There is a number that Justin McCarthy cannot put down. Close to eight million people alive today are living with a permanent disability caused by untreated clubfoot, a condition in which a baby’s foot or feet turn inward and downward at birth. The fix costs roughly US$500 per child. The math, therefore, is not the problem. The reach is.

“The problem is effectively solved on the medical side,” says McCarthy, Senior Adviser for Philanthropy and Partnerships at MiracleFeet, a non-profit that has supported treatment for more than 120,000 children across 39 countries. “The only thing missing is reach. So the question stops being whether it can be done and becomes why it hasn’t been done faster.”

Also Read: The hardest industries to disrupt and start in Asia: A focus on healthcare

It is a question McCarthy has now decided to make his professional obsession, having taken on a broader mandate for MiracleFeet across Asia Pacific, a region that holds a disproportionately large share of the world’s untreated cases and where, as he frames it, “rising incomes and infrastructure make closing the gap realistic within a career rather than a century.”

A return that would make a VC blush

The economics of clubfoot treatment are, by any serious measure, extraordinary. A US$500 course of care generates an estimated US$130,000 in additional lifetime earnings for the child treated, a 260-to-one return. Left untreated, the condition locks children out of school, work, and full participation in their communities. Treated early, that same child walks, studies, and contributes to the economy.

So why hasn’t that argument unlocked more capital or government attention, particularly in a region that increasingly speaks the language of impact investing?

McCarthy is pragmatic about it. “Health budgets in low- and middle-income countries are already stretched across urgent, visible emergencies,” he says. Clubfoot affects approximately one in 800 births, which is significant in absolute terms, but not the kind of headline number that wins a triage fight against malaria or malnutrition. Crucially, it is not fatal. It is not loud. It loses.

What MiracleFeet is betting on is a shift: that philanthropic leaders and health ministries, once they understand the lifetime economics, become the most effective advocates for integrating clubfoot care into national systems.

Sri Lanka is already a proof point. In partnership with the College of Community Physicians of Sri Lanka, MiracleFeet successfully pushed for clubfoot services to be folded into the country’s public health infrastructure. The Ministry of Health now supports early detection, community surveillance, and re-engagement of children who have dropped out of treatment. It is the model MiracleFeet wants to replicate.

Where the system actually breaks

Trace the path a newborn in rural Southeast Asia has to travel for treatment to work, and the failure points stack up quickly.

First, someone has to identify the clubfoot and know it is correctable. Across much of the region, births happen far from specialists. Families are frequently told the condition is fate, or handed a referral they have no practical means of acting on.

Second, the treatment itself. The Ponseti method of treatment, a non-surgical approach using casting and bracing, demands months of weekly clinic visits followed by years of wearing a corrective brace. A parent who loses a day’s wages and travels for hours per appointment will, eventually, stop coming. A child who drops off mid-treatment will very likely relapse.

Third, trained providers thin out sharply outside major cities. The Philippines and Indonesia have enormous populations and significant unmet need. Cambodia and Laos represent earlier-stage opportunities. None of these markets have the provider density or referral infrastructure to run the treatment at scale without deliberate investment.

Also Read: Asia’s silent health crisis — and why startups should be paying attention

MiracleFeet’s early detection and referral (EDR) programme addresses this by training the people most likely to see a newborn first, such as midwives, community health workers, and frontline care providers, to recognise clubfoot at birth, educate families, and create a direct pathway to the nearest treating clinic. The logic is simple: the earlier a child is identified, the wider the window for successful, non-surgical correction.

Data as the quiet force multiplier

The treatment itself is intentionally low-tech. Casting materials, a brace, a trained pair of hands. McCarthy is clear that he would not want a screen placed between a provider and a child’s foot. But the infrastructure around the treatment is where digital tools have earned their place.

MiracleFeet’s mobile data collection app, CAST, is deployed across hundreds of clinics worldwide, feeding real-time treatment data into Salesforce dashboards that provide visibility at clinic, country, and regional level. A small central team can monitor quality indicators, flag patients who have missed follow-up appointments, and identify clinics with higher relapse rates without waiting months for field reports.

