Practical steps for the next 24 months
Europe often frames the AI race as a competition in foundation models,GPU clusters, and billions in capital. In that race, the US and China will remain ahead for the foreseeable future.
But that is the wrong battlefield.
The real economic value of AI will not be created by those who train the largest models. It will be created by those who deploy AI at scale in real industries—in factories, hospitals, banks, energy systems, and supply chains.
In that race, Switzerland and Europe can still win.
Not by becoming model leaders.
But by becoming application leaders.
The Strategic Shift: From Model Race to Deployment Race
Instead of asking:
- “How do we build the next GPT?”
- “How do we finance €100B compute clusters?”
Europe should ask:
- “How do we deploy AI faster and more safely than anyone else?”
- “How do we turn regulated industries into AI advantage zones?”
Switzerland and Europe is uniquely positioned to lead this shift.
It has:
- Well regulated and high-value industries
- Deep trust in institutions
- Strong engineering culture
- High concentration of global headquarters
- World-class universities
If used correctly, these are not constraints.
They are strategic advantages.
Step 1: Pick 5–7 Strategic AI Application Domains
No country wins by trying to lead everywhere.
Switzerland and Europe should focus on sectors where they already have:
- Global market share
- Deep expertise
- High regulatory barriers
- High trust requirements
Priority sectors:
- Pharma and biotech
- Insurance and reinsurance
- Banking and wealth management
- Precision manufacturing
- Energy and infrastructure
- Medtech and hospitals
- Legal, tax, and compliance automation
Success should not be measured by
- Number of AI papers
- Number of unicorns
- Size of compute clusters
- Number of patents
Instead, measure:
- Productivity per employee
- Error reduction
- Time-to-decision
- Compliance incidents
- Cost per transaction
Step 2: Build “Trusted Data Collaboration” as a Core Capability
The real bottleneck in AI is not models.
It is data access, governance, and liability.
Europe can become the global leader if it builds the world’s best datacollaboration infrastructure.
Practical actions:
Within 12 months
- Standard contracts for inter-company data sharing
- Industry data rooms for regulated sectors
- Federated learning frameworks for sensitive data
- Legal templates for AI liability and accountability
Within 24 months
- Sector-wide data ecosystems (e.g. insurance, healthcare)
- Cross-border EU/Swiss data collaboration frameworks
- Standard “AI audit trails” for enterprise systems
Goal:
Make Switzerland and Europe the easiest place in the world to deploy AI with sensitive data.
Step 3: Turn Regulation into a Competitive Product
Europe already has strong regulation.
But today, it slows down deployment.
It should instead become a productized advantage.
Positioning:
“If your AI works in Switzerland or the EU, it will work anywhere.”
Practical steps:
Create a European “AI Assurance Stack”
Standardized components:
- Model risk management frameworks
- Documentation templates
- Monitoring and logging standards
- Red-teaming protocols
- Incident response procedures
Introduce voluntary “High-Trust AI” labels
For sectors like:
- Healthcare
- Finance
- Critical infrastructure
- Public services
Companies should be able to obtain a recognized compliance certification within weeks, not years.
Step 4: Use Government Procurement as the Main Accelerator
In the US, the government is a major technology buyer.
In Europe, procurement is often slow, fragmented, and risk-averse.
This must change.
Within 6–12 months
- Pre-approved AI vendor frameworks
- Short procurement cycles for AI pilots
- Standard evaluation metrics
Within 24 months
- Outcome-based contracts
- National AI deployment programs
- Cross-agency AI platforms
Governments should pay for:
- Reduced waiting times
- Lower administrative costs
- Fewer compliance errors
- Faster processing
Not for:
- Slides
- Concepts
- Endless pilot projects
Step 5: Build a European “AI Scale-Up Engine”
Europe has strong research and many startups.
But it lacks global enterprise-scale AI companies.
To fix this:
Financial measures
- Growth-stage funds focused on applied AI
- Public-private scale-up vehicles
- Incentives for late-stage capital in Europe
Commercial measures
- Enterprise sales training programs
- Cross-border procurement access
- Partnerships with system integrators
The goal:
Create 20–30 European AI scale-ups in regulated industries within five years.
Step 6: Targeted Compute Sovereignty
Europe does not need to outbuild the US or China in compute.
But it must guarantee:
- Secure inference environments
- Data-sovereign AI for sensitive sectors
- Access to compute for strategic industries
Practical approach
- Swiss/EU-based secure inference zones
- Public-private compute partnerships
- Reserved capacity for research and critical sectors
Focus on availability and trust, not prestige supercomputers.
Step 7: Train Thousands of AI Deployment Professionals
AI leadership will not come from more PhDs alone.
It will come from:
- AI product owners
- MLOps engineers
- LLMOps specialists
- Data governance professionals
Within 24 months
- AI deployment programs in every large enterprise
- National training initiatives
- Industry-university rotation programs
- Fast-track visas for AI practitioners
Goal:
Train 10,000+ AI deployment professionals across Switzerland and Europe.
Step 8: Launch 10 National Lighthouse Projects
Every successful tech ecosystem has visible success stories.
Europe needs:
- Not more strategy papers
- But real, measurable deployments
Example lighthouse projects
- Insurance claims automation with audited accuracy
- Hospital scheduling with measurable waiting-time reduction
- Manufacturing yield optimization
- Banking compliance copilots
- Energy grid optimization systems
Each project should publish:
- Productivity gains
- Cost savings
- Error reduction
- Implementation time
These become global reference cases.
Step 9: Build the Brand: “High-Trust AI Made in Europe”
Europe cannot win the “fastest and cheapest” narrative.
But it can win the most trusted and enterprise-ready narrative.
Positioning:
European AI = secure, traceable, compliant, and economically proven.
Just as:
- Switzerland stands for precision
- Germany stands for engineering
- France stands for aerospace
Europe could stand for:
Trusted AI for the real economy.
The Three Moves That Matter Most (Next 18 Months)
If Switzerland and Europe only did three things, it should be these:
- Reform procurement to scale AI deployments quickly
- Create trusted data collaboration frameworks across industries
- Standardize audit-ready AI assurance as an exportable advantage
If those three are executed well, Europe could become the global leader in real-world AI applications, even without leading in foundation models.
Final Thought
The US may dominate AI models.
China may dominate AI infrastructure.
But Switzerland and Europe can dominate something more valuable:
AI that actually works in the real economy.
And that race is still wide open.
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