This thesis explores how generative AI is changing the value of financial knowledge in wealth management. Based on 12 interviews with wealth managers in Germany, the findings show that financial knowledge is becoming more accessible, while trust and personal relationships are becoming more important. The study highlights how wealth management firms need to rethink their strategies to remain competitive.
Financial knowledge was once much harder for private clients to access than it is today. Nonfinancial experts who wanted to understand markets, construct a portfolio, or plan their wealth usually depended on professional advice. This gave wealth managers an informational advantage over their clients. That advantage has gradually weakened. Prices and product information became easier to obtain through the internet, regulation increased transparency around costs and performance, and retail platforms allowed clients to invest without relying on a traditional advisor.
Table of Contents
1. Introduction
2. Literature Background
2.1 The RBV and the KBV
2.2 AI and GenAI
2.3 GenAI as a Challenge to KBV Assumptions
2.4 Wealth Management
3. Methodology
3.1 Research Design
3.2 Data Collection
3.3 Sampling
3.4 Data Analysis
4. Findings
4.1 Knowledge as a Product Is Losing Its Value
4.1.1 From Edge to Entry Ticket
4.1.2 From Gatekeeper to Curator
4.2 Individualized Rather Than Organizational Use of GenAI
4.2.1 GenAI as the New Colleague
4.2.2 Less Excel, More Counsel
4.3 The Search for Advantage Beyond Knowledge
4.3.1 Buffering Effects Due to Technology Adoption Rates
4.3.2 Generations, Not Technology, Set the Clock
4.4 Missing Knowledge Strategy
5. Discussion
5.1 Theoretical Implications for the KBV
5.1.1 The Provision and Strategy Gap
5.1.2 Person-Bound Value and the Migration of Rents
5.1.3 Machine-Made Trust and the Interface Question
5.1.4 Temporal Heterogeneity of Knowledge Value
5.1.5 Proprietary AI as the New Knowledge Carrier
5.2 Why Might Firms Leave the Layer Ungoverned?
5.3 Practical Implications
5.4 Limitations
5.5 Future Research
6. Conclusion
Research Objectives and Core Themes
This master's thesis investigates how Generative Artificial Intelligence (GenAI) reshapes the strategic value of organizational knowledge within the wealth management sector, utilizing the Knowledge-Based View (KBV) and empirical expert interviews to construct patterns of shared meaning.
- Analysis of the intersection between Generative AI and the Knowledge-Based View (KBV) of the firm
- Evaluation of knowledge democratization and its effect on traditional financial advisory business models
- Investigation of advisor-bound tacit knowledge versus codified AI capabilities
- Examination of wealth management as a critical case study for knowledge-intensive service industries
- Identification of organizational governance gaps regarding AI tool adoption and risk management
Excerpt from the Book
3.1 Research Design
The interview material of this study was analyzed by means of RTA (Braun & Clarke, 2019, 2021), which builds on the foundational account of thematic analysis introduced by Braun and Clarke (2006). The study follows an interpretive orientation and treats the accounts of WMs as situated constructions of meaning rather than as neutral reports of an external reality, which corresponds to the qualitative research paradigm in which Braun and Clarke (2021) locate their approach. Grounded theory, although firmly established in qualitative research (Thornberg & Dunne, 2019), was not adopted for this study because its central purpose is the development of new theory from data. The present study pursues a different aim. It examines how the core assumptions of an existing theory, the KBV, could behave when clients gain access to financial knowledge through GenAI.
Because the KBV provides a defined theoretical lens, a more structured analytical procedure initially appeared plausible. Framework analysis, for instance, offers a systematic way of working through qualitative material along categories that are fixed in advance (Golndsmith, 2021), and Braun and Clarke (2021) describe related codebook approaches as occupying a middle position between coding reliability methods and fully reflexive forms of thematic analysis. For the present study, this degree of predetermination was considered too restrictive. Research on how GenAI affects the strategic value of financial knowledge in wealth management is still at an early stage, and an analysis bound to predefined categories would risk suppressing meanings that participants raise in their own terms. The KBV therefore orients the analysis without determining its categories in advance, and room remains for patterns that the theory does not anticipate.
Summary of Chapters
1. Introduction: Introduces the research topic, highlighting how GenAI democratizes financial knowledge access and challenges core assumptions of the Knowledge-Based View.
2. Literature Background: Reviews the Resource-Based View (RBV), the Knowledge-Based View (KBV), AI developments, and the specific dynamics of the wealth management industry.
3. Methodology: Details the research design, semi-structured expert interviews with wealth managers in Germany, sampling strategy, and reflexive thematic analysis procedures.
4. Findings: Presents empirical results across four overarching themes, addressing knowledge devaluation, individualized AI use, temporal advantages, and missing knowledge strategies.
5. Discussion: Evaluates theoretical implications for the KBV, explores governance gaps, practical consequences, limitations, and future research directions.
6. Conclusion: Summarizes the thesis findings regarding the shifting strategic value of organizational knowledge in wealth management.
Keywords
Generative Artificial Intelligence, GenAI, Knowledge-Based View, KBV, Wealth Management, Strategic Value, Organizational Knowledge, Reflexive Thematic Analysis, Expert Interviews, Financial Advisory, Tacit Knowledge, Artificial Intelligence
Frequently Asked Questions
What is the fundamental topic of this thesis?
The thesis investigates the intersection of Generative Artificial Intelligence (GenAI) and strategic management, specifically examining how AI tools impact the value and management of organizational knowledge in knowledge-intensive sectors.
What are the central thematic fields addressed in the work?
The core thematic fields include strategic management theory (Resource-Based View and Knowledge-Based View), Generative AI adoption in professional services, knowledge management practices, and the evolving dynamics of client-advisor relationships in wealth management.
What is the primary research question of the study?
The research question asks: How do GenAI-enabled platforms and tools reshape the strategic value of organizational knowledge in wealth management?
Which scientific methodology is applied in the research?
The study utilizes a qualitative, interpretive research design involving semi-structured expert interviews with wealth managers in the German market, analyzed through reflexive thematic analysis (RTA).
What topics are covered in the main body of the work?
The main body covers foundational literature on the KBV and AI, detailed methodological steps, empirical findings derived from interviews structured into overarching themes, and an extensive theoretical discussion outlining new propositions.
Which keywords best characterize the publication?
Key terms include Generative AI, Knowledge-Based View, Wealth Management, Tacit Knowledge, Strategic Value, Reflexive Thematic Analysis, and Professional Services.
Why was the wealth management sector chosen as the empirical setting?
Wealth management serves as a critical case study because its core business model relies on superior financial knowledge and judgment, making the democratization of such knowledge through GenAI immediately observable and impactful.
How do wealth managers integrate GenAI into their daily workflows according to the findings?
Advisors primarily integrate GenAI into backend workflows for task acceleration and drafting, while client-facing interactions continue to rely heavily on personal trust, empathy, and relationship management.
What is the "provision and strategy gap" identified in the discussion?
The gap refers to the division where wealth management firms heavily invest in codified AI infrastructure (provision) while leaving the tacit and relational layer involving human advisors largely ungoverned (strategy).
- Citar trabajo
- Tamilla Javadi (Autor), 2026, Democratizing Wealth Creation, Múnich, GRIN Verlag, https://www.grin.com/document/1780630