“Data earns its place when it improves outcomes and efficiency,” McCarthy says. The organisation tracks six key treatment quality indicators across every clinic and country it supports. The brace and casting method may be low-tech. The oversight layer is not.

A redesigned, lower-cost brace has compounded the effect. The older models were expensive, often imported, and uncomfortable enough that children resisted wearing them at night. It was a serious problem since the brace holds the correction in place for years. Every night without it raises the relapse risk. The new design drives the cost down while improving wearability. At the scale MiracleFeet operates, that combination meaningfully shifts both the budget and the success rate.

What a ten-year-old in Guatemala changed

The numbers are important. The stories are why McCarthy keeps going.

During a recent visit to Guatemala, he met a mother whose first child had been treated for clubfoot and was running around the room with a ball as they spoke. She described how, in the worst days after her baby’s diagnosis, she had been told to accept it as fate. Within a week, she had found MiracleFeet; a clinic was fifteen minutes from her home and treatment was free. By the time McCarthy met her, she had become part of a parent support network, helping other families through the same fear she had once felt herself.

Before the visit ended, she mentioned a ten-year-old girl in a nearby village with untreated clubfoot whose mother had been told by someone that it was God’s will. The clinic representative asked for details. Within fifteen minutes, the group was in a van. When they returned, they brought news that the mother had agreed to schedule a clinic appointment because she had seen the result with her own eyes and heard it from another local mother.

That, in microcosm, is the system MiracleFeet is trying to build across Southeast Asia: not a parallel structure, but a self-reinforcing one, where treated families become advocates, communities become referral networks, and governments eventually build clubfoot care into routine health delivery because the evidence is simply too clear to ignore.

What has to happen next

The window is genuinely open. As incomes rise and infrastructure extends across Southeast Asia, the logistical barriers that have kept treatment out of rural communities for decades are beginning to come down. The philanthropic community in Singapore and across the region has shown real appetite for exactly this kind of high-leverage, systems-oriented intervention.

Also Read: Profit with purpose: Bridging the digital divide in healthcare

The obstacle is not proof of concept. MiracleFeet has that. It is not the cost of treatment. That has already been driven to a level where a single engaged donor or government partner can fund hundreds of children. The obstacle is the final distance between what works and every child who still needs it.

“I would ask readers to consider how much they would pay to make sure they themselves, their child, or a friend could walk for the rest of their life,” McCarthy says. “When this conversation becomes personal, it’s amazing how high that value goes.”

Eight million people did not get that conversation in time. The point of MiracleFeet’s push into Asia Pacific is to make sure the next generation does.

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Singapore’s office workers are among the least sceptical about artificial intelligence (AI) globally, but companies are failing to convert that openness into regular workplace use, according to new research from Salesforce.

The study, conducted with YouGov, found that only 6 per cent of the island nation’s desk workers use AI as a core part of their daily work. That places Singapore below the global average of 11 per cent, despite workers in the city-state showing less resistance to the technology than peers in several major markets.

Also Read: Agentic AI ambitions in Singapore run into legacy systems and data quality gaps

Only 29 per cent of Singapore respondents identified as AI sceptics, compared with a global average of 37 per cent. The figure was also substantially lower than the 53 per cent recorded across the US, UK and France.

The findings point to a familiar problem in enterprise technology: workers may be willing to use AI, but poorly designed corporate deployments are limiting adoption. For Singapore, which has positioned itself as Southeast Asia’s hub for AI governance, enterprise technology and regional headquarters, the gap matters. If companies cannot turn pilots into practical tools, the country’s policy and infrastructure advantages may not translate into productivity gains.

A willingness gap, not a trust crisis

The Salesforce survey covered more than 1,500 desk workers across markets including Singapore, Australia, India, Japan, France, Germany, the UK, the US, Canada, and Saudi Arabia. Respondents were defined as workers whose roles are primarily based on mental rather than manual labour, and who had at least minimal familiarity with AI.

