title: The role of artificial intelligence in actioning customer insight to manage customer experience throughout the customer journey within service organizations in Jordan
Creators: name: Kasaji, Abdulrahman Abdulaziz
Publisher: name: University of Reading
Year: 2025

description: Customer experience management (CXM) has emerged as a significant aspect of business 
strategy, acknowledged for its role in fostering sustainable competitive differentiation. In this 
context, the utilisation of artificial intelligence (AI) technology in CXM assumes paramount 
importance, as it enables organizations to revolutionize customer journeys through AI-driven 
CXM. Such AI advancements hold immense potential in improving customer experience and 
enhancing organizational performance. However, the CXM field faces challenges in reaching 
the expected level of maturity. This research focuses on the role of AI in actioning customer 
insights throughout the Customer Journey (CJ) to manage Customer Experience (CX). The 
study aims to explore how organizations can effectively utilize AI-derived customer insights to 
continuously enhance CX and develop a framework for understanding and managing CX based 
on AI-generated insights within service organizations in Jordan. This includes understanding 
how AI can be incorporated into the customer insight process and exploring the various ways 
organizations utilize AI-driven customer insights to comprehend and manage customer 
experience. Additionally, the research seeks to explore the different ways organisations assess 
the value of actioning customer insights derived from AI.
Due to the exploratory nature of the topic, a case study approach was deemed appropriate for 
investigating the contemporary phenomenon in-depth and within its real-life context. The study 
employed a multiple embedded case study design, with the organization as the holistic unit of 
analysis and the process of generating and actioning AI-enabled customer insights as the 
embedded sub-unit of analysis. 
The researcher selected four service organizations in the banking and telecommunications 
sectors that have a customer experience management function, program, or practice in place, 
have adopted AI technologies, and are accessible and willing to participate. The data collection 
techniques provided a rich, detailed, and complete picture of the phenomenon under study. 
Purposeful sampling was utilized to collect evidence from multiple informants within each 
organization through semi-structured and in-depth interviews and document reviews. 
The analysis of the case studies followed the 'stacking comparable cases’ approach 
recommended by Miles and Huberman (2014), which involved within-case analysis, cross-case 
analysis, and systematic comparison and synthesis. Within-case analysis was used to describe,
understand, and explain what happened in a single, bounded context, while cross-case analysis 
was used to identify themes that cut across cases. Finally, systematic comparison and 
synthesis were used to compare and contrast findings across sector-specific and all sectors.
The key findings of this thesis contribute to the realms of theory and empirical research, as well 
as the practical role and implementation of AI in the field of CXM. Firstly, the development of 
the Customer Experience-Based View (CXBV) framework introduces a significant theoretical 
advancement, demonstrating AI’s transformative implications in CXM. Building upon the 
Resource-Based View (RBV) and Knowledge-Based View (KBV), the CXBV framework 
incorporates six critical capabilities central to successful CXM, which include customer 
experience strategy, customer journey management, customer intelligence approach, agile 
operations, the CX data-to-value process, and the harnessing of AI capabilities.
Further, this study brings to light the importance of additional dimensions such as customer 
experience strategy, customer journey management, customer intelligence, and agile 
operations, collectively substantiating the CX data-to-value creation process. It emphasizes the 
need for a comprehensive understanding of the transformation of customer data into customer 
experience actions.
Moreover, this research establishes the intermediary role of customer intelligence in the data�to-value creation process. It posits the necessity of interpreting and understanding customer 
insights prior to their implementation. The study identifies two specific categories of AI analytics, 
namely AI-enabled data-to-insights analytics and AI-enabled intelligence-to-action analytics, 
demonstrating AI’s potential to transform customer data into actionable insights.
From a practitioner standpoint, this thesis offers actionable insights and recommendations, 
particularly tailored to the telecommunications and banking sectors. The study's findings 
provide insights into how organizations can leverage AI-generated customer insights to manage 
customer experience effectively. This is exemplified by strategies such as providing customized 
consumer experiences, delivering seamless customer service across touchpoints, innovating,
and developing data-driven products, and refining organizational processes. The research also 
highlights the importance of assessing the value of AI-generated customer insights, which can 
enable organizations to optimize their CXM strategies and improve customer loyalty. 
Lastly, the findings illuminate the immense latent value of AI-generated customer insights in 
enhancing various customer experience outcomes. It outlines how AI can enhance customer 
satisfaction, loyalty, and even facilitate revenue growth while enabling strategic customer 
acquisition. Furthermore, AI’s predictive capabilities are highlighted as instrumental in 
facilitating proactive decision-making and aligning business strategies with future trends and 
customer expectations. These findings culminate in providing a comprehensive scholarly 
understanding of the intersection of AI and CXM in the business and management domain, 
enhancing the discourse in both academia and industry.