In 2025, nearly every business in Switzerland is “doing something” with Artificial Intelligence—whether it’s automation, data insights, predictive analytics, or machine learning. But while companies are investing in strategy and platforms, many are still struggling to build the right teams to deliver results.
At Swisslinx, we work with leading financial institutions, tech firms, healthcare innovators, and manufacturers across the Swiss market. One thing is consistent: hiring for AI and data roles remains one of the most misunderstood areas of recruitment today.
Here’s what hiring managers still get wrong—and how to get it right.
❌ Mistake 1: Expecting Unicorns
One of the most common issues we see is job descriptions packed with contradictory or unrealistic requirements. Data scientists expected to deploy production-ready systems. Machine learning engineers asked to also handle data governance and stakeholder presentations. Or AI leads required to have “10+ years of experience” in a field that’s barely that old.
💡 What works: Split roles by function and maturity. If you’re early in your AI journey, hire hybrid generalists. If you’re scaling, invest in clearly defined profiles:
Data Scientist for modelling and insights
ML Engineer for deployment and scalability
Data Engineer for pipeline building and architecture
AI Product Manager to bridge business and tech
Ethics or Governance Lead for compliance
❌ Mistake 2: Overlooking Soft Skills and Business Acumen
AI teams don’t work in isolation. Yet many hiring decisions focus solely on technical skills. This often results in brilliant developers who struggle to translate insights into business value—or to collaborate effectively with cross-functional stakeholders.
💡 What works: Prioritise curiosity, communication, and commercial thinking. The best AI talent understands not just how to build a model—but why it matters to the business. Strong candidates often come from non-traditional or mixed academic backgrounds. Don’t filter them out too early.
❌ Mistake 3: Underestimating Market Dynamics
In Switzerland, the demand for AI talent far exceeds supply. We see strong candidates fielding multiple offers at once—and declining processes that are slow, unclear, or misaligned with their expectations.
💡 What works:
Keep processes lean (2-3 rounds max)
Communicate salary and benefits early
Move decisively when you find a good fit
Speed and clarity are crucial. If you delay, you’ll lose top candidates to more agile competitors—often international ones.
❌ Mistake 4: Ignoring Retention During Recruitment
Many companies focus so much on hiring that they forget to sell the opportunity in a compelling way. In a tight market, candidates need to know:
Why your company?
What problems will they solve?
How will they grow?
💡 What works: Build a value proposition for your data and AI team. Share your roadmap, your investment in upskilling, your tech stack—and your commitment to ethical AI. People want to know they’re joining something meaningful, not just a headcount.
✅ The Swisslinx Advantage
At Swisslinx, we combine market intelligence with deep networks across AI, data, and digital transformation. We don’t just source CVs—we help you define roles, benchmark compensation, sharpen employer brand messaging, and deliver qualified candidates who fit your culture and long-term vision.
AI may be the future—but people remain the priority. Let us help you build the team that will unlock the potential of your data, securely and sustainably.
Want to discuss your AI talent strategy in 2025?
Reach out to our specialist consultants today.
