Pre-Stages of GenAI Governance via Managerial Communication

Generative artificial intelligence (GenAI) tools such as ChatGPT, Gemini, or Microsoft Copilot are associated with a number of promises for small and medium-sized enterprises (SMEs), relating to efficiency gains or process automation [1]. Combined with comparatively low costs, broad accessibility, and diverse application possibilities, GenAI changes the competitive landscape for SMEs as it democratizes scalability and creativity [2, 3]. The exploitation of GenAI potential requires an organizational governance structure that contends with the ethical challenges of AI usage. This is particularly important for SMEs [3–6]. 

While the underdevelopment of institutional corporate responsibility and the ensuing hindrance to GenAI implementation is a well-understood fact [7], little is known about how GenAI risks and responsibilities are perceived in SMEs and what practices have so far been established from a procedural perspective. Deeper insights are valuable for building a support structure that expands on existing approaches. 

In the following sections, we characterize AI governance as a reference framework, explore the state-of-the-art of GenAI governance in the Ruhr region, further substantiate the findings with insights from a case study, and finally suggest next steps.

Gen AI governance—A conceptual framework 

GenAI in SMEs is not only considered a possible game changer [8], but also an ethical challenge that faces new regulatory demands (e.g.; the EU AI Act) [9]. Coping with ethical challenges is an established practice in large corporations to avoid unchecked use of GenAI tools [10]. Even though SMEs operate largely outside public control, they are no less in need of institutionalized mechanisms and practices that ensure responsible use of GenAI [3, 11].

Governance aims to unlock the potential of GenAI in organizations while mitigating the associated risks. Governance describes a formalized and institutionalized structure embodied in legal regulations, in this case the EU AI Act [12], as well as in established reporting mechanisms for dealing with stakeholder demands in areas where risk-based externalities might occur. This can include codes of conduct, corporate declarations, and similar company-wide guidelines [13]. 

With respect to GenAI governance, scholars further distinguish structural, procedural, and relational governance mechanisms [14, 15]. Structural mechanisms cover aspects like the location of responsibility and decision-making authority, while procedural mechanisms include defining an AI strategy, developing guidelines and processes, and monitoring the use of GenAI to ensure compliance with legal and organizational requirements. Supplementary relational mechanisms support cooperation between stakeholders by transparently sharing information about the use of GenAI and by creating training opportunities for its safe and competent use [14, 15]. 

This is of particular relevance when a new governance topic such as GenAI requires institutionalization. Moreover, it is a method of defining governance requirements for SMEs. Governance in SMEs is leadership-oriented and therefore needs to reflect managerial accountability and leadership roles [16, 17, 18]. This is why managers’ responsibility, openness, and answerability also indicate first steps towards organizational governance [19].

Taken together, framing GenAI governance for SMEs includes institutionalized reporting structures in reference to the EU AI Act, but also less institutionalized ways of demonstrating managerial responsibility in terms of specifying responsible authorities, formulating explicit guidelines, establishing means of exchange for overcoming ethical challenges, and generating individual risk awareness. Excluding these managerial aspects would likely cause research to overlook possible first steps towards GenAI governance.

Empirical insights from an initial screening among SMEs in the Ruhr area 

As part of the HUMAINE project, we conducted a screening among the member companies of the Chamber of Industry and Commerce for the Central Ruhr area (IHK Mittleres Ruhrgebiet). The Chamber represents the interests of more than 37,500 companies from various industries operating in the German Ruhr area, thus constituting an invaluable source for exploring the current state of GenAI governance on a regional level. 

The analysis was based on a standardized questionnaire exploring five thematic areas: (1) forms of GenAI usage in the organization, (2) perceived risks, (3) implemented practices for enhancing responsibility, (4) core managerial activities, and (5) basic organizational data, particularly company size, for further contextualization [9, 15, 19, 20, 21]. Items were rated on a scale of 1 to 5 from “strongly disagree” to “strongly agree.” To ensure the applicability of the questionnaire, all items were pre-tested with members of a medium-sized company.

The screening instrument with standardized measures was sent to 6,846 organizations via an IHK distribution list. The survey was aimed at managing directors and owners of SMEs as key informants and yielded a total of 56 evaluable responses within one week – a low turnout that can be attributed to the brief survey period, the target group being addressed via top management [22, 23], and the specialized nature of the topic. Low response rates indicate that GenAI governance is not high on the agenda for most SMEs.

