AI in New Product Development: Opportunities, Applications, and Managerial Implications
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
New product development (NPD) requires multidisciplinary collaboration between internal (e.g., designers, engineers, project managers) and increasingly external stakeholders (e.g., customers). These collaborations aim to create new products that meet market needs, deliver value to customers and end-users, and generate revenue for firms. However, the rate of NPD failure is high with traditional NPD often facing significant challenges that can limit productivity and product innovation performance; these include lengthy development cycles and limited market insights. In this context, artificial intelligence (AI) has emerged as a potential collaborator for NPD teams. Much like the emergence of rapid prototyping in the 1980s, which is now widely accepted as a standard NPD tool in most engineering firms, AI promises to revolutionize NPD by improving decision-making, reducing development time, and providing deeper market insights. This article examines the current state of AI in NPD, reviewing its application across various industries and at different stages of the NPD lifecycle. In addition, this article outlines some of the key implications of AI adoption for technology and engineering managers, emphasizing the need for AI infrastructure investment, regulatory compliance, strategic planning and cultural change, cross-functional collaboration and stakeholder engagement, and employee development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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