How Generative Artificial Intelligence Shapes Innovation Processes: An Evolutionary Perspective
Bibliographic record
Abstract
The rapid development of generative AI (GenAI) tools has ignited debates about their dual role in organizational innovation, capable of enhancing creativity and efficiency while introducing risks like instability, randomness, and hallucinations. Interestingly, the innovation process in organizations is not only a linear and planned procedure but also accommodates variation and randomness as the building blocks of serendipitous discoveries. Drawing on evolutionary theories of innovation, specifically the blind variation and selective retention framework, this study explores how GenAI-induced randomness can foster both deliberate and serendipitous innovations. By synthesizing the literature on AI, digital innovation, and serendipity in management, we have developed a set of propositions and research questions for leveraging AI-driven variation and ensuring responsible AI use in the selective retention of innovations. Specifically, we propose that incorporating GenAI into innovation processes increases the chances of encountering both non-random and random forms of variation, but firms must keep in mind the implicit impacts of GenAI on selective retention of innovative ideas. Our conceptualization advances the understanding of GenAI's impact on innovation processes, offering actionable insights for organizations aiming to harness innovation in dynamic environments.
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.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".