Insights and perspectives from practice
In our publications, we share experiences, perspectives, and practical insights from automation and AI projects. Our goal is to provide guidance, address common misconceptions, and clearly show what works in practice – and what doesn't.
Why Processes Are the Real Starting Point for AI
AI is often seen as a technology solution. What truly matters is whether the underlying processes are understood and well-structured.
Read moreData Quality: What Really Matters – and What Doesn't
Data quality is considered a key prerequisite for AI. In practice, it's less about perfection and more about context and fitness for purpose.
Read moreWhy Many AI Projects Don't Fail Because of the Model
Most AI projects don't fail because of the model, but due to lack of integration, acceptance, and organizational anchoring.
Read moreStandard Software vs. Custom Automation: A Sober Assessment
Standard software and custom automation follow different approaches. An objective view helps in making the right choice.
Read moreWhat "ROI in AI" Realistically Means
The ROI of AI isn't just about cost savings. It comes from structural improvements in efficiency, quality, and scalability.
Read moreHuman-in-the-Loop: Why Control Is Not a Step Backward
Human control is not a disadvantage but a key prerequisite for sustainable AI deployment in complex processes.
Read moreThe Role of Employees in AI-Supported Work
The success of AI depends less on algorithms and more on the people who work with these systems.
Read moreOrientation Over Haste: How Organizations Start with AI the Right Way
Successful AI adoption doesn't start with speed but with orientation. Haste often leads to wrong decisions.
Read moreReady to automate what slows you down?
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