Standard AI metrics often focus solely on the number of organizations adopting technology. A new study in Statistics and Economics (DOI: 10.21686/2500-3925-2026-5-23-33) proposes a more nuanced index system to measure real-world impact.
Introduction
Current regional statistics regarding artificial intelligence (AI) are frequently limited to counting the number of organizations that have implemented these solutions. This approach leaves the actual outcomes of such implementations largely unmeasured.
Authors Natalya V. Novikova and Ivan A. Strakhov address this gap by constructing a system of indexes to evaluate AI effectiveness at the regional level. Their work aims to identify the factors behind territorial differentiation in how AI is utilized.
What the study examined
According to the abstract, the study utilized federal statistical data from 2024 and 2025 across 85 regions of the Russian Federation. The dataset included organizational assessments of AI's impact on ten distinct types of outcomes.
The researchers analyzed the proportions of positive, negative, and uncertain assessments. They also examined the types of technology used, the specific purposes of implementation, and the modes through which organizations acquired these AI solutions.
Findings and interpretation
According to the abstract, 62.7% of users acknowledge a positive impact on business process quality, while 51.1% report improvements in production processes. Conversely, only 11.5% report cost reductions, and 24.1% report labor productivity growth.
A significant finding is that between one-third and one-half of organizations are unable to assess the effect of AI at all. The study suggests that effectiveness is determined by the depth and breadth of technology use rather than the sheer size of the regional market.
Methodology and limitations
The methodology included descriptive statistics, Spearman rank correlation, multiple regression with robust standard errors, and two-dimensional typological grouping. The Ryabtsev index was used to analyze structural differences in effect profiles.
This editorial is based strictly on the provided abstract. As the full text was not reviewed, readers should be aware that the findings are specific to the Russian context and may not be directly generalizable to other global markets or industries.
WEBU practical proposal
For small business owners and web designers, we recommend prioritizing the 'breadth' of AI application. Instead of focusing on a single large-scale AI tool, integrate AI into multiple smaller workflows, such as automated customer support, content SEO optimization, and data-driven user experience testing.
In workplace management, we suggest implementing internal feedback loops to reduce 'uncertainty' in AI assessments. If your team cannot quantify the impact of an AI tool, it is likely that the tool is not being used to its full potential or is not aligned with specific business goals.
Conclusion
The study by Novikova and Strakhov (2026) shifts the focus from the scale of AI implementation to measuring its actual return. According to the abstract, regional policy should prioritize supporting the multi-purpose development of technology.
Ultimately, organizations should move away from simply 'adopting' AI and toward a strategy of deep integration, ensuring that every AI tool deployed has a measurable impact on business processes.
Sources and editorial transparency
Natalya V. Novikova, Ivan A. Strakhov (2026). Statistical Assessment of the Effectiveness of Artificial Intelligence Use by organizations in Russian Regions. Statistics and Economics. https://doi.org/10.21686/2500-3925-2026-5-23-33