Lack of trust and lack of acceptance caused by strategic mismatches in problem-solving have been identified as obstacles in the introduction of workload-alleviating automation in air traffic control. One possible way to overcome these obstacles is by creating automation capable of providing personalized advisories conformal to the individual controller. This paper focuses on performing an exploratory investigation into the tools and methodology required for creating conformal automation. Central in the creation of individualized prediction models is the combination of a visual feature, capturing traffic situations, and a tailored convolutional neural network model trained on individual controller data recorded from a human-in-the-loop simulation. The main advantage of using a visual feature is that it could facilitate “transparency” of the machine learning model. Results show that the trained models can reasonably predict command type, direction, and magnitude. Furthermore, a correlation is found between controller consistency and achieved prediction performance. A comparison between individual-sensitive and general models showed a benefit of individually trained models, confirming the strategy heterogeneity of the population, which is a critical assumption for personalized automation. Future research should be done in refining the model architecture, finding richer visual features that capture the breadth of human decision-making behavior and feedback model outputs back to individuals for measuring controller agreement.