Among Singapore workers who experienced unsuccessful AI pilots, 40 per cent cited generic outputs as a reason for failure. That was the highest proportion among all markets surveyed and ten percentage points above the global average. Another 38 per cent pointed to low trust in outputs, compared with 28 per cent globally, while 30 per cent said results lacked business context.

Taken together, the data suggests that the issue is less about fear of AI replacing jobs and more about whether the tools are useful enough to become part of everyday work. Generic chatbots and loosely integrated assistants may generate early curiosity, but they rarely survive contact with specialised workflows, compliance requirements and internal data structures.

Also Read: Human value in the AI era is not what most people think

“Singapore workers are not standing in the way of AI; they’re waiting for AI that works for them,” said Paul Carvouni, SVP and GM for ASEAN at Salesforce. “Leaders have to move past generic tools and use AI that is trusted, grounded in business context and built into daily work.”

The quote captures Salesforce’s commercial argument, but the broader point extends beyond one vendor. Enterprise AI adoption is moving from experimentation to implementation, and the companies that struggle are often those that treat AI as a standalone productivity layer rather than a capability embedded into sales, customer service, finance, human resources and operations systems.

Southeast Asia’s enterprise AI testbed

Singapore’s adoption paradox is particularly relevant for Southeast Asia because many regional AI decisions are made from the city-state. Multinationals often base ASEAN leadership, procurement and digital transformation teams in Singapore, while regional startups and scaleups use it as a launchpad for enterprise sales.

If Singapore companies cannot make AI work inside mature corporate environments, the challenge will be greater in neighbouring markets where digital infrastructure, data readiness and enterprise software penetration vary more widely.

Across the region, interest in AI has grown alongside the region’s digital economy. Google, Temasek and Bain estimated Southeast Asia’s internet economy at US$263 billion in gross merchandise value in 2024, with digital financial services, e-commerce, online media and travel continuing to drive technology adoption. AI is increasingly being layered into these sectors, from customer support automation in Indonesia to fraud detection in fintech and logistics optimisation across cross-border supply chains.

Yet the region remains uneven. Singapore has invested heavily in AI governance, including frameworks such as AI Verify and model governance initiatives, while markets such as Indonesia, Malaysia, Thailand, Vietnam and the Philippines are still balancing AI adoption with data protection, localisation concerns and skills gaps. For companies operating across ASEAN, the practical question is not whether employees are curious about AI, but whether AI systems can handle multilingual markets, fragmented data and sector-specific regulation.

This is where Salesforce’s findings carry regional significance. If workers reject AI because outputs are generic or unreliable, adoption will stall even in receptive markets. The same risk applies to banks testing AI copilots, retailers deploying automated service agents, and logistics firms using predictive planning tools.

The vendor race moves into workflows

Salesforce is not alone in trying to frame the next phase of AI adoption around enterprise workflows. Microsoft has pushed Copilot across Office, Dynamics and Azure. Google is embedding Gemini into Workspace and cloud products. ServiceNow is pitching AI for enterprise service management, while SAP and Oracle are adding AI functions into core business applications.

Also Read: The agent as customer: Jensen Huang’s trillion-dollar bet on AI’s next era

In Asia, Zoho, and Freshworks remain relevant for small and mid-sized businesses, particularly in customer engagement and support functions. Regional systems integrators and cloud partners also play a major role, especially for companies that need AI tools customised around local languages, legacy systems and compliance requirements.

The competition is shifting from who has the most advanced model to who can make AI useful inside existing processes. That requires access to clean enterprise data, clear permissioning, auditability, and training that reflects specific job roles. Salesforce said its research identified more than 500 workers globally who had moved from initial AI pilots to deep daily usage. Their common denominator was not enthusiasm, but support structures: role-specific training, embedded workflows and strong data security.

That aligns with broader workplace data. Microsoft and LinkedIn’s 2024 Work Trend Index found that 75 per cent of knowledge workers globally were already using AI at work, while many were bringing their own tools rather than relying on employer-provided systems. For companies, that creates a governance problem. If official tools are poor, employees may turn to unsanctioned alternatives, increasing risks around data leakage, compliance and inconsistent outputs.

