SMEs' use of AI for new product development: Adoption rates by application and readiness-to-adopt
Why this work is in the frame
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Bibliographic record
Abstract
Artificial Intelligence (AI) is poised to transform all aspects of business, and with it, new product development (NPD). Pioneering companies that are early adopters of AI for NPD have reaped substantial rewards, seeing notable reductions in development timelines and a heightened pace of innovation. These are larger firms like Siemens, GE, Nestle, and Pfizer; but what about the more typical or smaller firm? To address this question, we surveyed Irish small-to-medium-sized enterprises (SMEs), organized by the Innovation and Research Development Group (IRDG) in Ireland. This article unveils the study's findings, shedding light on the current implementation status of AI across 13 crucial applications in NPD. It also delves into the SMEs' intentions to adopt AI in their NP processes in the foreseeable future, along with the improvements that AI has already brought. Importantly, the study also focuses on SMEs' readiness to adopt AI for NPD, the most important metrics gauging readiness, and possible causes of hesitancy to adopt AI. SMEs in the study have not implemented AI across any of the 13 possible application areas in NPD to a great extent, and the intent-to-adopt is also not strong. Performance results from deploying AI in NPD to date are modest, averaging about 27 % improvement on each of the five KPIs. Further, SMEs' readiness-to-adopt AI for NPD reveals that they are not strongly committed to moving ahead with AI in NPD for a variety of reasons, including the high costs of acquiring AI; challenges in building a strong business case; cybersecurity and IP risks; and recent AI failures. The urgency to act and embrace AI in NPD becomes evident as we uncover the immense potential it holds for propelling businesses into a future of enhanced productivity, efficiency, and innovation. • AI has many potential applications and benefits for NPD, but few SMEs have adopted AI for NPD. • Readiness to adopt AI is lacking among SMEs – no management commitment, a lack of trust, and no demonstrated value. • SMEs must start the AI journey now or be left behind. The AI wave will crest before the end of this decade. • Firms should follow a proven technology adoption and deployment map, much like the RAPID process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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