AI skills have shifted from specialist niches into mainstream hiring practices, with AI‑related job postings leaping to capture 5.3 per cent of all roles in 2025, up from 3.3 per cent the year before.

That rise represents roughly 30,000 additional listings in a labour market otherwise facing global headwinds, according to PwC’s analysis of job-posting data and government surveys.

The numbers underline a broader transformation: rather than phasing out roles, AI is reshaping them. Occupations that are more exposed to AI, where day‑to‑day tasks and core abilities overlap with AI capabilities, are seeing more job openings and a faster rate of skill turnover.

Also Read: AI will replace inertia before it replaces people

The trend has important implications for Singapore, which aims to remain Southeast Asia’s technology and finance hub, and for neighbouring markets that look to the city‑state as a bellwether.

AI exposure correlates with job growth and skill churn

PwC’s measure of AI exposure shows a clear pattern: the greater an occupation’s exposure, the larger the number of job postings. Between 2019 and 2025, there was a +0.30 correlation between AI exposure and net skills change, suggesting that occupations more entwined with AI are also evolving fastest in terms of required competencies.

The reshaping is visible in hiring data. AI‑related roles rose to about 84,000 in 2025, an increase from the previous year, and over half of all job postings now fall within occupations with higher AI exposure. This suggests that employers are not merely replacing human tasks with automation; they are redesigning job descriptions and adding new responsibilities that include working alongside AI tools.

Public sector, finance and tech lead hiring

Sectoral analysis shows that technology, media, and telecom (TMT), government and public sector, and financial services are leading AI hiring in Singapore. These sectors also report high rates of AI adoption in surveys by the Ministry of Manpower (MOM), where 18.9 per cent of firms said they were redesigning job functions and 13.9 per cent reported creating new AI roles in the first quarter of 2026.

The government and public sector, in particular, are offering large wage premiums for AI talent, with advertised wages approximately 107 per cent higher for AI‑related roles than for non‑AI roles in the same sector in 2025. Consumer Markets reported a 96 per cent premium. High premiums in lower‑volume sectors point to concentrated demand for specialised skills. In contrast, more broadly, AI‑enabled sectors show narrower pay gaps as AI becomes part of routine job requirements.

AI users, not just developers

A striking signal of mainstreaming is the concentration of demand. About 82 per cent of AI‑related job postings in Singapore are for AI user roles — non‑specialist or hybrid positions that require working fluency with AI tools — rather than for developers. AI user roles accounted for approximately +26,000 of the increase in postings, while developer roles added around +4,200 in 2025 versus the prior year.

This split shows employers favouring a model where AI augments existing workforces rather than remaining the preserve of elite engineering teams. For Southeast Asia’s startups and fast‑scaling firms, that means hiring managers will increasingly prioritise candidates who can blend domain expertise with practical proficiency in AI tools, rather than recruiting only core machine‑learning engineers.

Policy and upskilling: Singapore’s push and regional spillovers

Singapore’s policy moves in 2026, from a National AI Council to dedicated AI missions and an AI Impact Programme, underpin this labour market shift. Those initiatives aim to boost adoption across sectors and encourage workforce upskilling. As organisations transition from pilots to scaled deployments, the demand for job redesign and structured reskilling will only ratchet up.

Also Read: AI’s first real casualties: The tech jobs that vanished in 2025

For the region, Singapore’s policy and market signals matter. Regional governments and corporations often benchmark against Singapore, and multinational firms based in the city serve as hubs for talent and investment that spill over into Indonesia, Vietnam, the Philippines and Malaysia. Startups in those markets could both benefit and face talent competition as Singapore firms soak up AI‑literate candidates and pay premiums for specialised roles.

What this means for startups and talent markets

For startups across the region, several practical consequences follow:

• Hiring strategy: Expect competition for AI‑literate generalists. Startups will need clearer role definitions that combine domain knowledge with AI fluency and may have to offer training pathways rather than expecting fully formed skills.
• Costs and pricing: As wage premiums persist for specialised AI roles, early‑stage firms may face higher personnel costs or choose to outsource AI development to contractors and partner firms in lower‑cost markets.
• Upskilling and retention: Investing in internal reskilling programmes could become a cost‑effective alternative to poaching senior AI talent, especially where long‑term cultural fit and domain expertise are critical.
• Product roadmaps: Startups that embed AI into their core propositions, not merely as an add‑on feature, will be better positioned to attract customers and talent in an ecosystem where AI capability signals competitive parity.

Risk and governance

As roles proliferate, governance becomes central. PwC highlights AI governance frameworks as one way to manage risk and foster trusted deployments. For Southeast Asian firms, adopting governance standards early could reduce regulatory friction and build user trust across markets where consumer privacy and algorithmic fairness are growing concerns.

The regional picture

Singapore’s labour market is the most visible example in the study, but the underlying dynamics are relevant across Southeast Asia. Countries with maturing digital economies will see similar shifts, albeit tempered by local talent supply, wage structures and policy timelines. For regional policymakers and startup founders, the imperative is clear: investing in reskilling and responsible AI practices now will determine who captures the productivity gains of the next wave.

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For most of my adult life, I have been paid to write. That sentence used to carry a certain clarity. It meant you could report, interview, structure arguments, meet deadlines, understand audiences and turn half-formed ideas into something people wanted to read. Over the years, I have written for media platforms across Asia and globally, worked with founders, PR teams, editors, startups and business leaders, and seen how the definition of a “good writer” changes depending on the publication, market and moment.

Then generative AI arrived in the mainstream. This is no longer a niche productivity shift. McKinsey’s 2025 global AI survey found that 88 per cent of organisations now report regular AI use in at least one business function, up from 78 per cent a year earlier. For content and communications teams, that means AI-assisted writing is quickly becoming part of the operating environment, not a novelty

Today, ChatGPT, Gemini, Perplexity and a long list of other tools can produce a clean first draft in seconds. They can summarise research, suggest headlines, rewrite copy, generate social captions and mimic the structure of a thought leadership article. For hiring managers in media, PR, marketing and content, this raises an uncomfortable but necessary question: if everyone can now “write”, who is actually a good writer anymore?

More importantly, who is still worth paying to write? This is not a question only for editors or agency leads. It is increasingly an HR question. Across APAC, where companies operate across multiple languages, cultures, regulatory environments and media markets, the ability to communicate clearly is becoming more important, not less. But the signals we use to assess communication talent need to change.

A polished writing test is no longer enough. A portfolio full of neat articles is no longer enough. Even years of experience may not mean what it used to. The real differentiator now is not whether someone can produce words. It is whether they can think, judge, question, adapt and take responsibility for what those words do.