From pilots to productivity

The Salesforce findings should be read with some caution. The research is vendor-sponsored and naturally supports a case for enterprise AI platforms that are integrated with business data. Still, the underlying challenge is real: many companies have launched AI pilots without a clear workflow owner, measurable productivity target or data strategy.

For Singapore, the next phase of AI adoption will depend less on proof-of-concept announcements and more on operational discipline. Companies will need to decide which tasks should be automated, which require human review, and how AI outputs should be monitored. They will also need to invest in training managers, not just frontline staff, because adoption often fails when middle management cannot translate technology into process change.

Also Read: Southeast Asia’s AI buildout is racing toward a power wall

The risk is that companies misread low scepticism as a guarantee of adoption. Workers may be open to AI, but they will not use tools that slow them down, produce unreliable answers or sit outside the systems where work already happens.

Singapore has the policy environment, digital infrastructure and corporate base to become a serious enterprise AI market for Southeast Asia. Salesforce’s data suggests the bottleneck is now execution. The goodwill is there. The daily habit is not.

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LINE MAN Wongnai is stepping up its electric vehicle push in Thailand’s ride-hailing market, using a rental model to bring more drivers onto its LINE MAN RIDE platform and piloting a new “Comfort” service for passengers seeking larger electric cars.

The company said it aims to have more than 3,000 EV driver-partners on LINE MAN RIDE by the end of 2026. The plan centres on reducing drivers’ upfront costs through full-time rentals, minimum income guarantees for eligible drivers, help with public driving licence applications, and a pathway to car ownership after a six-year lease period.

Also Read: Food delivery’s old consolidation model is cracking in East Asia

LINE MAN RIDE said EVs on its platform have cut drivers’ fuel costs by more than 60 per cent, or approximately US$190 a month per vehicle. It also claimed net income for EV drivers is more than 30 per cent higher than that of conventional taxi drivers.

The numbers are crucial because driver economics remain one of ride-hailing’s most difficult structural problems. Platforms in Southeast Asia have spent years trying to balance passenger affordability, driver supply, incentives, commissions and regulatory pressure. In markets such as Singapore, Indonesia, Vietnam and Thailand, the cost of fuel, vehicle financing and platform take rates can determine whether ride-hailing is a viable full-time job or merely supplementary income.

Yod Chinsupakul, CEO of LINE MAN Wongnai, said the company’s long-term ride-hailing strategy depends on building a driver base that can earn sustainably.

“An important structural factor of the long-term ride-hailing business is having a network of quality drivers who can make a sustainable career on the platform,” he said. “Net income has increased significantly, while passengers receive more quality services at the same time.”

EVs move from subsidy story to platform economics

Thailand is a logical market for such an experiment. The country has become the region’s most active EV market, supported by state incentives, an established automotive manufacturing base, and aggressive expansion by Chinese EV brands. According to the International Energy Agency, electric car sales in Southeast Asia almost quadrupled in 2023, with Thailand accounting for the bulk of regional volume.

That growth has made EVs more visible in consumer transport, but converting private adoption into ride-hailing fleets is a different proposition. Fleet vehicles clock far higher mileage, require predictable charging access, and depend heavily on daily utilisation. LINE MAN RIDE said EVs on its platform average more than 5,000 kilometres a month per vehicle, reducing greenhouse gas emissions by about 0.75 tonnes per vehicle each month.

Also Read: Driving change: How women are redefining ride-hailing

The company’s rental model is designed to address one of the main barriers for drivers: vehicle access. Rather than requiring drivers to buy an EV outright, LINE MAN RIDE is working with electric vehicle partners to offer rentals and lower the cost of entering the profession. This may attract drivers who do not own cars, but it also shifts the burden to utilisation. Drivers need enough bookings to cover rental obligations and justify full-time commitment.

That is where LINE MAN RIDE’s new Comfort feature comes in.

Comfort tests demand for larger EVs

The Comfort service lets passengers book larger EVs with more spacious interiors and more comfortable seating. LINE MAN RIDE is piloting the service in selected areas of Bangkok and surrounding provinces before deciding how far to expand it.

