Translated with DeepL
Typical decision-making scenarios are highlighted along the process map: from strategy and compliance to research and development, production, logistics and marketing through to human resources and invoicing. The article makes it clear how AI improves information bases, makes forecasts more precise and makes processes more efficient and sustainable.
The use cases presented are taken from over fifty AI action plans that were developed as part of the "AI Management for Business Value" continuing education programme at Lucerne University of Applied Sciences and Arts and the University of Lucerne. They provide an insight into specific AI projects at Swiss companies and show where direct business benefits can be realised.
The article was published in KMU-Magazin and is aimed at decision-makers and specialists who want to systematically tap into and strategically utilise the potential of AI.
This article in the SME magazine is part of a series that has emerged as a follow-up activity to the publication Orientation AI: Challenges and Opportunities for Swiss SMEs. This SATW publication supports small and medium-sized enterprises in recognising the potential of artificial intelligence and planning concrete next steps.
The content was created in close collaboration with stakeholders from the SAIROP (Swiss AI Research Overview Platform) network. SAIROP promotes the exchange between science, business and society, makes Swiss AI expertise visible and provides orientation in the dynamic AI ecosystem.
The article uses real-life examples to show the processes in which AI creates measurable added value: more precise demand forecasts, optimised resource planning, a more sound basis for management decisions, more efficient logistics or a more targeted customer approach. The decisive factor is not the technology itself, but the economic leverage.
Priorities in the processes, the availability and quality of the data, the expected benefits and possible risks need to be clarified. The article structures these questions along the lines of management, core and support processes.
AI can improve forecasts, scenario analyses and controlling. It provides reliable information more quickly, but does not replace the strategic responsibility of management. The article shows where AI has a supporting effect and where human judgement remains central.
Production planning, purchasing, logistics, marketing and quality and compliance monitoring are particularly important areas of leverage. The article describes how companies can reduce stock levels, stabilise processes and optimise capacity utilisation.
AI affects data protection, compliance and, where applicable, regulatory requirements such as the EU AI Act. Governance issues should be clarified at an early stage, especially in the case of sensitive data or automated decisions.
Successful initiatives start with clearly defined decision problems and measurable goals. Methods such as the AI Action Plan help to systematically identify, prioritise and realistically evaluate use cases.
In addition to data quality and IT infrastructure, competences, clear responsibilities and change management play a central role. AI is not an isolated IT project, but a strategic transformation topic.
Today, many solutions can be configured and integrated into existing systems with a manageable amount of effort. At the same time, many companies are still in the early stages of implementation. A structured approach makes it possible to control risks and gradually build up competitive advantages.
| Role | Title + Name |
|---|---|
| Text by | Leif Brandes , Jana Koehler |
