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APPLICATION OF AI FOR MONITORING AND OPTIMIZING IT INFRASTRUCTURE: ECONOMIC PROSPECTS FOR IMPLEMENTING PREDICTIVE ANALYTICS IN ENTERPRISE OPERATIONS
S. Bushuev, specialist's degree
South-Russian State University of Economics and Service (Russia, Shakhty)
DOI:10.24412/2500-1000-2024-8-3-125-129
Abstract. This research explores the application of artificial intelligence (AI) in monitoring and optimizing IT infrastructure, with a focus on the economic prospects of implementing predictive analytics (PA) in enterprise operations. It examines the benefits of AI-powered technologies and PA, including reduced downtime, increased efficiency, and enhanced decision-making capabilities. It addresses the challenges associated with implementing these technologies, such as data privacy concerns and organizational barriers. Through case studies examples, it demonstrates how AI can transform IT infrastructure management, offering enterprises a pathway to sustainable growth and long-term success.
Keywords: IT infrastructure, artificial intelligence (AI), machine learning (ML), optimization, predictive analytics, monitoring.
In rapidly evolving technological landscape, the management of IT infrastructure has become increasingly complex and vital to enterprise success. As organizations seek to optimize operations and maintain a competitive edge, the application of different techniques for monitoring and optimizing IT infrastructure presents a transformative opportunity. Artificial intelligence (AI) is being increasingly deployed on a global scale, with applications already evident in a range of sectors, including energy, wastewater management, transportation, and telecommunications [1]. AI-powered tools and technologies offer the potential to transform how enterprises manage their IT resources. It provides insights that can lead to enhanced performance, reduced costs, and improved strategic decision-making.
This approach utilizes machine learning (ML) algorithms to analyze historical and real-time data, identifying patterns and trends that can forecast future events [2]. This capability is particularly valuable in IT infrastructure management, where AI can predict hardware failures, optimize network performance, and automate routine maintenance tasks. From an economic perspective, the implementation of AI-driven PA offers significant advantages. By optimizing IT infrastruc-
ture, enterprises can reduce operational costs, enhance productivity, and achieve a higher return on investment (ROI). As a result, companies can respond more rapidly to market changes, capitalize on emerging opportunities, and maintain a competitive advantage.
The aim of this paper is to explore the application of AI for monitoring and optimizing IT infrastructure, focusing on the economic prospects of implementing PA in enterprise operations.
Main part. Impact of AI technologies on IT infrastructure management
The incorporation of AI technologies across a range of sectors has attracted considerable interest due to its capacity to transform established methods and facilitate sustainable growth. According to a 2023 BofA Global Research study [3], AI is expected to have a positive financial impact on 75 % of companies in the next five years. Through more accurate demand forecasting and inventory management, companies can improve operational efficiency, reduce costs, and increase customer satisfaction. According to a report 2024 by McKinsey & Company [4], over the past six years, the rate of AI adoption by organizations has remained relatively consistent, hovering at approximately 50 % (fig. 1).
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2017 2018 2019 2020 2021 2022 2023 2024 Adoption of Al — Use of generative Al
Figure 1. Worldwide adoption of AI in organizations, %
AI technologies are integral in creating more intelligent and efficient IT systems, enabling businesses to not only react to problems as they arise but also predict and prevent them, thereby enhancing overall productivity and innovation [5]. By integrating ML into IT
AI-based technologies enable a holistic approach to IT infrastructure management, providing the tools necessary for predictive
infrastructure management, enterprises can reduce costs, improve service quality, and increase agility in responding to changing business needs. A number of different tools have been deployed in the field of IT management, each offering distinctive benefits (table 1).
analytics, automation, and optimization. Processing NLP in IT infrastructure allows organizations to automate and enhance commu-
Table 1. Application of AI-based technologies in infrastructure management [6]
AI technology Application
ML It enables systems to learn from data and improve their performance over time without being explicitly programmed. ML algorithms can analyze historical and real-time data to identify patterns, make predictions, and automate decision-making processes. ML is particularly valuable for predictive maintenance, where it can forecast potential equipment failures and schedule maintenance activities before issues arise.
