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This thesis addresses the question of how companies can improve their peripheral vision to perceive relevant changes in their corporate environment at an early stage. With this foresight capability, these companies are left with more time to develop appropriate responses to the imminent changes and to quickly trigger innovation processes. To this end, this research studies how artificial intelligence techniques can be meaningfully applied for foresight and innovation processes. This challenge offers a vast research domain in which new insights can be gained by empirical and inductive research and then published as a foundation for future research. With this essay-based dissertation, five research articles contribute to this research domain.

First, the theoretical framework of corporate foresight, innovation management, and their mutual interaction is described. Next, the three phenomena, namely weak signals, strong signals, and trends, are introduced in the context of environmental scanning. Subsequently, a systematic literature review is presented to obtain an overview of the current state of research. In addition to a growing interest in this research domain, the results reveal several challenges, including inadequate search strategies, excessive involvement of human experts, and a one-sided focus on trends. Remarkably, the demand for a continuous environmental scanning process is expressed particularly frequently.