Unlocking Marketing Creativity Using Artificial Intelligence
Bibliographic record
Abstract
This article examines the role of artificial intelligence (AI) in enhancing marketing creativity by analyzing the synergy between computational and human creative processes. Through two studies, the authors investigate nongenerative and generative AI applications within marketing contexts using a conceptually driven and empirically derived approach. In Study 1, the authors observe how creative individuals, particularly artists, utilize AI and its effects on their creative experiences, revealing AI's role as (1) a new instrumental resource, (2) a tool for exploring possibilities, and (3) a means to deconstruct the creative process. Study 2 assesses 1,036 AI systems (2015–2021) and 241,292 AI models (2022–2024), categorizing them into four clusters and three levels of observed creativity. From these insights, the authors introduce a framework for AI-enabled creativity: (1) inspiring agile methods, (2) augmenting human creativity, and (3) inspiring unconventional thinking. Validated by three workshops, this framework equips marketing leaders with a deeper comprehension of AI's creative potential. The authors advocate for AI integration within agile, augmented, and unconventional marketing approaches, advancing our understanding of AI's contribution to marketing creativity. Additionally, they propose a research roadmap for empirical validation in real-world applications.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".