Rethinking Engineering and Production: Acceleration and Automation as Leverage for Productivity

July 23, 2026

How can companies increase their productivity without relying exclusively on new technologies? This question was the focus of the webinar “Rethinking Engineering and Production: Acceleration and Automation as Leverage for Productivity” as part of the series “Transformation. In Dialogue.” The two speakers, Matthias Kollberg, Managing Director of insert effect GmbH, and Dr. Rainer Stetter, Managing Director of ITQ GmbH, examined the topic from different perspectives. Together, they made it clear that successful automation does not begin with artificial intelligence, but with an understanding of a company’s own processes and the people who shape them.

Automation Requires Structure, Not Hasty Action

A central theme of both presentations was the realization that some companies are currently relying on AI technologies too soon. Before intelligent systems can create added value, processes must be clearly defined, data must be structured, and goals must be unambiguously formulated.
Matthias Kollberg presented a three-stage model of automation to illustrate this. The first level consists of traditional process automation, which handles recurring tasks in the background. Building on this comes the automated consolidation and visualization of data, which reveals opportunities for optimization. It is only at the third level that artificial intelligence comes into play—as a tool for analyzing, evaluating, and continuously improving existing processes.
The key starting point is always the same: companies must understand which tasks are time-consuming, cause errors, or unnecessarily tie up employees. Automation should begin where the biggest pain point lies, not where the technology appears most spectacular.

Small Steps Deliver Quick Benefits

Both speakers cautioned against viewing automation exclusively as a comprehensive transformation project. Instead of relying on a single, large-scale solution, a step-by-step approach is recommended.
Small, clearly defined automation projects can often be implemented quickly and produce immediately measurable results. They reduce risks, build acceptance within the organization, and lay the foundation for further steps toward digitalization. Over time, this creates a network of efficient processes that often yields greater benefits than individual large-scale projects.
Consistent documentation is key here. Only when processes, goals, and results are described in a transparent manner can automation initiatives be operated, expanded, and evaluated over the long term.

Digital Twins as a Bridge Between Development and AI

Dr. Rainer Stetter demonstrated how modern engineering approaches can be combined with the capabilities of artificial intelligence. Digital twins and simulation environments play a key role in his approach.
They enable companies to virtually model and test products, machines, or processes as early as the initial development phases. Errors can be identified before real systems are built. At the same time, such simulations create ideal training environments for AI applications. Millions of scenarios can be run through virtually without disrupting production processes or putting real equipment at risk.
Particularly in robotics, mechanical engineering, and automation technology, the combination of simulation, digital twins, and AI is increasingly becoming a key competitive factor.

People Remain the Decisive Factor for Success

Both speakers were in particular agreement on one point: automation does not replace humans. While routine tasks are increasingly being automated, the importance of human skills is growing. In the future, there will be a demand for skilled workers who can understand processes, define requirements, evaluate results, and control complex systems.
This also changes the requirements for training and continuing education. Companies need employees who can bridge different technological fields—from mechanics and automation to software development and AI applications. At the same time, continuous learning is becoming a strategic necessity, as technologies and tools are evolving at an ever-faster pace.

Infrastructure and Security Are Gaining Importance

In addition to qualified employees, technological infrastructure is increasingly becoming a decisive factor in the success of AI and automation projects. High-performance hardware, secure data processing, and appropriate computing resources are becoming key competitive factors.
Particularly in industrial settings, companies must carefully weigh which data can be transferred to external systems and which applications should be run on their own infrastructure. Data protection, compliance, and cybersecurity are thus becoming integral components of every automation strategy.

Conclusion

The webinar made it clear that productivity gains do not result solely from new technologies. Successful automation begins with clear processes, a structured approach, and the courage to implement small improvements first. Artificial intelligence realizes its full potential especially where these foundations have already been laid.
At the same time, it is clear that technological innovation and human expertise remain inextricably linked. Companies that invest in processes, infrastructure, and employee training create the conditions necessary to leverage automation and AI as sustainable drivers of productivity and to strengthen their competitiveness in the long term.