Using AI Effectively: Five Strategies for SMEs
For Swiss SMEs, AI is no longer just a topic for the future. Value is created when it reduces the workload on skilled workers, connects data, and supports employees in making decisions. Five key factors highlight what matters most.
1. Highlighting Skills
When it is difficult to find external specialists, taking a closer look at existing skills becomes a strategic advantage. SMEs need clarity on which skills are already in place, which ones will be in demand in the future, and how employees can develop their skills in a targeted manner.
With a competency-based HR strategy, skills become a common language across roles, departments, and generations. This facilitates internal mobility, professional development, and succession planning. At the same time, it creates attractive career development opportunities that make the most of existing talent and help retain employees.
2. Break Free from Routines
Recurring inquiries, data entry, approvals, and coordination take up valuable time in small and medium-sized businesses. AI can provide context-sensitive information, prepare process steps, and thus speed up routine tasks.
This frees up time for teams to focus on customer relationships, leadership, consulting, and complex decision-making—tasks where experience and human judgment matter. AI supports this work, but it does not replace it.
3. Putting Data into Context
Human resources, financial, and planning data are often stored in different systems. This makes it difficult to maintain an overview of costs, capacities, and risks. Small and medium-sized businesses, in particular, often rely on isolated tools that have evolved over time.
A shared database makes correlations visible earlier and supports more informed planning. AI can structure analyses within this database, flag deviations, and identify possible courses of action. This enables decision-makers to review, evaluate, and prioritize staffing needs, costs, and capacities based on a consistent set of data.
4. 
Building Trust from the Start
When it comes to sensitive personnel and financial data, AI must not be a black box. Employees must be able to understand what data is being processed, how recommendations are generated, and what rules apply.
Clear permissions, data sovereignty, and transparent review and escalation processes provide guidance. Where an employee representative body exists, it is advisable to involve it in the implementation process at an early stage. IT, data protection, management, and the affected teams should also be brought to the table. This allows for a joint clarification of usage, the data foundation, and the impact on day-to-day work.
5. Grow with a roadmap, not with siloed solutions
SMEs do not have to implement all use cases at once. A sensible starting point is a frequently recurring, clearly defined task with verifiable benefits. Afterward, processing times, the quality of results, feedback from the teams, and the testing effort will show whether the implementation is proving effective.
Only on this basis can further processes follow. The technological foundation must be able to grow alongside the company without creating new data silos or unnecessary complexity. In this way, AI evolves from an isolated pilot project into a reliable part of everyday work.
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