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How Application of Artificial Intelligence in Information Technology Transforms the Industry

Konstantin Mirin
6 Aug 2021
6 min
How Application of Artificial Intelligence in Information Technology Transforms the Industry

Artificial intelligence (AI) and information technology (IT) go hand in hand, together creating new industry opportunities. The advancement of technology is accelerating AI application in the IT sector, facilitating numerous tasks and human efforts.

The latest release of International Data Corporation’s (IDC) Worldwide Semiannual Artificial Intelligence Tracker shows that the global revenue for the AI market is expected to grow by 16.4% in 2021, reaching $327.5bn. This includes software, hardware, and services related to AI.

So how exactly is AI transforming the IT industry, and what are its applications?

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    Defining artificial intelligence and information technology

    Information technology allows computers to perform various tasks; including storing, retrieving, transmitting, and manipulating data. At the same time, artificial intelligence causes computers to exhibit signs of intelligence. They can perform complex tasks which formerly only humans could process, or even outperform them as AlphaGo, a computer program that plays board games, showed.  

    “Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs,” said computer scientist John McCarthy, widely known as the father of AI.

    How Application of Artificial Intelligence in Information Technology Transforms the Industry - photo 1

    When it comes to AI application in information technology, the following components play the dominant role: machine learning (ML) with its subset deep learning (DL); natural language processing (NLP) with speech recognition as its subset; and AIOps powered by ML.

    Thanks to these, the IT sector continues to develop, presenting new AI-powered solutions for business and common users alike. They include machine-based translation, voice assistants like Siri and Alexa, face recognition technologies in smartphones, chatbots, automation of software development stages, and so on.

    Let’s take a closer look at each of the segments.

    Machine learning

    Machine learning takes center stage in the IT sector. A subset of AI, it is trained to analyze large amounts of data and perform certain relevant tasks.

    Deep learning, in turn, enables training computer systems to carry out classification tasks working directly from images, texts, and sounds. It relies on labeled data and neural network architectures.

    ML has found its implementation in application testing, efficiency analysis, social media analysis, automation of processes, and spotting bugs.

    Software testing with the help of ML is a promising area. Some companies already use ML-powered quality assurance (QA) to facilitate human work, and much of this happens on the backend. ML bots are used to study end-user experience. They can be trained to run more test cases than regression testing. ML testing efforts are also helpful with the API layer, as algorithms can easily take over the analysis of test scripts, relieving a tester from making numerous API calls.

    Deployment control is another ML application in IT, where AI can boost the efficiency of deployment control activities within the software development process. It helps developers avoid the risk of failed deployment by scanning for bugs and issues with the help of ML algorithms.

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      Natural language processing

      While ML could be the most popular artificial intelligence application in information technology, NLP has been the one making headlines in the media lately. 

      NLP is a branch of AI application which enables computers to read, analyze, interpret, and generate human language through voice or text. NLP models perform a number of tasks, including but not limited to identifying emotion in human speech, completing sentences, and even writing articles that evaluators can barely distinguish from those written by humans. 

      Some of the most common applications of NLP techniques include online search engines, voice assistants, email filters, chatbots, autocorrect and predictive text, automation of customer feedback processing, etc… 

      The NLP market is projected to grow nearly 14 times larger in 2025 than it was in 2017, rising in value from $3bn to over $43bn, Statista’s predictions show.

      Dozens of NLP models are used in businesses. Here are some of the most popular ones:

      • BERT, or Bidirectional Encoder Representations from Transformers was developed by Google AI. BERT is used for answering questions, named entity recognition, and other tasks related to language understanding (i.e. chatbots, customer review analysis, and relevant information search).
      • XLNet is an autoregressive method effectuating the best of two worlds: autoregressive language modeling and autoencoding. It’s capable of completing 18 NLP tasks (such as chatbots, answering customers’ questions, emotional analysis, and relevant information search).
      • RoBERTa, or Robustly Optimized BERTA approach, was jointly developed by Facebook AI and  the University of Washington. RoBERTa outperforms BERT in tasks according to the General Language Understanding Evaluation (GLUE) benchmark implemented in dialogue systems. (Question answering, document classification, etc.).
      • ELECTRA, or Efficiently Learning Encoder that Classifies Token Replacements Accurately, is a self-supervised pre-training method, used to distinguish “real” input tokens from “fake” ones generated by another neural network.
      • GPT2 and GPT3, or Generative Pre-trained Transformer. Created by the Open AI laboratory, this advanced autoregressive language model uses 175bn parameters to perform text classification, question answering, emotional analysis, text summarization, and translation.

      A promising example of the application of an NLP model in IT is GitHub Copilot. This app is powered by GPT3 and writes code automatically. All you have to do is write a description of functions or enter relevant comments, and it will automatically fill in implementation details. The tool looks at code you’ve already created for your project and seeks to write a new one based on this information. In this way, GitHub Copilot powers novel code generation. 

      Enhancing IT operations with AIOps 

      Another domain transforming the IT sector using artificial intelligence is AIOps. Standing for Artificial Intelligence for IT Operations, this concept employs multi-layered tech platforms to automate and enhance IT operations, with the help of ML and analytics powered by big data. AIOps platforms collect information from various IT devices, run analysis related to retrieved data, automatically spot any issues, and respond to them in real-time.

      How Application of Artificial Intelligence in Information Technology Transforms the Industry - photo 2

      Utilizing ML capabilities, AIOps makes it possible to autonomously identify causes of IT operational problems. It offers solutions, automates responses, sifts out the “noise” from important information alerts, and learns and upgrades.

      Besides optimizing IT operation, AIOPs facilitates digital transformation, DevOps adoption, and cloud adoption.

      According to the findings of Gartner’s 2021 Market Guide for AIOps platforms, the estimated market size for the sector was between $900m and $1.5bn in 2020. Its compound annual growth rate is expected to reach 15% by 2025. 

      “There is no future of IT operations that does not include AIOps. This is due to the rapid growth in data volume and a pace of change (exemplified by rate of application delivery and event-driven business models) that cannot wait on humans to derive insights,” Gartner states.


      The advancement of AI has accelerated its application in many industries, with the IT sector being at the forefront. Integration of these two domains creates more intelligent and efficient solutions for businesses and common users. 

      ML and NLP have already been proven to be effective in the cases mentioned above. Further development of these technologies will boost business productivity and facilitate business processes.

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