Barriers to AI Adoption in Supply Chain Management: Perspectives from Industry Leaders
Bibliographic record
Abstract
This study explores the adoption of artificial intelligence (AI) in supply chain management, focusing on the challenges, benefits, and strategic considerations organizations face when integrating AI technologies into their operations. The research investigates how AI is reshaping traditional supply chain models, enhancing decision-making, and improving efficiency across various sectors. Through qualitative analysis, the study identifies key barriers to AI adoption, including organizational resistance, lack of skilled personnel, high implementation costs, and insufficient data infrastructure. Furthermore, the research highlights the transformative potential of AI in optimizing supply chain processes such as demand forecasting, inventory management, and logistics coordination. It also examines the critical role of leadership in driving AI initiatives, emphasizing the need for strategic alignment, cross-functional collaboration, and a culture of continuous learning. The study's findings suggest that while AI adoption can lead to significant performance improvements, its successful integration depends on a combination of technological, organizational, and human factors. The paper concludes with a discussion on the future of AI in supply chain management, stressing the importance of addressing both technological and organizational challenges to fully realize the benefits of AI. This research contributes to the growing body of knowledge on AI in supply chain management, offering valuable insights for both academics and practitioners seeking to understand and navigate the complexities of AI implementation.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".