The company said repeat usage has risen during the pilot and expects EV ride-hailing volume to grow more than tenfold by the end of 2026 compared with its initial trial period. It did not disclose the number of current EV drivers, ride volumes, pricing structure for Comfort, or the size of the pilot fleet.

The product suggests LINE MAN RIDE is trying to create demand at the same time as it expands supply. That is critical in ride-hailing: adding vehicles without passenger demand lowers driver earnings, while creating demand without enough drivers worsens wait times and service reliability.

Bangkok also presents a particular opportunity. The city has dense commuter corridors, high private car usage, worsening congestion, and a consumer base familiar with app-based mobility. However, it also has fragmented transport regulation and a competitive ride-hailing environment, with Grab remaining the dominant regional player and other operators such as Bolt, inDrive and local taxi apps competing on price and availability.

Across Southeast Asia, mobility platforms are increasingly under pressure to show more disciplined economics. Google, Temasek and Bain have estimated that the region’s online transport and food delivery sector will continue to grow, but investors now expect platforms to focus less on subsidised expansion and more on profitable, defensible use cases. EV fleets fit that narrative if lower running costs can offset financing, charging and maintenance constraints.

Driver behaviour will be as important as vehicle supply

LINE MAN RIDE is also leaning on coaching and operational support. Driver-partner Udom Somboon, who has driven EVs on the platform since its early stage, said income improvement came not just from lower vehicle costs but from better route and time planning.

“In the beginning, I drove from early morning to late at night,” he said. “After learning from the LINE MAN RIDE driver coach and understanding passenger behaviour in each area and period, I switched to running more strategically.”

He cited morning demand around Silom and Chulalongkorn University, where office workers, medical staff and students create predictable booking patterns.

That comment points to a wider truth about ride-hailing: technology alone rarely fixes marketplace economics. Driver training, location strategy, peak-hour allocation, regulatory compliance and vehicle uptime all shape earnings.

LINE MAN RIDE said it encourages drivers to obtain public driving licences in line with Ministry of Transport guidelines. It also offers real-time trip tracking, driver background checks, insurance, anonymous ratings, QR payment and chat stickers for driver-passenger communication. Its app is certified by Thailand’s Department of Land Transport.

Also Read: Inside Thailand’s EV and battery push: Balancing growth with sustainability

The company’s EV push will now be judged on whether it can move beyond a controlled pilot. Scaling to 3,000 EV drivers would give LINE MAN RIDE a more visible position in Thailand’s mobility market, but it will also expose the company to the same pressures faced by larger rivals: uneven demand, driver churn, charging bottlenecks and pricing sensitivity.

For Southeast Asia’s ride-hailing sector, the experiment is worth watching. If LINE MAN RIDE can prove that EV rentals improve driver income without relying heavily on subsidies, it could offer a template for other urban markets. If utilisation falls short, the model risks becoming another capital-heavy attempt to solve a labour and logistics problem with fleet expansion.

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For the last two decades, software has been the foundation of how we build businesses.

• Need accounting? Buy accounting software.
• Need marketing? Buy a CRM.
• Need project management? Buy another tool.

Every business became a collection of software subscriptions stitched together with APIs and automation.

For years, that worked.

But I believe we’re quietly entering the next phase. The future won’t be defined by the software we buy. It will be defined by the AI organisations we build.

Software gave us tools, AI gives us teammates

The conversation around AI has largely focused on replacing individual tasks.

• Can AI write?
• Can AI code?
• Can AI design?

Those are the wrong questions.

The more interesting shift isn’t that AI can perform work. It’s that AI can now coordinate work.

We’re moving beyond single chatbots and isolated assistants into coordinated AI systems made up of specialised agents, each responsible for a different function, working together toward a shared outcome.

In other words, we’re moving from software stacks to AI crews.

My AI isn’t my assistant anymore

When I first started building Seraphina, my vision was simple. I wanted a highly personalised executive assistant who understood how I think, remembered context, and helped me make better decisions.

