THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE QUALITY OF WORK OF DEVELOPMENT INSTITUTIONS IN THE DIGITAL WORLD: MANAGEMENT AND ADMINISTRATION ASPECTS
Bibliographic record
Abstract
The introduction of high technologies and breakthrough innovations, the active use of AI in management and administration is fundamentally changing the quality of functioning of development institutions, making it faster, more efficient and freer from the influence of the human factor. To achieve this, emphasis should be placed on developing tools and mechanisms to support AI. The purpose of the article is to present the readiness for AI in different countries of the world, to determine the manifestation of the impact of AI on the quality of management in development institutions. To achieve the specified goal, the work used methods of analysis, grouping, generalization, comparison, which allowed to comprehensively process the existing scientific works on the impact of AI and the potential effects that it “carries with it”, to outline the prospects for future research on the new generation of digital innovations. The article substantiates and reveals the fact that through the application of AI, the organizational culture in the development institute is improved, the institutional mechanism is implemented more effectively, and the community and teams will receive a new quality of digital life. It is indicated that artificial intelligence is currently perceived by development institutes as a new opportunity for the high-quality generation of work functions in management. It was found that the USA, Great Britain, Finland, South Korea, Germany, the Netherlands, Sweden, Denmark, and Norway weakened their AI readiness index in 2024 compared to 2021 within the framework of the “Electronic Government” criterion. Singapore, Canada, France, and Japan strengthened their positions. The authors express the opinion that technological skills and readiness for changes in management already determine the pace and qualitative evolution of business processes and business systems today. Scientists are of the opinion that digital technologies automate management beyond recognition. The processes of making managerial decisions are changing. In addition, the skills and competencies of managers and executives are changing through advanced training and retraining of employees through involvement in machine learning based on AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".