Barriers to the Implementation of AI in Organizations: Findings from a Delphi Study
Why this work is in the frame
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Bibliographic record
Abstract
Artificial intelligence (AI), like many technological innovations before it, promises to revolutionize organizations. However, implementing AI in organizations is not as simple as it may appear. This exploratory research aims to unearth barriers to the implementation of AI in organizations. The methodology is based on a ranked-order Delphi study with 18 AI experts. By comparing our results with previous research on barriers to implementation of other information systems and to conceptual and practitioner literature on AI implementation, our findings underscore specific AI implementation challenges for organizations. Barriers to AI implementation fall under three main categories: (1) a lack of organizational capabilities related to data; (2) a lack of individual competencies related specifically to AI; and (3) generic implementation barriers previously observed in implementation research that persist with this innovation.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.008 | 0.002 |
| 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 it