Deep learning It is a subset of ML that uses neural networks with multiple layers to model complex patterns in large datasets. It excels in scenarios where vast amounts of data are available, allowing systems to learn intricate patterns and make high-level abstractions. In IT infrastructure management, deep learning can be used to automate complex processes such as anomaly detection, network traffic analysis, and predictive maintenance.
Neural networks It can learn to recognize patterns and make decisions. Neural networks are highly effective in IT infrastructure optimization because they can process and analyze vast amounts of data from various sources, enabling real-time insights and proactive management. They are used for tasks such as fault detection, capacity planning, and automated troubleshooting, providing a foundation for more intelligent and adaptive IT systems.
Natural Language Processing (NLP) It enables computers to understand, interpret, and generate human language, making it an essential technology for automating communication and interaction within IT systems. NLP can be utilized to analyze log files, extract insights from unstructured data, and automate help desk functions through chatbots and virtual assistants. Organizations can improve user experience, streamline support processes, and enhance collaboration between IT teams and stakeholders.
Computer vision AI technology that allows machines to interpret and make decisions based on visual data from the physical world. Is is increasingly being used in IT infrastructure to monitor hardware components and detect physical anomalies. It enables automated visual inspections and real-time monitoring of physical assets, contributing to more proactive maintenance strategies and reducing the risk of hardware failures.
nication between systems and users by interpreting and processing human language, leading to more efficient interactions and faster issue resolution [7]. By leveraging deep learning, neural networks, and computer vision, enterprises can achieve greater operational efficiency, reduce costs, and improve their ability to respond to changing business needs.
Predictive analytics in IT infrastructure management
Implementation of PA is a powerful AI-driven approach that leverages data, statistical algorithms, and ML techniques to identify patterns and predict future outcomes. In the realm of IT infrastructure management, PA provides organizations with the ability to anticipate and address potential issues before they escalate, optimizing operations and reducing costs. By analyzing historical and realtime data, PA helps enterprises transition from reactive to proactive management strategies, leading to enhanced operational efficiency and resilience.
The advent of such innovations has additional beneficial implications for the IT infrastructure. Predictive maintenance is one of the most significant applications of PA in IT companies. By analyzing data from logs and system metrics, it can forecast equipment failures and schedule maintenance activities proactively. This approach minimizes unexpected downtime, reduces repair costs, and extends the lifespan of critical infrastructure components. For example, data from server temperature sensors and usage logs can be used to predict when a server might overheat and fail, allowing IT teams to intervene before the issue occurs.
Utilization of PA also plays a crucial role in capacity planning by forecasting future resource demands based on historical usage patterns. This enables organizations to allocate resources more effectively, ensuring that infrastructure is neither overutilized nor underutilized. By analyzing trends in network traffic, PA can help IT teams determine the optimal bandwidth allocation during peak usage times, preventing bottlenecks and maintaining a smooth user experience.
Anomaly detection is another important application of PA in IT infrastructure. By learning the baseline patterns of normal be-
havior within systems, it can quickly identify deviations that may indicate security threats, system failures, or other issues. This capability allows organizations to respond swiftly to potential threats, minimizing the impact on operations. PA can detect unusual patterns in data access or login attempts that may signal a security breach, enabling rapid response and mitigation.
Companies can optimize IT infrastructure performance by analyzing data to identify areas for improvement. By continuously monitoring performance metrics, PA can suggest adjustments to system configurations, workload distribution, and resource utilization to enhance efficiency. This ensures that IT systems operate at peak performance, delivering better service quality and user satisfaction.
The economic benefits of implementing PA in IT infrastructure are substantial. By optimizing maintenance schedules, resource allocation, and system performance, organizations can reduce operational costs, increase efficiency, and improve ROI. This approach enables more informed decision-making, helping enterprises align IT strategies with business goals and respond more effectively to market changes. This strategic advantage not only enhances competitiveness but also supports long-term growth and sustainability.
Examples of the implementation different AI-based technologies in IT
Popular streaming platform Netflix has implemented AI and ML tools for automated scaling and resource optimization [8]. The challenge was the management of a vast and geographically dispersed IT infrastructure. It is essential for the delivery of high-quality video services on a global scale. The implementation resulted in a reduction in cloud infrastructure costs and an improvement in content delivery efficiency, which in turn led to an increase in the quantity of users.
