An analysis of the optimal advertising format for artificial intelligence (AI) tools

Jens Cornelsen and Anna Mählck

IU Discussion Papers - Marketing & Communication from IU International University of Applied Sciences

Abstract: This discussion paper examines the success factors of different advertising formats on AI platforms and based on this, develops a practice-oriented guide for the successful implementation of operational advertising measures for AI tools. The relevance of the topic results from the growing importance of AI in everyday digital life as well as the challenges for existing advertising measures to "prevail" in an increasingly advertising-resistant environment. This article is based on an IU master's thesis in which comprehensive literature research was combined with expert interviews and focus groups. Methodologically, potential success factors of different forms of advertising are identified and then evaluated regarding their "target group performance" using a self-developed "AI Advertising Relevance Score" (AARS) based on an utility model. The work further develops existing research on the integration of advertising formats in AI tools while also providing valuable implementation recommendations for operational practice. The still largely limited use of advertising in AI tools shows that further empirical studies are required to validate the direct applicability of the research results presented here in different scenarios. Overall, however, the work makes a valuable contribution to the fundamental understanding of the possibilities and limits of different advertising formats in AI tools and provides practical implications for the sustainable economic stabilization or "monetization" of conversational AI systems such as ChatGPT, Google Gemini or CoPilot.

Keywords: Digitalization; artificial intelligence; ChatGPT; Google Gemini; CoPilot; AI tools; advertising; communication; success factors; utility analysis (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iubhma:323242

DOI: 10.56250/4071

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