ARCHITECTING AI-DRIVEN COMMERCIAL DECISION SYSTEMS: A MANAGERIAL FRAMEWORK FOR DATA-CENTRIC SALES ORGANIZATIONS

Abstract The increasing complexity of modern sales environments has fundamentally challenged traditional managerial decision-making models. As organizations generate and process unprecedented volumes of commercial data, conventional business intelligence and human-centered judgment mechanisms are no longer sufficient to ensure timely, consistent, and scalable decisions. In response, artificial intelligence has emerged not merely as an analytical support tool, but as a foundational element in the architecture of commercial decision systems. This paper introduces the concept of AI-Driven Commercial Decision Systems and positions it as a distinct managerial domain within business management scholarship. Rather than focusing on algorithmic design, the study adopts a managerial perspective to examine how artificial intelligence reshapes the structure, governance, and execution of commercial decisions in data-centric sales organizations. The paper traces the evolution from descriptive reporting systems to AI-enabled decision architectures capable of generating recommendations, optimizing commercial actions, and supporting real-time managerial control. Building on this analysis, the study proposes an original managerial framework for architecting AI-driven commercial decision systems. The framework integrates data infrastructure, analytical intelligence, human–AI interaction models, and governance mechanisms into a cohesive decision architecture aligned with strategic, tactical, and operational sales objectives. Particular attention is given to issues of decision authority, accountability, and organizational readiness, highlighting the managerial responsibilities that accompany increasing algorithmic influence. By conceptualizing AI-driven decision systems as managerial architectures rather than purely technical solutions, this paper contributes to business management theory and offers practical guidance for executives seeking to institutionalize AI within commercial decision-making processes. The findings underscore that sustainable competitive advantage arises not from AI adoption alone, but from the deliberate managerial design of decision systems that balance algorithmic intelligence with human judgment.