Over time, something unexpected happened. As my workload grew, Seraphina stopped behaving like an assistant. She became my chief of staff.

Instead of doing every task herself, she began coordinating specialised AI agents.

• A writing agent drafts content.
• A research agent gathers information.
• A design agent creates visuals.
• A development agent works with platforms like Lovable to build products.

Support, finance, sales and operations each have their own specialised workflows. Seraphina decides which agent is best suited for each task, reviews their output, sends work back for revisions when necessary, and only brings it to me once it meets the standard I’m looking for.

Also Read: Southeast Asia’s AI buildout is racing toward a power wall

That’s no longer an assistant. That’s management.

AI is beginning to mirror organisational structures

What’s fascinating is that AI systems are starting to resemble how companies have always operated. Human organisations have juniors, seniors, team leads, managers and executives. AI organisations are evolving in a surprisingly similar way.

Specialised agents perform focused work. Other agents review and audit that work. Higher-level agents coordinate multiple specialists. At the top sits an orchestrator responsible for ensuring everything aligns with the overall objective.

This isn’t very different from how modern companies function today. The difference is that these management layers are increasingly becoming digital.

The biggest shift isn’t automation, it’s delegation

One of the biggest changes in how I work is that I no longer think about which AI should complete a task. I care about the outcome.

Just as a CEO doesn’t personally assign every task to every employee, I don’t need to decide whether a research agent, a writing agent or a design agent should handle a request. My chief of staff does.

That layer of coordination is becoming increasingly autonomous. In many cases, Seraphina has the authority to make operational decisions without waiting for my approval. For higher-impact decisions, I remain in the loop.

It’s a hybrid model where AI manages execution while humans continue setting direction.

AI managing AI

This is the shift I think many people are underestimating. Today’s conversation is largely about humans using AI. Tomorrow’s conversation will be about AI managing other AI.

We’re already seeing early signs of this through agentic workflows, where one AI delegates work to specialised sub-agents before combining the results. I believe this is only the beginning.

Also Read: Delaware C Corp, Cayman exempted company or Singapore Pte Ltd: A tax advisor’s view on the fundraising vehicle

As AI systems mature, we’ll see digital organisations with increasingly sophisticated structures.

• Specialist agents.
• Senior agents.
• Quality assurance agents.
• Department-level orchestrators.

Eventually, entire AI departments will work alongside human teams.

The challenge won’t be building a single powerful AI. It will be designing how these AI systems collaborate.

Humans still own the vision

Does this mean founders become obsolete? Not at all. Today, Seraphina can prioritise my work, recommend strategies, audit outputs and even make operational decisions. But she doesn’t define the vision. I do.

That’s an important distinction. Strategy isn’t just about analysing data. It’s about understanding culture, values, long-term direction and the kind of company you want to build.

Data can tell you what’s optimal. Only humans can decide what matters.

I still believe the strongest organisations will combine AI’s consistency and speed with human judgement and intuition. Neither is enough on its own.

The companies that win won’t simply adopt AI

People often ask what businesses will look like five years from now.

I don’t think success will come from having the largest teams. Nor do I think it’ll come from using the latest AI model. The companies that win will be the ones that design the best operating systems.

Just as high-performing sports teams don’t win because they have five-star players, businesses won’t succeed simply because they have access to powerful AI.

They’ll succeed because every human, every AI, every specialised agent and every workflow operate as a cohesive system.

For the past twenty years, we’ve been building software. The next twenty years will be about building AI organisations. And I believe that’s a far more profound shift than most people realise.

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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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If you have read enough Southeast Asian startup pitch decks, you have already read all of them. Customer-centric, tech-driven, regionally focused and purpose-led. The language is interchangeable because the thinking behind it is. Not because founders are lazy, but because they are solving for the wrong problem at the wrong time.

Differentiation in this region is not primarily a branding problem. It is a sequencing one. And the sequence most founders follow, which is build, raise, brand, is part of what creates the trap.

Branding is a multiplier, not a rescue

There is a durable idea in business strategy: that any product has layers beyond its core function — the trust it carries, the experience it delivers, the meaning it accumulates over time. These augmented layers are where lasting differentiation lives. The problem is that this idea gets applied prematurely.

