© Nuthawut / stock.adobe.com
Expectations Towards GenAI
This study explores the expectations of corporate communication professionals towards generative AI. By clustering the expectations of 40 communication experts, four generic types were identified based on their attitudes toward AI adoption: Efficiency-Driven Strategists, Responsible AI Advocates, Interpersonal Traditionalists, and Pragmatic Competitors. Each type reflects distinct perspectives and blind spots, which can lead to inclusive implementation strategies in communication teams. The findings offer practical guidance for uncovering different AI expectations, aligning team dynamics, and designing effective, individual training programs.
GenAI in Corporate Communications
Generative AI (GenAI) tools like ChatGPT or Copilot are here to stay and are shaping the practice of corporate communications. AI is commonly regarded as a means of increasing efficiency and enhancing creativity within
corporate communication departments (Uysal & Deng, 2025). At the same time, its implementation is frequently hindered by limited AI literacy of employees and concerns over potential job displacement, biases in training data, or the loss of authenticity in communication (Yue et al., 2024). As a result, the extent and manner in which communication departments adopt these technologies vary considerably.
In the case of GenAI, the interaction between humans and technology is inherent. For this interaction to be effective, it is essential to understand how individuals perceive technology and what they expect from it.
This study examines the professional attitudes and self-perceptions that shape practitioners’ engagement with GenAI and identifies four distinct types of expectation. These types go beyond Rogers’ diffusion types and offer practical value especially when considering specialized GenAI training programs, selecting team members for AI tasks, or organizing day-to-day business in communication departments.
How Practitioners Perceive AI Tools: A Typology
Each of the four types expects different outcomes from AI tools and is characterized by unique strengths but also blind spots:
- Efficiency-Driven Strategists;
- Responsible AI Advocates;
- Interpersonal Traditionalists; and
- Pragmatic Competitors.
Efficiency – Driven Strategists value the efficiency gains enabled by GenAI because it allows them to dedicate more time to strategic tasks. They regard GenAI as a sparring partner that supports faster data analysis, giving them a competitive edge through quicker decision-making. They firmly believe that GenAI is more than just a passing trend and reject the notion that it might negatively impact organizational workflows. As strategists, they embrace the opportunity to critically reflect on the workflows, resources, and assets, and reshape them in tune with the adoption of AI.
While acknowledging the positive aspects of GenAI, such as its ability to support creative tasks, Responsible AI Advocates raise concerns about the lack of transparency and the black box nature of these tools. They argue that the use of AI in corporate contexts must be guided by clear ethical and legal standards and call for the implementation of a dedicated code of conduct. Similar to Efficiency-Driven Strategists, Responsible AI Advocates believe that GenAI is here to stay. However, they dismiss the notion that a tool could fulfill the role of a human person. They are usually a good counterpoint to the strategists because they remind them of the legal, ethical, and political frameworks involved.
Placing the highest value on human connection, Interpersonal Traditionalists remain skeptical of technological trends. They feel that each new tool simply adds to their workload rather than reducing it. Unsurprisingly, they do not perceive the promised efficiency gains of GenAI to be that relevant. The primary driver of AI adoption is the pressure caused by competitors, and the fear of falling behind. The supposed benefits of AI are eclipsed by the training demand, creating cognitive pressure and emotional barriers towards adoption. Traditionalists perceive experimenting with technology as a distraction that could be better spent on nurturing interpersonal connections.
Placing the highest value on human connection, Interpersonal Traditionalists remain skeptical of technological trends. They feel that each new tool simply adds to their workload rather than reducing it. Unsurprisingly, they do not perceive the promised efficiency gains of GenAI to be that relevant. The primary driver of AI adoption is the pressure caused by competitors, and the fear of falling behind. The supposed benefits of AI are eclipsed by the training demand, creating cognitive pressure and emotional barriers towards adoption. Traditionalists perceive experimenting with technology as a distraction that could be better spent on nurturing interpersonal connections.
How the Different AI Expectation Types Can Learn from Each Other
Our typology does not imply any normative judgment about which type is more responsible or innovative. On the contrary, each type reflects essential perspectives that contribute to the successful implementation and adoption of GenAI. Since there are often unvoiced assumptions and blind spots, we encourage occasional shifts in perspective
A clear understanding of the different AI expectation types within a given team can provide a foundation for successfully implementing GenAI tools and adapting to evolving workflows. Given the central role of AI competencies in the profession, communication departments should consider offering tailored training programs aligned with the specific needs of each type.
Using benchmarking tools like the COMM-AIT, the first AI literacy test (www.comm-ait.com), which is specifically geared towards communication professionals, can help to set the baseline for specific training actions.
Diverging Expectations, Aligned AI Strategy
Many AI strategies focus on implementing the right tools for the job or by presenting prompt libraries, yet without a deep understanding of employees’ needs, competencies, and expectations, such efforts risk remaining superficial and disconnected from everyday communication practice. Balancing these diverse needs is essential to leverage the unique strengths of all team members and turn them into a collective advantage for the communication team.
By using this typology as a foundation, communication departments can design AI strategies that resonate with different user mindsets. The following three recommendations offer a starting point for putting these insights into practice:
- Which AI expectations exist in your team? Start by documenting the different AI expectation types within your team. This can be done with a small survey or team workshop.
- What does each type need? Consider tailored management support and training for the different types within your team.
- Where are your blind spots? Encourage team members to take on another type’s perspective to ensure your AI strategy is balanced and sustainable.
About The Study
This study is part of the larger research project “Future Lab: Generative AI in Corporate Communications” led by Swaran Sandhu (Hochschule der Medien Stuttgart) and Ansgar Zerfass (Leipzig University). The authors are Katja Leifheit and Swaran Sandhu. The project includes three full-day workshops in 2025 with corporate communication professionals, each involving 40 to 50 AI experts. This part of the study focused on expectations and was conducted during the project’s online phase in spring 2025.