Europe's AI Moment: Physical Intelligence, Industrial Transformation, and the Rise of Dialogue-Oriented Systems

11 March 2026

At the Plug and Play Germany Summit 2026 on March 10, industry leaders, startups, and investors came together to discuss how artificial intelligence (AI) is transforming industry and everyday life. The discussions focused on three emerging areas: physical AI, industrial AI, and conversational AI—each representing a different level at which algorithmic intelligence is applied to the real world. One message stood out in particular during the keynote discussions and expert panels: While the United States currently leads in AI development overall, Europe still has significant opportunities to take a leading role where AI intersects with the physical world.

AI embedded in robots, machines, and devices that interacts directly with the real world was highlighted as one of the most promising fields for the future. While large-scale AI platforms and foundational models are already largely dominated by American technology companies, the race for physical AI is still wide open, according to the speakers. Thanks to a strong industrial base, technical expertise, and an existing robotics ecosystem, Europe is well-positioned to compete on the global stage. However, the success of physical AI also depends heavily on the user experience. The goal, the panelists emphasized, is not to replace humans entirely, but to support them. Physical AI systems should make daily life easier and more enjoyable, reduce repetitive tasks, and give people more time to focus on meaningful work, thereby ultimately improving overall quality of life. Automation should therefore not be viewed solely as a means to greater efficiency.

AI is already having a measurable impact on industry and manufacturing. Industrial AI applications are making rapid strides in areas such as process monitoring, task verification, safety and protection systems, and predictive maintenance. AI enables companies to transition from reactive workflows to predictable and plannable processes. By analyzing sensor data, machine movements, and operational patterns, AI can predict problems before they occur and plan and coordinate responses in advance. A key technical challenge remains the creation of a semantic world model—that is, a system capable of interpreting physical inputs from sensors and translating them into meaningful operational insights. Currently, many industrial AI systems are still heavily application-oriented and designed for very specific use cases. In the future, the importance of AI agents is expected to grow: autonomous systems will be able to interact with one another and coordinate tasks. Interoperability between these agents — especially when deployed at the edge (directly on machines or local infrastructure) — will be of critical importance. This so-called edge AI offers several advantages, such as reduced latency, increased operational reliability, and better control over sensitive industrial data. The speakers also emphasized that AI solutions are rarely ready for immediate use. Implementing deep AI often requires integration across multiple services, devices, and software layers. Above all, however, data sovereignty and control remain of critical importance. Companies that retain sovereignty over their operational data are currently best positioned to build reliable AI systems.

From chatbots in customer service to voice assistants and corporate support systems: conversational AI is increasingly becoming part of everyday life. The speakers urged caution when it comes to AI-generated responses, as they are not always accurate. Several challenges were identified, including linguistic ambiguities, dialects and accents, background noise, and limitations in training. AI responses are only as accurate as the data used to train them. One example of this is the use of AI in customer support. While it can significantly speed up the processing of inquiries, it often requires extensive preliminary information from the user. Even then, misunderstandings can arise due to natural variations in human language. As conversational AI is becoming widespread in nearly all industries, the focus must therefore continue to be on transparency, validation, and human oversight. 

All of these different areas of application were also the subject of three interesting panel discussions, the results and insights from which are summarized below.

During the first panel, “AI Implementation Across Various Industries: Sharing Measurable Results,” it became clear that the greatest challenge in implementing AI is not technological in nature, but rather lies in strategic alignment. Companies must start with a clear value proposition and identify the right business metrics to measure success. If this is not done, even technically successful AI projects may fail to deliver meaningful results. Understanding customers is also crucial for assessing the impact of AI. Companies should also question assumptions regarding implementation. For example, 100 percent implementation is not always necessary. The final 5% could require a disproportionately high expenditure of resources compared to the benefits they provide. Data remains the fundamental component of successful AI systems. However, it must be correctly linked to AI models and continuously refined. The panelists also highlighted the growing importance of edge AI, in which intelligence is processed directly on the machines rather than in large, centralized models. This helps companies maintain control over their own data and reduce their dependence on external providers and infrastructure. Finally, companies were encouraged to remain active beyond the digital realm by interacting directly with customers, operations, and real-world environments. At the same time, they must prepare for risks so they can address them quickly and responsibly.

In Panel 2, “The Perspective of Venture Capitalists (VCs) on AI Investments: Concrete Use Cases or the Next Big Bubble?,” the topic of AI was examined from an investment perspective. The AI landscape is rapidly shifting toward vertical AI solutions that require specialized systems for specific industries. Although many AI tools are already available, their quality varies just as much as the level of funding behind them. For investors, two factors remain crucial: first, clear customer value, and second, proven adoption. The investment environment also reflects the complexity of the AI ecosystem. From infrastructure to applications, different levels would require different investment strategies and timelines. A particularly promising opportunity lies where AI and the physical world intersect. Startups and SMEs that combine intelligent software with robotics, machines, and industrial systems are expected to see significant growth in the coming years.

In the concluding Panel 3, which built on the perspectives presented in Panel 2, it was emphasized that innovation through partnerships is no longer merely optional. Given the rapid pace of AI development and the complexity of modern technology stacks, it is unrealistic for companies to build all the necessary capabilities in-house. Collaborations between companies, startups, research institutions, and technology providers are becoming indispensable. AI partnerships are already demonstrating strong potential in assisted decision-making, engineering and design processes, shortening development cycles, and reducing the need for physical testing through simulations. Companies were encouraged to integrate external AI expertise into their daily operations. At the same time, however, several governance challenges must also be addressed, such as data sovereignty, AI governance frameworks, compliance and regulation, as well as integration into existing systems and workflows. Companies should remain open-minded and willing to experiment, but they should also carefully evaluate business models and partnerships before committing resources. According to the speakers, there should be no fear of failure. Companies should also not feel pressured to move at the same pace as their competitors if their business model requires a different pace. The key lies in building strong ecosystems and finding partners who complement internal capabilities, rather than trying to develop every technology in-house.

In addition to offering interesting insights, various startups affiliated with Plug and Play had the opportunity to present themselves both on stage and in a “meet-and-greet” area. This gave everyone the chance to delve deeper into the topics discussed earlier and the specific solutions. Prof. Dr. Corinna Schmitt took this opportunity to promote the 2026 CODE Annual Conference and the associated Cyber/IT Innovation Conference. She also highlighted opportunities for collaboration with the CODE Research Institute and for contacting the National Coordination Center for Cybersecurity Germany (NKCS) to obtain information on funding opportunities.

In summary, the event demonstrated that physical AI could become a strategic advantage for Europe, that industrial AI will transform manufacturing through predictions and automation, and that conversational AI is reshaping human-machine interaction — though it must be used responsibly. As AI continues to evolve, success depends not only on technological breakthroughs but also on trust, collaboration, and responsible use, so that AI ultimately improves people’s lives rather than replacing them.

Photos: © SeCoSys/Schmitt