AI in IT is a concept that determines the future and everything that it holds. AI has not only transformed traditional computing methods but has also been penetrating many industries, significantly transforming them. As the IT infrastructures become more complex and clients -more sophisticated, IT departments must look for the most effective solutions to enhance IT operations management and accelerate problem resolution in complex modern IT environments. AI, being a tremendous breakthrough, has found great use in the diverse, dynamic, and difficult-to-manage IT landscape.
According to IDC, by 2027, global spending on AI systems will reach more than $500 billion.
AI: Technology Segments
You can divide AI into technology segments, such as machine learning, deep learning, natural language processing, image processing, and speech recognition. However, machine learning and deep learning are central to the IT industry.
Machine Learning
The essence of intelligence is learning. Machine learning (ML) is a subset of AI that focuses on a computer program that can parse data using specific algorithms. Such a program modifies itself without human intervention, producing the desired output based on analyzed data. Using ML techniques, a machine is trained to analyze vast amounts of data and then learn to perform specific tasks.
Deep Learning
Deep Learning (DL) is a subset of ML whose algorithms and techniques are similar to machine learning but whose capabilities are not analogous. In DL, a computer system is trained to perform classification tasks directly from sounds, texts, or images by using a large amount of labeled data and neural network architectures.
Natural Language Processing
Natural Language Processing (NLP) allows AI to understand and manipulate natural language as humans do. Despite the inherent complexity, it offers the possibility of computers reading text or interpreting spoken words with the same ease and fluidity. NLP relies on two basic concepts: Natural Language Understanding and Natural Language Generation. These two engines power chatbots and intelligent virtual assistants to communicate with users. Moreover, sentiment analysis driven by NLP has proved to be a valuable tool in IT.
Computer Vision
Computer vision allows AI to derive meaningful insights from digital images, videos, and other visual content. The AI system can take action or make recommendations based on the extracted information. If AI enables computers to think, computer vision allows them to see, observe, and understand.
AI Applications in IT
In the IT industry, AI-driven applications are used in 3 major areas: Quality Assurance, Service Management, and Process Automation.
QA: Software Testing
Each time a development team introduces a new code, it has to test it before letting this code enter the market. Regression testing cycles take a lot of effort and time if QA experts manually do them. With the ability of AI to determine repetitive patterns, this process can be run easier and faster. Using AI for data analysis allows QA departments to eliminate human errors, reduce running test time, and quickly identify possible defects. As a result, a QA team is not overloaded with large amounts of data to handle.
QA: Application Testing
An AI-based system builds test suites by processing behavioral patterns according to location, device, and demographics. This allows QA departments to facilitate testing processes and enhance the effectiveness of an application.
QA: Defect Analysis
AI systems monitor and analyze data and then compare them to prescribed parameters to detect errors or areas requiring special attention. The system generates a warning if it detects a problem or error. Additionally, the AI system can perform a deep analysis of errors, defining areas most apt to defects and providing possible solutions for further optimization.
QA: Efficiency Analysis
By analyzing and summarizing relevant information from many sources, an AI system provides QAs with valuable information, giving QA engineers a complete view of the alterations they must carry out. Using this information, Quality Assurance can make more informative decisions.
AI for Service Management
AI technology is also widely used in service management. Leveraging AI for service automation allows companies to utilize their resource more effectively, making service delivery faster, cheaper, and more effective.
AI for Process Automation
Humans and manual processes can no longer keep pace with network innovation, evolution, complexity, and change. The next evolution of automation is AI. Various business processes will become more intelligent, aware, and contextual. AI-powered automation will allow IT companies to automate many operational processes easily, reducing expenses and minimizing manual work. IT process automation can be used to streamline various IT operations in a vast number of situations, replacing repetitive manual tasks and business processes with automated solutions.
AI-Driven Computer Engineering
AI is the future of computer programming. In traditional programming, code is a series of rule-based decisions in highly complex conditionals. An advanced AI system will soon be able to run and manage the software development cycle by itself, understanding the core of a code. By now, AI helps human programmers navigate the increasingly complex number of APIs, making coding easier for developers.
AIOps: AI for IT Operations
The term “AIOps” was first coined by Gartner and refers to using AI to manage information technology based on a multi-level platform. Specifically, AIOps uses big data, analytics, and machine learning capabilities to automate data processing and decision-making. The AIOps platform enables comprehensive insight into past and present states of IT systems based on real-time and historical data analysis.
Based on AI technology, AIOps simplifies IT operations management and accelerates problem resolution in complex IT infrastructures.
Continuously increasing volume from primary data collection systems, the constant rise of information sources, and the ongoing enhancement of system modifications complicate the performances of IT companies. AIOps is an excellent solution to tame the immense complexity and quantity of data.
According to Gartner, using AIOps and digital experience tools to monitor applications and infrastructure will rise from 5% in 2018 to 30% in 2023.
Conclusion
The rapid rise of innovative technologies has led to more intelligent and efficient businesses. The IT industry turns to Artificial Intelligence to resolve and prevent high-severity outrages. Its Machine Learning and Deep Learning capabilities allow AI to transform traditional IT operations, making them intelligent, time-saving, and efficient. Quality Assurance, Service Management, and Process Automation are the main areas in which AI has proved to be an effective tool. Moreover, AIOps offers a better and more productive way to manage IT operations.