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              <text>Madan Kumar, C.; Shekhar Jadhav, Rohini; Sondawale, Shweta P; Angulakshmi, M.; Bikash Acharjee, Purnendu; Arockia Dhanraj, Joshuva</text>
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              <text>AI-Powered Analytics in IT Services and the Opportunities and Challenges for Scalable Growth</text>
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              <text>01-01-2025</text>
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              <text>Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.734-739</text>
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              <text>&lt;a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439473" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/SISIMPACT67725.2025.11439473&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105037464223?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105037464223?origin=resultslist&lt;/a&gt;</text>
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              <text>Madan Kumar C., School of CS &amp;amp; AI, SR University, Telangana, Warangal, India; Shekhar Jadhav R., Vishwakarma Institute of Technology, Department of Computer Engineering, Maharashtra, Pune, India; Sondawale S.P., School of Engineering and Sciences, MIT ADT University GLA University, Maharashtra, Pune, 412201, India; Angulakshmi M., School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Tamil Nadu, Vellore, India; Bikash Acharjee P., Computer Science, CHRIST University, Bengaluru, India; Arockia Dhanraj J., Research and Development Cell, Lovely Professional University, Punjab, Phagwara, 144411, India</text>
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              <text>The technological advancement has witnessed high levels of efficiency in operations, decision-making and customer engagement because of the integration of AI enabled analytics into IT-based services. The paper examines the influence of AI-based solutions that are transforming IT service models, with focus on the effect on scalability, predictive maintenance, and intelligent automation. The studies to point out the opportunities presented by AI describe improved personalization of service delivery, fewer instances of downtime, and data-based optimization approaches. Nonetheless, it also addresses such vital issues as the privacy or interpretability of data and the infrastructural requirements of scaling AI solutions. It is suggested to replace these limitations with a hybrid framework resolving limitations by integrating the advantages of cloud-native frameworks with edge-intelligent systems. Experimental study conducted among medium sized IT companies revealed that the speed in delivering the services increased by 5 0%, 6 0% lower error rates and over 55% reduction in downtime. The results hint at the possibility of bearing AI-driven analytics-based IT services when the required control and strategic design are employed.  2025 IEEE.</text>
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              <text>AI-powered analytics; Cloud-native architecture; Edge intelligence; Ethical AI deployment; IT services</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>ISBN: 979-833155787-4;</text>
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