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.
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
Objectives and Thematic Focus
This thesis investigates the transformative role of Artificial Intelligence (AI) within price management, specifically examining its impact across the four fundamental phases: strategy, analysis, decision-making, and implementation. The primary objective is to develop a comprehensive, structured framework that assists businesses in evaluating the value and risks associated with integrating AI into their pricing strategies.
- Analysis of phase-specific value propositions and risks introduced by AI technologies.
- Evaluation of AI’s role in reshaping traditional pricing processes (e.g., from static to dynamic models).
- Synthesis of current literature (2022-2024) to create a decision-making framework for corporate use.
- Identification of strategic challenges, including algorithmic bias, data requirements, and organizational change management.
Excerpt from the Book
Rule-based pricing: Systematizing pricing rules
With the rise of increasingly sophisticated computer systems in the late 20th century, businesses began to implement rule-based pricing strategies, where specific rules dictated price adjustments under given conditions. Rule-based pricing emerged as businesses sought greater consistency in pricing decisions, reducing reliance on intuition by instituting predefined responses to market or cost changes (Maxwell, 2018). Typically, these systems used a single or small set of factors to inform rules, relying on basic tools like databases or Excel to automate decisions based on competitor actions or desired markups.
Often, these rules were applied business-wide without a lot of differentiation of customers or markets. Rule-based systems were particularly beneficial in stable markets, as they enhanced efficiency and scalability by automating repetitive pricing tasks and reduced errors linked to manual processes. In highly regulated industries, they also helped ensure compliance with pricing standards.
However, the rigidity of rule-based systems often hindered adaptability in dynamic markets. In the retail and fast-moving consumer goods (FMCG) industries, for instance, companies found that strict adherence to rules, such as competitor-aligned pricing, limited their ability to respond to unique market fluctuations effectively. Despite its limitations, rule-based pricing was a significant advancement, facilitating more systematic and scalable pricing processes that reduced dependency on human intuition alone (Galkin, 2023).
Summary of Chapters
1.0 Introduction: This chapter introduces the research context, defines the problem of AI integration in pricing, and outlines the two overarching research questions guiding the thesis.
2.0 Main part: This section establishes the theoretical foundation by defining AI and price management, detailing the pricing phases, and explaining the methodology used for the narrative literature review.
3.0 Results: This chapter presents the findings regarding AI’s value potentials, associated risks such as algorithmic bias, and the specific impacts of AI across the distinct price management phases.
4.0 Discussion and conclusion: This chapter synthesizes the literature review results into a comprehensive practical framework and reflects on the broader implications of AI in price management.
5.0 Outlook and research limitations: This chapter discusses the inherent constraints of the research and identifies areas for future study, such as the cross-functional impacts of AI.
Keywords
Artificial Intelligence, Price Management, Dynamic Pricing, Algorithmic Bias, Machine Learning, Pricing Strategy, Data-Driven Decisions, Consumer Behavior, Pricing Optimization, B2B, B2C, Real-Time Market, Decision-Making Framework, Price Elasticity, Explainable AI
Frequently Asked Questions
What is the core focus of this thesis?
The thesis explores how Artificial Intelligence transforms the traditional four-phase price management process (strategy, analysis, decision, and implementation) and seeks to provide a decision-making framework for businesses.
What are the primary thematic areas covered?
The work covers technological advancements in pricing, AI’s value contributions, identified risks such as algorithmic bias or the "black box" problem, and the organizational requirements for AI adoption.
What is the primary objective of the research?
The goal is to develop a practical, structured framework that helps organizations assess AI integration opportunities and risks within their specific pricing strategies.
Which research methodology is applied?
The study utilizes a narrative literature review approach, including snowball sampling of recent publications (2022-2024), to synthesize qualitative findings into a conceptual model.
What does the main part of the thesis address?
It covers the theoretical background of AI and pricing models, explains the four-phase pricing framework, and details the systemic evolution from experience-based to AI-powered pricing.
Which keywords best characterize the research?
Key terms include AI, price management, algorithmic bias, machine learning, dynamic pricing, and strategy, emphasizing the intersection of technology and pricing efficiency.
How does AI specifically impact the price analysis phase?
AI maximizes data processing capabilities in this phase, enabling deep insights into competitor pricing, customer behavior, and demand elasticity to facilitate smarter, more responsive models.
What are the major ethical risks linked to AI-driven pricing?
Significant risks include algorithmic bias resulting in unfair discriminatory pricing, the "black box" issue hindering transparency, and the potential for unintended algorithmic collusion between competitors.
- Citation du texte
- Lennard Heyder (Auteur), 2024, Artificial Intelligence in Price Management. A Qualitative Assessment of Potentials, Risks and Impacts, Munich, GRIN Verlag, https://www.grin.com/document/1524079
