Leveraging Artificial Intelligence for Sustainable Economic Growth: Lessons from the United States and China to Address the UK’s Economic Challenges

Thomas Kiu & Jason Cheng-Cheng Yang

Journal of Knowledge Management Practice2025https://doi.org/10.62477/jkmp.v25i3.521article
ABDC C
Weight
0.41

What the paper says

The United Kingdom (UK) has historically been a pivotal player in global economic development, excelling in sectors such as finance, insurance, and professional services. However, the past decade has presented significant challenges, including the outflow of skilled professionals, the aftermath of the 2008 global financial crisis, the economic impacts of the COVID-19 pandemic, and the complexities introduced by Brexit. These factors have collectively hindered the UK's economic growth and stability. This article aims to explore best practices and innovative strategies from global economic leaders, particularly the United States and China, to address these challenges and enhance the UK's competitiveness. The methodology involves desk research. Section 1 revisits the challenges faced by the UK economy, while Section 2 evaluates the economic strategies of the United States and China, focusing on their AI sectors. The article concludes by identifying six key areas where the UK should leverage AI to navigate the evolving economic landscape and foster sustainable development. By drawing on global insights, this study provides actionable approaches for UK policymakers to enhance long-term sustainable growth and competitiveness.

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https://doi.org/https://doi.org/10.62477/jkmp.v25i3.521

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@article{thomas2025,
  title        = {{Leveraging Artificial Intelligence for Sustainable Economic Growth: Lessons from the United States and China to Address the UK’s Economic Challenges}},
  author       = {Thomas Kiu & Jason Cheng-Cheng Yang},
  journal      = {Journal of Knowledge Management Practice},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.62477/jkmp.v25i3.521},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.