INTELLIGENT TECHNOLOGIES IN BUSINESS: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON SUSTAINABLE DEVELOPMENT

The purpose of the paper is to evaluate the impact of artificial intelligence (AI) adoption on enterprise-level sustainability in the context of digital transformation. The study aims to determine how different levels of AI implementation and business process automation influence key sustainability indicators – namely, energy efficiency, CO₂ reduction, and cost savings. Metodology. The research is based on a synthetic dataset generated through a simulation approach using probabilistic distributions and literature-based assumptions. The dataset includes 250 observations and four independent variables: level of AI adoption, investment in AI, automation level, and degree of policy support. The dependent variable is an integrated sustainability index composed of three sub-indicators. Regression modeling was conducted using the Random Forest algorithm to detect both linear and nonlinear relationships, identify key drivers, and ensure model robustness. Model accuracy was evaluated through R², RMSE, MAE, and MAPE metrics. Results. The model demonstrated a high level of predictive performance (R² = 0.78), confirming its validity. The most influential factors were the actual use of AI technologies and business process automation. Investment in AI without concrete implementation and state policy support had a lower impact on sustainability outcomes. The feature importance analysis confirmed that energy efficiency, cost savings, and CO₂ reduction are directly correlated with digital implementation rather than with formal spending or subsidies. Practical implications. The findings can support enterprise-level strategic planning by highlighting the need for actionable AI integration instead of declarative investment. For policymakers, the study indicates that future support mechanisms should focus on incentivizing outcomes rather than inputs. The proposed model may also serve as a tool for evaluating the effectiveness of national digital and environmental policies. Value / Originality. The study provides a novel combination of simulation-based data generation and ensemble modeling to explore the relationship between AI and sustainable development. It offers a transferable methodology for countries with limited access to real enterprise data and contributes to a deeper understanding of digital sustainability transitions in emerging economies.