The old markers of writing talent are becoming weaker signals

Three years ago, if I were hiring a writer or content person, I would have paid close attention to bylines, writing samples, industry exposure, speed and grammar. These things still matter, but they are no longer sufficient. A candidate can now submit a clean sample with very little original thinking behind it. They can use AI to improve sentence flow, generate article structures or create a competent-looking draft on a topic they barely understand. This does not make them dishonest. In many cases, it simply reflects the new reality of work. Most content teams are already using AI in some form, formally or informally.

The problem is that hiring processes have not caught up. Many companies still assess writers as though the main scarcity is sentence construction. But in 2026, sentence construction is becoming cheaper. What remains scarce is judgment.

Can this person tell when a claim is weak? Can they spot when a statistic is outdated or being used out of context? Can they interview someone and hear the actual story beneath the corporate talking points? Can they understand why a founder’s opinion matters to one outlet but sounds self-promotional to another? Can they write differently for e27, Tech Collective, a lifestyle publication, a LinkedIn post and a client byline without flattening everything into the same generic tone? That is where talent now shows up.

Also Read: Is our talent pipeline ready for the AI economy? Not in the way we think

AI proficiency matters, but not in the way many people think

There is a temptation to treat “AI skills” as a new line item on a job description. Can the candidate use ChatGPT? Can they write prompts? Can they generate content faster? These are useful questions, but they are shallow on their own.

In content and media roles, AI proficiency should not mean the ability to outsource thinking to a tool. It should mean knowing how to use AI without losing editorial judgment. A strong candidate should be able to explain what they would use AI for, what they would never use it for and how they would verify the output.

For example, I would be more impressed by a candidate who says, “I use AI to test headline options and identify gaps in structure, but I do my own source checking and rewrite the argument myself,” than one who simply says, “I can produce five articles a day using AI.”

Speed is useful, but speed without discernment creates risk. In media and communications, that risk may appear as factual errors, bland thought leadership, weak attribution, cultural tone-deafness or content that sounds polished but says very little. For startups and agencies in APAC, where one article may need to work across Singapore, Malaysia, Indonesia, Vietnam or broader regional audiences, that lack of judgement can damage credibility quickly. The best talent today is not anti-AI. It is AI-literate and editorially accountable.

What I now look for in writers and content talent

The first thing I look for is curiosity. Not the performative kind, but the kind that shows up in the questions someone asks before they write. A good writer does not simply ask, “What is the word count?” They ask who the audience is, why this topic matters now, what has already been said, what the client or publication wants to avoid, what claim needs proof and what the reader should walk away understanding. In an AI-saturated content market, curiosity is a competitive advantage because it leads to better inputs. Better inputs still produce better work, whether AI is involved or not.

The second signal is taste. This is harder to teach than grammar. Taste is knowing when a sentence sounds too inflated, when an opening paragraph is dragging, when a quote is weak, when a headline is technically accurate but emotionally flat. It is what helps a writer avoid the generic “in today’s rapidly evolving landscape” style of content that AI tools produce so easily.

The third is accountability. I want to know whether someone feels responsible for the accuracy and usefulness of the work. This is especially important in journalism-adjacent roles, PR and thought leadership. A writer who cannot explain why they used a certain source, framed an argument in a certain way or removed a claim from a draft is not ready to operate independently.

The fourth is adaptability. The strongest content professionals are not locked into one format or one voice. They can write a founder byline, edit a client comment, turn a press release into a story, prepare interview questions, write a social caption and understand why each one requires a different approach. Finally, I look for perspective. AI can summarise what is already online. A strong writer can tell you what is missing from the conversation.

What matters less than it used to

This may be uncomfortable, but credentials matter less to me than they once did. A journalism degree, a communications qualification or a well-known previous employer can be useful signals, but they are not guarantees. Some of the strongest writers I have worked with were not the most credentialed. They were the ones who could listen carefully, think clearly and revise without ego.

Also Read: The creative gap: Why GenAI is outpacing the talent it was meant to empower

Years of experience also need to be examined more carefully. Someone may have spent five years producing content without ever learning how to shape an argument. Another person may have two years of experience but sharper editorial instincts, stronger research habits and a better grasp of digital audiences.

Even technical writing skill, while still important, is no longer the entire game. Grammar can be cleaned up. Structure can be improved. What is harder to fix is a lack of thinking. This does not mean lowering standards. It means raising them in the right places.

What this means for APAC’s HR and media ecosystem

Across APAC, companies are under pressure to produce more content, more quickly and across more channels. Startups need founder visibility. Tech companies need thought leadership. HR teams need employer branding. PR agencies need bylines, pitches, commentary and media-ready narratives. Publications need contributors who understand their audience and do not waste editorial time.

At the same time, budgets are tight, and AI tools are making leaders question what they should still pay humans to do. The answer is not to pay people merely to generate text. That work will continue to be automated, compressed or devalued. The answer is to pay people who can combine domain understanding, editorial judgement and strategic communication.

For HR leaders, this means rethinking how writing and content roles are assessed. Instead of asking candidates to produce a generic article from scratch, give them a messy brief. Ask them what they would question. Give them a weak AI-generated draft and ask them to improve it. Ask them to fact-check a paragraph. Ask them to explain which angle would work for which publication and why. In other words, test the thinking around the writing.

A practical framework for hiring content talent now

When hiring writers, editors, PR consultants or content strategists today, I would ask five questions.

• Can they think beyond the brief? A great hire does not simply execute instructions. They can identify what is missing, what is unclear and what needs to be challenged.
• Can they use AI without becoming dependent on it? The best candidates should be able to use tools for efficiency while still owning the final judgment.
• Can they adapt to the audience and context? A strong writer knows that a startup founder byline, a lifestyle feature and a regional tech analysis cannot sound the same.
• Can they handle feedback without losing the thread? In content work, revision is not a punishment. It is part of the job. Good talent can take feedback, improve the piece and still protect the core argument.
• Can they make the work more useful? This is the ultimate test. After they touch a draft, is it clearer, sharper, more accurate and more valuable to the reader?

Also Read: What hiring a high school graduate taught me about talent in the AI economy

The future belongs to writers who can think

AI has not made writing irrelevant. It has made average writing easier to produce. That distinction matters. For those of us who have built our careers on words, the shift can feel unsettling. But it is also clarifying. The market is no longer rewarding people simply because they can fill a page. It is rewarding those who can bring judgment, context, taste and responsibility to communication.

In media, PR, marketing and content roles, “great talent” no longer means the person who can write the cleanest first draft. It means the person who can understand what needs to be said, why it matters, who it is for and how to make it credible.

