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.
Inhaltsverzeichnis (Table of Contents)
- 1.0 Introduction
- 1.1 Problem statement
- 1.2 Objective and research questions
- 1.3 Outline of the thesis
- 2.0 Main part
- 2.1 Theoretical background and fundamentals
- 2.1.1 Artificial intelligence (AI)
- 2.1.2 Price management
- 2.1.3 Technological impact on price management
- 2.2 Literature review - aim and methodology of using AI in pricing
- 3.0 Results
- 3.1 Value potentials of AI in pricing
- 3.2 Challenges and risks of AI in pricing
- 3.3 AI's impact on the price management phases
- 4.0 Discussion and conclusion
- 5.0 Outlook and research limitations
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis aims to provide a qualitative assessment of the potentials, risks, and impacts of artificial intelligence (AI) in price management. It explores the theoretical foundations of AI and price management, reviews existing literature on AI's application in pricing, and analyzes the value potentials, challenges, and impacts of AI across different phases of price management.
- The application of AI in price management
- The potential benefits and risks associated with AI-driven pricing
- The impact of AI on various stages of the price management process
- A review of relevant literature and methodologies
- The challenges and limitations of AI in price management
Zusammenfassung der Kapitel (Chapter Summaries)
1.0 Introduction: This introductory chapter sets the stage for the thesis by outlining the problem statement, which focuses on the increasing use of AI in price management and the need to understand its potential benefits and risks. It clearly defines the objective and research questions that guide the study, establishing a framework for the subsequent chapters. The chapter concludes by providing a brief outline of the thesis structure, providing the reader with a roadmap for navigating the subsequent analysis.
2.0 Main part: This chapter lays the groundwork for the subsequent analysis by providing a robust theoretical background. It defines key concepts like artificial intelligence and price management, exploring their individual characteristics and functionalities. Crucially, this chapter establishes the relationship between AI and its implications for price management. This section serves as a foundational pillar for understanding the complexities of integrating AI into pricing strategies and for interpreting the results presented in later chapters.
2.1 Theoretical background and fundamentals: This section delves deeper into the theoretical underpinnings of AI and price management, setting the conceptual framework for the analysis. It distinguishes between different types of AI and price management methodologies, examining their respective strengths and weaknesses. The discussion extends to the technological impact of AI on price management, highlighting how advancements in AI are reshaping traditional pricing strategies. This provides a context for understanding the complexities of integrating AI into pricing.
2.2 Literature review - aim and methodology of using AI in pricing: This chapter systematically reviews existing literature on the use of AI in pricing. It details the methodologies employed to identify and synthesize relevant research, offering a critical evaluation of existing studies and their findings. It establishes the current state of knowledge regarding AI's role in pricing and identifies gaps in research that the current thesis aims to address. This provides a comprehensive overview of the existing scholarly work in the area.
3.0 Results: This section presents the findings of the research. It details the value potentials of AI in pricing, offering concrete examples and potential benefits for businesses. It then proceeds to analyze the challenges and risks, offering a balanced perspective by highlighting potential drawbacks and limitations of AI integration. The impact of AI across the different phases of price management is also investigated, examining how AI transforms traditional processes. This section provides a systematic overview of the research's empirical findings.
Schlüsselwörter (Keywords)
Artificial Intelligence, Price Management, Dynamic Pricing, Machine Learning, Value Potential, Risks, Challenges, Technological Impact, Business to Business (B2B), Business to Consumer (B2C), Explainable AI (XAI).
Häufig gestellte Fragen
What is the purpose of this document?
This document provides a comprehensive language preview of a thesis. It includes the title, table of contents, objectives and key themes, chapter summaries, and keywords, intended solely for academic use, analyzing themes in a structured and professional manner.
What is covered in the table of contents?
The table of contents includes sections on the introduction, main part (covering theoretical background, fundamentals of AI and price management, and a literature review), results (value potentials, challenges, risks, and impact on price management phases), discussion and conclusion, and outlook and research limitations.
What are the objectives and key themes of the thesis?
The thesis aims to provide a qualitative assessment of the potentials, risks, and impacts of artificial intelligence (AI) in price management. Key themes include the application of AI in price management, potential benefits and risks of AI-driven pricing, the impact of AI on price management processes, a literature review, and the challenges and limitations of AI in price management.
What is the main topic explored in the introduction chapter (1.0)?
The introduction outlines the problem statement, focusing on the increasing use of AI in price management and the need to understand its potential benefits and risks. It also defines the objective and research questions guiding the study and provides a brief outline of the thesis structure.
What does the main part of the thesis (2.0) cover?
The main part provides a theoretical background by defining key concepts like artificial intelligence and price management. It also establishes the relationship between AI and its implications for price management.
What topics are covered in the section on theoretical background and fundamentals (2.1)?
This section delves into the theoretical underpinnings of AI and price management, distinguishing between different types of AI and price management methodologies and their respective strengths and weaknesses. It highlights the technological impact of AI on price management.
What is the focus of the literature review (2.2)?
The literature review systematically reviews existing research on the use of AI in pricing, details the methodologies used to identify and synthesize relevant research, and offers a critical evaluation of existing studies and their findings. It identifies gaps in research that the thesis aims to address.
What kind of content is included in the results section (3.0)?
The results section presents the findings of the research, detailing the value potentials of AI in pricing, analyzing the challenges and risks, and examining the impact of AI across different phases of price management.
What are the listed keywords for this research?
The keywords include: Artificial Intelligence, Price Management, Dynamic Pricing, Machine Learning, Value Potential, Risks, Challenges, Technological Impact, Business to Business (B2B), Business to Consumer (B2C), and Explainable AI (XAI).
- Quote paper
- Lennard Heyder (Author), 2024, Artificial Intelligence in Price Management. A Qualitative Assessment of Potentials, Risks and Impacts, Munich, GRIN Verlag, https://www.grin.com/document/1524079
