
Every AI conversation seems to begin with the same question: what can AI do better than humans?
It is an understandable question since AI can now analyse information quickly, summarise long reports, generate first drafts, support customer service, and automate tasks that used to take hours. For many companies, the appeal is immediate. If a tool can help a team work faster, reduce repetitive work, and make better use of existing resources, it is difficult to ignore.
But I think there is another question we should be asking more often, especially in Southeast Asia: Who actually gets to benefit from this shift?
The current AI conversation often assumes that everyone starts from the same place. It assumes that workers have time to experiment with tools, businesses have budgets for training, and communities have equal access to digital infrastructure. In reality, the gap between those who are ready for AI and those who are not is still very visible.
This is where the discussion about human value becomes more interesting. The issue is not simply whether AI will replace certain tasks. It is whether we are building an AI economy where more people can meaningfully participate.
Human value is changing, but it is not disappearing
Much of the anxiety surrounding AI comes from the belief that machines are replacing human value. I understand where that concern comes from, but I do not think it tells the full story.
For a long time, many professional skills were built around access to information. People were valued for how quickly they could research, organise knowledge, analyse trends, or turn information into a useful output. Those skills still matter, but AI has changed the baseline. A first draft, a summary, or a basic analysis is no longer as difficult to produce as it once was. That does not mean human value has disappeared. It means the source of value is moving.
In an AI-enabled workplace, the people who stand out are often not the ones who can simply produce the most output. They are the ones who can ask better questions, understand context, make sound judgments, and connect technology to real human needs.
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AI can generate a list of ideas, but it cannot always know which idea is right for a specific market, community, or moment. It can analyse patterns, but it does not carry the lived experience needed to understand why people behave the way they do. It can help optimise a process, but humans still need to decide what kind of outcome is worth optimising for.
This is why I do not see the future of work as a simple story of humans versus machines. It is more likely to become a story of who can use machines with enough judgment, empathy, and responsibility.
The real divide is access, not interest
In Southeast Asia, interest in AI is not the problem. Many people and businesses are curious about it. However, the harder question is whether they have the same opportunity to learn, test, and apply it.
The World Economic Forum’s Future of Jobs 2025 coverage on Southeast Asia notes that digital skills are becoming more important for companies across the region, but many employers still see significant gaps. Upskilling and reskilling are becoming priorities because the pace of change is already affecting what businesses need from their teams.
This matches what many of us are seeing on the ground. Larger companies can invest in AI tools, internal training, consultants, and structured experimentation. Smaller companies often have to make do with limited time, limited budget, and limited guidance.
For workers, the difference can be just as stark. Someone in a major city with strong internet access, an English-language education, and exposure to global tools may find it easier to learn AI. While a frontline worker, informal worker, or small business owner in a less connected area may not have the same starting point.
The risk is that AI becomes another layer of advantage for people and organisations that already have access to capital, infrastructure, and education.
Southeast Asia needs inclusive AI growth, not just faster AI adoption
The region’s digital economy is still growing quickly. The e-Conomy SEA 2025 report says Southeast Asia’s digital economy has grown from US$40 billion in GMV a decade ago to more than US$300 billion in 2025.
Indonesia is a useful example of why inclusion matters in this conversation. MDI Ventures’ recent white paper, Catalysing Digital Resilience and Sustainable Growth: Advancing Inclusive Innovation and AI-Driven Impact Across Indonesia’s Digital Economy, notes that the country has around 65 million MSMEs, contributing 60.5 per cent to GDP and absorbing 96.5 per cent of the national workforce. It also points out that Indonesia’s digital economy is projected to reach between US$180 billion and US$340 billion by 2030, while many small businesses still face challenges in financing access, digital infrastructure, cybersecurity, and AI readiness.
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That context matters because Indonesia’s digital economy cannot be considered truly strong if its smaller businesses are left behind. Growth may happen at the top, but resilience depends on whether the broader business ecosystem can participate.
This is where AI should be seen as more than a productivity tool. If applied well, it can support better credit scoring, improve access to digital financial services, strengthen cybersecurity, and help small businesses operate with more confidence. But these benefits will not spread automatically. They need infrastructure, trust, relevant products, and patient ecosystem-building.
The MDI white paper makes this point indirectly through its focus on impact capital, digital trust, AI, cybersecurity, and inclusive digital infrastructure. Its portfolio examples, including Amartha, Qoala, Privy, and CYFIRMA, show how technology can support access, protection, identity, and trust within the wider digital economy.
We should also think about how people learn
There is another part of this shift that deserves more attention. As companies automate more entry-level tasks, we may accidentally weaken the pathways that help people build experience.
Many junior roles are built on tasks that are not glamorous but are deeply educational. Writing meeting notes, preparing research, drafting reports, checking details, and supporting senior colleagues are often how people learn how an industry works. These tasks teach judgment slowly. They expose people to context, mistakes, client expectations, and decision-making.
If AI takes over too much of that early work without a replacement learning path, companies may solve one efficiency problem while creating a future talent problem.
This is why the talent conversation should not stop at whether people know how to use AI tools. The deeper question is how quickly people can keep learning as the nature of work changes. LinkedIn estimates that 70 per cent of the skills used in most jobs will change by 2030, while PwC’s 2025 Global AI Jobs Barometer found that workers with AI skills command a 56 per cent wage premium. This suggests that AI is not simply reducing the value of human talent. It is raising the value of people who can keep adapting.
For organisations, the risk is that workers who already have access to training, tools, and experimentation time will move further ahead, while those without that access fall behind. This does not mean companies should avoid automation. It means they need to be more intentional about learning.
If AI handles the first draft, junior employees still need to learn how to evaluate that draft. If AI summarises research, people still need to learn how to question the source, spot missing context, and decide what matters. If AI supports execution, teams still need to teach accountability, communication, and ethical judgment.
AI can speed up work, but it should not remove the process through which people become thoughtful professionals.
Great talent now looks different
This also changes what we should look for in talent. A few years ago, the strongest candidate might have been the person with the most polished technical skills or the most impressive credentials. Those things still have value, but they are no longer enough on their own.
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In an AI-enabled environment, I would pay closer attention to curiosity, adaptability, clarity of thinking, and the ability to work with ambiguity. I would also look for people who know how to use AI without outsourcing their judgment to it.
That last part matters. There is a difference between someone who uses AI to think better and someone who uses AI to avoid thinking. The first person becomes more capable. The second person becomes more dependent.
This is why AI literacy should not be treated as a narrow technical skill. It is becoming part of how people communicate, analyse, make decisions, and build trust. The strongest professionals will be those who can combine technological fluency with human understanding.
The future of AI should be measured by who gets included
Many businesses are asking how AI can help them do more with fewer people. That is a practical question, and it will not disappear.
But I hope more leaders also ask a broader question: how can AI help more people contribute?
That question leads to a different set of priorities. It pushes organisations to invest in training beyond senior teams. It encourages businesses to think about frontline workers, small merchants, regional entrepreneurs, and communities that may not be first in line for new technology.
Southeast Asia’s future growth will depend not only on how quickly AI is adopted, but on how widely its benefits are shared. If smaller businesses, young workers, and underserved communities are left behind, the digital economy may become more advanced without becoming more resilient. That would be a loss for everyone.
In the end, the most important human contribution in an AI-powered world may not be competing with machines. It may be making sure the future we build with them still works for more humans.
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The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.
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