17.06.2026

From Usage to Impact: Where Companies stand with GenAI today

The German Social Collaboration Study 2026 shows that generative AI has long since become part of everyday work in many companies. At the same time, only a few organizations have so far succeeded in systematically translating the technology into value creation. The key factor will be how effectively strategy, technology, and organization interact.

The German Social Collaboration Study 2026 examines the use of generative AI in the digital workplace in German companies. It is based on a survey of more than 200 executives from various industries and focuses in particular on maturity levels, usage patterns, and organizational and strategic frameworks.

The study is conducted annually by Campana & Schott in collaboration with the Chair of Information Systems | Software & AI Business at the Technical University of Darmstadt and provides a vendor-neutral overview of developments in digital collaboration and the use of emerging technologies.

The current edition focuses on how companies are already using generative AI productively, which structural challenges are hindering scalability, and under what conditions GenAI can truly deliver measurable impact.

The following key findings summarize the study’s main results and highlight the conditions under which the use of GenAI translates into sustainable impact.

1. Lack of Strategic and Organizational Embedding

But a look at the structural level also reveals clear areas for action. While the technological foundation is well developed in many organizations, strategic and organizational embedding is not keeping pace with the rapid adoption of GenAI.

In many cases, GenAI is introduced before it is clear how objectives, governance, responsibilities, and operating models should interact. This gap in implementation and structure limits scalability—not the technology itself.

2. Insufficient Employee Enablement

Employee enablement is also lagging behind what growing adoption might suggest. While willingness to use GenAI productively is high, uncertainties regarding responsibility, quality, transparency, and the pace of change persist.

Without targeted training and clear guidance, a tension emerges between individual experimentation and organizational strain.

3. Scaling requires a robust Operating Model

Against this backdrop, the structural question of scaling is gaining importance. Individual use cases work well, but their transfer into processes and value creation is rarely systematic. What is needed are approaches that do not treat successful solutions in isolation, but instead transform them into standardized, reusable, and governance-compliant building blocks.

The goal is to translate the rapid pace of technological progress into a robust and repeatable operating model, for example through concepts such as the Agent Factory approach.

4. Transparency on Value remains limited

Finally, there is still significant room for improvement when it comes to measuring impact. Despite initial evidence of ROI, a consistent KPI framework that reliably captures effects and supports decision-making is often lacking.

Without transparency regarding value, risks, and quality, GenAI remains prone to misperceptions—both overly optimistic and overly skeptical.

Conclusion:

The overall finding is therefore that progress is generally positive, but structural professionalization is not keeping pace. Companies that now consistently align strategy, technology, and organization create the conditions for GenAI not only to function, but to truly deliver impact—measurably, responsibly, and sustainably.

Would you like to dive deeper?

Explore the full study here!