AI-powered marketing: What, where, and how?
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) 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it