Comparative study of AI adoption strategy using Hofstede model between Kazakhstan and Uzbekistan

Artificial intelligence is no longer a distant concept reserved for science fiction; it is a practical tool reshaping how nations run their economies and deliver services. Yet, the path to adopting these technologies is not the same for every country. While some nations move quickly based on market forces, others rely heavily on government direction. To understand why, researchers often look at culture. One of the most established ways to measure cultural differences is through a framework developed by Geert Hofstede, which examines how societies handle power, risk, and group loyalty. For instance, in cultures with high "power distance," people generally accept that authority figures make the rules and expect to be told what to do. In cultures with high "uncertainty avoidance," people feel uncomfortable with ambiguity and prefer strict rules and proven methods over risky experiments. When these cultural traits meet the rapid rise of artificial intelligence, they create unique challenges and opportunities that go beyond simple economics or technology.

A recent study by researchers at Nazarbayev University and Astana IT University explores this exact intersection in Central Asia, comparing two of the region's largest economies: Kazakhstan and Uzbekistan. Both countries have launched ambitious national plans to integrate artificial intelligence into their public services and industries. However, the researchers found that while their goals are similar, their approaches are fundamentally different. By analyzing government reports, investment data, and cultural frameworks, the team discovered that Kazakhstan is building a foundation of hardware and digital infrastructure, whereas Uzbekistan is relying on strong, top-down government coordination to drive progress. The study suggests that neither approach is inherently superior, but both are deeply rooted in the specific cultural and institutional realities of each nation.

The researchers began by looking at the hard numbers. They examined how much each country has invested in technology, how many skilled workers they have, and how ready their governments are to use artificial intelligence. The data revealed a clear split. Kazakhstan has built a robust physical backbone for digital work. The country has established powerful supercomputing centers capable of processing massive amounts of data and has attracted significant private investment into its technology sector. In contrast, Uzbekistan has fewer of these physical resources and less private money flowing into tech startups. Instead, Uzbekistan has focused its energy on creating a unified national strategy. The government there has taken a lead role, setting strict timelines and legal frameworks to guide how artificial intelligence is used in public services, from healthcare to anti-corruption efforts.

This difference in strategy aligns closely with the cultural patterns the researchers observed. The study points to "power distance" and "uncertainty avoidance" as key drivers. In Uzbekistan, where there is a strong tendency to respect hierarchy and a desire to minimize risk, the government's centralized approach makes sense. People and businesses are more likely to adopt new technology if it comes with the clear backing of the state, which reduces the fear of making a wrong choice. The data supports this: Uzbekistan's government is highly rated for its readiness to manage artificial intelligence, but the private sector lags behind. Many small business owners in the country express fear that these new tools will replace their jobs, leading them to stick with traditional methods unless the government explicitly tells them to change.

Kazakhstan, on the other hand, shows a different pattern. While it also has a strong government role, its culture and economic structure have allowed for a more infrastructure-focused strategy. The country has invested heavily in the "plumbing" of the digital world—high-speed internet, data centers, and supercomputers. This approach assumes that if the tools are available and reliable, businesses and organizations will find ways to use them. The study found that Kazakhstan has a higher number of technology specialists per person and a larger share of its economy dedicated to information technology compared to its neighbor. However, even here, the private sector is not fully mature. The researchers noted that while the government has built the stage, the actors—the private companies—are still learning how to perform.

A critical finding of the study is that in both countries, the private sector is not yet driving the adoption of artificial intelligence. In neither nation did the researchers find a vibrant ecosystem where small businesses were independently experimenting with these technologies. Instead, progress is almost entirely dependent on government mandates and public-sector projects. The study suggests that this is not a failure of planning, but a reflection of the current reality. In transitional economies where trust in institutions is vital and the risks of new technology are felt deeply, the government must act as the primary engine for change. The researchers argue that trying to force a "bottom-up" approach, where individual companies lead the way, might fail because the cultural environment is not yet ready for that kind of independent risk-taking.

The researchers were careful to note that their work does not prove that culture is the only reason for these differences. They acknowledged that factors like available funding, education levels, and existing laws play a massive role. They also pointed out that their study relied on existing reports and data rather than new surveys or interviews, which means their conclusions are based on patterns they observed in the available information rather than direct testing of every hypothesis. Nevertheless, the alignment between the cultural framework and the observed strategies is striking. The study suggests that for artificial intelligence to truly take root in Central Asia, policies must be tailored to these specific cultural contexts. In Uzbekistan, this might mean continuing to use strong government leadership to build trust and reduce fear. In Kazakhstan, it might mean focusing on converting the country's strong technical infrastructure into practical tools that everyday businesses can use.

Ultimately, the paper offers a clear roadmap for understanding how technology spreads in different parts of the world. It challenges the idea that there is a single, universal way to adopt artificial intelligence. Instead, it shows that successful strategies must respect the local culture. In places where people look to authority for guidance and fear uncertainty, the government must lead the way. In places where technical capacity is high, the focus should be on building the tools that allow that capacity to flourish. For policymakers and business leaders in Central Asia, the message is that there is no one-size-fits-all solution. The path forward requires a deep understanding of the people and the institutions that will ultimately decide whether these new technologies become a part of daily life or remain a distant promise.