For years, Southeast Asia’s startup economy has rewarded speed. Founders were expected to launch quickly, localise faster than global rivals, and chase market share across a region where digital adoption often outpaced regulation. That instinct still matters. But as artificial intelligence moves from pilot projects into banking, healthcare, government services, logistics and public infrastructure, speed is no longer enough.

A different test is emerging: trust.

Malaysia’s National AI Action Plan 2026-2030, or AI Nation 2030, makes this shift explicit. One of its core foundations is “Trust via Responsible Governance”, a signal that the country wants AI adoption to be measured not only by productivity gains or startup growth, but also by whether systems can be explained, audited and governed.

Also Read: Malaysia’s AI Nation 2030 puts cities and farms at the heart of climate resilience

For startups, this should not be read simply as another compliance burden. In the coming corporate AI market, governance may become a sales advantage. Enterprise buyers will increasingly ask not just what an AI product can do, but how it was built, what data it uses, where the risks sit, and whether those risks can be defended in front of boards, regulators and shareholders.

In other words, the next competitive edge in AI may belong to companies that can make trust operational.

From black-box tools to board-level accountability

The clearest sign of this shift is the AI-Aware Stewardship initiative under Malaysia’s AI Action Plan. The policy targets corporate boards and seeks to prepare them to oversee risks linked to AI and emerging technologies.

The targets are specific. Malaysia wants 30 per cent of large public listed companies to adopt emerging technology governance best practices by 2028, rising to 50 per cent by 2030. To move this from aspiration to practice, the Securities Commission Malaysia, Bursa Malaysia, the National AI Office, the Personal Data Protection Commission, and the Ministry of Science, Technology and Innovation are coordinating updates to the Malaysian Code on Corporate Governance and Listing Rules.

Large listed companies are expected to be encouraged to publish an Emerging Technology Governance Statement in their annual reports. They may also use maturity scorecards to show shareholders how prepared they are to manage digital and AI-related risks.

That changes the buying environment for B2B startups. A bank, telco, insurer or major retailer will find it harder to adopt a black-box AI product if it cannot explain how the system works, what safeguards are in place, or where accountability lies when something goes wrong. Procurement teams may still care about price and performance, but boards will increasingly care about audit trails.

For founders selling into large enterprises, this means the product demo is no longer enough. The due diligence file matters just as much.

The rise of audit-ready AI

One of the more demanding elements of the new framework is the push for risk mapping. Large companies will be encouraged to maintain a board-approved AI system inventory and risk assessment map, subject to internal audit review.

Also Read: Malaysia wants 300,000 AI jobs by 2030. Talent will decide if it gets there

This has direct implications for vendors. If a startup’s product sits inside a corporate AI inventory, the enterprise customer will need details about the system’s model, data, dependencies, controls and failure risks. A vendor that cannot provide these details may slow down the buyer’s approval process, or be dropped altogether.

The practical response is for startups to become audit-ready by design.

That starts with data provenance. Founders need to know where their training and operational datasets came from, how personally identifiable information was handled, whether data was licensed properly, and how usage aligns with Malaysia’s emerging data-sharing frameworks, including the Akta Perkongsian Data 2025.

It also requires model explainability. Not every AI system can be made simple, especially those built on complex machine learning methods, but startups should be able to explain how decisions are generated, what variables matter, and where human oversight is required.

Bias and safety logs will also become more important. Startups should be able to show how they test for unfair outcomes, handle edge cases, document incidents, and update models when risks appear. This is especially relevant in Southeast Asia, where AI tools often operate across multiple languages, dialects, income groups and cultural contexts. A model trained for one market may behave differently in another.

The companies that build this documentation early will have an advantage. They can plug more easily into enterprise governance processes, shorten procurement cycles, and reassure investors that the business will not collapse under regulatory scrutiny as it scales.

A risk-based route for founders

A common fear among startups is that AI regulation will favour incumbents with large legal teams. Malaysia’s plan appears to recognise this risk by proposing a hybrid, risk-based governance framework.

Under this approach, not all AI systems are treated the same. Lower-risk applications can operate under voluntary guidance, while higher-stakes uses in areas such as finance, healthcare or communications may face tighter rules overseen by sector regulators, such as Bank Negara Malaysia or the Malaysian Communications and Multimedia Commission.

This distinction matters. A startup building an AI tool for internal workflow automation should not face the same burden as one automating credit decisions or clinical recommendations. Risk-based governance, if implemented clearly, can give young companies room to innovate while giving enterprises a clearer path for adoption in sensitive sectors.

Also Read: From paddy fields to small shops, Malaysia maps an inclusive AI future

Malaysia is also introducing a National AI Classification initiative, led by the National AI Office, to certify “Made-by-Malaysia” AI systems. The certification is expected to evaluate the AI lifecycle, from compute and data layers to the final model.