The tools will keep improving. More people will be able to produce acceptable content. But acceptable content is not the same as valuable communication. That is where good writers still matter. And that is why the best ones will still get paid.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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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India’s startup ecosystem is entering a more disciplined phase, one where capital efficiency, sustainable growth, and talent retention are taking precedence over unchecked expansion. In this environment, Employee Stock Ownership Plans (ESOPs) are no longer viewed as optional perks; they are becoming a critical lever in people strategy.

For founders and HR leaders alike, the question is no longer whether to offer equity, but how to structure it effectively. Despite their growing importance, ESOP pools are often designed reactively, shaped by investor expectations or immediate hiring needs rather than long-term workforce planning. This can lead to misalignment between business goals and employee incentives. Having said that, a more deliberate approach is needed.

ESOPs as a strategic people lever

At a fundamental level, an ESOP pool represents a portion of company ownership reserved for employees. But from a people and culture standpoint, it serves a deeper purpose. Well-designed ESOPs:

• Strengthen alignment between employee performance and business outcomes
• Enhance retention, particularly in critical and leadership roles
• Enable startups to compete for talent despite cash compensation constraints
• Foster a sense of ownership and long-term commitment

In talent-scarce sectors, ESOPs can significantly influence offer acceptance and employee loyalty, especially when employees clearly understand their potential value.

Also Read: From perk to power: Rethinking ESOPs in the modern talent economy

Moving beyond the “standard percentage” mindset

A common mistake organisations make is relying on broad benchmarks when determining ESOP pool size. While many Indian startups allocate between five per cent and 25 per cent, this range offers limited guidance without context. The more relevant considerations include:

• Workforce expansion plans over the next two to three years
• Seniority mix and critical roles to be hired
• Market competitiveness for key talent segments
• Investor expectations and future funding rounds

For HR leaders, this is an opportunity to play a more strategic role, linking equity allocation directly to workforce planning rather than treating it as a finance-driven decision.

Structuring ESOPs: governance matters

An effective ESOP programme is not just about allocation; it requires robust governance and operational clarity.

• Clear ownership and administration: Organisations should define who is responsible for ESOP management, typically a combination of leadership, HR, and finance. This includes grant approvals, compliance, and ongoing communication.
• Vesting design as a retention tool: Vesting schedules are one of the most powerful retention mechanisms within an ESOP framework. Standard structures—such as a four-year vesting period with a one-year cliff—encourage continuity while rewarding long-term contribution. However, companies may need to tailor vesting terms for senior hires or critical roles.
• Thoughtful grant strategy: Equity distribution should be intentional —

• Early-stage employees may receive higher allocations due to higher risk
• Performance-based grants can reinforce meritocracy
• Reserving equity for future leadership hiring is essential for scalability

A static, one-time allocation approach often limits flexibility as the organisation grows.

Managing dilution while driving value

Dilution remains a key concern for founders when creating or expanding ESOP pools. However, it should be viewed through a value-creation lens. Strategic dilution used to attract and retain high-impact talent can significantly enhance enterprise value over time. From a people perspective, the focus should be on ensuring that equity allocation drives:

• Business growth
• Leadership stability
• Long-term employee engagement

The trade-off is not ownership versus dilution; it is short-term control versus long-term value creation.

Also Read: The best new year resolutions for startup founders: Offering ESOPs that actually work

Choosing the right equity instruments

While stock options remain the most widely used ESOP structure in India, organisations are increasingly exploring alternatives such as:

• Restricted Stock Units (RSUs)
• Employee Stock Purchase Plans (ESPPs)
• Phantom stock or cash-settled plans

Each instrument differs in terms of taxation, complexity, and employee perception. HR and leadership teams must align the choice of instrument with:

• Company stage and liquidity outlook
• Employee demographics and financial awareness
• Administrative and compliance capabilities

Bridging the employee understanding gap

One of the most overlooked aspects of ESOP programmes is employee communication. While equity is often positioned as a high-value benefit, many employees lack a clear understanding of vesting timelines, exercise processes, tax implications and realistic value scenarios. This gap can reduce the perceived value of ESOPs, even when the underlying structure is strong. Organisations that invest in ESOP education, through workshops, dashboards, or transparent communication, tend to see higher engagement and retention outcomes.

Risks of poorly designed ESOP programmes

Without careful planning, ESOPs can create unintended challenges:

• Over-allocation leading to excessive dilution
• Under-allocation reduces competitiveness in hiring
• Lack of transparency impacting employee trust
• Compliance and regulatory risks
• Administrative complexity and cost

For HR leaders, this underscores the need to treat ESOPs as an ongoing programme rather than a one-time initiative.

Also Read: How to do ESOP right for your startup

Building a culture of ownership

As India’s startup ecosystem matures, ESOPs are becoming more meaningful due to increasing liquidity events such as IPOs, buybacks, and secondary transactions. However, the true impact of ESOPs extends beyond financial outcomes. When implemented effectively, they contribute to stronger accountability, long-term decision-making and a culture where employees think and act like owners. This cultural shift is often what differentiates high-performing organisations from the rest.

Final thoughts

ESOP pools are not merely financial structures; they are integral to how organisations attract, retain, and engage talent. For founders and HR leaders, the priority should be to:

• Align ESOP design with business and workforce strategy
• Build transparent and well-governed frameworks
• Continuously evolve programmes as the organisation scales

Ultimately, the success of an ESOP programme is not defined by how much equity is allocated, but by how effectively it aligns people with the company’s long-term vision. Because sustainable growth is rarely built by founders alone, it is built by teams that feel invested in the outcome. 

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

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There was a time when building a product was the hardest part of entrepreneurship.

Today, that is changing.

With AI, founders can generate code, design landing pages, create marketing assets, automate workflows, and launch products faster than ever before. What once took months can now happen in days.

But this shift introduces a new challenge.

If building becomes easier, founders risk creating things that nobody actually wants.

The new founder skill is no longer execution alone. It is learning how to validate demand before investing certainty.

For years, startup advice revolved around one central idea: Build the product, launch it, then figure out how to monetise it later.

That approach made sense when building itself was expensive. When engineering resources were scarce, simply getting a product into the market was an achievement.

But in today’s environment, where AI dramatically reduces the cost and speed of execution, the question has changed.

It is no longer, “Can we build this?”

It is, “Should we build this at all?”

This distinction matters.

Many founders still spend months researching, refining, and polishing their ideas before introducing them to the market. They work quietly behind the scenes, convinced that perfection increases the chances of success.

Then launch day arrives.

And nobody buys.

Also Read: B2B founders keep skipping brand, and it is costing them more than they realise

I have seen this pattern repeatedly among aspiring entrepreneurs. They pour countless hours into creating programmes, products, and services without ever testing whether genuine demand exists.

Interest and demand are not the same thing.

