Artificial intelligence (AI) applications are increasingly adopted by marketing practitioners to complement and substitute marketing tasks. However, their financial and non-financial performance implications are not yet clear. This dissertation examines whether and how AI applications used in marketing create value for firms. In Chapter 2, I draw on the customer experience management, customer touchpoint and marketing finance literatures to theorize that AI can be used to deliver personalization and convenience benefits to consumers and thus create financial value for firms. I use a multi-method approach is used to test the mediational process and whether stock market investors value AI-enabled conversational commerce applications (CCAs). I report key findings that the stock market positively values CCA announcement and the financial value for a firm with median market value in the sample increases by $56.9 million (+0.29%). CCA launch strategies and design functionalities explain the heterogeneity in financial market returns. Investors pay attention to CCA announcements. Firms communicate personalization and convenience benefits of CCAs to investors and the benefits mediate the effect of CCA on firm value. Also, customers perceive the personalization and convenience benefits, and it increases their purchase likelihood. In Chapter 3, I examine whether marketing AI startups utilize textual descriptions of AI applications to inform VCs and whether such textual communication predicts venture capitalist funding beyond other traditional factors (e.g., financial, demographic) commonly used to predict VC funding for startups. In addition, I study which business and marketing strategies communicated by AI startups through text descriptions are more likely to be associated with VC funding. In Chapter 4, I develop a conceptual model to describe how AI applications create value for B2B sellers across buyers’ purchase journey. I developed a touchpoint-based framework to theorize how AI applications add efficiency and effectiveness to help buyers achieve goals across different stages of the purchase journey. Insights from this dissertation has implications for both marketing theory and practice. It contributes to knowledge about how AI applications add value to marketing. It also helps marketing practitioners justify their investments in AI applications and provide guidance regarding how to extract better value from AI applications.