You can say that AI will destroy humanity like Elon Musk. You can be an AI evangelist. One thing is for certain: AI is changing the way we communicate, interact, work, eat, sleep. AI is changing it all.

AI, ML and DL

Artificial Intelligence

Artificial Intelligence, abbreviated as AI, is a branch of computer science that creates a system able to perform human-like tasks, such as speech and text recognition, data analysis, learning, problem-solving. Using AI-powered technologies, computers can accomplish specific tasks by analyzing huge amounts of data and recognizing in these data recurrent patterns.

Being an umbrella term, AI can be divided into different technology segments, such as machine learning, deep learning, natural language processing, image processing, and speech recognition. However, a central role in the IT industry belongs to machine learning and deep learning.

Machine Learning 

Machine learning (ML) is a subset of AI, which focuses on a computer program that is able to parse data using specific algorithms. Such a program modifies itself without human intervention, producing the desired output based on analyzed data. In essence, using ML techniques, a machine is trained to analyze huge amounts of data and then learn to perform specific tasks.

Deep Learning 

Deep Learning (DL) is a subset of machine learning, whose algorithms and techniques are similar to machine learning, but capabilities are not analogous. The main difference between ML and DL lies in the interpretation of the data they feed on. 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, as well as neural network architectures. 

In the IT industry AI-driven applications have found its use in three major areas: Quality Assurance, Service Management, and Business Processes Automation. Below we will look at all of these areas and the AI usage in them.


Each time a development team introduces a new code, it has to test it before let this code enter the market. Regression testing cycles can take a lot of efforts and time if it is manually done by QAs. With the ability of AI to determine repetitive patterns, this process can be done easier and faster. Using AI for data analysis helps QA departments eliminate human error probability, reduce running test time, and easily identify possible defects. As a result, a QA team is not overloaded with large amounts of data to handle.

AI-powered technology has found its use in the following areas of software testing:

Application Testing

An AI-based system builds test suites by generating behavioral patterns according to geography, devices, and demographics. This allows QA departments to facilitate testing processes and enhance correctness in the program tremendously.

Defect analysis

AI systems can monitor and analyze data and then compare them to prescribed parameters in order to detect errors or areas that require special attention.  If the system detects a problem or an error, it generates a warning. Additionally, the AI system is able to perform a deep analysis of occurred errors, defining areas most apt to defects as well as providing possible solutions for further optimization

Efficiency analysis

By analyzing and summarizing relevant information from a large range of the source, an AI system provides QAs with valuable information, giving QA engineers a complete view of the alterations that they must carry out. Using this information, QAs can make more informed decisions.


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.

Today, AI with its machine learning technologies offers IT companies a self-resolving service desk, which is capable to analyze all the company input data and, as a result, provide users with proper suggestions and possible solutions. Applying AI, companies are able to track user behaviour, make suggestions, and consequently provide self-help options to make service management more effective. In this case, AI ultimately gives users a better experience through improved self-service.

Also, AI is used to develop Computer Vision technology. With CV, Optical Character Recognition has become very popular.

ML and DL capabilities of AI, allow the system analyze a request submitted to a service desk. The AI system finds out concurring requests, compares newly submitted with previously resolved ones, and then based on the past experience gets instant understanding, which solution to opt.

Being a powerful business tool, AI assists an IT team in operational processes, helping them to act more strategically. By tracking and analyzing user behavior, the AI system is able to make suggestions for the process optimization and even develop an effective business strategy.

Particularly effective Artificial Intelligence technology for the development of the procurement system.


AIOps is an abbreviation for the use of AI for IT operations. The essence of this term: the use of AI to manage information technology based on a multi-level platform. The main technologies are Big Data and Machine Learning, which automate data processing and decision making. The AIOps platform accepts for input not only historical data but also online.

The expected result from the work of AIOps is a continuous analysis that will give answers and allow implementing continuous improvements and corrections in the work of the IT infrastructure. The AIOps platform connects three disciplines – service management, performance management, and automation – to achieve the desired result and can be considered as continuous improvement of information systems.

The reasons for the growing popularity of AIOps are the continuously increasing volume from primary data collection systems, an increase in the number of information sources and an enhancing in the number of changes in controlled systems. It’s harder and harder for specialists to keep track of all systems, much less respond to alerts.

At this stage in the development of AIOps, not all companies are able to provide true AIOps. We provide you with a list of 9 functional elements that AIOps platform should have.

  • Accumulated data management 
  • Stream data management 
  • Log reception 
  • Receive data packets 
  • Reception of digital indicators 
  • Reception of documents 
  • Automated pattern discovery and prediction 
  • Anomaly detection 
  • Identification of the true source of problems 

AIOps is undeniably the next evolutionary step in IT management. Proof of this is the published Gartner AIOps forecast: the use of AIOps will grow from 5% in 2018 to 30% in 2023. The faster the company begins to switch to data processing using AIOps, the faster it adapts to a market that is changing rapidly and requires new technologies.


One great benefit AI offers for IT sector is automation. With AI embedded almost every work process can be done without human intervention. The capabilities of deep learning technologies allow IT companies easily automate many operational processes, reducing expenses and minimizing manual work. Besides, the AI algorithms are designed to learn from previous experience, continually improving themselves.  

AI is the future of computer programming. An advanced AI system will soon be able to run and manage software development by itself, understanding even all the intentions behind a code. If the system is dissatisfied with a provided code, finding there some defects, it will fix it immediately without human assistance.

AI will also automate the process of running and managing company networks. It is able to understand patterns, created with network fingerprints while using it. Via leveraging AI for automation, IT companies enhance AI applications in other niches. AI assists in running and managing computers and thus contributes to all other forms of computation.

The rapid rise of innovative technologies has led to A more intelligent and efficient business. In the IT industry Artificial Intelligence has been gaining a lot of traction, today. Its Machine Learning and Deep Learning capabilities allow AI applications transforming many areas of an IT segment. Among them are Quality Assurance, Service Management, and Coding, that are the major ones undergo the AI disruption. Applying AI systems there provides IT companies with a number of benefits, maintaining a competitive edge on the global market.


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