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Publicação
Augmented AI for Decision Making: Leveraging Generative AI for Enhanced Decision-Making
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| 2.25 MB | Adobe PDF |
Autores
Resumo(s)
For more than a century, successive technological waves have reshaped managerial work, from
Taylor’s stopwatch to enterprise resource-planning systems and, today, large language models. This
research investigates the integration of Generative AI into the Business Process Management life cycle
by positioning ChatGPT at the decision nodes of three different scenarios: strategic market entry,
tactical budget allocation and operational staff scheduling. Therefore, a design-science approach
combined with a critical literature review and an online survey captured 119 decisions from senior,
middle and junior managers paired each with ChatGPT’s response to the same business case.
Consistency, perceived feasibility, trust and willingness to delegate were analyzed with nonparametric
statistics. Findings reveal a clear structure-ambiguity gradient: the model reliably cut planning time
and achieved 86 % delegation acceptance in the highly structured operational task, matching managers
in the tactical case only when its output was reviewed for soft constraints, and lost credibility at the
strategic tier where political nuance dominated. The artifact of the study sets on analyzing both
strengths and limitations on applying generative AI in business decision-making. This study delivers
actionable guidance for responsible AI deployment: automate where rules dominate; retain human
oversight where context, ethics, or stakeholder politics prevail; and embed explainability and
governance at every stage.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Artificial Intelligence Generative AI AI and data-driven decision-making Decision Making AI Limitations SDG 9 - Industry, innovation and infrastructure SDG 17 - Partnerships for the goals