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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".