The article is devoted to the integration of ESG principles into project management in the context of Russia’s changing regulatory framework. The study analyses the challenges of implementing sustainable development principles throughout the project lifecycle, changes in the functions of project managers, and models of interaction with stakeholders. The necessity of transitioning from classical management models to a paradigm that combines economic efficiency, environmental safety, social responsibility, and corporate policy is substantiated. A typology of ESG risks and a methodology for their quantitative assessment based on an integral indicator of expected losses are proposed. The authors have developed a model of the relationship between classical groups of project management processes and aspects of sustainable development. The aim of the study is to systematize approaches to integrating ESG principles into project management and to formulate practical recommendations considering new regulatory requirements. The methodological framework is based on an analysis of the regulatory framework, a comparative analysis of international standards, as well as a synthesis of best practices and identified challenges, followed by the development of practical recommendations. Sustainable development is becoming a fundamental factor in the long-term competitiveness of companies, and the successful integration of ESG principles requires the consolidation of efforts by business, the state, and society, the develop-ment of flexible planning systems, and the improvement of project managers’ qualifications.
The purpose of this article is to conduct a comprehensive review of the existing experience in the use of artificial intelligence in project management and identify key barriers and risks preventing its widespread and successful implementation. A special focus is on assessing not only technological, but also organizational, cultural, and economic constraints that form a complex context for integrating AI solutions. The main hypothesis of the study is the assumption that most Russian organizations have either very limited experience in implementing artificial intelligence in project processes, or do not use it at all in this area, which is explained by a combination of risks and barriers. This situation creates a significant potential for scientific analysis and the development of practical recommendations aimed at overcoming these obstacles. The authors managed not only to capture the overall picture, but also to diagnose the underlying causes of the current situation. The research is based on an analysis of scientific literature sources on the topic of the article, professional standards for project management (Russian, Japanese, American, English and international), analytical reviews. The results of the analysis formed the basis of a survey developed by the authors, which was conducted among professional groups of project managers. The results obtained indicate that there is a significant gap between the declared strategic interest in the possibilities of AI and its real, extremely limited use in the project activities of domestic companies. In conclusion, the authors proposed a number of recommendations and outlined promising development paths for companies seeking to effectively integrate AI into project management.
This article discusses the role of Artificial Intelligence (AI) in governance. AI completely changes the role of the finance director: from being a budget keeper and controller, he becomes a strategist who relies on data and forecasts. This study examines how AI — from robotization of routine operations to machine learning and predictive analytics — is embedded in key processes of the finance department: accounting, reporting, budgeting, risk management. The author proposes a model of restructuring the financial department with three centers of competences (data, automation, strategic partnership) and describes the full life cycle of predictive models. The real barriers are separately considered: low data quality, lack of staff with dual skills, staff resistance, ethical risks, and ways to overcome them. The study may be of interest to financial executives who are just beginning the digital transformation of their organizations.
The article examines how the quality and level of verification of a professional knowledge base on the reliability of responses generated by expert systems based on artificial intelligence technologies. Using project management as a case study, the authors test the hypothesis that a specialized system working with professionally verified sources provides higher-quality responses than general-purpose large language models relying on knowledge acquired during pre-training. To test the hypothesis, a prototype of the SOVNET–Prooftech IT service was developed, materials from the scientific journal «Project and Program Management» and the IPMA ICB4 standard. An experimental dataset of 130 open-ended questions covering the competence elements of ICB4 was created, including 74 questions on risk management. A total of 520 responses generated by four AI systems were evaluated by certified SOVNET assessors using blind independent expert assessment. Inter-rater agreement was measured at ICC(2,1) = 0.801. The specialized system achieved a mean score of 9.09 out of 10, outperforming Gemini NotebookLM (7.85), GigaChat (6.07), and DeepSeek (5.56). In addition, existing Russian and international services for searching and analyzing professional publications were reviewed, and an Osterwalder Business Model Canvas was developed to plan the further development and scaling of the solution. The results confirm the value of a specialized, verified knowledge base as a factor in improving the quality of professional AI-generated responses.
The article considers a typical Cutover methodology which allows «painless» go-live of software product. Existing Cutover procedure is focused on the implementation of small and medium-complex information systems. The analysis of common stages of Cutover and document flow generated during their execution is carried out. At mobilization stage, it is proposed to designate additional project roles of Cutover leader and coordinator responsible for the preparation and execution of Cutover plan. It is recommended to create Cutover plan at next phase iteratively: first, you should shape a top-level plan that allows indicative estimation of company’s blackout period, and only then you can build a detailed plan combining business process, data migration, technical preparation of IT-systems and change management. Phases of preparation for implementation and execution of resulting Cutover plan are enriched by a well thought out communication strategy that regulates who should do what and when at the time of software system go-live. These improvements extend the content of Cutover methodology for its successful application in complex projects of corporate information systems deployment. There is matrix provided showing a list of Cutover tasks and documents depending on the complexity of IT project in conclusion.
