Think locally when planning globally with AI

To implement AI in a way that is sustainable and beneficial for all, leaders must account for the needs, norms and tech readiness levels of local cultures.

As I’m fond of telling people, I have one of the best jobs in the world. In my travels throughout Australia, New Zealand, Greater China, ASEAN, Japan and India, I experience the richness and diversity of these countries and cultures, with environments as different from one another as they are beautiful.

Something that’s often overlooked is how greatly attitudes toward technology differ across these regions. When analyzing the growth of artificial intelligence, and of generative AI in particular, it’s easy to get caught up in monocultural thinking. It can seem as though everybody is adopting generative AI or laying the groundwork for adoption. Especially for those of us working in technology, the pressing questions seem to focus on implementation, risk and benefits.

But this thinking can be harmful. Before rushing headlong toward an AI-driven future, leaders must pause to consider how adoption will benefit or damage communities. (This map, while flawed, illustrates the different levels of interest in generative AI in various countries.)

Failure to account for cultural differences could set back or destroy AI initiatives and erode trust citizens place in businesses and other institutions. By contrast, thoughtful adoption will allow for variations across communities and industries. Ultimately, what matters most is how we navigate the convergence of industry, technology and humanity to achieve optimal outcomes.

States of readiness for AI

Across the Asia-Pacific-Japan region, we see very different starting points and attitudes toward AI adoption. Here are the major categories:

• Fast-paced adopters are markets and industries with a high level of technology maturity. An example is Japan, which is known both for eager adoption of technology and for its growing technology sector. Japan’s aging population and resulting worker shortage makes AI a necessity there. As one executive told me, “We are used to seeing cartoon versions of ourselves, to living in a virtual world. AI is our buddy, and we will move fast.”
  
  Whether out of necessity or cost reduction pressures, these markets (or sectors within them) are quick to adopt. Fast-paced adopters benefit from having the discipline to build business cases to underpin their AI adoption and ensure they are broader-reaching in their assessment of risks and benefits.
  
  
• Reluctant experimenters are markets and industries where technology solutions and infrastructure are mature, but there is hesitancy to adopt AI. Here we see more skepticism about AI due to concerns about ethics, trust, privacy and security.
  
  Australia, where I live, falls into this category—many leaders are now learning the basics of generative AI. Recently, the nation has experienced high-profile cyber breaches that have eroded consumer trust and caused boards and senior executives to think seriously about how they are managing business risk and exposure.
  
  Concerns about those risks and general fears about AI are slowing adoption, especially compared with fast movers. In countries that fall into this category, business cases are relevant—but they need to be accompanied by robust risk assessments.
  
  
• Fast-advancing developing countries have significant technology maturity. India is at the top of this list. To understand India’s situation, it’s instructive to look at China’s tech adoption in recent decades, as it shifted from a rural, agrarian economy to one driven by technology and social media. In my 20s, I traveled in China when it was a sea of bicycles and bells. The change there has been mind-boggling—and you can think of India’s development with embracing AI as a sort of turbocharged China.
  
  It's difficult to predict the resulting social impact. India has a labor-based economy. But while the country is incredibly adept at turning out highly skilled engineers, what will happen to the non-engineers, to the people whose jobs are automated? Fast-advancing countries like India need to determine both what they want to achieve and what they want to avoid to make the best adoption and regulatory decisions.
  
  
• Emerging countries whose tech maturity is evolving is another sizable category. Because these nations may be just starting to use cloud computing at scale, access to the data that feeds AI is in its early stages. Countries in this category need to consider how they can advance and compete with these limitations, with a focus on investing in both the technology foundation and the skills to needed to support future advances.
  
  Emerging countries with a small number of employment or technology hubs will likely see young people move to these hubs. This will change both the fabric of families and the character of rural, agricultural regions. New construction will be needed in cities to serve the needs of the new residents. In time, a middle class will emerge, as will businesses to serve the needs of those citizens. All these factors must be carefully considered by businesses and governments.
  
  Sustainable technology is another regional and national consideration that lives upstream of AI. Infrastructure needs to expand with processing power and data requirements—which are notoriously prodigious where AI is concerned. This is crucial for emerging countries, in which the potential infrastructure build-out must be examined through the lens of sustainability and overall benefit (or detriment) to the community.

Big questions about the big picture

Rather than seeing generative AI as a worldwide fait accompli, we should consider the many implications that will play out according to a country’s character, traditions, and infrastructure.

The good, and perhaps ironic, news is that the very technology under discussion is sufficiently advanced to address these questions. AI allows us to crunch enough data and variables to grasp virtually all ramifications of its implementation. Using that unprecedented quantity of information, leaders can make plans to consider productivity, sustainability, transport, demographics and other factors before rushing forward.

As leaders, we need to explore how to guide and advise in these environments. The first step is to recognize that AI adoption, like technology adoption in general, is not equal across the globe. Each government and business must develop a conscious view of their intent across tech advancement, economic prosperity, trust, responsibility and social impact.

To learn more, follow us at the World Economic Forum Annual Meeting at Davos or visit the Generative AI section of our website.