Why we must think locally when planning globally with AI  

Wall map with pins in the South-East Asia region: Failure to account for cultural differences could diminish AI initiatives and public trust.

Failure to account for cultural differences could diminish AI initiatives and public trust. Image: Unsplash/Z

Jane Livesey
Member, Global Executive Committee; Head, Asia-Pacific and Japan, Cognizant
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Artificial Intelligence

  • Implementing generative artificial intelligence carries the risk of monocultural thinking when regions and countries differ greatly in how they approach the technology.
  • Governments and businesses could sacrifice public trust in AI if they rush into an AI-driven future without considering differing risks and benefits to communities and industries, cultural differences and needs.
  • A necessary and conscious strategy to AI will seek to balance technological advancement with economic prosperity, trust, responsibility and social impact.

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, states in the Association of Southeast Asian Nations, 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 often overlooked is how greatly attitudes toward technology differ across these regions. When analyzing the growth of artificial intelligence (AI) and generative AI (genAI) in particular, it’s easy to get caught up in monocultural thinking. It seems that everybody is adopting genAI or laying the groundwork for adoption. Especially for those 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.

The map shows different levels of interest in genAI across the world.
The map shows different levels of interest in genAI across the world. Image: Electronics Hub

Failure to account for cultural differences could set back or destroy AI initiatives and erode citizens’ trust 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 region, we see very different starting points and attitudes toward AI adoption. Here are the major categories:


How is the World Economic Forum creating guardrails for Artificial Intelligence?

Fast-paced adopters

These are markets and industries with a high level of technology maturity. An example is Japan, known for its eager adoption of technology and growing technology sector. Japan’s ageing population and resulting worker shortage make 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

Markets and industries where technology solutions and infrastructure are mature but there is hesitancy to adopt AI can be where there is more scepticism about AI due to ethics, trust, privacy and security concerns.

Australia, where I live, falls into this category – many leaders are only now learning the basics of genAI. 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. Business cases are relevant in countries that fall into this category but need to be accompanied by robust risk assessments.

Fast-advancing developing countries

India comes to the top of the list when it comes to countries that have significant technology maturity. 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 travelled 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 labour-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 such as India must determine what they want to achieve and avoid to make the best adoption and regulatory decisions.

Emerging countries

Countries whose tech maturity is evolving are another sizeable 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 must consider how they can advance and compete with these limitations, focusing on investing in the technology foundation and the skills required to support future advances.

Emerging countries with few employment or technology hubs will likely see young people move to these hubs. This migration will change 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. Businesses and governments must carefully consider all these factors.

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. That is crucial for emerging countries, where the potential infrastructure build-out must be examined through the lens of sustainability and overall benefit or detriment to the community.

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Big questions about the big picture

Rather than seeing genAI 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 – although most models are limited by their basis on Western data. Using that unprecedented quantity of information, leaders can make plans to consider productivity, sustainability, transport, demographics, emerging regulations 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.

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