AI in New Product Development: Opportunities, Applications, and Managerial Implications
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".