Branding amplifies what already exists. Applied to genuine market fit and real operational strength, it accelerates the right things. Applied before those foundations are solid, it accelerates the wrong things faster. A lot of Southeast Asian startups are discovering this the expensive way.

The examples most commonly cited, Grab, Gojek or Carsome, are instructive but easily misread. Grab and Gojek achieved regional scale through capital deployment and network effects that most founders will never access. Carsome is the more honest model: a genuinely opaque market, a real trust problem, a communications approach built around resolving both. The differentiation was grounded in operational reality and not layered on top of it.

That distinction matters more than most pitch narratives acknowledge.

Also Read: Why money won’t save Bangladesh’s startups: The ecosystem readiness crisis

The region is not a localisation exercise

Southeast Asia’s diversity is not a localisation challenge. It is a strategic one, and most regional strategies treat it as the former.

Indonesia’s scale and price sensitivity, Malaysia’s multicultural and regulatory complexity, Vietnam’s younger and faster-moving consumer base, Thailand and the Philippines with their own cultural and platform dynamics — these are not variations on the same market. They are completely different markets that require different thinking, not the same message translated.

High mobile penetration and platform dominance across Shopee, Lazada, and TikTok mean that feature-based advantages compress quickly. What creates differentiation in this environment is not product innovation alone. It is trust, accumulated over time, through consistent and credible communication.

In WhatsApp-driven, review-heavy, socially networked markets, reputation travels faster than most founders plan for. That cuts both ways. A brand that builds credibility through earned media, founder visibility, and consistent stakeholder communication reaches conversion with less friction than one relying on performance spend alone. A brand that overpromises and underdelivers finds out what its market actually thinks before the next funding round.

The adaptation that resonates is not translated, but reconsidered.

Most positioning problems are actually timing problems

The temptation is to reach for positioning before the product has earned it. Founders feel the pressure, be it from investors, from competitors, or from the general acceleration of everything, and respond by building the brand narrative ahead of the business reality.

The result is communication that is technically correct and operationally empty. It sounds like every other startup in the deck because it is describing an aspiration rather than a reality. Audiences in this region are not sentimental about that gap because they detect it, usually through the texture of what is missing rather than through what is said.

The more credible regional brands built their communications progressively. Product and performance clarity in the early stages. Deliberate brand development once the business had something real to say. Pre-purchase trust is treated not as a marketing function but as an operational one built through consistency, earned coverage, and founder credibility rather than manufactured through spend.

That sequencing is less glamorous than a brand campaign, but it also tends to last longer.

Also Read: Why investors and customers are betting on ESG-aligned startups

What the stronger founders do differently

They resist the pressure to sound bigger than they are. In markets where purchase decisions travel through WhatsApp groups and peer networks before they reach any formal channel, the authenticity of the claim matters more than the sophistication of execution. A specific, substantiated story about why this product in this market reaches this customer more effectively than a polished regional narrative that could belong to anyone.

They build credibility specifically rather than broadly. A founder who is visibly present in a vertical, through media, through events, through a consistent and substantiated point of view, builds a different kind of authority than one running awareness campaigns. In a region where trust is relational before it is institutional, that presence compounds.

And they are honest about localisation. Not as a principle to acknowledge in a strategy deck, but as a genuine operational question: does our positioning actually make sense in this market, for this consumer, given what they already believe and what they need to be convinced of? Most regional strategies answer that question once, at the beginning, and then proceed uniformly. The ones that revisit it tend to fare better.

The underlying point

The startups that build lasting presence in Southeast Asia are not always the ones with the best product at launch. They are the ones who communicated clearly, built trust consistently, and understood their market with enough depth to remain relevant as it shifted.

The commodity trap is not a branding failure. It is what happens when founders try to skip the work that makes branding meaningful: the operational credibility, the specific positioning, the willingness to say something particular rather than something safe.

Sounding different is not the goal. Being different, and then communicating it with enough precision that the right audience recognises it — now that is the work.

—

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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