INTEGRATION OF ARTIFICIAL INTELLIGENCE METHODS INTO PROJECT QUALITY MANAGEMENT: REAL-TIME CONTROL OF COMPLIANCE WITH STANDARDS

The subject of the present study is the processes, tools and technological solutions that ensure the use of artificial intelligence to automate and increase the accuracy of quality control in projects, as well as the factors that affect the success of their implementation. The purpose of the study is to analyse and generalise approaches to the integration of artificial intelligence (AI) methods into project quality management, with a particular focus on real-time control of compliance with standards. The objective of the research is to ascertain the economic, organisational and technological viability of implementing AI, in addition to identifying risks and barriers that may impede the effectiveness of the project. Methodology. The study is grounded in a comprehensive analysis of international and industry cases (Ocado, Siemens, Nissan), a meticulous review of reports from leading consulting companies (Accenture, Deloitte, PMI), and a systematic summary of practical recommendations for the stages of AI implementation: data preparation, understanding stage, modelling, implementation, scaling and support. Four financial models were considered to assess economic efficiency: ROI, TCO, NPV and IRR. Additionally, typical integration issues were analysed, including a lack of qualified personnel, inconsistent data, staff resistance, and an absence of a clear strategy. Results. Implementing AI in quality control reduces the proportion of defects by an average of 5.2%, cuts readjustment costs by up to 70%, cuts the need for inspection personnel by 40%, and achieves an ROI of 45% in the first year. In manufacturing processes, (ML), (CNN), (RNN), (NLP) algorithms and expert systems automate routine checks, accelerate defect detection and increase accuracy to 99.995%. Practical examples demonstrate that comprehensive implementation can pay for itself within 1–3 years. Practical significance. The results and recommendations obtained can be used by enterprises to create effective digital transformation strategies, to optimise quality control processes, to reduce costs and to increase competitiveness. The developed approaches to the phasing of implementation and assessment of economic efficiency allow for the minimisation of risks associated with the integration of AI. Research value / Novelty. The study offers a systematic approach to implementing AI in project quality management, taking into account technological, organisational and economic factors. Its novelty lies in its comprehensive consideration of practical cases and the risks that impede the effective implementation of AI, while providing tools to overcome these risks. This promotes more informed decision-making in the field of digital modernisation and increases trust in innovative technologies within the business environment.