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      New Digital Work : Digital Sovereignty at the Workplace 

      Scenario-Based Foresight in the Age of Digital Technologies and AI

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      Springer International Publishing

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          Abstract

          Scenario-based foresight is used less and less in the corporate world despite continued high satisfaction with the obtained results. In the age of digitalization, many companies feel increasingly forced to short-termism instead of strategic planning. However, emerging digital technologies, such as artificial intelligence (AI), represent a promising approach to cope with the traditional challenges of scenario-based foresight as well as new challenges added by digitalization. Therefore, this work-in-progress paper identifies and analyzes use cases for scenario-based foresight with digital technologies employing a systematic analysis of the relevant literature.

          In the paper at hand, we show that the use of digital technologies for improving the performance of scenario-based foresight is an emerging field. We identify 14 so-called use cases, i.e., unique goal-oriented applications of digital technologies for scenario-based foresight. In general, the use cases show that currently digital technologies can enhance, not substitute the capabilities of scenario-based foresight practitioners. Digital technologies primarily support the analysis of large amounts of data, e.g., for collecting futuristic data and identifying key influence factors. However, activities that require implicit knowledge and creativity, like the interpretation of scenarios, are currently still left to humans.

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          Guidance on Conducting a Systematic Literature Review

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            Big data: A review

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              Is Open Access

              Machine learning for metabolic engineering: A review

              Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined.
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                Author and book information

                Book Chapter
                2023
                April 27 2023
                : 51-67
                10.1007/978-3-031-26490-0_4
                3bcafabd-33b4-4327-abde-91f93cb8c5b5
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