Artificial Intelligence in Price Management. A Qualitative Assessment of Potentials, Risks and Impacts

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

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.