The Adoption and Impact of AI by SMEs for New Product Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artificial intelligence (AI) is transforming business, including new product development (NPD), yet smaller firms face challenges in adoption. This article presents findings from a survey of Irish small and medium enterprises (SMEs) conducted with the Industry Research & Development Group. Despite AI's potential, the study reveals low AI implementation across 13 NPD applications, but more positive intentions to adopt AI in the near future. Performance improvements from AI are modest, with an average of 27% improvement across five key metrics. Readiness to adopt AI for NPD, however, is weak with SMEs exhibiting low trust, limited senior management commitment, and minimal demonstrated value from AI. The article concludes with five managerial recommendations, emphasizing the urgency for SMEs to leverage AI to boost innovation, and provides a simple process map for AI deployment.
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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 | 0.002 | 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 it