IEN Europe: Not all of our readers are familiar with the term “Connected Worker-Platform”. Could you please describe the idea behind it?
Norman Hartmann: A Connected Worker Platform is a digital tool set that empowers frontline workers in industrial environments. While most digital transformation efforts have historically focused on machines and automation, human workers have remained on the sidelines in terms of digital integration. The concept of a Connected Worker Platform changes that by providing shop floor employees with the right information at the right time, directly at the point of activity. It enables them to perform their jobs more efficiently, avoid mistakes and collaborate better with other departments. The integration of machines, people and materials in a unified digital ecosystem bridges the gap between humans and industrial IT systems. Beyond task management, it enables teams to document work in real time, access contextual data and benefit from AI-supported assistance. It’s about creating a closed feedback loop. Workers not only receive instructions but also generate valuable data that improves future processes.
IEN Europe: What is the software and hardware needed for implementation? Which role does the “Industrial Smartwatch” play in this concept?
Norman Hartmann: We started out with the industrial smartwatch because experienced workers usually know how to do their jobs. They just need to know what to do next. A smartwatch is hands-free, always on the wrist, and doesn’t interfere with manual tasks. It was ideal for quick task dispatching, alerts and confirmations. It also helped us communicate the concept in a very tangible way during our early days. However, today the Workerbase platform is device-agnostic. It runs on smartwatches, smartphones, tablets, laptops and desktop PCs. A smartwatch is still very useful for specific use cases, but not a requirement. This flexibility means our solution can scale with the needs and preferences of each customer site. Whether it’s maintenance, logistics or quality assurance. Each role can use the device that best fits their tasks.
IEN Europe: AI is a term that is hard to avoid in the industrial landscape at the moment, no matter where you look. How would you rate the current situation regarding the use of AI in production? What is your opinion on how things will develop in the near future?
Norman Hartmann: We’re definitely in the early stages of AI adoption in manufacturing. There’s a lot of experimentation happening. Some of it quite promising, some of it still struggling with real-world applicability.
The big challenge is that AI alone isn’t useful without context. That's why we’ve designed our platform to act as an AI enabler. Companies can integrate their preferred AI engines, whether it’s OpenAI, Gemini, or an in-house model. What we do is provide the infrastructure: contextual data from the shop floor, user input, task flows and historical performance. With this, the AI can do meaningful things, like helping workers make better decisions based on past events, summarizing procedures or generating documentation automatically. Conversely, once AI helps produce structured documentation, it can then analyze it to surface patterns and opportunities. This two-way interaction between human activity and artificial intelligence is what unlocks real value, and we make that accessible.
IEN Europe: What skills are needed in a company and how many specialists are needed to prepare and implement a Worker Platform project?
Norman Hartmann: One of the most powerful aspects of a Connected Worker Platform is its simplicity. Most tasks, like building workflows or digitizing checklists, can be done with no IT background. It’s designed to be as intuitive as writing a Word document. This allows companies to start small, experiment and scale at their own pace. For more advanced use cases, we offer the ability to integrate custom code. This is optional and only needed if a company has very specific requirements. At that point, collaboration between domain experts and IT teams can unlock even more potential. In practice, each factory site usually has at least one key user, someone who acts as the local expert. In larger organizations, we sometimes have hundreds of key users across regions. These teams create new applications and continuously refine the system. It’s a very decentralized and dynamic approach to digital transformation.
IEN Europe: What industries and sectors customers come from? What are the typical benefits and what period of time can be expected for the return of invest?
Norman Hartmann: We originally started in discrete manufacturing, but today our customers come from virtually every industry: automotive, electronics, consumer goods, food & beverage — you name it. The challenges we solve are universal: bringing the right information to workers, documenting their actions, coordinating across teams and accelerating onboarding. The return on investment is typically seen within a few months. We’ve seen companies save millions of euros by preventing errors, reducing downtime and streamlining processes. The platform improves traceability, compliance and efficiency, all of which contribute to a significantly more agile and responsive production environment. Because the software integrates seamlessly with existing systems like ERP and MES, it doesn’t require customers to rip and replace. Instead, it extends their digital capabilities directly to the frontline, creating immediate and measurable value.
IEN Europe: Can you give us one or two examples of challenges you solved for customers ?
Norman Hartmann: One of the most impactful cases was with Gazelle, a leading bicycle manufacturer. They were struggling with frequent and unexplained assembly line stops. Previously, when a stop occurred, it was simply recorded as a binary event, “stop” or “go”, with no context. Our system changed this by capturing additional data directly from the workers. Now we also record the reasons behind each stop, such as workers not knowing how to proceed or missing materials. This approach allows us to provide a different level of support. For instance, if a worker doesn't know how to solve a problem, the system can route the task to the appropriate person to offer guidance. If missing materials are the issue, the system can automatically notify logistics or the supply chain team to deliver the resources. By adding context and traceability, we were able to reduce downtime by 35%, significantly improving production efficiency. Furthermore, by capturing detailed reasons for each interruption, we’re able to identify recurring problems and implement measures to prevent them.
Another great example comes from Bosch's SMT production lines and GKN Powder Metallurgy, a company which specializes in metal sintering. In both cases, they connected all their machines to our platform. Now, when something breaks down, our system ensures the repair process is handled efficiently. It routes the problem to the right expert depending on the issue. For example, if a machine breakdown occurs, it finds a technician who’s not only qualified but also available and nearby. We also take care of logistics. For instance, if a technician needs specific tools, the system ensures that he knows to bring them. Our platform orchestrates multiple people to handle different parts of the task. Logistics might deliver the necessary material, while the technician handles the actual repair. This way no one has to waste time fetching tools, calling colleagues for help or waiting for parts to arrive. Additionally, our system leverages AI to provide workers with real-time guidance, helping them complete tasks more efficiently. If a new issue arises that hasn’t been documented before, the platform allows users to easily add this problem to the AI knowledge base simply by recording a video of the work being performed. The AI analyses the video and ensures that in the future, workers can quickly access solutions for the new problem.
IEN Europe: Thank you for these insights!