Someone might follow you because they find you entertaining. They may like your posts, comment enthusiastically, or even share your content with others.

None of those behaviours guarantees they will become paying customers.

Revenue reveals something that engagement alone cannot.

It reveals commitment.

Monetisation is often viewed as the finish line. In reality, it can be one of the earliest and most valuable forms of validation available to founders.

Revenue is information.

It tells us whether the problem is significant enough for people to pay to solve it. It tells us whether our positioning resonates. It tells us whether the timing is right.

Most importantly, it tells us whether we should continue investing our time, energy, and resources into building.

This was a lesson I learned firsthand.

Years ago, I started a media and technology venture that began as a school project. The focus was on creating something valuable and useful. Monetisation was never part of the original strategy.

People enjoyed the content.

They consumed it consistently.

However, because the audience had been conditioned to receive everything for free, introducing paid offerings later became extremely difficult.

The challenge wasn’t generating attention.

The challenge was converting attention into commercial intent.

That experience fundamentally changed how I approach new ventures today.

Also Read: Funded: SEA founders need a capital sequence, not another funding scramble

When I conceptualised Seraphina AI, I already had a version that I used internally. It helped me streamline workflows and supported my day-to-day operations.

What I didn’t have was a consumer product.

Instead of immediately building one, I asked a different question:

Would other people value this enough to pay for it?

Rather than spending months creating features based on assumptions, I started with a waitlist.

I shared the idea.

I sent newsletters.

I nurtured conversations around the problem the product was designed to solve.

Eventually, I opened pre-orders.

Only after people committed financially did I decide to invest fully in developing the consumer version of the product.

Those early customers joined in the first half of the year.

The product itself launched approximately nine months later.

Validation came before development.

Today, this philosophy shapes how I launch almost everything.

When exploring a new programme or initiative, I rarely begin by building the entire experience upfront.

Instead, I start with a waitlist.

If there is enough interest, I invite people to place a small deposit.

That deposit is not simply about generating revenue.

It is about measuring conviction.

If people are unwilling to commit a modest amount towards solving a problem, it raises important questions about whether the market truly exists.

This approach helps founders avoid one of the most expensive mistakes in entrepreneurship: building based on assumptions rather than evidence.

In an AI-powered world, ideas are abundant.

Execution is increasingly accessible.

The real constraint is no longer technical capability.

It is a judgment.

The founders who thrive in this environment will not necessarily be the ones who build the fastest.

They will be the ones who validate the smartest.

The ones who understand the difference between curiosity and commitment.

The ones who recognise that not every idea deserves to become a product.

The ones who are willing to test demand before investing in certainty.

Because when building becomes easier, discernment becomes more valuable.

I could build countless products, programmes, and systems.

Many founders can.

But if nobody is willing to use them, what is the point?

The future belongs not to founders who build everything they can.

It belongs to those who know exactly what is worth building in the first place.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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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I was chatting with my mother last week, and she mentioned the weavers of Bengal.

Not as history. As family memory, the way an older generation talks about things their grandparents lived through. Dhaka muslin had once been the finest textile in the world, exported across Europe, Asia, and the Arab world for centuries. Then the Industrial Revolution arrived. Manchester mills, British tariffs against Indian cotton, and a few decades later, the weavers of Bengal — generations of inherited craft, an entire economic ecosystem — were destitute. The skill did not save them. The market for the skill simply went away.

I have been thinking about that conversation ever since. Because I am also a professor at a business school, and the question I get asked most often, by students and by parents of students, is some version of: what should they study, what should they do, how should they prepare for the workforce of tomorrow?

And I do not have an honest answer that is also a comfortable one.

The thing we cannot keep saying

For two years, the comfortable position in education circles has been that AI is a productivity tool. That it will augmentknowledge workers, not replace them. That the disruption will be gradual, manageable, similar to other technology cycles.

That position is becoming harder to hold honestly.

In May 2025, Dario Amodei, the CEO of Anthropic — one of the companies actually building this technology — told Axios that AI could eliminate roughly 50 per cent of entry-level white-collar jobs within one to five years, and push unemployment to between 10 per cent and 20 per cent. He named tech, finance, law, and consulting specifically. The line that has stayed with me: “We, as the producers of this technology, have a duty and an obligation to be honest about what is coming. Most of them are unaware that this is about to happen.”

A year later, the data is moving in that direction. Big Tech hiring of new graduates has dropped roughly 50 per cent from pre-pandemic levels, according to venture firm SignalFire. Wall Street banks have announced cuts concentrated in entry-level analyst seats. Tech entry-level hiring fell 30–50 per cent across 2025. The first rung of the white-collar ladder is the one being sawed off.

Also Read: The real AI threat isn’t your job, it’s your mind

This is not the metaverse. This is not crypto. Those were narratives in search of use cases. What is happening now is the opposite — capability arriving faster than the use cases, faster than the labour market, faster than education systems can adapt. Every senior leader I speak with this year is seeing it inside their own organisation.

And the next wave is physical

The instinct so far has been to tell young people: go into the trades. Become a plumber or an electrician. The body is safe even if the desk job is not.

I do not think we get to say that for much longer, either.

Self-driving vehicles, until recently a punchline, are now running commercial robotaxi services in multiple cities across the US and China. Humanoid robotics that two years ago could barely walk are now folding laundry and stocking shelves in pilots. The combination — large models meeting physical actuators — is what people in the field are starting to call physical AI. It is at roughly the stage knowledge AI was at in 2022. Look at how far that has come in three years.

I am not predicting that plumbers will disappear by 2030. I am saying I am no longer willing to tell a sixteen-year-old that physical work is a permanent moat. The honest answer is we don’t know. And the pace at which that answer keeps moving makes any specific prediction we make today suspect by next year.

What we cannot predict, and what that means

Here is the other half of the honesty.

The most lucrative careers of the last twenty years are the ones nobody in 2005 could have advised a child to prepare for. The full-time YouTuber. The Twitch streamer. The prompt engineer. The TikTok creator earns more than a partner at a top consultancy. The DevOps engineer. The growth marketer. The mobile app indie developer. None of these was on a syllabus. None had a college pathway. The most we could have done in 2005 was say: the internet seems important; learn to use it, follow your interests, be ready to invent the rest.

This will be true again. Almost certainly more so. There will be wealth, professions, and entire categories of human work that we cannot picture from here and that will become obvious in retrospect. The graduates of 2026 are going to invent jobs we do not yet have words for.

This is the strangely hopeful part of the answer. The thing we cannot do is hand them a map. The thing we can do is make sure they are equipped to draw one.

Also Read: The future is full of humans working with humans, AI systems and other technologies

What I tell graduates now

I have stopped trying to point to specific professions as safe harbours. Instead, I share three things, in roughly this order.

