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Critical success factors in BPM implementation: Creating AI supported decision engine for the business
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Orientador(es)
Resumo(s)
In today's rapidly evolving business environment, achieving operational efficiency,
transparency, and market responsiveness is crucial for success. Business Process Management
(BPM) is a critical tool in this pursuit, encompassing the design, configuration, enactment, and
analysis of business processes to drive continuous improvement. Recent advancements in
Generative Pre-trained Transformer (GPT) models offer new opportunities to enhance BPM
through improved decision-making and progress-tracking capabilities. However, there is a
significant gap in the literature concerning the systematic development of AI-powered
decision engines to support BPM implementation.
This thesis addresses this gap by partially employing Design Science Research Methodology
(DSRM) with some limitations described in the document to develop a decision engine artifact
tailored for BPM adoption. The research utilizes concept of Critical Success Factors (CSFs)
essential for BPM, incorporating AI to automate and optimize decision-making processes. The
methodology involves a thorough literature review, the conceptualization and design of a
decision engine model, and the development of a prototype. This prototype is evaluated
through expert reviews, providing critical insights into its effectiveness and practical
applicability.
The findings indicate that the integration of AI, particularly GPT models, can significantly
enhance BPM by providing dynamic insights and automating complex decision-making
processes. The developed decision engine prototype offers a structured approach to BPM
adoption, tailored to different organizational stages and supported by a comprehensive list of
CSFs. This research contributes to both academic knowledge and practical applications,
offering a foundational framework for organizations to enhance their BPM efforts and advance the dialogue on data-driven decision-making in business process implementation.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
Palavras-chave
Business Process Management Artificial Intelligence Decision Engine Critical Success Factors SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure