Bibliographic record
Abstract
Artificial intelligence (AI) has become a disruptive force that has revolutionized industries and changed business practices. The integration of AI has brought numerous benefits to various functional areas within organizations, with marketing experiencing a significant positive impact. AI technologies have empowered marketers with advanced tools and insights, fostering unparalleled efficiency, personalization, and strategic campaign decision-making. Despite these advancements, the scholarly focus on AI's transformative effects on marketing is limited. This research investigates how AI is currently applied across different marketing functions and its potential future evolution and impact on marketing processes. In a rapidly evolving world, businesses must navigate complexity, innovate, and sustain competitive advantages. Grounding our analysis in previous AI marketing literature, we adopt the dynamic capability theoretical lens, emphasizing how organizations adapt and prosper in changing environments. This study highlights six key marketing areas where AI promises transformative effects, aiming to illuminate the path for future marketing innovations and strategies, including AI-driven customer insights, measuring marketing performance, automated marketing strategies, ethical implications, enhancing customer experiences, and growth opportunities with AI Implementation. While recognizing AI as a positive disruptive force, we also highlight its limitations, potential threats to privacy and security, as well as ramifications of biases, misuse, and dissemination of misinformation. Finally, the article delineates the gaps in the research and formulates questions aimed at advancing knowledge in AI marketing.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".