Become fluent with AI before it becomes furniture

Not as a search engine. As a thinking partner, a builder, a critic, a research team in your pocket. The graduates who treat AI as a tool to dodge will be displaced by the graduates who treat it as a force multiplier. The latter group is small today. It will be the entry condition tomorrow.

Build judgment around something you genuinely care about

AI is flattening the cost of producing anything; what becomes scarce is taste, judgment, and the ability to decide what is worth producing. That cannot be taught from a syllabus. It is built by going deep on something — a craft, a domain, a question — that you would care about even if nobody paid you for it. The depth becomes the platform from which you can leverage AI. Breadth without depth produces nothing memorable.

Expect to reinvent yourself, and treat it as normal

My generation built careers around the idea that you would do one thing well for thirty years. The next generation will need to be comfortable doing several things across thirty years, with two-to-three-year reinvention cycles. This is uncomfortable to us. It is not, it turns out, uncomfortable to them. The teenagers I meet are already pattern-matching to this faster than their parents are.

What my mother actually said

After we talked about the weavers, my mother said something I keep returning to. She said the weavers’ children eventually found new ways to live. Not the same way. Not as wealthy, not for a long time. But Bengal did not end with the looms. Something else came after.

That is the most honest thing I can say to a young person right now. The looms you were trained for are changing under your feet. We do not know exactly what comes next. But something will. And the people who do best in any disruption are the ones who stop arguing with the change and start positioning for what is on the other side of it.

The youth I meet are already doing this. Quietly, mostly without us. They do not need us to predict their future. They need us to be honest about ours.

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As AI reshapes the marketing function, Southeast Asian startup founders face a deceptively simple question: what does good actually look like now?

AI is restructuring the marketing function faster than most startups have had time to notice. The skills that made a strong marketing hire in 2022 are being automated. The skills that actually matter now are different, and most hiring managers don’t yet have a clear framework for identifying them.

It’s not a question of whether AI will replace marketers. It has largely already replaced specific tasks. The more useful question for founders and operators is: given that, what should your marketing team actually look like?

The execution layer is gone

For lean startup marketing teams, which describe most of the Asia region, AI has effectively eliminated the cost of execution. Content production, campaign setup, basic reporting, and social scheduling: these are now table stakes that AI handles faster and cheaper than a junior hire.

That sounds like good news. In some ways it is. But it creates a structural problem. Many early marketing hires in startups were valued precisely for their ability to execute at volume. If that’s the primary value proposition, the role is under pressure.

A recent conversation with fintech marketing leaders across the region made this tension explicit. Teams are at wildly different stages of AI adoption, from basic prompting to fully agentic workflows, and the gap between early adopters and the rest is widening fast. The consensus: the most valuable marketing hire right now is someone who can adapt to change, operate across multiple functions, and direct AI systems rather than just use them.

Also Read: AI as an audience: Welcome to the citation economy

The profile that keeps coming up: T-shaped specialists who can act as orchestrators. Depth in one discipline, whether that’s demand generation, brand, or content strategy, combined with enough breadth to work across the AI toolchain. Pure generalists, interestingly, may be losing ground. The winning profile is depth plus adaptability, not breadth alone.

Three questions worth asking before your next hire

• Can they tell when AI output is wrong?

Anyone can generate copy, build a campaign brief, or pull a competitive analysis with AI now. The rarer skill is editorial judgment: knowing immediately when the tone is off, the claim is shaky, or the output doesn’t reflect your brand. For APAC startups operating across multiple markets, this is especially critical. AI tools trained predominantly on Western data consistently underrepresent Asian consumer behaviour, local nuance, and regional context. A marketer who can catch that gap and correct for it is genuinely valuable. One who can’t will ship content that quietly erodes trust.

• Are they waiting to be trained, or training themselves?

Only 25 per cent of workers receive formal AI training from their employers, even as skills in AI-exposed roles are evolving 66 per cent faster than other jobs. The marketers pulling ahead aren’t waiting for a curriculum. They’re running experiments, building workflows, and developing their own framework. For founders evaluating candidates, this is a useful signal. Ask what they’ve built or tested with AI in the last three months. The answer tells you a lot.

• Do they understand the trust problem?

This one is particularly relevant in fintech and financial services, but it applies across sectors. AI-generated content at scale risks producing what some are calling “AI slop”: homogenised, generic output that erodes brand differentiation and credibility. In categories where trust is the product, that’s an existential risk, not a content quality issue. The marketer who understands this, who treats AI as a tool for amplification rather than a replacement for judgment, is the one who protects your brand as you scale.

The build vs buy question

One unresolved tension for startup founders right now: whether to build AI marketing capabilities in-house or buy them through agencies and tools. The honest answer is that most startups are doing both, somewhat chaotically, without a clear framework for when each makes sense.

Also Read: The future is full of humans working with humans, AI systems and other technologies

A few rough principles worth considering. Use AI tools for execution that’s repeatable and low-stakes: content variations, SEO drafts, campaign copy. Retain human judgment for anything that touches brand voice, customer trust, or strategic positioning. And be cautious about cutting agency relationships entirely in favour of AI-generated output, the consensus among marketing leaders is that AI isn’t yet ready to own branding at scale. The cost savings can be real; the brand risk is also real.

What this means for how you structure the function

The CMO or marketing lead role is shifting toward orchestration, setting creative and strategic direction while AI handles activation.

AI fluency across the function is now a baseline requirement, not a specialist skill. That doesn’t mean everyone needs to be a prompt engineer. It means everyone needs to understand enough to work with AI intelligently, to direct it, evaluate its output, and know when to override it.

APAC’s talent scarcity makes this more acute. Skills shortages already affect 77 per cent of employers in the region, with sales and marketing among the hardest roles to fill. The pool of candidates who combine domain expertise, AI fluency, and genuine regional judgment is small. Founders who know what they’re looking for and can articulate it clearly in a job description have a meaningful advantage.

The talent reset is already underway. The startups that adapt their hiring frameworks now will be better positioned than those still hiring for the job that existed three years ago.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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One of the fastest ways to weaken an AI programme is to declare it responsible before the organisation has agreed on what that word means in practice.

This is a common mistake because responsible AI sounds mature, board-ready, and difficult to argue against. It travels well in policy documents, governance forums, investor language, and internal announcements. It signals seriousness. It suggests the organisation has thought ahead. It gives the impression that the hard questions are already under control.

Often, they are not.

In many companies, responsible AI is still being treated as a label applied after the real decisions have already been made. The model is selected, the use case is funded, the vendor is approved, the pilot is underway, and then the organisation asks how to make the initiative responsible. By that point, the most important definitional work has usually been deferred. Nobody has been forced to settle what kind of system this actually is, what kind of judgment it is influencing, what kind of harm matters most, what level of error is acceptable, what counts as meaningful human oversight, or which decisions should never be delegated to probabilistic systems at all. 

