The Economic Value of AI-Driven Customer Experience in Digital Business Models

Authors

  • Fahmida Akand Department of Management, Guru Kashi University, Talwandi Sabo, India
  • Hemant Kumar Watts Department of Management, Guru Kashi University, Talwandi Sabo, India

DOI:

https://doi.org/10.54536/ajfti.v4i1.8197

Keywords:

Artificial Intelligence, Competitive Advantage, Customer Experience, Digital Business Model, Economic Value

Abstract

AI was increasingly revolutionizing digital business models by delivering superior customer experience and creating identifiable financial value. This study sought to understand the economic consequences of AI-enabled customer experience on digital media platforms in terms of consumer mindset, business functionality, and competitive edge. The purpose was to investigate how AI enhances customer interaction, what it contributes economically in terms of cost-cutting and increased profits, as well as identification of challenges, threats, and future trends. A quantitative research design was adopted, and a structured questionnaire was distributed to 250 users of digital business service. The results demonstrated that AI was found to facilitate increased customer satisfaction, transaction time saving and personalization and thus led to greater inclination of spending and loyalty. A ranking analysis showed that the most valued outcomes were cost savings and customer satisfaction, and the former was found to be the most preferred. Results indicated that the firms that integrated AI had received a more positive brand reputation and competitive advantage than those without. The report found that AI-based customer experience had generated significant economic value, but also encountered challenges in trust, data security and implementation cost continued to be challenging.

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Published

2026-10-19

How to Cite

Akand, F. ., & Watts, H. K. (2026). The Economic Value of AI-Driven Customer Experience in Digital Business Models. American Journal of Financial Technology and Innovation, 4(1), 219-228. https://doi.org/10.54536/ajfti.v4i1.8197

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