Artificial Intelligence in Price Management. A Qualitative Assessment of Potentials, Risks and Impacts
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Master's Thesis from the year 2024 in the subject Business economics - Information Management, grade: 1.0, Otto Beisheim School of Management Vallendar (Chair of Strategy and Marketing), course: Price Management, language: English, abstract: This thesis explores the transformative role of Artificial Intelligence (AI) in price management, focusing on its impact across the four key phases: strategy, analysis, decision-making, and implementation, as defined by Simon and Fassnacht. The motivation for this research arises from a gap in existing literature-no conceptual framework currently addresses...
Master's Thesis from the year 2024 in the subject Business economics - Information Management, grade: 1.0, Otto Beisheim School of Management Vallendar (Chair of Strategy and Marketing), course: Price Management, language: English, abstract: This thesis explores the transformative role of Artificial Intelligence (AI) in price management, focusing on its impact across the four key phases: strategy, analysis, decision-making, and implementation, as defined by Simon and Fassnacht. The motivation for this research arises from a gap in existing literature-no conceptual framework currently addresses AI's specific impacts on each phase of the price management process. The primary aim is to assess AI's influence, including opportunities, risks, and structural changes, within each phase. Two research questions guide this study: What phase-specific value propositions and risk factors will AI introduce in each of the four price management phases? How can these impacts be synthesized into a comprehensive decision-making framework? To address these questions, a narrative literature review of recent studies (2022-2024) was conducted, culminating in a structured framework. This framework provides a cross-industry overview of AI applications in pricing, equipping organizations with a practical tool to evaluate their pricing strategies and make informed decisions about AI integration. The framework presents a varied value-risk profile across phases, helping organizations identify where AI can best support pricing and where risks, such as algorithmic bias and transparency issues, require oversight. A phased approach to AI adoption is recommended, beginning with phases where AI supports human judgment to limit risk. Higher levels of automation and decision authority can be introduced later to maximize efficiency and value, provided there is a balanced approach to risk, technological maturity, and alignment with organizational goals.
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