Most responsible AI programmes are stronger on language than on meaning

The surface signs of seriousness are now familiar. Principles are published. Review committees are formed. Risk templates are created. Training is rolled out. Human in the loop language appears in design documents. Fairness, transparency, explainability, and accountability are all referenced in the right places.

None of this is useless. Much of it is necessary. But none of it matters enough if the core terms remain vague.

What exactly counts as a high impact use case?  What counts as decision support rather than decision making? What counts as a customer affecting output? What counts as automated action? What counts as a material model change? What counts as explainable enough for the real context in which the system will be used? What counts as acceptable performance when the harm is not evenly distributed? What counts as sufficient review when the humans involved do not fully understand the model but are still expected to sign off on its behaviour?

These are not drafting issues. There are operating issues.

The real weakness is the definition debt

Every organisation understands technical debt. Fewer understand the definition of debt.

Definition debt accumulates when an institution moves faster on deployment than on conceptual clarity. It uses broad terms that sound robust but remain internally unstable. It talks about safety, fairness, explainability, oversight, harmful use, customer impact, model drift, and accountability as though these were settled ideas, while different teams are quietly operating with different meanings.

Also Read: Responsible AI won’t scale on good intentions alone

This creates the worst kind of governance problem because it often looks like alignment from a distance.

Legal may think human oversight means a named approver exists in the process. The product may think it means a user can technically ignore the model output. Engineering may think it means the model is not directly triggering an automated downstream action. Operations may think it means an analyst glances at the result before moving on. Audit may think it means there is an evidential record after the fact. Everyone uses the same phrase. Nobody is governing the same reality.

That is the definition of debt in action. The language of control exists, but the operational meaning remains fractured. Over time, this debt becomes expensive. 

Responsible AI fails first as a framing problem

Much of the current debate still assumes that responsible AI is mainly a model problem. How do we reduce bias? How do we improve explainability? How do we strengthen monitoring? How do we govern vendors? How do we prevent misuse?

Those are important questions, but they often arrive too late.

The first failure is usually one of framing. The organisation does not define the system in a way that matches the consequences it is about to create.

A model assisting with internal drafting is one thing. A model shaping customer communications, fraud handling, cyber response, financial recommendations, hiring decisions, investigation summaries, claims triage, or exception management is something else entirely. Yet many institutions still group these under the same technology umbrella and then try to manage them through generic policy language.

That is not governance. That is category collapse.

A serious responsible AI programme starts by distinguishing what kind of influence the system is being granted. Is it informing, recommending, ranking, screening, approving, acting, or persuading? Is it being used in a reversible context or an accumulative one? Is the output advisory in theory but determinative in practice? Is the system affecting a user directly, or affecting the employee who affects the user? Is the harm visible immediately, or does it compound quietly through repeated use?

A more mature approach begins by accepting that the big words in responsible AI are not self-executing.

Fairness for what decision, against what baseline, across which groups, measured over what period, with what acceptable trade-offs. Safety for what use case, against which harms, under what misuse assumptions, with what residual risk tolerance? Oversight by whom, with what expertise, with what authority to intervene, and with what evidence available at the moment intervention is needed. Explainability for which audience, for what decision, and with what purpose. Accountability is assigned to which actor when the output was produced by one team, approved by another, deployed by a third, and acted on by a fourth.

Also Read: 5 dimensions of responsible AI: Enhancing societal needs with blockchain

These are definitional questions disguised as governance questions.

That matters because responsible AI has become crowded with high-level commitments and light on decision-grade clarity. Too much of the discussion still assumes that shared vocabulary means shared understanding. It does not.

Real governance starts when the organisation is willing to pin terms down hard enough that they shape investment, architecture, approval rights, monitoring design, incident response, and executive accountability.

Until then, the programme is mostly speaking in values while operating in approximation.

Process matters, but only when it is tied to consequence

To say responsible AI is a process is not to defend bureaucracy. It is to argue that responsibility must be continuously produced, not merely declared.

A serious process does not begin and end at model approval. It starts with use case framing, continues through design, testing, deployment, monitoring, escalation, retraining, change management, incident learning, and sometimes withdrawal. It recognises that the model will be used differently from how it was originally described, that humans will adapt around it, that workflows will stretch it into adjacent roles, and that the meaning of harm may change once the system interacts with real customers, regulators, operations, and frontline pressure.

That is why a checkbox cannot work. A checkbox assumes the relevant question has been settled at a single moment. Responsible AI assumes the opposite. It assumes the organisation must keep asking whether the system is still behaving within the boundaries that were originally judged acceptable, whether those boundaries were defined well enough in the first place, and whether the real use of the system has drifted beyond what was approved.

This is not red tape. It is the minimum discipline required when deploying systems whose outputs can look more stable than their consequences.

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The rise of artificial intelligence is not simply changing how students learn. It may be fundamentally reshaping what expertise itself means.

A student recently presented an AI-assisted proposal that was technically polished, logically structured, and supported by convincing recommendations. Only a few years ago, producing work of that quality would likely have required substantial effort in research, synthesis, modelling, and technical writing.

But once the discussion moved beyond the proposal itself, the limitations became visible.

What assumptions had been embedded within the recommendation? Would the proposed solution still hold if manufacturing conditions shifted, ingredient behaviour changed, or commercial priorities evolved? How should decisions adapt once new constraints emerge across cost, sustainability, quality, or operational feasibility?

The challenge was no longer about generating technically plausible answers. It was about understanding how to interpret, contextualise, and adapt those answers once realities became dynamic, interconnected, and uncertain.

This distinction matters increasingly.

Across industries, AI tools are rapidly lowering the effort required to generate polished outputs. Analyses, reports, recommendations, coding support, technical summaries, strategic frameworks, and even research synthesis can now be produced at remarkable speed and sophistication.

Historically, the ability to produce coherent analyses and technically sound outputs often served as evidence of expertise. Much of higher education and professional advancement has been built around this premise.

AI is now compressing that advantage.

As informational and cognitive production becomes increasingly automated, the basis of differentiation begins to shift. The question is no longer simply whether individuals can generate answers. Increasingly, differentiation lies in the ability to frame meaningful questions, recognise hidden assumptions, interpret outputs within context, navigate ambiguity, and exercise sound judgement when conditions no longer remain stable.

Also Read: AI as an audience: Welcome to the citation economy

In other words, expertise is moving beyond informational mastery alone towards contextual intelligence.