While self-reported data from organizational decision-makers may be subject to social desirability bias [24], measures were taken to reduce bias, including the use of anonymous data collection and neutral item phrasing. Figure 1 gives a brief summary of the demographic characteristics of the respondents. Statistical analyses showed that response rates do not correlate with company size.

The survey results indicate that over 80% of organizations use GenAI primarily for information gathering and research (84%) as well as for formulating, summarizing, and translating (86%). It seems that GenAI software is primarily used to substitute pre-GenAI applications, especially Google applications. Almost half of SMEs use GenAI for brainstorming (52%) and creating templates and draft concepts (48%). Only a small group uses GenAI for image generation (29%) or programming and formula tasks (23%). The findings suggest a conservative use of GenAI that does not explore new dimensions of technological potential. In these environments, neither risk awareness nor an institutionalized AI governance structure can reasonably be expected.

Asked about uncertainties and risks, the respondents classify almost all potential risk categories as below average. It is only with regard to data privacy, the legal framework of the EU AI Act, and the opacity of black-box decision-making that a moderate risk awareness is shown (Fig. 2). 

Corresponding to the low risk awareness, respondents admit a significant lack of established authorities or practices for dealing with GenAI risks (Fig. 3). It is only with regard to communication practices that managers indicate a medium level of responsibility. 

In view of the lack of institutionalized responsibility, it is interesting to note how managers describe their individual role and activities. It becomes evident that respondents see themselves as personally and organizationally responsible for the use of GenAI in their organizations, at least on a medium level (Fig. 4). 

In summary, the screening of regional SMEs from the Ruhr area shows that the topic of GenAI governance is on the agenda for only a few companies. Where reflection is taking place, rather conventional ways of using the technology, similar to those offered by former software products, tend to emerge. Risk awareness is rather low and an institutionalized AI governance is missing. Responsible AI is considered and practiced as a managerial communication task. Thus, SMEs from the Ruhr area are far from practicing GenAI governance, revealing both a high demand for more advanced technology usage and a corresponding need for GenAI governance.

Insights from the SEEPEX case study

The screening provides a first overview but does not offer any information about the development process within SMEs. In this regard, the qualitative data from the SEEPEX case, a medium-sized pump manufacturer from the Ruhr area, affords a more nuanced picture. The case study includes a series of interviews with employees confronted with GenAI implementation during a six-month period in 2025. The interviews were conducted at two measurement points by two independent researchers.

At the beginning of the implementation period, employees used the GenAI tool rather conservatively, given their concerns about exposing themselves to penalties for sharing personal or company-related data in their prompts and requests. 

Already here, the relevance of managerial communication as a first step towards AI governance becomes clear. Extensive clarification and clear communication about how sensitive data should be handled when interacting with GenAI mitigated initial uncertainty. Even though an institutional approach was missing, managerial responsibility was a substitute to cope with GenAI risks at the beginning of the process. In the next stage of deployment, SEEPEX defined responsibilities for the GenAI implementation process and specified the guidelines for the use of GenAI in the further implementation process. These guidelines have a technical focus, on the trustworthiness of data [20].

The case study indicates that GenAI governance does not always take the form of an established report system but can constitute an interactive way of clearing the implementation process. During the early stage of implementation, it is managerial risk awareness and responsibility that favors risk avoidance. Therefore, managerial practices should be considered as early-stage governance [14]. Obviously, this approach has limitations when it comes to company-wide use of GenAI and more advanced ways of leveraging its technological potential. It must therefore be complemented by more deeply embedded, structural measures in future development.

Discussion: Evolving GenAI governance in SMEs

As the empirical findings from the Ruhr area suggest, structural or procedural control mechanisms, such as formal guidelines or monitoring systems, are almost entirely missing. The current stage of development shows that a responsible use of GenAI is instead based on communicative practices and social interaction. Although these informal methods of coping with ethical challenges bear risks and indicate deficits, they are nonetheless highly valuable for a nascent GenAI governance.

In line with other scholars [16, 17], we affirm that the relevance of informal governance should not be underestimated when it comes to GenAI implementation in SMEs, as it is often an important starting point for establishing explicit guidelines and control structures [25].

The presented findings from the screening in combination with the more detailed insights from a local case study of the Ruhr area made clear that, as long as more informal substitutes in managerial communication and practices exist, a lack of GenAI governance does not necessarily encourage risky behavior.

