- Titel
- Towards a Framework for Assessing the Business Impact of Artificial Intelligence
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-838628
- Datum der Einreichung
- 05.10.2022
- Datum der Verteidigung
- 27.01.2023
- Abstract (EN)
- Artificial intelligence (AI) will shape the future of life and business like no other technology. AI generates insights that humans alone would not be able to recognize, supporting human decision-making. In combination with the ever-increasing amount of data and processing capabilities, AI-based innovation offers unprecedented potential for companies to gain a competitive advantage. Due to the high technical complexity of AI, organizations face difficulties to assess the individual benefits and drawbacks. A glimpse into the history books reveals parallels with the advent of the steam engine: Some companies are aware that, AI as a so-called ‘general purpose technology’ offers immense cross-industry business potential. They may therefore be tempted to implement so-called ‘moon shot’ projects to exploit the full AI potential at once. Other companies, in contrast, are unaware of the technology’s possibilities, either because they lack AI expertise or fear the disruption of existing value creation structures. Science and practice primarily evaluate AI from either the business potential, organizational change, or system requirements perspective. But organizations must equally consider business potentials and organizational implications for deriving sustainable AI innovation decisions and developing or integrating appropriate systems. The present dissertation hence develops an AI impact assessment framework that integrates both perspectives by taking a non-technical, functional perspective on the technology. With the integration of artifacts such as the periodic table of AI v2 and the AI application taxonomy developed as part of the dissertation project, the AI impact assessment approach facilitates communication around AI and supports AI innovation decisions. The AI impact assessment framework and the related research results were generated by applying the design science research method in the consortium research project ‘Competence Center Ecosystems’ in the financial industry. The present dissertation extends knowledge on the conceptual configuration of AI use cases and applications, AI strategy derivation, and the analysis of AI-induced organizational implications. For practical use, the research results (1) contribute to an AI understanding allowing even digital non-experts to analyze AI opportunities and challenges, (2) enable organizations to develop industry-specific AI strategies, (3) support the sustainable organizational AI deployment, and (4) provide a multi-perspective decision-support for AI innovation decisions. Overall, the generated research results and the AI impact assessment framework aim to foster the business benefits of AI as well as its sustainable deployment for both humans and society.
- Verweis
- Assessing the business impact of Artificial Intelligence
The conference paper is part of the dissertation.
Link: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/2fdc40e9-c36e-43c3-8ad2-06e6384286ad/content - How IT-related financial innovation influences bank risk-taking: results from an empirical analysis of patent applications
The conference paper is part of the dissertation.
Link: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8808073&casa_token=7sJi648NrCkAAAAA:TBdMk-fpkvoturZXPv8BapydKo5Hdo9vnG2R9RS1ALoQCEtguilol6lkEbHR7OsVLtNZAzby5aB6&tag=1 - Implications of AI-based Robo-Advisory for Private Banking Investment Advisory
The journal paper is part of the dissertation.
Link: https://www.emerald.com/insight/content/doi/10.1108/JEBDE-09-2022-0037/full/html - The Convergence of Distributed Ledger Technology and Artificial Intelligence: And End-to-End Reference Lending Process for Financial Services
The conference paper is part of the dissertation.
Link: https://www.alexandria.unisg.ch/260329/1/The%20Convergence%20of%20Distributed%20Ledger%20Technology%20and%20Artificial%20Intelligence%20-%20An%20end-to-end%20Reference%20Lending%20Process%20for%20Financial%20Services.pdf - Artificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload
The conference paper is part of the dissertation.
