Leverage White-Collar Workers with AI

While in the manufacturing industry robots do the majority of the assembly tasks, robotics process automation, where software robots are taking over repetitive tasks from humans have been introduced only recently. Many routine tasks continue to be executed without adequate assistance from tools that would be in reach of the current technical capabilities of AI. Using the example of taking meeting minutes, the paper presents some intermediate results of the capabilities and problems of currently available natural language processing systems to automatically record meeting minutes. It further highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible. Treating AI as actors enables a more effective allocation of tasks upfront, which makes it easier to come up with a hybrid workplace scenario where AI can support humans in doing their work more efficiently.