The benefits of AI depend less on the technology itself than on the quality and availability of a company’s data.
Many SMEs can already create significant added value simply by using well-organised data, dashboards and reports.
Successful data projects start with specific business problems – not with the question of which AI tool to use.
Data quality must be appropriate to the specific business purpose: it is not perfection, but sufficient reliability that is crucial.
By establishing data quality, data literacy and clear lines of responsibility, organisations lay the foundations for successful AI applications and better decision-making.
In the fourth instalment of the ‘Switzerland: A Hub for Innovation’ series, Sandro Saitta explains why data is the real fuel for AI. The article sets out why companies should first define their business challenge and only then decide whether AI is the right solution at all.
He also shows how SMEs can get off to a pragmatic start using existing data, why a strong data culture and clear roles are important, and why even simple solutions such as reporting, dashboards or automated data processing often deliver significant benefits.
This article in KMU-Magazin forms part of the series ‘Switzerland as a Hub for Innovation’, which emerged as a follow-up to the SATW publication ‘AI Orientation: Challenges and Opportunities for Swiss SMEs’.
The content was developed in collaboration with stakeholders from the SAIROP (Swiss AI Research Overview Platform) network and the SATW’s network of experts. SAIROP promotes collaboration between research, industry and public administration and supports knowledge transfer within the Swiss AI ecosystem.
Virtually every data-driven application – from dashboards and forecasts to generative AI and AI agents – relies on reliable data. Without a suitable data foundation, even the most advanced AI tools can only have a limited impact.
Not necessarily. In many SMEs, better-organised data, structured reporting, clear dashboards or automated data processing already deliver significant added value. The business challenge should therefore take precedence over the choice of technology.
Data does not have to be perfect. What matters is that it is reliable enough for its intended business purpose. The more critical an application is, the higher the requirements for quality and consistency.
The starting point should be a specific business problem: Which process needs improving? Which decision requires better information? Only then should one assess what data is available and whether AI is actually necessary.
Data-driven transformation is not purely a technological challenge. Organisations need clear lines of responsibility and a shared basic understanding of why data is important, how visualisations are interpreted, and what the possibilities and limitations of AI are.
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