calculate the importance for the user and personalise the display modes. The result is a holistic view of complex interrelationships and facts.
Only as good as the input
The advantage of AI is its ability to evolve independently and thus to solve problems autonomously – ideally more precisely and faster than humans. So much about the theory. In practice, however, ethical and legal questions arise and are currently being discussed worldwide. The European Commission (EC) is addressing these concerns and is trying to ensure that AI systems placed on the market and used in the EU are safe and respect fundamental rights.
The draft AI act (Regulation COM/2021/206 final) aims to ensure legal certainty for the providers of AI systems in order to promote investment and innovation in the field of AI and to prevent market fragmentation. The EC's draft contains horizontal rules that will apply to the development, marketing and use of AI-driven products, services and systems. With this draft, the EC is trying to mitigate the risk of incorrect or even discriminatory results deriving from AI. However, an output can only be as good as the input. Distorted or inadequately selected data will always lead to misguided answers.
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