The AI race belongs to those who deploy it quickly: Opinion

Source: Straits Times
Article Date: 19 Oct 2026

Regional execution is showing that deploying intelligence matters far more than inventing it.

Anyone who wonders what impact artificial intelligence has probably doesn’t live in Asia.

In India, a digital platform called BHASHINI allows citizens to access government services in more than 22 local languages using multilingual AI. In Japan, civil servants across government ministries began deploying generative AI tools in 2026 to streamline daily administration.

And in Singapore, over 70 corporate AI Centres of Excellence have been set up to embed AI in healthcare, finance and logistics.

These cases share a common thread: They are not about creating the world’s most powerful frontier model, but involve embedding existing, increasingly accessible intelligence directly in everyday public and economic institutions.

Singapore offers a glimpse of what successful AI deployment can look like. In healthcare, SingHealth’s Note Buddy was already assisting more than 2,100 healthcare workers by mid-2025 and had produced over 16,000 clinical and administrative notes – figures that represent hours of administrative work taken off clinicians’ desks and returned to patient care.

The impact of AI

For many Asian economies, these should be encouraging signals. Every week brings a new prediction that AI models are improving at a remarkable pace.

In contrast, across the globe, governments are treating AI as a top strategic priority. Yet most of the debate remains framed as a race: Who will build the most powerful model? Who will dominate computing power? Who will win the AI century?

Much of the underlying assumption is identical: Whoever gets to the technological frontier first wins. From Asia’s experience, however, that may be the wrong way to think about it.

The more important challenge is not building the smartest AI. It is figuring out how to use increasingly abundant intelligence well and embedding that intelligence in everyday business operations.

Asia risks spending billions competing for benchmark intelligence when the real challenge is learning how to reorganise around AI.

We have seen this paradigm before. At the start of the personal computer revolution decades ago, a PC cost as much as a car and they were largely confined to governments, universities and large corporations. Today, powerful computing sits in almost every home and office. The advantage no longer comes from owning a computer. It comes from using it better than everyone else.

Implementation is strategy

Why does execution matter more? Because two companies can have access to the same models and yet produce vastly different results. One becomes significantly more productive, while the other experiences only minor operational gains.

The cause of this difference is almost never the technology itself, but the organisation using it.

The banking sector suggests that the real race is no longer for the best model, but for the most effective way to generate returns.

JPMorgan Chase has rolled out its LLM Suite to more than 200,000 employees. Revolut uses AI to assist customers with routine financial tasks, while HSBC is investing in new AI capabilities in Singapore across businesses ranging from wealth management to payments.

Access to advanced models is becoming increasingly widespread and what now separates leaders from followers is the ability to turn AI use into a daily habit that aids customers and removes friction.

For most organisations, the bottleneck is the ability to restructure jobs, decisions and information flows.

Competitiveness will depend less on model benchmarks and more on adoption, organisational learning and institutional adaptation. This shift could potentially give Asian economies an edge beyond conventional AI rankings.

Deployment, not invention

To be sure, regional governments here are already investing heavily in sovereign models, domestic computing and national AI champions. While strategically critical, these efforts may matter less economically than integrating practical AI into the broad base of companies, hospitals, factories, schools, ports and civil service agencies.

Responsibility is also emerging as an issue in AI deployment as questions are raised over how to use machines to improve decisions while keeping people accountable for them. Yet such issues need not be a stumbling block, as recent examples show.

In healthcare, AI can help flag abnormalities in mammograms, but doctors remain responsible for diagnosis and treatment.

Organisations that get this balance right will learn faster, adopt more confidently and make better use of the technology.

The most important divide of the 2030s may not be between countries that possess cutting-edge AI models and those that do not. As models become cheaper and widely available, the gap between tech haves and have-nots could gradually narrow.

The larger divide will emerge between organisations that learn to work effectively with AI and those that fail to adapt their structures, processes and workforce.

Most Asian countries can benefit immensely from AI without winning the frontier model race. They need infrastructure and skills to help workers and organisations adopt the best available tools.

The greatest beneficiaries will be countries that make AI affordable, trustworthy and easy to use through targeted investments in workforce training, governance and digital infrastructure.

The prospects here are strong. Anthropic’s decision to open its fifth Asia-Pacific office in Singapore reflects growing demand for AI tools, especially in highly regulated sectors. OpenAI and Google DeepMind have also deepened their presence in the city-state.

Asia’s history shows its greatest economic successes came from adopting and spreading powerful technologies at scale, not inventing them. The biggest winners will not be those building the smartest models, but those making AI useful to millions of ordinary workers, teachers, nurses, engineers and civil servants.

Bruno S. Sergi is an instructor at the Harvard Division of Continuing Education and editor-in-chief of the Cambridge Elements in the Economics of Emerging Markets at Cambridge University Press.