Link: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/2fdc40e9-c36e-43c3-8ad2-06e6384286ad/content - Freie Schlagwörter (EN)
- Artificial Intelligence, Business Impact Analysis, Framework
- Klassifikation (DDC)
- 330
- Den akademischen Grad verleihende / prüfende Institution
- Universität Leipzig, Leipzig
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-838628
- Veröffentlichungsdatum Qucosa
- 01.03.2023
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY-NC-ND 4.0- Inhaltsverzeichnis
Acknowledgements Abstract Table of Contents List of Figures List of Tables List of Abbreviations 1 Introduction 1.1 Motivation 1.2 Research background and research gap 1.3 Research questions 1.4 Structure of the thesis 2 Foundations 2.1 Artificial intelligence 2.1.1 Human intelligence as the fundament of artificial intelligence 2.1.2 Viewpoints on the definition of artificial intelligence 2.1.3 Artificial intelligence from a non-expert perspective 2.1.4 A functional viewpoint toward artificial intelligence 2.2 Technology innovation decision-making 2.2.1 Lessons from earlier and recent history 2.2.2 Current AI developments and societal disruption 2.2.3 The innovation decision-making process 2.2.4 Impact analysis as a decision support approach 3 Theoretical anchoring 3.1 Theory selection and assessment 3.1.1 Selection process 3.1.2 Specific requirements assessment 3.1.3 General requirements assessment 3.2 Applied theories and contributions 3.2.1 Technology-organization-environment framework as a guidance for AI impact assessment 3.2.2 Agent-based view as a theoretical lens for AI functionality structure and AI application configuration 3.2.3 Theory of competitive strategy as a fundament for AI-related strategy decision-making 3.2.4 Task-technology fit model and unified theory of acceptance and use of technology for the analysis of AI-induced organizational implications 4 Research approach 4.1 Design science research 4.2 Consortium research 4.3 Research instantiation 5 Research studies on the business impact of artificial intelligence 5.1 Research overview and applied methodology 5.2 Research question 1: Functional AI structure and conceptional AI application configuration 5.2.1 Study 1.1: Assessing the business impact of Artificial Intelligence 5.2.2 Study 1.2: The AI application taxonomy – structuring AI functionalities for business potential analysis 5.2.3 Relation between study results 5.3 Research question 2: AI-related business potentials 5.3.1 Study 2.1: How IT-related financial innovation influences bank risk-taking: results from an empirical analysis of patent applications 5.3.2 Study 2.2: Assessing the business impact of artificial intelligence in the financial industry – An investigation of AI-FinTechs applying an AI application taxonomy 5.3.3 Relation between study results 5.4 Research question 3: Organizational impacts of AI-based applications 5.4.1 Study 3.1: Implications of Artificial Intelligence-based Robo-Advisory for Private Banking Investment Advisory 5.4.2 Study 3.2: The convergence of distributed ledger technology and artificial intelligence: An end-to-end reference lending process for financial services 5.4.3 Study 3.3: Artificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload 5.4.4 Relation between study results 6 Integration of the research study findings 6.1 AI-related innovation decision-making 6.2 The AI impact assessment framework 6.2.1 Scope 6.2.2 Description 6.2.3 Evaluation 6.3 Practical application in a consulting project with a Swiss financial service infrastructure provider 7 Results, contributions, and outlook 7.1 Summary of results 7.2 Contributions 7.2.1 Theoretical contributions 7.2.2 Practical contributions 7.3 Limitations 7.4 Future research 7.5 Conclusion Appendix Appendix A: Study 1.1 – “Assessing the business impact of Artificial Intelligence“ Appendix B: Study 1.2 – “The AI Application Taxonomy – structuring AI Functionalities for business impact analysis” Appendix C: Study 2.1 – “How IT-related financial innovation influences bank risk-taking: results from an empirical analysis of patent applications” Appendix D: Study 2.2 – “Assessing the business impact of artificial intelligence in the financial industry – An investigation of AI-FinTechs applying an AI application taxonomy“ Appendix E: Study 3.1 – “Implications of AI-based Robo-Advisory for Private Banking Investment Advisory” Appendix F: Study 3.2 – “The Convergence of Distributed Ledger Technology and Artificial Intelligence: And End-to-End Reference Lending Process for Financial Services” Appendix G: Study 3.3 – “Artificial Intelligence for Managerial Information Processing and Decision-Making in the Era of Information Overload” Appendix H: Description of AI periodic table elements Appendix I: AI impact assessment framework questionnaire Appendix J: AI-Fintech business model analysis results References Curriculum Vitae Declaration of academic integrity Bibliographic description