Not everything needs to be automated. Not every process is better because it’s faster. We have to be brave enough to ask: is this what good work looks like? And, is this what we want progress to feel like?
That kind of reflection is not a soft skill. It’s a leadership skill. And it’s what will set resilient organisations apart in the next decade.
Our wisdom about technology has never come solely from Silicon Valley. It has emerged from kampungs and callejones, from night markets and gaming cafés, from shared family phones and gig economy hacks. We already know how to remix, adapt, question, and make things our own.
So as AI reshapes what work looks like, maybe our goal isn’t to pick a side—to work with or without it—but to hold space for complexity. To recognise that we may need to do both, or neither, depending on the situation.
A region in flux, and not just digitally
We’re used to navigating multiple truths at once. A GoJek rider in Jakarta might stream TikToks between gigs, using the same phone to track his mother’s blood pressure remotely via an AI-powered app. A sari-sari store owner in Cebu might use a chatbot to reorder stock, while still tracking sales manually in a notebook. In Phnom Penh, factory workers are now operating alongside semi-automated conveyor belts, even as their wages remain flat.
Also Read: Are you a human resource?
This is a region that moves fluidly between high-tech and low-tech, tradition and reinvention. So why would our response to AI be any different?
To ask “should we train for jobs AI can’t do?” or “should we learn to work alongside it?” implies a clear line between human and machine. But in practice, that line is often blurred.
Human jobs? Or human judgment?
There are jobs AI can’t do—at least not well. Emotional care work, creative direction, conflict mediation. A machine can write a poem, but can it feel the pulse of a nation in protest? It can generate a sympathy message, but can it sit beside a grieving mother and know when to stay silent?
These are deeply human tasks. But let’s not idealise them.
Not every job left behind by AI will be meaningful or well-paying. A lot of what remains will still be hard, under-appreciated labor. In many cases, the more “human” the work, the less valued it tends to be—especially in lower-income parts of our region. Are we ready to address that?
At the same time, learning to “work with AI” isn’t as simple as handing out prompt engineering guides. Real collaboration requires something deeper: judgment.
Can a teacher tell when an AI-generated test has biases built in? Can a logistics manager see when the optimisation algorithm is shortchanging rural routes because it doesn’t see their worth? Can a startup founder ask: who trained this model, and in whose language? These are skills of interpretation, of contextual reading—of power, not just productivity.
The most important skill might be asking better questions
Up-skilling shouldn’t just mean learning Python or how to use Midjourney. It should mean asking: “What’s at stake in how this AI tool is used?”
Also Read: The perfect storm: Jobs plunge, tariffs hit, and crypto volatility soars
For people leaders, HR professionals, policymakers, and educators in Southeast Asia, this means rethinking how we talk about readiness. It’s about seeing systems: to understand where power lies in algorithmic decisions, to recognise how AI might amplify inequality if left unchecked, to bring lived experience into technology design.
What we need is not just skills to work with AI or outside of it. We need a mindset that is:
- Curious about how AI works,
- Skeptical about where it fails,
- Empowered to push back when it doesn’t serve people,
- And imaginative about how to build better tools that reflect our values and cultures.
This is not an easy thing to teach. But it may be the most essential thing to learn.
Because the future of work won’t be written only in code. It will be written in culture. And culture, thankfully, is something Southeast Asia knows how to build.
—
Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.
Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.
Image courtesy: DALL-E
The post Up-skilling for AI: Train for what machines can’t do or learn to work with them? appeared first on e27.