For local startups, this could become more than a badge. Certified companies may gain prioritised access to the National Data Exchange, compute voucher programmes, local supply chain registries, government procurement opportunities and large corporate tenders. That would make governance a market access tool, not just a legal exercise.

Why this matters beyond Malaysia

Malaysia’s approach also sits within a broader Southeast Asian moment. Governments across the region are trying to balance AI adoption with public trust. Singapore has pushed governance through tools such as AI Verify, Indonesia and Thailand are examining digital rules through their own policy lenses, and ASEAN has been building regional guidance for responsible AI.

For startups, the regional lesson is simple: compliance designed only for one buyer or one jurisdiction will not be enough. A Malaysian startup seeking to sell across ASEAN should design internal controls that can travel. That means aligning safety, data and documentation practices with international standards and emerging regional frameworks, including the ASEAN AI Safety Network.

This is particularly important because Southeast Asian startups often scale regionally before they are fully mature internally. A company may start with a Malaysian bank, then pitch a Singaporean insurer, an Indonesian fintech, or a Philippine conglomerate. Each buyer may have different rules, but all will increasingly ask similar questions about data, accountability and risk.

Governance as a growth engine

For founders, the roadmap is becoming clearer. Start with an internal AI register that maps models, datasets, third-party APIs, security controls and human oversight points. Train engineering, product and leadership teams to understand responsible AI, not as a slogan but as part of product management. Build documentation that can withstand review by enterprise risk teams, investors and regulators.

The bigger point is cultural. AI governance should not sit only with lawyers at the end of a sales process. It needs to be built into product design, model development, customer onboarding and post-deployment monitoring.

Also Read: Malaysia’s sovereign AI bet: Local context becomes the next startup moat

Malaysia’s AI Nation 2030 plan suggests that the region’s AI market is entering a more mature phase. Startups that treat governance as paperwork may struggle. Those that treat it as infrastructure may find it opens doors.

The next wave of AI adoption in Southeast Asia will not be won by the fastest builders alone. It will be won by companies that can show their systems work, explain why they can be trusted, and prove they are ready for the scrutiny that comes with scale.

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Three months ago, I sat in a quarterly risk committee meeting at an Indonesian bank, watching a climate risk update presented in twelve slides over fifteen minutes. The presentation covered taxonomy alignment, sustainable finance commitments, and progress against the bank’s net-zero pathway. It was professional, well-researched, and accurate. It also did not mention the bank’s exposure to physical flood risk across its real estate book, the transition risk inside its coal-related loans, or the basis on which any of those risks were being priced into provisioning.

After fifteen years inside Indonesian risk functions, I have come to see that pattern as the defining shape of climate risk inside the region’s banking sector. The reporting infrastructure has matured rapidly. The provisioning infrastructure underneath has not. The gap between what banks disclose about climate and what their balance sheets actually carry has become the most consequential unpriced exposure in ASEAN banking.

The framework that was built

Indonesia’s OJK has, over the past three years, built one of the more thoughtful climate risk frameworks in ASEAN. The Sustainable Finance Roadmap, the green taxonomy, the climate disclosure requirements, the architecture is in place, and Singapore, Malaysia, and the Philippines have moved in parallel. Most major banks now publish annual climate disclosures, often aligned to TCFD recommendations. The disclosures are real work. They are not the same thing as risk management.

Where the exposure actually sits

Three categories of climate exposure inside Indonesian bank balance sheets are visible enough to name and large enough to matter.

Physical climate risk in property and infrastructure. A significant share of commercial real estate financing sits in coastal cities exposed to subsidence, tidal flooding, and increasingly severe wet-season rainfall. The collateral underlying these loans is rarely revalued against forward-looking climate scenarios. The provisioning logic assumes the asset retains its current value. The asset, increasingly, does not.

Transition risk in carbon-intensive sectors. Loans extended to coal, palm oil, and heavy industrial sectors carry exposure to a rapidly evolving regulatory environment, domestic carbon pricing, the European Union’s deforestation regulation, and sector-specific phase-out commitments. The credit framework that priced these loans five years ago did not anticipate that some underlying assets could become stranded inside the loan tenor.

Also Read: Indonesia’s AI hiring gap is real, just not 28×

Cascading climate risk in adjacent sectors. The most under-discussed exposure is not the direct one. It is the credit risk inside borrowers whose own portfolios, supply chains, or customer bases are climate-exposed. A logistics company is not a climate-exposed borrower in the conventional sense. A logistics company whose largest customer is a flood-prone factory is.

Why the framework misses it

The disclosure architecture and the provisioning architecture were built for different purposes, and they have not been reconciled.