A multinational investment financial organization Bank of America developed an AI-powered platform Erica for monitoring, managing, anomaly detection and proactive maintenance [9]. In 2024, the company reported a 35% increase in the utilization of its artificial intelligence assistant, with nearly three-quarters of all households (73%) active-
ly engaged with digital platforms since its creation in 2018. Implementation lead to reduction in IT incident resolution time and improved service uptime and reliability.
The sales process at XYZ Software Solutions encountered difficulties, particularly in identifying suitable leads, prioritizing sales activities, and anticipating customer behavior. In order to solve the problem, they utilized AI-powered automation system and employed ML algorithms. The system analyzed a multitude of data points, encompassing customer data, social media interactions, and sales patterns from previous transactions. The implementation of the aforementioned strategy yielded a notable enhancement in both lead conversion rates and sales productivity [10].
The rapid advancement of AI technologies has opened up new ways for small businesses and multi-national organizations to optimize their business processes. These implementations enable the financial industry to drive innovation and contribute to economic growth. Corporate experience has shown the significant impact of innovating with AI to improve economic performance and product stability.
The incorporation of AI technologies into sustainable infrastructure is becoming increasingly prevalent, thereby creating a range of challenges and limitations for stakeholders and IT specialists [11]. The efficacy of AI-driven PA is contingent upon the quality of the data it analyzes. The implementation and management of AI-driven systems necessitates a workforce with expertise in data science, ML, and IT management. The short-
age of such proficient personnel can impede the effective utilization of AI in an infrastructure. Another problem is inaccurate or outdated data. It can result in erroneous predictions, which may prove more detrimental than beneficial to the operations of the company. The deployment of AI technologies in infrastructure management gives rise to concerns pertaining to data privacy and cybersecuri-ty. Infrastructure-related data may encompass information of a sensitive nature that necessitates protection from unauthorized access and misuse.
Conclusions
The application of AI for monitoring and optimizing IT infrastructure represents a significant advancement in enterprise operations, offering numerous economic benefits and enhancing overall efficiency. By implementing AI-driven PA, organizations can transition from reactive management strategies to proactive approaches. It enables to anticipate issues, optimize resource allocation, and improve system performance. This shift not only reduces downtime and operational costs but also enhances decision-making processes, providing businesses with a competitive edge in a rapidly evolving technological landscape. Through the use of various advanced AI tools, enterprises can effectively monitor and manage their IT infrastructure, ensuring that systems run seamlessly and efficiently. These tools enable real-time data analysis, anomaly detection, and predictive maintenance, allowing organizations to address potential problems before they escalate into significant issues.
References
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ПРИМЕНЕНИЕ ИИ ДЛЯ МОНИТОРИНГА И ОПТИМИЗАЦИИ ИТ-ИНФРАСТРУКТУРЫ: ЭКОНОМИЧЕСКИЕ ПЕРСПЕКТИВЫ ВНЕДРЕНИЯ ПРЕДИКТИВНОЙ АНАЛИТИКИ В ОПЕРАЦИЯХ ПРЕДПРИЯТИЯ
С. Бушуев, специалист
Южно-Российский государственный университет экономики и сервиса (Россия, г. Шахты)
Аннотация. В данной статье рассматривается применение искусственного интеллекта (ИИ) для мониторинга и оптимизации ИТ-инфраструктуры с акцентом на экономические перспективы внедрения предиктивной аналитики (ПА) в операциях предприятия. Исследование освещает преимущества технологий на основе ИИ и ПА, включая снижение времени простоя, повышение эффективности и улучшение возможностей для принятия решений. Также рассматриваются проблемы, связанные с внедрением этих технологий, такие как вопросы конфиденциальности данных и организационные барьеры. Демонстрируется, как ИИ может помочь преобразовать управление ИТ-инфраструктурой, предлагая предприятиям путь к устойчивому росту и долгосрочному успеху.
Ключевые слова: ИТ-инфраструктура, искусственный интеллект (ИИ), машинное обучение (МО), оптимизация, предиктивная аналитика, мониторинг.
