The Impact of Artificial Intelligence on Public Relations Strategies: A Quantitative Analysis of the Middle East’s 10 Most Valuable Brands
Bibliographic record
Abstract
Purpose: This paper’s research question is: How does artificial intelligence impact the main elements of public relations strategies: communication objectives, target audiences, brand positioning, and creative concepts (art and message)? Methodology: To answer this question, we conducted a literature review about how this technology impacts the marketing industry, branding campaigns, and public relations strategies. Then, we resorted to Brand Finance’s “Middle East 150” report and defined 20 indicators to quantitively analyze how the Middle East’s 10 most valuable brands used artificial intelligence to enhance their public relations strategies. Findings: Our results revealed that the three most respected indicators were the use of several target audiences simultaneously (78), impactful images (74), and storytelling (73). The brands respecting the most criteria were Sabic and Stc. Unique Contribution to Theory, Practice and Policy: Companies must use this technology to revisit the public relations profession and implement more credible branding models based on sharing meaningful content that improves stakeholders’ lives. We recommend brands to integrate artificial intelligence professionally into their public relations departments, recruit experts in this area, update their communication plans, and implement new internal practices and indicators guiding public relations campaigns.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".