U.S. Leads the Global AI Race — But Widespread Adoption Faces Major Challenges, Says New Report
The global push toward artificial intelligence is gaining unprecedented momentum in 2025. Organizations across continents are rushing to adopt AI to drive competitiveness and transformation. But while boardroom ambition is high, new data suggests that internal alignment gaps may derail some efforts. The speed of AI deployment may no longer be the sole indicator of success; execution strength and data infrastructure are proving just as critical.
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Ambition vs. execution
The 2025 AI Space Race report by NetApp surveyed 800 CEOs and IT leaders across the United States, China, the United Kingdom, and India. It found that 81% of organizations are already piloting or scaling AI initiatives. Furthermore, 88% of global executives believe their companies are ready for AI transformation. However, the report warned that regional disparities and internal disconnects could shape the final outcome of this innovation race.
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In the United States, results showed a high level of internal alignment. Around 86% of IT executives and 77% of CEOs reported that their companies had active AI deployments. This near-unity between leadership and technical teams suggests that American firms may be better positioned to convert ambition into tangible outcomes.
China’s fast-paced push reveals a disconnect
In China, the pace of AI ambition is also rapid, especially at the executive level. According to the survey, 92% of Chinese CEOs claimed their organizations were actively pursuing AI. But only 74% of IT leaders within those same firms confirmed this view.
The disparity extends to perceptions of readiness 68% of Chinese CEOs said their companies were prepared for AI transformation, compared to just 58% of IT counterparts.This internal divergence, NetApp’s report stated, could undercut China’s efforts. When companies emphasize speed over foundational infrastructure, execution can falter. The report noted that without organizational unity, even the most ambitious AI strategies may suffer from delays, underperformance, or security challenges.
India and the UK
India and UK lag slightly behind the US and China in terms of current AI leadership. Yet both countries showed tighter alignment between executive ambition and IT readiness. This synchronicity could become a strategic advantage as these nations scale AI capabilities.
Indian and British respondents also expressed higher-than-average confidence in their future roles in global AI leadership. Around 40% of Indian leaders and 34% of UK leaders said they believed their regions would lead the AI space in the coming years. These figures significantly exceed the global average of 16% and 19%, respectively.
NetApp’s findings suggest that consistent alignment between strategic vision and technical deployment may enable India and the UK to bridge their current lag and play stronger roles going forward.
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The foundation of AI success
Across all regions, one common factor emerged as critical for sustainable AI deployment: intelligent data infrastructure. According to the report, companies that built secure, scalable architectures experienced fewer barriers during AI rollout. Executives in the US, UK, and India emphasized integration with core business systems as the most vital requirement for success.
China differed slightly in its priorities. There, scalability emerged as the top concern. About 35% of Chinese respondents cited scalability as the most crucial AI capability—a figure 11 percentage points higher than the global average. This focus reflects China’s urgency to implement AI across massive business operations quickly and effectively.
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Russell Fishman, senior director of product management at NetApp, stated that the organizations most likely to succeed would be those that “invest in secure, scalable data architecture that removes friction from AI deployment.”
Weak Data and Cloud Strategies
Despite optimism around AI adoption, most global leaders remain wary of execution pitfalls. According to NetApp, 79% of respondents voiced concern that inadequate data and cloud strategies could derail AI projects. These worries span issues from broken AI models to serious cybersecurity breaches.
The report noted that even companies with active deployments can face risk if their underlying data systems are not robust. Errors in integration, poor governance, or legacy infrastructure may slow adoption and reduce ROI. In some cases, these flaws may lead to failure of AI models that depend on real-time, accurate, and context-rich information.
Leaders across all surveyed regions recognized this challenge. Even those confident in their AI ambition acknowledged that operational success depends on foundational IT strength.
Aligning Leadership and Tech
The gap between executives and IT leaders—particularly in countries like China—reveals a potential obstacle for global AI transformation. Without alignment, miscommunication or mismatched timelines can compromise strategic outcomes.
On the contrary, in regions where leadership and technical gaps are narrow as in the US, the UK, and India, organizations can adapt faster and better to AI changing requirements. According to the report by NetApp, when there is better internal cohesion, it is easy to come up with a faster prototyping process, clearer timelines, and fewer implementation failures.
Integration between departments, ongoing training, and a shared understanding of both risks and rewards are essential components of successful AI transformation.
A Race With More Than One Finish Line
The AI race is no longer just about who moves fastest. According to NetApp’s research, the winners will be those who balance ambition with infrastructure, speed with stability, and leadership with cross-functional alignment.
There are regional disparities yet there are opportunities. Eighty-eight percent of the world moves toward a change as far as their firms are ready, and 81 percent of the global firms are already experimenting with AI, but they are facing the problem of implementation. Data protection, data platforms, and integration of internal strategies will probably lead to the number of companies that rise to the top in terms of leading AI in 2025 and beyond.
While the world is racing toward AI transformation, only those prepared for the complex realities of execution may reach the destination.
