Trade spend and sales planning represent two of the most complex and resource-intensive decision domains in large commercial organizations. Decisions regarding promotional investments, customer incentives, and sales targets involve significant financial exposure, competing objectives, and high levels of uncertainty. Traditionally, these decisions have relied on managerial experience, historical performance analysis, and negotiated planning processes. While such approaches offer contextual flexibility, they struggle to scale and often result in inconsistent outcomes across markets and channels. This paper examines the growing role of algorithmic decision support in trade spend and sales planning, with a particular focus on AI-driven optimization models. Adopting a business management perspective, the study moves beyond technical model performance to analyze how algorithmic decision support reshapes managerial roles, decision authority, and governance structures. The paper conceptualizes AI-driven optimization as a decision design capability that augments managerial judgment rather than replacing it. The analysis highlights how algorithmic decision support enables organizations to evaluate complex trade-offs among volume growth, profitability, and relational considerations at scale. At the same time, it reveals new managerial challenges related to transparency, accountability, and trust in algorithmic recommendations. The paper argues that the strategic value of AI-driven optimization depends on how decision authority is allocated between human managers and intelligent systems, particularly in high-stakes domains such as trade spend allocation. To address these challenges, the study proposes a managerial framework for integrating algorithmic decision support into trade spend and sales planning processes. The framework emphasizes decision-type differentiation, controlled delegation of authority, and governance mechanisms that align algorithmic outputs with strategic intent. The paper contributes to business management literature by clarifying the managerial implications of AI-driven optimization models and provides practitioners with guidance for institutionalizing algorithmic decision support as a scalable and governable capability in commercial planning.
Ufuk Elevli (Mon,) studied this question.