Advances in the method development that combines deep learning and reinforcement learning (RL) show promise that dynamic decision-making problems can be solved with a strong performance by a machine alone. Operating an electron microscope in an automated fashion could therefore benefit from this development. A first step towards this direction has been proposed in Ref. , where the combination of deep learning and RL offers the possibility to perform low-dose experiments for electron ptychography through adaptive scanning. A schematic of the adaptive scanning workflow is shown in Figure . The advantage of this approach is that it is highly adaptable to a wide range of scanning microscopy techniques through the modification of a reward function that expresses the research goal. Hence, various imaging and spectroscopy techniques, such as STEM EELS, that have already been shown to benefit from an optimized scanning scheme, could be further advanced through a successful automation of the experiment. But also many other parameters of the experiment, such as adjustable aberrations, lens currents, or the phase shifts in programmable phase plates (see Fig. ) can be optimized to improve the efficiency of the experiment with which a given research question is being addressed. Recent developments in software for processing natural language are likely to result in the highly technical user interface of electron microscopes being extended by chatbots and comparable features.