DIGITAL BUSINESS TRANSFORMATION MANAGEMENT IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE IMPLEMENTATION
Bibliographic record
Abstract
The research explores the impact of artificial intelligence (AI) on digital business transformation management. It differentiates the concepys of digitization, digitalization and digital trandformation. It begins by highlighting the increasing importance of digital technologies and AI for enhancing business competitiveness and fostering international collaboration. However, the study also acknowledges the challenges associated with AI adoption, such as infrastructure limitations, skill gaps, and ethical considerations. The study is aimed to identify and analyze the key aspects of managing digital transformation with AI. The research employs a survey methodology, gathering data from 56 professionals across Ukraine and international companies in the US, Europe, and Canada. The survey, conducted via Google Forms, focused on the 5P framework (People, Policy, Process, Partners, and Platforms) to assess AI’s impact on various business dimensions. The results reveal a significant disparity in AI adoption between Ukrainian and international companies, with the latter showing greater integration. The study identifies key AI tools used (e.g., ChatGPT, Gemini, Midjourney) and their application in areas like data analysis, content creation, and process automation. It also highlights the challenges of AI implementation, including data security concerns and the need for workforce retraining. Furthermore, the research points out that the IT sector; marketing and finance are the leaders in AI implementation. Notably, the research underlines the importance of strategic partnerships for successful AI integration and the necessity of adapting existing IT infrastructure to support new AI technologies. The study concludes that AI is crucial for enhancing business efficiency and competitiveness. However, it also emphasizes the need for a holistic approach to digital transformation, addressing both technological and human factors. It suggests that future research should focus on long-term AI impacts and employees’ adaptation to AI integration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".