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Commercial Decision Systems in the Age of Artificial Intelligence: Managerial Capabilities, Risks, and Control Mechanisms
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Commercial decision-making has entered a period of unprecedented complexity as artificial intelligence becomes embedded across sales, pricing, ordering, inventory, and resource allocation processes. Decisions that were once episodic and manager-driven are increasingly continuous, data-intensive, and algorithmically supported. In this environment, traditional managerial decision-making frameworks—centered on experience, periodic reporting, and hierarchical control—struggle to provide sufficient scale, consistency, and oversight. This paper examines the transformation of commercial decision systems in the age of artificial intelligence from a business management perspective. It argues that AI-driven decision systems do not merely enhance analytical efficiency, but fundamentally reshape managerial capabilities, risk exposure, and control mechanisms. By embedding predictive, prescriptive, and adaptive logic into commercial processes, artificial intelligence expands the scope of decisions that managers can realistically evaluate while simultaneously introducing new forms of dependency and governance challenges. The study conceptualizes commercial decision systems as socio-technical architectures in which managerial intent, algorithmic logic, and organizational control interact. It analyzes how AI-enabled systems enhance managerial capabilities by increasing decision visibility, enabling systematic trade-off evaluation, and supporting real-time guidance across complex commercial environments. At the same time, it highlights risks associated with over-automation, data bias, misaligned optimization objectives, and reduced transparency. Building on management control and decision systems literature, the paper develops a managerial governance framework that clarifies how control mechanisms, accountability structures, and ethical safeguards can be integrated into AI-driven commercial decision systems. The framework emphasizes that effective use of AI requires deliberate managerial design rather than passive reliance on algorithmic outputs. The paper contributes to business management research by reframing artificial intelligence as a driver of new managerial capabilities and control challenges within commercial decision systems. For practitioners, it provides guidance on how to govern AI-enabled decisions in a manner that preserves accountability, strategic alignment, and organizational trust. The findings suggest that sustained value creation in AI-enabled commercial environments depends on managers’ ability to balance expanded decision capacity with robust control and governance mechanisms.
Keywords
Commercial Decision Systems, Artificial Intelligence in Management, Managerial Decision-Making, Control Mechanisms and Governance, Algorithmic Accountability
References
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How to cite this paper
@article{1713979,
author = {Ufuk Elevli},
title = {Commercial Decision Systems in the Age of Artificial Intelligence: Managerial Capabilities, Risks, and Control Mechanisms},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {1833-1841},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713979.pdf},
abstract = {Commercial decision-making has entered a period of unprecedented complexity as artificial intelligence becomes embedded across sales, pricing, ordering, inventory, and resource allocation processes. Decisions that were once episodic and manager-driven are increasingly continuous, data-intensive, and algorithmically supported. In this environment, traditional managerial decision-making frameworks—centered on experience, periodic reporting, and hierarchical control—struggle to provide sufficient scale, consistency, and oversight. This paper examines the transformation of commercial decision systems in the age of artificial intelligence from a business management perspective. It argues that AI-driven decision systems do not merely enhance analytical efficiency, but fundamentally reshape managerial capabilities, risk exposure, and control mechanisms. By embedding predictive, prescriptive, and adaptive logic into commercial processes, artificial intelligence expands the scope of decisions that managers can realistically evaluate while simultaneously introducing new forms of dependency and governance challenges. The study conceptualizes commercial decision systems as socio-technical architectures in which managerial intent, algorithmic logic, and organizational control interact. It analyzes how AI-enabled systems enhance managerial capabilities by increasing decision visibility, enabling systematic trade-off evaluation, and supporting real-time guidance across complex commercial environments. At the same time, it highlights risks associated with over-automation, data bias, misaligned optimization objectives, and reduced transparency. Building on management control and decision systems literature, the paper develops a managerial governance framework that clarifies how control mechanisms, accountability structures, and ethical safeguards can be integrated into AI-driven commercial decision systems. The framework emphasizes that effective use of AI requires deliberate managerial design rather than passive reliance on algorithmic outputs. The paper contributes to business management research by reframing artificial intelligence as a driver of new managerial capabilities and control challenges within commercial decision systems. For practitioners, it provides guidance on how to govern AI-enabled decisions in a manner that preserves accountability, strategic alignment, and organizational trust. The findings suggest that sustained value creation in AI-enabled commercial environments depends on managers’ ability to balance expanded decision capacity with robust control and governance mechanisms.},
keywords = {Commercial Decision Systems, Artificial Intelligence in Management, Managerial Decision-Making, Control Mechanisms and Governance, Algorithmic Accountability},
month = {March},
doi = {https://doi.org/10.64388/IREV8I9-1713979}
}