Elena Kuts, head of the expert and analytical

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According to Igor Bessarab, head of the IW Group testing department, AI is well suited for automating routine processes: writing a test model based on ready-made documentation, decomposing analytics into tasks, unit tests for development: "Everything that is a routine operation can and should be transferred to AI. The question of data security remains, but this will be resolved in the near future. The main effect of using AI is a reduction in labor costs. According to average estimates, the use of AI can reduce up to 30% of a team's labor costs, eliminating routine and labor-intensive processes."
department of the Russian south korea whatsapp resource software developer ALMI Partner LLC, called 44% an approximate estimate: "The actual use of AI may be both higher and lower than this figure. Many companies are implementing various processes based on AI. For example, automated tests, data analytics, machine learning for making business decisions. The assessment of the use of AI in DevOps also depends on the specific industry and regional characteristics. For example, companies operating in the financial sector and healthcare, where a high degree of reliability and software performance is required, can more actively implement AI in DevOps processes compared to organizations from other industries. The implementation of AI significantly reduces labor costs in many areas of activity, which leads to an increase in productivity and efficiency several times over. Thanks to the use of machine learning algorithms and neural networks, AI is able to perform many repetitive tasks much faster and more accurately than a person."

"There are many scenarios for using AI in DevOps, and they are not limited to code testing. Among the current tasks: generating test cases, helping to migrate simple pipelines and masking (anonymizing) data. AI tools can increase the efficiency of writing code - they help fix errors, optimize cycles and code written by the developer, search for functions that can lead to a defect in the solution at the assembly stage. Technologies can also be used to search for vulnerabilities in information security and analyze pull requests. Unlike a person who can miss or not notice something, AI checks absolutely everything and gives more high-quality comments to improve the performance and efficiency of the system. The main effect of implementing AI is an increase in the quality and speed of writing code. It is difficult to give an exact estimate of the reduction in labor costs in development, but some companies say that the implementation of AI has reduced business costs for software creation by 30-40%," said the head of the "Engineering Tools" stream on the "Sfera" platform. Evgeny Kalashnikov.

According to Maxim Milkov, Lead Product Manager at Softline Digital, possible scenarios for using AI are not limited to code testing, configuration management, and identifying anomalies in data: "AI can be integrated into various business processes, significantly improving their efficiency and productivity. For example, this could be data analysis and forecasting, for example, predicting demand for products and services. It could also be automation of business processes when it is necessary to process documents or simplify the accounting system. AI technologies are widely used in the areas of marketing, customer service, cybersecurity, personnel management, and resource optimization. The gain in this case can reach tens of percent."