From the «human factor» to digital objectivity: transforming the expert role in the analysis of contract performance results

The article presents a comprehensive analysis of the transformation of expert activities under conditions of digitalization and the emerging potential of artificial intelligence technologies. It examines the key limitations of the traditional expert model based on the human factor, including subjective judgments, cognitive biases, fragmented data, and the high labor-intensity of expert procedures. The study demonstrates that the development of intelligent automation, machine learning, natural language processing, and computer vision creates the prerequisites for a new model of expert analysis built on digital objectivity, reproducibility, and transparency. The article analyzes the stages of digital transformation in expert activities, changes in the expert’s role, the distribution of responsibility between the specialist and the algorithm, and the potential architecture of a hybrid expert-evaluation system. Particular attention is paid to the technical, legal, methodological, and organizational barriers to AI implementation, as well as opportunities for building sustainable synergy between humans and digital tools. The paper proposes a conceptual model of a next-generation expert system in which AI performs automated data analysis, while the expert ensures verification, interpretation of complex cases, and quality control of decisions. The study concludes that a hybrid model represents the most effective direction for the development of expertise amid increasing complexity of management processes and rising requirements for transparency in procurement oversight.