The influence of artificial intelligence technologies on business model transformation for marketing agencies
Abstract
One of the most impactful trends in digital technology is artificial intelligence (AI). Ai acts as a core catalyst for digital transformation, compelling organizations, particularly in the field of marketing, to react to its disruptive nature. Consequently, this master thesis investigates the disruptive impact of AI-technologies on marketing agencies, exploring how it necessitates business model transformation and alters the competitive environment. Based on a two-round Delphi study involving 22 industry experts, the research analyzes internal structural changes using the Business Model Canvas and examines the external business environment through the Five Forces Model. The outcomes of the Delphi study confirm that AI-technologies not only improve operational efficiency but also act as a driver for strategic change. As generative AI commoditizes standardized executional tasks, agencies can no longer sustain their traditional business model. Hence, a shift in the value proposition from a service provider to a strategic advisor is necessary. Agencies must transform into high-level strategic consultants, provide data driven strategies and leverage newly arising value creating business activities enabled by AI-technologies. Alongside this shift comes the need to adapt all other elements of the business model, as business model transformation requires a holistic approach. The analysis using the Five Forces model reveals an increasingly intense competitive landscape, making it difficult for marketing agencies to survive without adapting to this changing environment. The thesis concludes that future competitiveness depends on successful business model transformation.
Fields of Study & Subjects
Digital Innovation in Industries
Business Strategies and Innovation
AI in Service Interactions
Publication details
- Journal / source
- University Library Linz repository (Johannes Kepler Universitat Linz)
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Thesis