“We're right in the foothills”: the adoption of artificial intelligence in corporate communication departments

Alexander Buhmann et al.

Corporate Communications2026https://doi.org/10.1108/ccij-09-2025-0296article
AJG 1ABDC B
Weight
0.37

Abstract

Purpose The adoption of artificial intelligence (AI) is among the most pressing topics in contemporary corporate communication research. However, there are, so far, little empirical studies on how AI is actually implemented and how it is changing professional communication practice. Design/methodology/approach We apply the perspective of sociotechnical systems theory to explore current AI use and application in large corporate communication departments. Through qualitative interviews with a sample (n = 30) of chief communication officers (CCOs) from the 300 largest corporations in Europe, we examine AI implementation at the intersection of tasks, organizational structures, processes and technological properties of AI tools. Findings Findings reveal that the often-touted AI-induced structural shifts in communication departments are, thus far, hardly observable. The most advanced use cases of AI remain strategically limited to experimentation (i.e. “sandboxing”) or non-sensitive tasks. Currently, the primary areas of change involve training and upskilling. Results highlight important dualities in AI implementation: while AI enhances operational efficiency, it also presents challenges related to governance, technology integration and the ongoing need for human engagement in creative and strategic tasks. Originality/value The present study draws on a highly senior sample of communication executives to provide novel insight into the state of AI implementation and use in large corporate communication departments.

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https://doi.org/https://doi.org/10.1108/ccij-09-2025-0296

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@article{alexander2026,
  title        = {{“We're right in the foothills”: the adoption of artificial intelligence in corporate communication departments}},
  author       = {Alexander Buhmann et al.},
  journal      = {Corporate Communications},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ccij-09-2025-0296},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.