Business Informatics

Maturity model and success factors for the implementation of AI agents in Russian companies

2026-07-02T17:55:05+03:00

By early 2026, AI agents ceased to be experimental tools and began to be considered as a form of business process automation. Based on the analysis of in-depth interviews, an expert session, a quantitative survey of Russian companies, and sources reflecting Russian and foreign experience in the deployment of AI agents, a five level maturity model was developed. This model includes the following levels: initial, repeatable, defined, managed, and optimizing. The model allows diagnosing an organization’s readiness for industrial use of agent systems. It was revealed that full autonomy of AI agents is virtually non existent in Russian practice; hybrid operating models with clearly defined human in the loop thresholds dominate. The shortage of qualified specialists is the main constraint. The greatest potential of AI agents is observed in repetitive operational tasks where non standard situations are rare. The effect of implementing AI agents should most often be assessed not as direct cost reduction, but as an increase in team efficiency. The main success factors are data quality and depth of integration with the corporate IT landscape.

A multimodal model for commercial real estate valuation based on the integration of geospatial data and language-model-derived features

2026-07-02T17:55:09+03:00

The aim of this paper is to substantiate methodological approaches to commercial real estate valuation through the integration of heterogeneous data, and to demonstrate their application within the Moscow market. A methodology for constructing a multimodal automated valuation model (AVM) is presented, built on the integration of structured, geospatial, and semantic data. The study was conducted in the street retail segment of the Moscow commercial real estate market. Three sequential feature configurations were developed: a baseline model, a model extended with geospatial characteristics, and a model further augmented with interpretable features extracted from property text descriptions via the GigaChat language model. Modeling was performed with CatBoost and LightGBM algorithms. Hyperparameters were optimized through cross-validation on the training sample, and final model quality was assessed on a held-out test set. Adding geospatial features reduces the mean absolute percentage error by 17–20% for both algorithms-a significant reduction. The inclusion of LLM-derived semantic features yields a further accuracy gain of 1.6–3.3%. SHAP analysis confirms the dominant role of spatial factors, while qualitative property characteristics also contribute meaningfully to model output. The multimodal approach delivers higher accuracy and interpretability than conventional single-source models, demonstrating its applicability to mass appraisal tasks and its practical value for the banking sector, real estate developers, and valuation firms. The theoretical contribution of this work lies in identifying a viable direction for the development of valuation methodology in the era of digital data. The approach does not substitute for expert judgment. It reinforces it, providing practitioners with a scalable, reproducible, and objective analytical instrument that formally incorporates both spatial and semantic context. The results have practical relevance for real estate lending institutions, valuation companies, and entities owning or managing commercial property portfolios. They are also directly applicable to regional state budgetary institutions of the Russian Federation responsible for cadastral valuation, and to Rosreestr (the Federal Service for State Registration, Cadaster and Cartography) in its efforts to advance mass property appraisal methodology at the national level.

Development of a conceptual model and metrics for evaluating the effectiveness of knowledge management in the process of digital business transformation using the example of consulting companies

2026-07-02T17:55:06+03:00

Knowledge management is a strategic priority for organizations in the context of digital business transformation. Consulting companies, whose activities are based on intellectual capital, are faced with a growing gap between the pace of new knowledge generation and the organizational capacity to master it. The life cycle of knowledge is shortening and traditional management models are losing their effectiveness. At the same time, there is a shortage of comprehensive studies in the scientific literature that take into account the specifics of digital business transformation and allow us to evaluate the effectiveness of models in the context of business results of organizations. The purpose of this work is to develop and empirically verify the provisions of the conceptual model of knowledge management in the process of digital business transformation using the example of consulting companies. The research methodology involves the formation of a multi-layered author’s conceptual model and the development of a structured set of metrics for evaluating the effectiveness of knowledge management in an organization. The verification of the regulations was carried out based on the results of a survey of 1,267 employees of five major Russian consulting companies, followed by analysis using descriptive statistics, contingency tables, cluster analysis (Ward’s method) and binary logistic regression. Empirical results have confirmed a statistically significant relationship between the results of digital business transformation and the effectiveness of knowledge management. The results contribute to the development of knowledge management theory in relation to the conditions of digital business transformation and can be used in the formation of strategies for the development of knowledge management systems. Specific recommendations for consulting companies have been formulated and substantiated.

