Research Paper
Today we will explore the findings of a research paper examining the impact of AI-driven analytics on IT service delivery. The research, conducted by a team of experts, delves into how AI transforms IT services from reactive to proactive, enhancing client satisfaction and operational efficiency.
Authors
- Junaid Baig Mirza
- Ali Hassan
- MD Nadil Khan
- MD Mohaiminul Hasan
- Rajesh Paul
- Mohammad Rakibul Hasan
Short DOI: 10.62127/aijmr.2025.v03i01.1122
Link: https://www.aijmr.com/research-paper.php?id=1122
Cite This: Optimizing IT Service Delivery with AI-Powered Digital Marketing Analytics: Understanding Client Needs for Enhanced Support Solutions – MD Nadil khan, Ali Hassan, MD Mohaiminul Hasan, Junaid Baig Mirza, Rajesh Paul, Mohammad Rakibul Hasan – AIJMR Volume 3, Issue 1, January-February 2025.
AI-powered analytics increased client satisfaction by 25% and reduced service response times by 21%, transforming traditional IT support into proactive, predictive service models.
Data Sources
This research uses a mixed-methods approach, collecting primary data from structured surveys and interviews with over 150 IT service professionals across industries including finance, healthcare, and e-commerce. Secondary data was drawn from datasets on client behavior, service logs, and AI platform performance, alongside peer-reviewed literature and industry reports to ensure a comprehensive, data-driven foundation.
Analysis Techniques
The study applied descriptive and inferential statistical methods to quantify AI’s impact on service response times and client satisfaction. Machine learning models—regression, decision trees, and random forests—were developed using Python, supported by SPSS 27.0 for data precision. Case studies and data triangulation reinforced insights, while all processes adhered to GDPR standards and ethical research practices.
Predictive models achieved 92% accuracy in identifying client needs, enabling IT teams to deliver timely, tailored support solutions that minimize downtime and optimize operations.
Key Findings & Insights
Reduced Response Times
AI reduced service response times by 21%, enabling faster resolution of client issues and improving overall service efficiency.
Increased Satisfaction
Client satisfaction increased by 25% due to the proactive and personalized support enabled by AI-driven analytics.
Predictive Accuracy
Predictive models achieved 92% accuracy in anticipating client needs, allowing for proactive problem-solving and improved service delivery.
Automation Impact
Automation cut manual intervention in ticket resolution by 40%, freeing up IT staff to focus on more complex tasks and improving overall efficiency.
Challenges
Despite its benefits, AI adoption in IT services faces barriers such as high implementation costs, legacy system incompatibility, and workforce skill gaps. Ethical concerns, data privacy regulations (e.g., GDPR), and the need for transparent AI models further complicate deployment, particularly for small and mid-sized enterprises.
Recommendations & Future Directions
Invest in AI
Invest in AI tools for predictive maintenance and personalized services to enhance service efficiency and client satisfaction.
Data Privacy
Implement robust data privacy and ethical frameworks, such as GDPR compliance, to ensure responsible AI adoption.
Upskill Workforce
Upskill the workforce to effectively integrate AI into IT service delivery, ensuring seamless adoption and maximizing benefits.
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