SAS surveyed 1,600 global decision makers about their plans to invest in and implement GenAI. They’re enthusiastic about the technology, but they face a number of obstacles from business strategy to data security and governance.

China Leads in Generative AI Adoption, Survey Reveals

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Generative AI is rapidly transforming industries worldwide, with China at the forefront, according to a recent global study by SAS and Coleman Parkes Research Ltd. The study highlights that 83% of Chinese organizations have adopted Generative AI (GenAI), outpacing the UK (70%), the US (65%), and Australia (63%).

Despite leading in adoption, the US is ahead in GenAI maturity, with 24% of organizations fully implementing the technology, compared to 19% in China and 11% in the UK.

The economic impact of AI, including GenAI, is significant. McKinsey estimates that GenAI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors, potentially boosting AI's overall economic influence by 15% to 40%.

SAS and Coleman Parkes surveyed 1,600 decision-makers across industries such as banking, insurance, public sector, healthcare, telecommunications, manufacturing, retail, energy, and professional services. The study aimed to understand the adoption and implementation of GenAI across these sectors.

Global Adoption Rates and Challenges

  • North America: 20% fully using and implementing GenAI.
  • APAC: 10% fully using and implementing GenAI.
  • LATAM: 8% fully using and implementing GenAI.
  • Northern Europe: 7% fully using and implementing GenAI.
  • South West and Eastern Europe: 7% fully using and implementing GenAI.

Regarding GenAI use policies, APAC leads with 71% of organizations implementing policies, followed by North America (63%), South West and Eastern Europe (60%), Northern Europe (58%), and LATAM (52%).

Despite high adoption rates, challenges persist. Only 9% of leaders are extremely familiar with their organization's GenAI strategy. Data privacy and security, regulatory compliance, and insufficient data for fine-tuning large language models (LLMs) are major concerns.

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