Many Swiss SMEs are already using AI tools such as ChatGPT or DeepL. However, they often lack a clear strategy. The new practical guide helps companies to recognise potential and find practical solutions to challenges such as high costs, data protection or a lack of expertise.
It shows in which areas AI brings the greatest benefits - for example in increasing efficiency, in decision-making or in the development of new business models - and how its use can be organised in a concrete and secure manner.
SAIROP - short for Swiss Artificial Intelligence Research Overview Platform - provides a supplementary overview of suitable offers and funding opportunities.
Manuel Kugler, SATW
Lamia Friha, UNIGE | Alain Hugentobler, UNIGE | Iason Kastanis, CSEM | Jana Koehler, HSLU | Mascha Kurpicz-Briki, BFH | Gianfranco Moi, UNIGE | Andrea Rizzoli, IDSIA | Benjamin Sawicki, NCCR Automation | Gabriele Schwarz Innovista Management GmbH
These FAQs provide a concise overview of key topics and facts from the original text. They help to summarise important content in an understandable way, clarify key statements and provide specific answers to frequently asked questions.
AI can increase efficiency, open up new business opportunities and create competitive advantages – for example through automation, better decision-making or new digital services.
AI can increase productivity, automate processes and improve data-driven decisions – especially when resources are scarce in small and medium-sized enterprises.
SMEs should start with a needs analysis, test small pilot projects, seek external expertise and establish clear rules for AI use (governance).
Suitable applications include:
Text generation (e.g. for marketing, reports)
Forecasting models for demand or personnel planning
Quality control with computer vision
Proactive machine maintenance
AI-supported decision-making support
Typical hurdles:
Lack of knowledge and uncertainty about costs
Resistance or concerns among employees
Poor data quality
Legal requirements (data protection, EU AI Act)
Through small, low-risk pilot projects, the use of existing tools (e.g. ChatGPT, Copilot), partnerships with universities or funding such as that provided by Innosuisse.
By relying on explainable models, defining application limits, training employees and checking data for distortions or stereotypes.
Platforms such as SAIROP and programmes offered by universities of applied sciences provide practical training, coaching and access to experts.
