From Intuition to Insight: Leveraging AI to Forecast New Product Success
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) holds transformative potential for decision-making in new product development (NPD), yet firms remain hesitant to entrust investment Go/No-Go project decisions entirely to AI. This article explores how AI can address critical challenges in NPD, particularly by predicting product success using data-driven models. The author introduces AI-PRISM, an innovative seven-factor scorecard model powered by AI, designed to systematically assess NPD projects, fill information gaps, and provide unbiased success probabilities. AI-PRISM leverages external data sources and rigorous analysis to overcome the limitations of traditional methods, such as human biases and incomplete data. Validation tests demonstrate its reliability and accuracy, outperforming human evaluators in consistency. By integrating success probabilities into financial metrics like Expected Commercial Value (ECV), AI-PRISM enhances the accuracy of Go/No-Go decisions, potentially doubling productivity in RD&E
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it