The slow diffusion of AI into everyday work is only one part of the picture. Perhaps more revealing is how adoption is happening in the first place. The study finds that AI use is frequently driven by employees themselves rather than by strategic decisions made at the organizational level. Only 55 percent of AI users report that the tool they use most frequently was officially introduced by their employer. In other words, nearly half of workplace AI usage occurs informally, on the initiative of individual workers who experiment with chatbots, assistants and other applications without formal guidance, approval or support. This pattern has significant implications for how organizations manage technology, because it means that much of the practical knowledge about AI is accumulating outside official channels while companies are still catching up on governance, secure infrastructure and workforce training.

Florian Kunze, head of the study and professor of organizational behavior at the University of Konstanz, frames this as a gap between individual experimentation and organizational transformation. “In many organizations, the major AI revolution has not unfolded as a planned transformation process so far”, he explains. “Instead, many employees are experimenting with AI, while employers are still lagging behind when it comes to establishing clear policies, providing training and implementing secure AI solutions”. The image that emerges is not one of a top-down digital revolution but of a bottom-up, somewhat chaotic process in which workers teach themselves to use new tools while their employers struggle to formulate rules, safeguard data and build the institutional scaffolding that a genuine technological transition would require.

The unevenness of AI adoption extends across occupational boundaries. In office and knowledge-based work, 49 percent of employees now use AI, a figure that reflects the natural fit between generative tools and tasks involving text, analysis and information management. Among employees in production-related and manual occupations, by contrast, only 25 percent report using AI. Both groups have seen year-on-year growth, yet the divide between them remains largely unchanged. The researchers attribute this persistence at least in part to the differing scope for AI applications in the respective fields. Where the core of the work involves manipulating language, code or data, current AI systems offer immediate value; where the work is physical, embodied and hands-on, the technology has so far offered far fewer practical entry points.

Educational attainment produces an even sharper divide. While 56 percent of employees with a high level of education use AI in their work, only 21 percent of those with lower educational attainment do so. Employees with higher education also express more positive expectations about the technology and speak more openly about their adoption of it. The Konstanz researchers note that employees with higher educational qualifications are almost three times as likely to use AI as those with lower levels of education. This is a sobering statistic for anyone hoping that AI might act as a great equalizer in the labor market. Instead, the technology appears to be amplifying existing skill hierarchies, flowing most readily to those who already possess the credentials, confidence and task profiles that make adoption easy.

Organizational size adds a further layer of inequality. Small organizations are significantly less likely to offer AI training, establish formal guidelines for AI use or provide company-owned AI tools. The numbers are stark: only 11 percent of employees in small organizations report having received AI training, and just 10 percent report that binding rules on AI use exist in their workplace. In larger organizations, such support structures are far more common. The consequence is that workers in small firms are left to navigate a powerful and potentially risky technology largely on their own, without training, without clear rules and often without secure, company-sanctioned tools. This gap between large and small enterprises risks compounding the broader pattern in which advantage accrues to those already best positioned to benefit.

Elena Gerdiken, a doctoral researcher at the Future of Work Lab in Konstanz, warns that these disparities carry real consequences for social equality. “The findings show that the AI transformation is unfolding unevenly across society”, she says. “Employees in knowledge-intensive occupations, those with higher levels of education and those working in larger organizations are currently benefiting most from the new opportunities and advancement potential that AI offers. Without targeted support measures, there is a risk that existing inequalities will become further entrenched”. Her assessment points to a central tension in the current moment: a technology that is often discussed in universal terms is, in practice, being absorbed by the workforce in ways that track and reinforce pre-existing lines of privilege.

Attitudes toward AI reveal another striking divergence, this time between societal and personal perceptions of risk. Forty percent of employees expect AI and automation to have an overall negative impact on the labor market over the next ten years. Yet only 17 percent are worried about losing their own jobs. This gap between abstract concern and personal security is a well-recognized pattern in technology attitudes, but the Konstanz data show how pronounced it remains. Employees appear to view AI primarily as a future societal issue rather than as an immediate threat to their own work. Correspondingly, perceptions of concrete organizational change remain limited: only 12 percent of respondents think AI has reduced the number of entry-level positions, and just 23 percent perceive AI as already making a significant contribution to their organization’s value creation.

Carolina Opitz, also a doctoral researcher at the Future of Work Lab Konstanz, interprets this perceptual distance as a defining feature of the current phase. “Many employees view AI primarily as a future societal issue rather than as an immediate threat to their own work”, she says. “Both the risks and the economic potential associated with the technology are currently perceived more strongly at the societal level than in employees’ everyday working lives”. This disconnect matters for organizations and policymakers alike, because it suggests that the workforce may be neither sufficiently alarmed to demand protection nor sufficiently engaged to push for the training and infrastructure that would allow broader, safer adoption.

The Konstanz researchers close their report with a set of recommendations aimed at converting informal experimentation into managed, equitable transformation. They argue that companies should communicate AI initiatives more clearly and actively guide employees as they adapt to the new technologies. Key measures include establishing clear rules for the secure use of AI, expanding job-related training opportunities, providing targeted support for small and medium-sized enterprises, and developing AI solutions suited to a wide range of occupations rather than only to knowledge work. The study, funded by the DFG Cluster of Excellence “The Politics of Inequality” and conducted with the online panel provider Bilendi, builds on earlier waves surveyed in March 2024 with 2,019 respondents and in May 2025 with 1,024 respondents. Taken together, the three waves sketch a consistent trajectory: AI in German workplaces is growing, but slowly, largely from the bottom up, and along fault lines of education, occupation and organization size that society will need to address deliberately if the promised benefits are to be shared widely rather than concentrated among those already ahead.

Subject of Research: Workplace adoption of artificial intelligence and its social inequalities in Germany

Article Title: The use of AI at work: slow, informal and unequal

Article References: The use of AI at work: slow, informal and unequal. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, future of work, workplace adoption, digital inequality, Germany, University of Konstanz, labor market, generative AI, organizational behavior, employee training, occupational divide, education gap