AI-Driven Customer Insights in IT Services: A Framework for Personalization and Scalable Solutions

Аннотация

New developments in Artificial Intelligence (AI) in IT services have drastically altered how companies use customer insights to supply personalized and scalable responses to a wide variety of client necessities. The focus of this study consists in the use of AI tools and algorithms in customer data analysis, but also in the sense that they are useful for providing targeted and efficient IT service solutions. The findings are robust because a mixed-methods approach was employed, using qualitative analysis of case studies and quantitative evaluations of service outcomes. The results show that adding AI features into workflows of IT services can significantly improve satisfaction metrics for customer, operating efficiency, and the scalability of the service overall. Additionally, the paper organizes frameworks and different strategies for utilizing AI devices and investigating issues, for example, data secrecy, calculation predisposition, and extendibility. This research also helps bridge a few of the existing gaps in the existing body of knowledge about potential AI applications in customer–centric IT service and provides actionable insights for practitioners and policymakers. The main takeaways indicate how much organizations The American Journal of Engineering and Technology 35 https://www.theamericanjournals.com/index.php/tajet The American Journal of Engineering and Technology need to start seeing AI as a business growth strategy within the AI algorithms which lead to fallacious or and not as a technological advancement. Related to biased insights thereby disrespecting the credibility of this, future research needed to understand the ethical systems. A huge challenge to scalability is also involved considerations of artificial intelligence in customer here, even if the AI models function perfectly within a insights, and the overall implications of artificial controlled environment, its scaling for catering to the intelligence, in the context of media distributors and varied customer bases and the use cases often requires different cultural and regulatory environments. substantial resources and expertise. These challenges need to be addressed in order to truly achieve AI’s Keywords: AI-driven insights, Customer potential for transforming IT service delivery. personalization, IT service scalability, Algorithmic frameworks, Data-driven solutions. In this study, this problem has been addressed as — there exists no comprehensive framework to incorporate AI driven customer insights into IT services Introduction: With the speed of the digital in such a way as to deliver a personalized yet scalable transformation era, the IT service industry plays the solution. AI applications have been widely researched in key part of digital transformation. One of these the context of IT services, but work has not yet been advancements is Artificial Intelligence (AI) which is conducted to understand how these AI applications can taking the center stage in enabling organizations to be systematically designed and deployed with a goal of interact and to provide services to customers. It has achieving both personalization and scalability. This gap transformed into a requirement for companies that will be filled by this study through development of a want to deliver personalized service but also solutions framework that utilizes AI tools and technologies to that scale. Although traditional customer relationship provide custom solutions and address different management (CRM) systems work to a certain extent, customer needs in an efficient and scalable manner. they are also lacking when tasked to process the huge, The objectives of this study are three in number. It unstructured and complicated data in the digital era. attempts first to determine what the main components This is the gap that AI driven customer insights fill by of the AI driven customer insights in IT services are and utilizing machine learning algorithms, natural language those are the technologies, methodologies and processing (NLP) and predictive analytics to create real frameworks. Second, it seeks to measure via data the time actionable insights that can have a big impact on effects of AI on customer satisfaction, operation how decisions related to IT services can be made. efficiency and service scalability. The study finally Increasing complexity in consumer behavior and suggests actional recommendations that businesses can expectations amplify the need for AI powered implement in order to include integrating AI in their IT customer insights. Customers of today want services as service delivery processes, dealing with challenges like per their individual needs, preferences and contexts. In data privacy, scalability, and algorithmic bias. A mixed addition, businesses that fail to deliver personalized method research design, which includes quantitative solutions will be lost in its competitive edge. Since AI data analysis and qualitative case studies is utilized, b