Helbling channels AI into value creation
Zurich/Aarau - Helbling supports companies in generating sustainable added value through the use of artificial intelligence (AI). Within the approach, structures and work processes are adapted using a specialized methodology. Helbling has already successfully integrated AI into several projects, including at a Swiss hospital.
(CONNECT) A company can only generate real added value with artificial intelligence once it has aligned its processes, roles, and management structures, according to Helbling. Teams at the Zurich-based engineering and consulting firm have now developed a corresponding methodology focused on the so-called Target Operating Model (TOM) that is designed to facilitate the transition to an AI-driven organization. Presenting the approach in a specialist article, experts from Helbling’s Aarau and Zurich offices illustrated the steps using a practical example.
Being under time pressure puts companies at risk of turning to AI, just like earlier IT tools, uniquely for automating routine tasks, they write. Instead, the technology could help boost value creation – such as by supporting analysis or decision-making.
According to the article, Helbling’s methodology combines technological excellence with business and process expertise. It has six essential elements. Developing a long-term data strategy plays a key role and must be supported by senior leadership, it emphasizes. Another important aspect is selecting the right areas and use cases for AI. “High-volume or high-value areas often present the greatest potential to start with,” write the experts. They explain that each use case can be broken down into smaller decision units, helping establish effective AI integration. Other key considerations include cybersecurity and the redistribution of tasks between humans and machines.
In one recent project that Helbing gives as an example, the methodology enabled the successful integration of AI into a Swiss hospital: in oncology, time spent identifying treatment options, which previously took four hours, has been reduced to just a few minutes. To achieve this level of efficiency, Helbling’s team broke the process down into three steps: knowledge building, literature search, and documentation. Full operationalization requires date protection measures, they note, and organizational processes within the hospital itself must also be adapted.
Ultimately, the experts highlight that by embracing this transformation, organizations can help themselves be prepared for further developments in AI. Physical AI, for example, introduces additional complexity by integrating robotics and shifting the focus from cognitive to hands-on operational work. ce/yvh