The empirical results reveal early-stage practices evolving to become a governance structure for responsible GenAI use in SMEs. We agree with previous studies [16, 17] that existing governance models have not yet adequately considered the specific conditions of SMEs but could emphasize managerial communication and practices as a relevant pre-stage of GenAI governance for the quick adaptation of AI in corporate problem solving.

Beyond these research outcomes, the findings reveal a high demand for practitioners to advance not only GenAI usage, but also for corresponding governance mechanisms. For all actors and institutions who provide a support structure for regional development, e.g.; the IHK or the competence center HUMAINE, the requirements for future support activities are clear. 

This article was written as part of the project “HUMAINE (human-centered AI network) – Transfer-Hub of the Ruhr Metropolis for human-centered work with AI”, which is funded by the German Federal Ministry of Research, Technology and Space in the program “Future of Value Creation – Research on Production, Services and Work” and supervised by the Project Management Agency Karlsruhe (PTKA) (funding code: 02L19C200).

You might also be interested in

Serious Gaming and the Energy Transition Collaborative knowledge generation and interactive understanding of complex interrelationships Janine Gondolf , Gert Mehlmann, Jörn Hartung, Bernd Schweinshaut, Anne Bauer

Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole.

Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 62-69

Building the Future Workforce Today Trendiation as a strategic framework for employee qualification and training Jürgen Fritz, Sebastian Busse, Ingo Dieckmann, Torsten Laub

As Industry 4.0 and artificial intelligence reshape organizational capabilities, traditional training systems struggle to keep pace with evolving skill requirements. This paper introduces Trendiation—a structured methodology for translating emerging trends into actionable strategies—as a systematic approach to this challenge. Through a workshop-based application examining Edutainment, Human-Centered Design, and Workforce Transformation, we demonstrate how organizations can move from abstract trend identification to concrete qualification requirements and prioritized training initiatives. The method produces a traceable artifact chain spanning trend framing, capability-gap assessment, and implementation roadmaps. Participant evaluations indicate high perceived clarity and practical utility. By bridging foresight analysis with participatory design, Trendiation enables organizations to proactively cultivate adaptive capabilities and build learning cultures aligned with future work ...

Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 22-29 | DOI 10.30844/I4SE.26.2.22

Operationalizing Ethical AI with tachAId Validating an interactive advisory tool in two manufacturing use cases Pavlos Rath-Manakidis, Henry Huick, Björn Krämer , Laurenz Wiskott

Integrating artificial intelligence (AI) into workplace processes promises significant efficiency gains, yet organizations face numerous ethical challenges that stakeholders are often initially unaware of—from opacity in decision-making to algorithmic bias and premature automation risks. This paper presents the design and validation of tachAId, an interactive advisory tool aimed at embedding human-centered ethical considerations into the development of AI solutions. It reports on a validation study conducted across two distinct industrial AI applications with varying AI maturity. tachAId successfully directs attention to critical ethical considerations across the AI solution lifecycle that might be overlooked in technically-focused development. However, the findings also reveal a central tension: while effective in raising awareness, the tool’s non-linear design creates significant usability challenges, indicating a user preference for more structured, linear guidance, especially ...

Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 50-59 | DOI 10.30844/I4SE.26.1.48

Digital Competence Lab (DCL) for Speech Therapy Designing a learning platform to advance digital skills Anika Thurmann , Antonia Weirich , Kerstin Bilda, Fiona Dörr , Lars Tönges

The digital transformation of healthcare results in lasting changes in speech therapy. Smart technologies and artificial intelligence (AI) are creating new opportunities to ensure therapy quality, address care bottlenecks, and actively involve patients in exercise processes. At the same time, these developments are expanding the role of speech therapists, who increasingly use digital systems as supportive tools in addition to their core therapeutic tasks. Based on a feasibility study of the AI-supported application ISi-Speech-Sprechen in a real-world setting of complex Parkinson's therapy (PKT), this article outlines the key challenges associated with implementing smart technologies.

Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 110-118 | DOI 10.30844/I4SE.26.1.102

AI Implementation in Industrial Quality Control A design science approach bridging technical and human factors Erdi Ünal , Kathrin Nauth , Pavlos Rath-Manakidis, Jens Pöppelbuß , Felix Hoenig, Christian Meske

Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ...

Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844/I4SE.26.1.112

XAI for Predicting and Nudging Worker Decision-Making Feasibility and perceived ethical issues Jan-Phillip Herrmann , Catharina Baier, Sven Tackenberg , Verena Nitsch

Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students’ sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students’ preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ...

Industry 4.0 Science | Volume 42 | 2026 | Edition 1 | Pages 70-78