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Competitive Switzerland: Playing the long game: Can Switzerland lead the way in generative AI? | Accenture

Artificial Intelligence | Switzerland

Executive Summary

Opportunity Amid Disruption

Half of Swiss executives feel their companies are well-equipped to capitalize on opportunities from technological disruptions, with generative AI (gen AI) being a key driver. Research shows Switzerland ranks third globally in terms of the impact of generative AI on work time. This technology has the potential to significantly enhance the Swiss economy, potentially adding CHF 92 billion in economic value by 2030 under a “people-centric” scenario.

A People-Centric Approach for Switzerland

To fully leverage this economic potential, Switzerland’s workforce must be prepared. Accenture’s analysis shows that 45% of work time in Switzerland is likely to be impacted by generative AI. This shift is seen not as a threat but as a chance to boost productivity, especially in the financial services sector.

Revenue Growth Over Productivity Gains

Generative AI offers more than just productivity improvements. In Switzerland, 91% of executives believe that gen AI will have a greater impact on revenue growth than on cost reduction. This optimistic outlook is supported by the proactive measures taken by companies like Helvetia, Roche, Novartis, Givaudan, ABB, and Swisscom.

Switzerland is recognized as a top innovative country, ranking first in the WIPO Global Innovation Index for the past 13 years. It also leads the INSEAD Global Talent Competitiveness Index, holding the top spot for the last decade.

Challenges to Overcome

For Switzerland to become a global leader in generative AI, it must address challenges in enterprise adoption, workforce readiness, and regulatory frameworks. While top Swiss companies have room to expand their AI usage, only a small percentage are currently scaling gen AI initiatives, and progress is slower compared to global peers. Swiss workers are open to generative AI, valuing its potential, and willing to learn new skills. However, they remain cautious about job security, work quality, and overall well-being.

Global focus on AI regulation has surged in the last decade, and Swiss policies largely align with OECD principles, though not entirely. This has sparked an ongoing public debate within the federal parliament, evidenced by frequent discussions on generative AI from 2019 to February 2024.

Action Points for Swiss Companies

Swiss companies should focus on five key imperatives to scale generative AI across their organizations: leading with value, developing a secure AI-enabled digital core, reinventing talent and work practices, closing the gap on responsible AI, and driving continuous reinvention.

Recommendations for Swiss Policymakers

Building on its strengths, Switzerland can capture the full benefits of generative AI by defining a strategic vision, fostering international collaboration, improving role transition mechanisms, strengthening dialogue and oversight on AI, and promoting generative AI literacy in society.

Unlock insights into Switzerland’s competitive edge. Continue reading and download the full report for strategic recommendations.

Related report: Retail reinvented: Unleashing the power of generative AI | Accenture

The Table of Contents of “Competitive Switzerland: Playing the long game: Can Switzerland lead the way in generative AI?” Report:

  • Executive Summary
  • 01 | Generative AI Can Have a Profound Impact on the Swiss Workforce and Economy
  • 02 | Barriers to Unlocking the Full Potential of generative AI
  • 03 | Final Considerations
  • Methodology Appendix
  • References

Number of Pages:

  • 51 pages

Pricing: 

  • Free

Methodology

To assess the impact of generative AI on labor productivity, a study analyzed data from the U.S. Bureau of Labor Statistics and O*NET, covering over 19,000 tasks across 900 job families in 19 industries. Tasks were evaluated for their relevance to large language models (LLMs) based on language use and the necessity for human involvement. A model was created to estimate productivity gains by assigning time savings to tasks using experimental evidence and regression analysis.

To simulate the economic impact of generative AI on GDP growth, the study developed a predictive machine learning model based on U.S. labor data, examining transitions between occupations. Three scenarios were created—Aggressive, Cautious, and People-centric—each with different assumptions about AI adoption rates and labor market adjustments. The analysis predicted changes in the labor force, wage bill variations, and subsequent economic growth under these scenarios, using data from the Federal Swiss Statistical Office and Oxford Economics.

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