Sep 26, 2022. By Anil Abraham Kuriakose
Artificial Intelligence is a computer science discipline, dealing with how human intelligence can be created for machines. Artificial Intelligence is not just programming computers to obey rules and perform a specific task. It is similar to the data-driven approach to human learning and decision-making. Hence Artificial Intelligence becomes the central component of strategic decision-making in modern enterprises. AI enhances the capability of business leaders to make better decisions. It is disrupting the way industries function and operates and the rive varies from sales and marketing team to finance to IT. Enterprises are betting big on AI and ML to get them into a competitive edge.
There are three types of Artificial Intelligence systems. The first one is Artificial Narrow Intelligence (ANI), which is goal-oriented and targeting to complete a single task. The second one is Artificial General Intelligence (AGI), in which machines can learn and understand the way humans are given a situation. And the third is the Artificial Super Intelligence (ASI), where which machines will supersede the human being in decision making and problem-solving. The current AI systems are developed based on either statistical algorithm based (classic machine learning) or neural network-based (deep learning). Machine learning algorithms are mainly classified into supervised learning, unsupervised learning, and reinforcement learning. The deep learning systems mimic the human brain with interconnected neurons containing a huge number of parameters and layers. Using deep learning techniques it is possible to develop computer vision and natural language processing applications. Like any other business function, AI can be applied to real-world IT and help the IT organization to improve decision-making speed and save operational costs through automation. The different AI applications in the IT domain are given below.
Different AI Applications in IT domain:
AI application development
The AI-based application development is a new approach, and it always starts with data to build models instead of programs. The machine learning model development itself can be automated using automated machine learning (AutoML) and network architectural search (NAS) techniques.
Traditional application development.
If you take any application, only a fraction contains the machine learning code. Hence the traditional SDLC based software development model is not going away. However, AI can help several areas in conventional software development, including precise estimation, rapid prototyping, automatic error handling, and automatic code refactoring.
AI-based quality assurance
The AI-based approaches can be used to enhance the effectiveness of the quality assurance (QA) process. Several AI techniques exist to improve the test case generation for all valid user journeys, find all permutation and combinations of the bug hunting path, and maximize the code coverage. Additionally, AI-based testing tools can scan the source code, log files, and predict potential security vulnerabilities.
Application maintenance and support (AMS)
AI techniques improve the predictive maintenance capability of software solutions, thus reducing maintenance time and costs. The enterprises adopting the AI for application maintenance and support (AMS) area will get advantages of increased productivity and faster application lifecycle management.
In recent times, observability is a buzzword, several times it is replacing the definition of traditional monitoring concepts. The observability enhances the conventional monitoring system's ability to infer the internal state of the IT environment. The level of observability in the IT environment can be maximized with AI-based, KPI analytics, log analytics, anomaly detection, contextual event correlation, and incident recognition.
Better IT user engagement
The key question in front of the IT service management team is how to improve IT user engagements. Adopting artificial intelligence (AI) techniques like natural language processing, deep learning-based ticket and change risk analytics enhance the IT service management team's capability to have better IT user engagement and IT service management.
AI-based observability provides better insights and incident recognition. Getting to the point of having an actionable incident that supports automated workflows enhances the capability of the IT operations team to resolve different IT outages and performance problems. Also, they can automate different IT processes and tasks using cognitive automation techniques.
AI-based IT governance provides self-service capability and the highest level of policy compliance. This self-service model drives agility across the IT organization and helps IT teams to work on more innovative activities instead of wasting time on bureaucracy and organizational politics.
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