This becomes particularly visible in applied manufacturing environments, where technically correct answers frequently prove insufficient once operational realities evolve.

In these systems, outcomes are rarely shaped by isolated variables alone. Product performance emerges from interactions across formulation behaviour, equipment variability, environmental conditions, process stability, regulatory requirements, workforce capabilities, supply constraints, commercial pressures, and sustainability considerations.

A recommendation that appears technically optimal in theory may become operationally impractical once real-world constraints begin interacting across the system.

AI can increasingly optimise within represented conditions. But real environments do not remain static long enough for optimisation alone to be sufficient.

This is not unique to manufacturing.

Across sectors, AI is increasingly handling structured synthesis, retrieval, formatting, and routine analytical generation. As this happens, human value shifts further towards interpretation, systems thinking, adaptive judgement, and the ability to make decisions under evolving conditions.

This has significant implications for education.

Much of today’s conversation understandably focuses on AI literacy: helping students learn how to use emerging tools effectively and responsibly. These are necessary foundations. But they are unlikely to be sufficient.

If AI increasingly lowers the barrier to producing technically polished work, then education can no longer derive value primarily from answer production alone.

The more difficult challenge is preparing students to operate meaningfully within increasingly AI-mediated environments — environments where outputs are abundant, but interpretation, prioritisation, and judgement become the true constraints.

This changes the kinds of learning experiences that matter.

Also Read: The real AI threat isn’t your job, it’s your mind

In applied learning environments, students increasingly encounter situations where decisions must account for incomplete information, competing priorities, shifting objectives, and operational uncertainty. They may begin with technically sound AI-assisted recommendations, but are subsequently challenged to reconsider those recommendations as realities evolve between quality, cost, sustainability, scalability, and feasibility.

The educational emphasis, therefore, shifts from producing answers towards interrogating them.

Students are assessed not only on the recommendation itself, but also on their ability to explain assumptions, justify trade-offs, identify blind spots, integrate contextual considerations, and adapt thoughtfully when conditions change.

These are fundamentally different capabilities from informational recall alone.

Importantly, AI itself can become part of the learning environment rather than simply a productivity tool. Used well, it creates opportunities to move beyond routine answer generation and place greater emphasis on interpretation, complexity management, and reflective decision-making.

This also challenges how capability is assessed.

Traditional assessments have often rewarded polished reports, technically correct answers, and well-structured presentations. While these remain useful, they become less meaningful as standalone indicators of understanding when AI increasingly assists with their production.

The more important question is whether learners can navigate ambiguity when no single optimal answer exists.

Can they recognise when technically correct outputs become contextually inappropriate?

Can they adapt decisions responsibly when systems evolve?

Can they integrate competing considerations across technical, operational, ethical, environmental, and commercial domains?

These capabilities are difficult to cultivate through learning environments designed primarily around predictable solutions. They are developed through exposure to complexity, iteration, uncertainty, and authentic situations where decisions carry real consequences across interconnected systems.

Also Read: Hiring an AI-fluent junior is easy, building one with judgment is the problem

The implications extend beyond classrooms.

As AI continues to reshape work, organisations may also need to rethink how talent is evaluated. Credentials, technical fluency, and polished outputs may no longer function as sufficient proxies for capability when many of these can increasingly be augmented by AI systems.

The future value of talent may lie less in producing information and more in exercising discernment.

Those who thrive may not necessarily be individuals who can generate the fastest answers, but those who can understand which questions matter, identify what is missing, recognise shifting constraints, and make responsible decisions amidst uncertainty.

In many ways, the talent reset driven by AI is not reducing the importance of human expertise. It is redefining where human expertise becomes most valuable.

As AI capabilities continue to advance, human differentiation may increasingly reside in qualities that are deeply contextual and difficult to automate fully: systems thinking, adaptive judgement, ethical reasoning, contextual interpretation, and the ability to navigate complexity across evolving environments.

The future will not belong simply to those who know how to use AI tools.

It will belong to those who can work meaningfully with AI-generated knowledge while still understanding how to interpret reality when systems, priorities, and conditions inevitably continue to change.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Last year, I drew the org chart of the risk team I would hire if I were building from zero. I drew four boxes. Fifteen years earlier, when I first joined a risk team inside an Indonesian bank, the chart I worked under had eleven boxes — analysts feeding into officers feeding into heads feeding into a Chief Risk Officer, with parallel ladders for credit, operational, market, and compliance risk. The shape of the function was a pyramid. The shape I was now drawing was a flat trapezoid.

The pyramid is collapsing — not from above, where most of the attention goes, but from the middle. And the people working their way up through it are the ones who will feel the shift first.

The middle is going first

The analyst tier, the layer where most of us learned this craft, is the one being automated first. Risk dashboards that used to take a team a week to compile now generate themselves overnight. Reconciliation work that used to require three people now requires a workflow. The young analyst who used to spend three years learning the function by doing the compilation is no longer being hired into the seat that taught them.

Two roles that did not used to exist are quietly emerging in the space the analyst tier used to occupy. The first is the model overseer — someone who can read what a model is doing, validate its outputs against domain knowledge, and produce the evidence a regulator will accept. The second is the cross-functional translator — someone who can sit between engineering, risk, and product, and arbitrate between the languages each side speaks.

The Chief Risk Officer role is changing too. Three years ago the CRO’s job was largely to defend the function inside the organisation — to argue for risk constraints against revenue pressure, and to package the function’s work for the board. Today the CRO is increasingly an integrator. They must understand how AI models touching credit, fraud, and compliance interact with each other, and how the firm’s risk posture shifts as each of those models is retrained or replaced.

Also Read: The future is full of humans working with humans, AI systems and other technologies

What I should have changed sooner

I did not flatten the teams I was hiring for soon enough. I kept hiring analyst-tier roles into 2023 because the previous pyramid was familiar, because I worried about losing the apprenticeship pipeline the analyst tier represented, and because the alternative team shape was still genuinely uncertain. Twelve months of those hires turned out to be redundant within two years — not because the people were not capable, but because the work they were hired to do had already been automated underneath them. I should have spent that budget on the model overseer roles that turned out to matter.

The CRO pipeline problem

The flattening creates a new problem nobody in the industry is solving yet. The traditional pyramid was, among other things, a training apparatus. Analysts grew into officers, officers grew into heads, heads grew into chiefs — and each layer taught the person above it some of what the next layer needed to do.

Without that ladder, the function depends entirely on lateral hires. The supply of people who have already learned to be a CRO without going through the pyramid is small, and most of them are sitting in their current jobs at large institutions that built them. The next generation of CROs in ASEAN will either be poached or invented. There is no obvious third path yet.

