Пшиченко Дмитрий В

Researcher, author, lecturer, and technology executive specializing in artificial intelligence, digital transformation, data management, enterprise information systems, and the digital development of industrial organizations.

The main research focus is the practical application of artificial intelligence in industrial and corporate environments, including machine learning, predictive analytics, computer vision, generative AI, large language models, intelligent agents, optimization algorithms, digital twins, and decision-support systems.

Professional and academic experience spans more than twenty years in information technology, digital transformation, industrial automation, and the management of complex technology programmes in the oil and gas, energy, mining, engineering, and other asset-intensive industries.

Research activities are focused on the transition from experimental AI prototypes to scalable, secure, economically justified, and institutionally sustainable solutions. Particular attention is given to the organizational, data-related, technological, regulatory, and economic conditions required for successful AI adoption.

Artificial intelligence is considered not only as a technical instrument but also as part of a complex socio-technical system involving algorithms, data, business processes, organizational structures, employees, technology platforms, corporate governance, and regulatory requirements.

Key scientific interests include the development of conceptual frameworks, classification models, maturity models, implementation methodologies, and evaluation systems for artificial intelligence and digital transformation.

Research methodology includes systematic and structured literature reviews, comparative analysis, qualitative and quantitative case studies, expert interviews, stakeholder analysis, scenario analysis, strategic foresight, techno-economic assessment, lifecycle analysis, multi-criteria evaluation, pilot projects, and living-lab approaches.

A particular emphasis is placed on moving beyond narrow technical indicators such as accuracy, precision, and recall. AI systems are evaluated in terms of operational efficiency, productivity, financial impact, resource use, resilience, environmental effects, employee experience, governance quality, and long-term strategic value.

A major area of research is industrial artificial intelligence, including predictive maintenance, anomaly detection, equipment diagnostics, industrial computer vision, production optimization, intelligent process control, energy-efficiency management, supply-chain optimization, occupational safety, and decision support for complex industrial operations.

Another important research direction is data management and data governance. This includes enterprise data platforms, data architecture, data quality, metadata management, master data, data ownership, access-control models, data catalogues, data lineage, interoperability, and organizational mechanisms for responsible data use.

Research also covers enterprise architecture and digital platforms, with particular attention to the integration of operational systems, corporate applications, data platforms, AI services, integration technologies, cloud and on-premise infrastructure, cybersecurity controls, and digital products.

The economic dimension of digital transformation represents a separate area of study. Relevant topics include investment evaluation, productivity effects, total cost of ownership, scalability, implementation risks, financial and non-financial benefits, technology dependence, and alternative financing models for digital initiatives.

Academic interests also include the circular economy, sustainable industrial development, and the environmental impact of artificial intelligence. Research covers circular design, predictive maintenance, product-life extension, industrial symbiosis, waste reduction, recycling, secondary-resource markets, digital product passports, lifecycle intelligence, and efficient use of energy and materials.

Special attention is paid to the transition from isolated AI applications to integrated circular socio-technical systems based on interoperable data, common standards, transparent metrics, reliable environmental information, stakeholder coordination, and accountable governance.

Responsible and trustworthy artificial intelligence is another key research area. Topics include explainability, transparency, human oversight, model validation, cybersecurity, data sovereignty, algorithmic risk, auditability, ethical governance, and regulatory compliance.

Research interests also include digital product passports and lifecycle data systems, including interoperable product and material data, data-quality levels, access rights, verification mechanisms, traceability, remaining-useful-life prediction, repair and reuse recommendations, and cross-border data exchange.

A further area of research is the application of AI by small and medium-sized enterprises. This includes accessible AI tools, shared digital infrastructure, cloud and open-source technologies, practical implementation models, digital-skills development, data availability, cybersecurity, and technological dependence on large platforms.

International research interests cover comparative studies of AI adoption and digital transformation across Europe, Eurasia, India, Asia, and emerging economies, with attention to regulatory differences, industrial structure, infrastructure maturity, institutional capacity, and access to investment.

Research outputs include scientific articles, analytical reports, position papers, research agendas, project concepts, technology assessments, methodological frameworks, implementation guidelines, policy recommendations, technology roadmaps, and investment cases.

The academic profile combines research, teaching, and practical implementation experience. Educational activities cover artificial intelligence, digital transformation, information-systems management, enterprise architecture, data governance, corporate innovation, and the evolving role of CIOs and CDOs.

Open to international and interdisciplinary research cooperation, joint publications, conference papers, edited volumes, research grants, doctoral supervision, industry-sponsored studies, and collaborative pilot projects involving universities, industrial companies, technology firms, public institutions, standards organizations, investors, and startups.

The long-term research objective is to contribute to the development of rigorous and practically applicable knowledge at the intersection of artificial intelligence, industrial digitalization, corporate management, sustainability, and public policy.