How AI benefits Automation Plat-forms in DevSecOps Environments and their Measurement

This thesis examines the measurement of benefits derived from the incorporation of artificial intelligence (AI) into automation platforms within DevSecOps (Development, Security and Operations) environments. The advent of AI has led to a surge in the adoption of AI-driven solutions, with a focus on enhancing operational efficiency and the security of IT landscapes among others. However, quantifying these benefits presents significant challenges for organisations.

Gehrig, Raphael, 2024

Art der Arbeit Master Thesis
Auftraggebende
Betreuende Dozierende Martin, Andreas
Keywords
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By developing a framework to measure the benefits of AI, this research facilitates enhanced strategic planning and informed decision-making. Subsequently, this study addresses five core areas: the challenges in automating IT landscapes, the areas within DevSecOps that benefit most from automation platforms, the methodologies to measure AI benefits, the significance of quantifiable benefits, and the limitations of measuring AI benefits. The employed methodology encompasses a comprehensive literature review, in-depth stakeholder interviews, and an empirical validation of the proposed framework leading to the final conclusion.
Business Process Management (BPM) and Design Thinking principles are combined in the framework together with benefits management guidelines to ensure a structured yet flexible approach, incorporating clear metrics customisable to various business con-texts. Empirical validation demonstrates the framework's practicality with stakeholder feedback emphasising the need for ongoing education, especially in the domain of AI. The findings highlight the importance of systematically measuring AI benefits, leading to optimised resource allocation and enhanced decision-making.
This thesis contributes to research by providing a robust tool for organisations to assess and improve their processes. It offers practical insights for industry leaders and sets the foundation for future research to refine and expand its application across different sec-tors and organisations.
Studiengang: Business Information Systems (Master)
Vertraulichkeit: öffentlich
Art der Arbeit
Master Thesis
Autorinnen und Autoren
Gehrig, Raphael
Betreuende Dozierende
Martin, Andreas
Publikationsjahr
2024
Sprache der Arbeit
Englisch
Vertraulichkeit
öffentlich
Studiengang
Business Information Systems (Master)
Standort Studiengang
Olten