Artificial intelligence is becoming easier for businesses across Asia to access. From healthcare and manufacturing to marketing and customer service, companies can now use tools that once required substantial investment and specialised technical expertise.
But easier access does not necessarily mean equal opportunity.
For years, discussions about the technological divide have largely focused on differences between wealthier and less-developed countries. As cloud-based AI services become more widely available, that picture is becoming more complicated.
Businesses operating in very different markets can increasingly access the same off-the-shelf tools. Yet their ability to use those tools effectively can vary significantly.
The next AI divide in Asia may therefore emerge not only between countries, but also between companies within the same market.
The distinction is increasingly about more than who can access AI. It is about who can turn that access into measurable business value.
Highlights
AI Access Is Becoming Easier, but Is Adoption Keeping Up?
Cloud-based tools, application programming interfaces (APIs) and AI-as-a-service platforms have made AI capabilities available to businesses that previously lacked the resources to develop them independently.
Small businesses can now use these tools for marketing strategies, basic accounting, automated customer responses, simple software fixes, graphic design and routine operational tasks.
AI agents can also support day-to-day business processes around the clock, potentially reducing the time and staffing resources required for certain activities.
As these technologies become more widely available, a company’s initial capital and geographic location may no longer be the only factors determining its ability to benefit from AI.
How effectively it implements the technology is becoming increasingly important.
The Gap Between Access and Value Creation
Pradeep Saini, Co-Founder and CEO of ZeProp, believes the biggest change is how quickly smaller businesses can begin using AI.
“What has changed most is the speed at which a smaller company can start using AI. A few years ago, it needed significant investment and specialised teams. Today, cloud platforms and APIs allow a startup to automate customer interactions, documents, insights and routine decisions quite quickly. But the real value comes from applying AI to a genuine business problem, not simply adding another tool.”
Saini, a former COO and CTO with global technology-services leadership experience across IBM, Wipro, Hewlett Packard/EDS and Siemens Nixdorf, highlights an important distinction between adopting technology and creating value from it.
Lower barriers to entry do not automatically guarantee successful implementation.
Haojun See, Founder of Singapore-based AI adoption agency On The Ground (OTG), sees this challenge regularly while working with small and medium-sized enterprises (SMEs).
“They have, in the sense that AI is cheaper and more accessible than ever. But access isn’t the same as adoption. At On The Ground, the AI adoption agency I run here in Singapore, we work with SMEs across legal, HR, and non-profits. We constantly see that smaller businesses can’t assimilate new AI tools easily — not because the tools are bad, but because proper implementation takes time and resources they often lack. So the barrier to entry has dropped, but many SMEs hit a wall right after that.”
His observation points to a growing challenge for smaller businesses: gaining access to AI is only the first step. Integrating it into everyday operations requires additional resources, planning and organisational readiness.
From AI Access to Competitive Advantage
Having a subscription to an off-the-shelf AI tool is somewhat like renting a house and moving into it.
It can serve its purpose, but users must work within its existing design, features and limitations. It may not fully match their specific requirements.
However, gaining a competitive advantage does not necessarily require a company to build its own AI system.
The advantage can come from what a business adds around widely available technology: proprietary data, internal knowledge, tailored workflows, integration with existing processes and the ability to execute effectively.
Some companies may choose to develop proprietary systems when the business case supports that investment. Such systems can be tailored more closely to their operations and may create opportunities for continuous improvement through internal feedback.
But simply owning a proprietary AI model does not guarantee an advantage.
What matters is how effectively companies combine technology with their own data, processes and customer knowledge.
The Importance of Organizational Foundations
See argues that many smaller businesses struggle with the organisational foundations needed to make this possible.
“The biggest barrier isn’t really about AI at all — it’s that most smaller businesses aren’t clear about their own workflows, and don’t know exactly where all their data lives. That’s what stops basic AI access from becoming a real operational advantage. This is actually the exact sequence we take clients through at OTG: data migration and clean-up first (unglamorous, doesn’t sound very AI, but it’s the necessary foundation), then map out the major workflows, then prioritise which parts are worth automating — and only then have the real conversation about how AI helps. Skip that sequence, and most SMEs end up bolting AI onto messy processes, which doesn’t work.”
His approach suggests that effective AI adoption often begins with work that has little to do with AI itself.
Businesses must first understand their data, identify inefficiencies and establish which processes would genuinely benefit from automation.