Simulation of migration and demographic processes to support decision-making

2026-07-02T17:55:07+03:00

Currently, many enterprises in Russia are facing a shortage of labor resources. Unequal spatial development negatively affects the production characteristics of companies. In particular, declining birth rates and increasing migration from peripheral to central and European regions of the country significantly affect the labor market. Therefore, this article proposes a new approach to predicting and optimizing migration and demographic processes by using agent-based models and a hybrid particle swarm optimization algorithm. The goal of this approach is to find solutions that will contribute to implementing a balanced spatial development strategy and population growth in regions. In order to address this problem, a hybrid multi-swarm particle swarm optimization algorithm (R-HMSPSO) has been proposed. This approach aims to maximize the population size, while considering strategies for uniform population growth across regions and constraints on territorial population concentration measures (TPC). The search for optimal solutions using R-HMSPSO is based on a set of decision variables that determine the rate of wage growth, creation of workplaces, housing construction, and the pace of social infrastructure development. All these factors affect the quality of life and the demographic situation in regions. The TRC measure serves as primary constraints when solving the maximization problem for population size. The improved economic situation in a region affects its attractiveness, leading to a redistribution of migration flows and a change in population concentration. Therefore, an evolutionary search for optimal variable values is applied simultaneously across all regions. This model was aggregated through objective functions with an R-HMSPO algorithm. As a result of optimization experiments, the most preferable solutions for improving the demographic situation in Russia’s regions have been identified. These solutions contribute to both population growth and more balanced spatial development.

Penetration of artificial intelligence into the software development life cycle: An empirical labor market analysis

2026-07-02T17:55:10+03:00

The integration of artificial intelligence (AI) into information technologies is reshaping competency requirements for IT specialists. However, AI competency penetration into the early phases of the Software Development Life Cycle (SDLC) – requirements analysis and planning – remains underexplored. Existing studies either rely on static competency models lacking empirical validation or analyze the labor market at an aggregated occupational level without differentiation across SDLC functional clusters. The research objective is to empirically test the hypothesis of AI competency penetration into the skill sets associated with early SDLC phases. The scientific novelty lies in the methodological transition from static competency models to a process-oriented analysis. Roles are cross-referenced with SDLC phases through weighted clustering with normative alignment to SWEBOK v3.0, BABOK v3.0, and ISO/IEC/IEEE 12207:2017. The empirical basis comprises 182,447 unique IT job postings from the hh.ru platform for the period March–December 2025, covering 19 SDLC-relevant roles. A reference list of 307 AI competencies was compiled via LLM-based preliminary screening followed by expert verification. Eight functional clusters are identified. Weighted demand S(s) is distributed as follows: analytics – 16.0%, architecture – 16.0%, development – 13.6%, management – 12.2%, documentation – 12.2%, testing – 12.0%, support – 9.1%, design – 8.9%. Weighted penetration P(s) ranges from support (0.34%) to design (1.70%). The hypothesis is confirmed: penetration in the Analytics cluster (0.69%) corresponds to the market average, while in the Management cluster (0.76%) it exceeds it, though early SDLC phases lag behind the Design, Development, and Architecture clusters in absolute terms. The findings are applicable to corporate early-warning strategies, educational curriculum revision, and updating professional standards in the context of the proliferation of generative AI.

Hilbert spectrum support vector regression for public companies’ sales forecast

2026-07-02T17:55:04+03:00

Accurate and timely revenue forecasting is of critical importance to investors in public companies, who base their investment decisions on the Discounted Cash Flow (DCF) model. Market practice dictates that financial analysts develop financial models by focusing on revenue growth projections for the first year of the forecast horizon, which holds the highest weighting in discounted cash flow calculations. These projections are typically derived through expert assessment of publicly available quarterly reporting. However, the potential of contemporary data science tools, particularly machine learning approaches that employ linear approximation techniques to model nonlinear patterns to enhance current quarter revenue analysis is largely underutilized. The present study focused on developing source code to apply the Hilbert Spectrum Support Vector Regression (Hilbert spectrum SVR, HSVR) method for sales prediction. Testing HSVR on data of companies listed on the Moscow Exchange and comparing its performance metrics with those of classical Support Vector Regression (SVR), led to the conclusion that HSVR can be deployed in industrial settings for revenue forecasting of public companies using news vectors – vector representations (embeddings) of news articles.