Use of Artificial Intelligence in the Digital Economy

This paper examines the role of artificial intelligence (AI) in the development of the digital economy. It analyzes key areas of AI use in various industries: from forecasting market trends and optimizing production processes to improving the efficiency of logistics and financial transactions. Particular attention is paid to machine learning models that allow analyzing large volumes of data for strategic decision-making. It also touches on the challenges associated with the implementation of AI, including cybersecurity, job losses, and ethical aspects. The paper presents practical examples of using AI to analyze the market and assess the impact of automation on employment. The result of the study was the generalization that the competent implementation of AI contributes to increasing the competitiveness of countries, accelerating innovation, and sustainable economic growth in the context of global digitalization. Materials and methods . There are several simple methods that can be used in Python to perform market analysis using artificial intelligence, including data analysis using libraries such as pandas for data processing and scikit-learn for machine learning. One of the simplest options is trend analysis using the regression method. Here is an example of simple code for market analysis using linear regression. Results . Finally, we create a model that predicts sales volume based on the price of a product. We use simple linear regression to analyze the relationship between price and the number of units sold. The code also visualizes the relationship between price and sales volume, and outputs the model coefficients. Conclusion . Using artificial intelligence methods such as linear regression allows us to effectively analyze market trends and identify relationships between key indexes such as price and sales volume. Using Python libraries such as pandas and scikit-learn simplifies data processing and construction of predictive models. Visualization of results helps to better interpret the obtained dependencies, which can be a useful tool for making informed management decisions and optimizing marketing strategies.