From Descriptive Analytics to Autonomous Commercial Decisions: The Evolution of AI in Sales Management Systems
Abstract
Sales management has historically relied on human judgment supported by descriptive reporting and periodic performance analysis. While advances in business analytics have enhanced visibility into commercial activities, decision authority has largely remained human-centered, constrained by cognitive limitations, organizational complexity, and delayed response cycles. As sales environments become increasingly data-intensive and dynamic, these limitations have exposed the structural inadequacy of traditional analytics-driven decision models. This paper examines the evolutionary transformation of sales management systems from descriptive analytics toward autonomous commercial decision-making enabled by artificial intelligence. Adopting a business management perspective, the study traces the progression from reporting and diagnostic analytics to predictive, prescriptive, and ultimately AI-driven autonomous decision systems. Rather than focusing on technical model development, the paper emphasizes the managerial implications of this evolution, highlighting how decision authority, control mechanisms, and organizational roles are reshaped as AI systems move from advisory tools to active decision agents. The study introduces the concept of autonomous commercial decisions as outcomes generated within AI-enabled sales management systems operating under managerial constraints and governance structures. It argues that autonomy in commercial decision-making is not a binary state but a controlled continuum in which human managers retain strategic authority while delegating operational decision execution to intelligent systems. Through this lens, artificial intelligence is positioned as a catalyst for reconfiguring decision architectures rather than replacing managerial responsibility. By synthesizing insights from sales management, decision theory, and AI-enabled analytics, the paper proposes an evolutionary maturity model that explains how organizations transition from descriptive analytics to autonomous decision systems. The findings contribute to business management literature by clarifying the conditions under which AI-driven autonomy enhances commercial performance while preserving accountability and strategic alignment. For practitioners, the study provides a conceptual foundation for designing sales management systems that balance efficiency, control, and adaptability in AI-driven commercial environments.
Keywords
Autonomous Commercial Decisions, AI in Sales Management, Descriptive and Prescriptive Analytics, Decision Automation, AI-Driven Decision Systems
Citations
IRE Journals:
Ufuk Elevli "From Descriptive Analytics to Autonomous Commercial Decisions: The Evolution of AI in Sales Management Systems" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 2158-2171 https://doi.org/10.64388/IREV9I3-1713982
IEEE:
Ufuk Elevli
"From Descriptive Analytics to Autonomous Commercial Decisions: The Evolution of AI in Sales Management Systems" Iconic Research And Engineering Journals, 9(3) https://doi.org/10.64388/IREV9I3-1713982
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12