What good team design looks like now

The teams I am helping build today look nothing like the pyramid I came up through. The shape is closer to a small, senior cell than a hierarchy. Two or three model overseers. One or two cross-functional translators. A small, very senior decision layer at the top. Apprenticeship happens through rotation rather than ladder — people move between the model side, the policy side, and the engineering side, picking up the domain by exposure rather than by promotion.

Also Read: Hiring an AI-fluent junior is easy, building one with judgment is the problem

The teams are smaller. The seniority per head is higher. The compensation envelope per role is bigger. And the work done by each seat is more cross-domain than any single seat used to require.

The shape you draw next matters more than the tools you buy

If you are building or reshaping a risk function in 2026, the question is not how to digitise the pyramid you have. The question is whether the pyramid was ever the right shape for the work you now need to do. The teams that will sit inside ASEAN’s regulated institutions five years from now look nothing like the teams that staffed them when most of us learned this craft.

That gap will not close by adding tooling on top of the old structure. It will close by drawing a different shape, and being willing to hire against it before the rest of the industry catches up.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Singapore’s business leaders are the least optimistic about international expansion among the markets surveyed in Kreston Global’s latest Interpreneur Report, underscoring a more cautious approach to cross-border growth amid rising geopolitical friction, supply-chain fragility, and tariff pressure.

The report, based on a survey of 1,100 “interpreneurs” operating businesses with annual revenue between £10 million and £300 million (roughly US$12.7 million to US$381 million) across 11 countries, gives a rare window into how mid-market firms that have already expanded overseas are thinking about the next stage of foreign growth. Singapore respondents averaged a 7.2 out of 10 score for the current business climate for expansion, compared with 8.2 globally.

Also Read: How SMEs can become learning organisations, without the corporate bureaucracy

The more muted sentiment coexists with guarded optimism. Two-thirds of Singapore interpreneurs, or 66 per cent, expect the environment for international business expansion to become more favourable in the next two to three years. However, that optimism lags the 86 per cent seen among their global peers.

A city-state acutely exposed to global trade dynamics, Singapore unsurprisingly flags geopolitical instability, supply-chain disruption, and tariff-related costs as the most significant threats to overseas operations. In each case, the city recorded some of the highest concern levels among countries polled: 52 per cent cited geopolitical instability (versus a 45 per cent global average), 43 per cent named supply-chain disruption (global average 31 per cent), and 42 per cent pointed to tariff-related cost increases (global average 40 per cent).

“These are direct and profound impacts on the economy and business confidence,” said Helmi Talib, managing partner at Kreston Helmi Talib, Singapore. “As a city that heavily relies on trade, global headwinds such as geopolitical tensions and supply-chain disruption shape a more cautious, selective approach to international expansion.”

The Singapore context: still open, but choosy

Singapore’s priorities when assessing expansion destinations remain rooted in traditional fundamentals: future economic growth prospects (46 per cent), favourable tax policies (44 per cent), trade agreements (44 per cent), and alignment with long-term strategy (43 per cent). Access to skills and talent, and to digital infrastructure, was each featured by 38 per cent of respondents.

That emphasis signals how Singaporean firms are treating overseas expansion more as a calculated extension of their domestic strategies than as a leap into unfamiliar technology frontiers. Access to new customer markets (52 per cent), strategic partnerships or joint ventures (51 per cent), and the ability to lower production or operating costs (43 per cent) were cited as the most significant opportunities for international growth.

Also Read: 3 easy tips for SMEs to build overseas customer loyalty

For Southeast Asia, that spells an opening for deeper commercial ties built on partnerships rather than purely technology-driven propositions. ASEAN economies offering talent, lower operating costs, or attractive trade pacts may attract Singapore capital and management expertise, but likely on a more project-by-project basis than in the boom years of rapid outbound deals.

AI and technology: embedded, not transformative

One of the report’s more revealing findings concerns the role of artificial intelligence. While 97 per cent of Singapore respondents say AI influences their expansion strategy to some extent, only 52 per cent describe that impact as “significant” or “very significant”, well below the 74 per cent global average. Singapore interpreneurs are almost twice as likely as global peers (45 per cent versus 24 per cent) to regard AI’s impact as moderate or minor.

That pragmatism extends to technology more broadly. Just 25 per cent say access to digital technologies and innovation was a primary motivator for expanding overseas (global average 40 per cent), and only 37 per cent view advanced technology adoption as a major future opportunity (global average 52 per cent).

“Singapore is one of the most mature and hyper-connected digital markets globally,” Talib said. “Access to technology is less of a limiting or motivating factor in expansion decisions; AI appears to be embedded, business-as-usual rather than a transformational driver.”

For Southeast Asia, this has two implications. First, Singapore firms may look to regional markets for conventional expansion levers (market access, cost efficiencies, and partnerships) rather than leveraging a native technology advantage for exports. Second, local Southeast Asian startups and service providers that can offer targeted operational capabilities or support localisation will remain valuable to Singaporean entrepreneurs looking to scale abroad.

Practical responses: governance, processes and partnerships

Faced with a volatile, uncertain, complex and ambiguous (VUCA) environment, Kreston advises SMEs to prioritise internal alignment and operational readiness so they can move quickly when opportunities appear. “SMEs that strategically invest in strengthening governance, refining processes and establishing robust operating frameworks will be better equipped with the resilience and agility needed to act decisively as expansion opportunities regain momentum,” Talib said.

That message resonates across Southeast Asia, where mid-market firms often encounter regulatory complexity, talent gaps and fragmented supply chains. The report suggests Singapore-based firms may increasingly favour joint ventures, strategic alliances, and local partnerships to navigate those hurdles, a trend that could bring capital, governance standards and managerial know-how into the region.

A nuanced picture beneath a broadly positive headline

Liza Robbins, chief executive of Kreston Global, described the overall mood as resilient despite the challenges. “The finer details in the data tell a more nuanced story of businesses grappling with the challenges of AI, tariffs and geopolitical instability,” she said. “In spite of this, the resilience, drive and adaptability of interpreneurs have once again been underscored.”

For Southeast Asia, the Kreston findings emphasise a recalibration rather than retreat by Singapore firms: more selective, partnership-led expansion focused on market access, cost efficiency and regulatory alignment, with technology treated as an enabler rather than the primary rationale for outbound investment.

Also Read: Singapore SMEs outpace large firms in branding and networks but face AI skills gap

In practice, that could translate to more Singapore capital flowing into targeted manufacturing hubs, logistics nodes and consumer markets within ASEAN, accompanied by operational and governance expertise, rather than the headline-grabbing tech acquisitions of previous cycles. For regional startups and policymakers, the opportunity lies in aligning incentives, easing market entry and demonstrating reliable local capabilities that complement Singapore’s cautious but persistent outward push.

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