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Artificial intelligence (AI) might change the communication profession immensely, but the academic discourse is lacking an investigation of the perspective of practitioners on this. This article addresses this research gap. It offers a literature overview and reports about an empirical study on AI in communications, presenting first insights on how professionals in the field assess the technology.

A quantitative cross-national study among 2,689 European communication practitioners investigated four research questions: RQ1 – How much do professionals know about AI and to what extent are they already using AI technologies in their everyday lives? RQ2 – How do professionals rate the impact of AI on communication management? RQ3 – Which challenges do professionals identify for implementing AI in communication management? RQ4 – Which risks do they perceive?

Communication professionals revealed a limited understanding of AI and expected the technology to impact the profession as a whole more than the way their organisations or themselves work. Lack of individual competencies and organisations struggling with different levels of competency and unclear responsibilities were identified as key challenges and risks.

The results highlight the need for communication managers to educate themselves and their teams about the technology and to identify the implementation of AI as a leadership issue.

The article offers the first cross-national quantitative study on AI in communication management. It presents valuable empirical insights on a trending topic in the discipline, highly relevant for both academics and practitioners.

This article studies the digitalization of corporate communications and the emergence of communication technology (CommTech). The authors show communicators' expectations regarding digitalization, gauge the current level of digitalization across communication departments and agencies and examine the effectiveness of strategic approaches to manage digitalization.

The authors conceptualize the phenomenon of CommTech and propose a framework for studying CommTech's emergence and consequences by combining (1) recent theorizing on digitalization in corporate communications, (2) the concept of digital maturity from information systems research and (3) a socio-technical approach to analyze the development of work systems. The authors apply this framework in a quantitative study (n = 2,664) among communication practitioners from 46 countries.

While digitalization of both communication activities and the underlying support infrastructure is seen as critically important among communicators, a large fraction of communication departments and agencies are still assessed as digitally immature. Further, data reveal the relevance of different (technology, tasks, structure and people) dimensions of digitalization strategies and the influence of such strategies on the digital maturity of communications.

The framework and empirical instruments developed in this study help practitioners to uncover and evaluate the level of digital maturity of communication departments and agencies. This allows to identify current challenges and future opportunities for improvement.

The authors propose a concise definition for the much-debated concept of CommTech and develop a new theoretical framework for understanding CommTech's emergence and consequences in the profession. This empirical work constitutes the first large-scale study on the digital maturity of communication departments and agencies.

The purpose of this paper is to investigate to what extend an automated, algorithm-based analysis of online conversations of stakeholders in social media and other Internet media can be used for reputation management.

Examination of the reputation of the 5,000 companies with the largest number of employees in Germany based on communication with these companies in 350m online sources on the German-speaking Internet within one year. The method is grounded on an adapted reputation model based on Fombrun.

The central result of the study is the identification of the ideal balance between the different dimensions leading to the best overall reputation. The resulting correlation matrix with the respective correlation coefficients (according to Pearson) thus forms the basis for the optimal reputation architecture.

The discovered “optimal reputation architecture” refers to a German context. Future studies should investigate in how far the adapted model and the “optimal reputation architecture” also work for other cultures. It can be assumed that there may be differences as different dimensions, for example, sustainability, may have a different importance in other cultural contexts. Apart from the question if the “optimal reputation architecture” is also valid for other cultural contexts, the concept has to be validated for German companies as well as it is just based on the two described studies.

The method used shows that social listening can deliver valuable results for research in the field of reputation management as it expands the possibilities to investigate reputation on a large scale. The approach shows in how far scientific research can be expanded beyond classic content analysis as the number of items which can be analysed exceeds that of classic analytical approaches by far. Explicit and implicit experiences, which are the drivers of reputation, can be systematically recorded and analysed using social listening, thus delivering valuable insights in how stakeholders perceive the performance of a company in different dimensions.

Measuring the reputation on the basis of social listening is very important for practical applications in companies, because the data is available digitally and can deliver up-to-date reputation values almost in real time – so that the communication can be aligned very quickly with current events. This makes it easier to implement and control the interaction between companies and their environment in the digital space.

The classic approach in reputation management is traditional market research. It is relatively expensive and takes a relatively long time to produce results. Reputation management based on social listening digitises reputation measurement, lowers costs and delivers results in a very timely manner. It might be the future of reputation measurement. This is relevant not only for practical purposes but also for scientific approaches.