Turning Disruption into Potential: Key Capabilities for Leveraging Generative Artificial Intelligence Successfully

Abstract

The thesis investigates what capabilities are necessary to use the transformative potential of Generative AI (GAI) in companies, highlighting how early usage can be a source of competitive advantage. Given the rapid advancements in GAI since 2022 and its impact on business, this research identifies key capabilities to use GAI successfully. Addressing a gap in current literature, which is often limited by industry-specific perspectives and pre-2022 developments, this study adopts a cross-industry explanatory approach. It builds upon insights from eleven GAI experts across corporates, SMEs, startups, consultancies, and venture capital funds, while capturing diverse perspectives. The research initially establishes a fundamental understanding of GAI, exploring leading providers, market trends, and limitations. GAI’s unique sequence-to-sequence modeling automates repetitive tasks and enhances productivity, but also presents challenges like data inconsistencies, often requiring a human-in-the-loop. The study underscores the importance of broad AI education to ensure a holistic understanding of GAI alongside other AI technologies. By identifying common GAI use cases across industries, the research presents insights into assessing use case compatibility, determining return on investment, and navigating make-or-buy decisions for GAI applications. Organizational design emerged as a key capability, involving C-level commitment, strategic alignment, and a core GAI team as a central resource for cross-functional expertise. This team, reporting directly to top leadership, cascades tasks across departments while supporting scalable implementation. A digital hub facilitates ongoing education, and dedicated product managers oversee the performance and user satisfaction of GAI applications. Additionally, advantageous resources such as digital maturity, human capital, and partnerships are essential for leveraging GAI effectively. Compliance robustness is critical, encompassing adherence to regulations such as the EU AI Act, ensuring transparency in AI architecture, and upholding data security. Data security remains a priority, with orchestration layers safeguarding prompt quality and preventing sensitive data exposure. In conclusion, this thesis outlines key capabilities that guide C-level executives and managers in formulating GAI strategies for digital transformation. By offering a cross-industry perspective, it provides practical insights for companies to harness the potential of GAI effectively.