AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations

İbrahim Çataldaş

doi:10.31123/ajcs.1989609

AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations

Abstract

Purpose: This study examines the potential and limitations of artificial intelligence (AI)-generated visuals in public relations. It also examines the authenticity, credibility, and ethical dimensions of AI-generated visuals. Design/methodology/approach: A qualitative comparative visual analysis was conducted using 10 press releases from Türk Telekom's official website. Original visuals in these releases were compared with AI-generated visuals created in ChatGPT from the same texts. In total, 20 visuals were evaluated through qualitative analysis. Findings: Original corporate visuals featured real people, events, and partnerships, whereas AI-generated visuals were more explanatory and attention-grabbing. However, AI visuals often showed similar design patterns, sometimes looked artificial, and required cautious, ethical use. Research limitations/implications: The study focuses on visuals from ChatGPT's AI image-generation tool, which may limit findings' applicability to other AI systems with different visual and design features. Future research should compare various AI platforms. Practical implications: The findings guide PR practitioners on using generative AI in visual communication. AI visuals may support message clarity and engagement, but should complement, rather than replace, authentic images. Originality/value: This study adds to the emerging literature on generative AI in public relations, offering a practical and conceptual view on the opportunities and limitations of AI-generated visuals

Keywords

References

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Details

Primary Language

English

Subjects

Communication and Media Studies (Other)

Journal Section

Research Article

Authors

İbrahim Çataldaş ^*
0000-0001-8096-9177
Türkiye

Publication Date

September 29, 2026

Submission Date

July 8, 2026

Acceptance Date

September 5, 2026

Published in Issue

Year 2026 Volume: 53

DOI

https://doi.org/10.31123/ajcs.1989609

IZ

https://izlik.org/JA46JR76WX

Cite

RIS / Bibtex

APA

Çataldaş, İ. (2026). AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations. Akdeniz Journal of Communication Studies, 53, 1-20. https://doi.org/10.31123/ajcs.1989609

AMA

1.Çataldaş İ. AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations. AJCS. 2026;53:1-20. doi:10.31123/ajcs.1989609

Chicago

Çataldaş, İbrahim. 2026. “AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations”. Akdeniz Journal of Communication Studies 53 (September): 1-20. https://doi.org/10.31123/ajcs.1989609.

EndNote

Çataldaş İ (September 1, 2026) AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations. Akdeniz Journal of Communication Studies 53 1–20.

IEEE

[1]İ. Çataldaş, “AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations”, AJCS, vol. 53, pp. 1–20, Sept. 2026, doi: 10.31123/ajcs.1989609.

ISNAD

Çataldaş, İbrahim. “AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations”. Akdeniz Journal of Communication Studies 53 (September 1, 2026): 1-20. https://doi.org/10.31123/ajcs.1989609.

JAMA

1.Çataldaş İ. AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations. AJCS. 2026;53:1–20.

MLA

Çataldaş, İbrahim. “AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations”. Akdeniz Journal of Communication Studies, vol. 53, Sept. 2026, pp. 1-20, doi:10.31123/ajcs.1989609.

Vancouver

1.İbrahim Çataldaş. AI-Generated Alternatives to Corporate Press Release Images: A Comparative Visual Analysis in Public Relations. AJCS. 2026 Sep. 1;53:1-20. doi:10.31123/ajcs.1989609