Saini similarly cautions against treating AI strategy as a simple choice between purchasing existing software and building proprietary technology.
“In my experience, it is not a pure build-versus-buy decision. Strong organisations use standard software where it makes sense, but keep ownership of the data, workflows and customer experience that truly differentiate them. That requires clear leadership, disciplined data practices and an understanding of where technology can create a lasting advantage.”
Together, their perspectives suggest that competitive differentiation depends less on whether businesses use the same AI tools and more on how those tools are embedded within their operations.
Singapore and India Reveal the Gap Between AI Adoption and Capability
The emerging divide becomes clearer when examining how businesses are adopting AI in different Asian markets.
Singapore and India offer two useful examples.
Singapore: AI adoption is rising, but larger enterprises remain ahead
According to Singapore’s Infocomm Media Development Authority (IMDA), AI adoption among SMEs increased from 4.2% to 14.5% within one year.
The same figures show that adoption among larger enterprises reached 62.5%.
These numbers illustrate two developments occurring simultaneously.
AI is becoming more accessible to smaller businesses, but larger enterprises continue to demonstrate substantially higher levels of adoption.
Access to technology, therefore, does not necessarily translate into comparable implementation across businesses.
India: Smaller businesses face different barriers to AI maturity
India presents a different commercial environment but a similar pattern.
Findings from the NASSCOM–EY AI Adoption Index indicate that larger, technology-driven companies are progressing towards higher levels of AI maturity.
Meanwhile, micro, small and medium-sized enterprises (MSMEs) face distinct challenges, particularly in manufacturing.
The research indicates that AI adoption among manufacturing SMEs remains below 25%, with established organisational structures and perceptions of cost contributing to slower uptake.
At the same time, smaller Indian businesses are using affordable AI tools to reduce customer acquisition costs.
These developments show that AI adoption is not a uniform process, even within the same national economy.
Some businesses are finding practical ways to benefit from accessible tools, while others face challenges related to cost, organisational readiness and implementation.
The Singapore and India examples do not suggest that differences between countries have disappeared.
Rather, they highlight an additional dimension of the AI divide: companies operating within the same market can develop very different levels of technological capability.
Why the Next AI Divide Will Be About Execution
Discussions about artificial intelligence often focus on geopolitical competition and the technological ambitions of major global powers.
These issues remain important. Access to advanced computing infrastructure, investment and technical expertise continues to differ across countries.
However, examining Asia’s business landscape reveals another layer of competition.
As affordable software-as-a-service (SaaS) products, cloud APIs and off-the-shelf AI tools become more widely available, businesses face a different question.
What happens after they gain access?
The experiences shared by Saini and See suggest that successful adoption depends on several interconnected capabilities: organising data, understanding workflows, integrating technology into daily operations and identifying where AI can create genuine business value.
The evidence from Singapore and India reinforces this distinction.
While AI adoption is expanding, businesses differ considerably in their readiness and ability to use the technology effectively.
For companies across Asia, the challenge is no longer simply whether they can obtain AI tools. It is whether they have the organisational foundations to make those tools useful.
Geography still matters, and differences between national economies remain significant.
But access to the same technology does not guarantee the same results.
Asia’s next AI divide may increasingly be shaped by the companies that know how to turn accessible technology into lasting operational advantages – and those that are still struggling to move beyond access.
Editor’s Note
This article was contributed by NewInAsia Editorial Fellow Musfiq Mahmud, a journalist and digital media enthusiast from Bangladesh with interests in technology, international affairs, communications, media literacy and fact-checking.
In this piece, he explores how Asia’s emerging AI divide may increasingly be shaped by differences between businesses rather than national borders alone. Drawing on insights from technology leaders and AI adoption data from Singapore and India, he examines why access to artificial intelligence does not automatically translate into competitive advantage – and why data readiness, organisational processes and execution matter.
To pitch your story or share your perspectives on artificial intelligence, emerging technologies, digital transformation and business innovation across Asia, contact the NewInAsia editorial team.
Sources & References
Research and industry reports
- Infocomm Media Development Authority (IMDA) – Singapore Digital Economy Report 2025. Provides Singapore’s 2023–2024 AI adoption figures for SMEs and non-SMEs.
- EY–NASSCOM – AI Adoption Index: Is AI Still Incubating in Your Organisation or Driving Innovation?. Examines AI maturity, adoption barriers and organisational readiness across Indian industries.
Highlights
Read the Chinese article here.
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