Translated with DeepL
How can small and medium-sized companies use artificial intelligence sensibly without high barriers to entry or unrealistic expectations? The article "The right AI entry point for every company size" shows in a practical way how AI agents can already take on specific tasks today and which entry paths are suitable depending on technical maturity.
Specific examples are used to show how companies can gradually gain experience, ease the burden on processes and responsibly integrate AI into their day-to-day work: From easy-to-use AI tools that can be used without programming knowledge to individually developed applications that are specifically tailored to complex processes.
The article is aimed at decision-makers and specialists who want to realistically categorise artificial intelligence and use it with clear added value. The article was written by Lamia Friha, Giovanna Di Marzo Serugendo, Pierre-Yves Burgi and Jan Melichar from the University of Geneva.
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.
Practical examples will be used to show how AI supports recurring activities, such as information searches, document processing, customer contact and administrative processes.
No. The article explains that many AI tools can be used without prior technical knowledge and how companies can choose between simple solutions and customised applications depending on their initial situation.
The text provides orientation here by categorising different entry routes and showing how the appropriate approach can be derived from technical maturity, available resources and concrete benefits.
A transparent description is given of the time and financial outlay involved in different approaches and where quick relief is possible.
Key issues that should be clarified at an early stage, such as the handling of sensitive data, responsibility in the event of errors and the legal framework, are highlighted.
The article places particular emphasis on the role of training, clear rules and a step-by-step approach so that AI is perceived as support and acceptance is created.
The article explains why a clearly defined problem is more important than the technology itself and why a pragmatic start is often more successful than large pilot projects.