Disclosure frameworks make the institution’s climate position legible to external stakeholders. They are not designed to drive loan-level loss provisioning, capital adequacy, or pricing inside the bank.

Provisioning frameworks were built before climate was on the regulatory radar. The expected credit loss model accommodates forward-looking information in principle, but most banks still apply it with historical loss data and short-horizon scenarios. Climate risk operates on a longer horizon than the provisioning logic was built for.

What is starting to work

A few institutions are beginning to close the gap.

Climate-adjusted credit reviews. Some banks now incorporate climate scenarios into credit committee processes for large or long-dated exposures. The discipline of forcing the question into the same room as the lending decision is producing more honest pricing.

Sector concentration limits with climate triggers. Some institutions set internal limits on exposure to high transition-risk sectors and lower those limits as policy clarity improves. The mechanism is imperfect. It is the closest thing to a working transition risk control I have seen in the region.

Also Read: How to get beyond the chatbot and boost your AI productivity

Collateral revaluation under climate scenarios. The most rigorous response I have seen comes from institutions revaluing real estate collateral under multiple climate trajectories, not just the central case. The revaluation rarely changes a single loan’s status. It consistently changes the capital the bank holds against the portfolio.

What needs to happen

Three moves would meaningfully reduce systemic exposure.

Connect disclosure to provisioning. The climate analyses that flow into TCFD-style reports should also flow into expected credit loss calculations, capital planning, and pricing. The reports and the reserves should be telling the same story.

Require forward-looking collateral valuation for long-dated exposures. Where loan tenors extend across plausible climate horizons, the collateral assumption should be tested against those horizons rather than against present-day comparables.

Bring transition risk into supervisory stress testing. ASEAN supervisors already run credit, liquidity, and market stress tests. They should be running transition stress tests, modelling specific policy scenarios across carbon-intensive sectors and measuring portfolio capital impact.

The macro stakes

Indonesia is among the most climate-exposed major economies in the world, with a banking sector whose stability matters regionally. The disclosure architecture the country has built is genuinely good. The provisioning architecture has not caught up.

The climate risk inside Indonesian bank portfolios is not theoretical. It sits on balance sheets now, accruing exposure that is not being priced, against scenarios the institutions’ own disclosures already say are coming. The bill, when it arrives, will not be paid by the disclosure framework. It will be paid by the capital base. The window to close that gap is closing.

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Singapore’s fight against scams is moving deeper into the messaging apps people use every day, after police disrupted more than 30,000 Apple iMessage accounts linked to a campaign that has already caused about SGD2.2 million (US$1.7 million) in losses.

The Singapore Police Force said its Cyber Command has been detecting and disabling accounts tied to the scam since June 2026. The loss figure has climbed quickly: on August 5, police had put the damage at SGD1.2 million (~US$940,000). In other words, reported losses rose by nearly US$800,000 in a matter of weeks.

Also Read: Almost got “digitally arrested.” India needs Singapore’s playbook before the next scam call

The case underlines a problem that is becoming familiar across Southeast Asia: scammers are not relying only on old-fashioned SMS blasts or suspicious phone calls. They are moving across encrypted messaging apps, social platforms, marketplaces and ad networks, looking for whichever channel has the least friction and the most trust.

In this campaign, fraudsters sent iMessages pretending to be courier companies such as NinjaVan, J&T Express and SPX Express, as well as government agencies and financial institutions. The messages directed recipients to spoofed websites designed to look like the real thing. Victims were then asked to make a small payment or settle a fine, often by entering card or banking details.

The amounts requested may have seemed minor, but the information handed over was valuable. According to police, some victims who entered one-time passwords later discovered that their cards had been added to mobile wallets, bank security tokens had been registered on unfamiliar devices, or their accounts had been accessed without permission. Many only realised what had happened after seeing unauthorised transactions.

Why iMessage is a harder target

The campaign also exposes a regulatory and technical gap. In Singapore, SMS scams have been targeted through network-level filters and a sender ID registry, which helps prevent fraudsters from impersonating trusted organisations through text message headers.

Also Read: Phishing threats: Protecting your online shopping and banking

But iMessage runs on Apple’s own system, outside the traditional telecoms layer. That means it is not covered by the same filters and registry used for SMS. For a scammer, that difference matters. A message delivered in Apple’s blue bubble can appear familiar and personal, especially to users who do not think of iMessage as a risky channel.

Police stressed that government agencies and courier companies do not use iMessage to communicate with the public. That simple point is important because many delivery-related scams rely on timing and plausibility. In a city where online shopping, food delivery and parcel tracking are part of daily life, a message about a failed delivery or unpaid fee can feel routine enough to click.

Singapore is not alone in facing this shift. Across Southeast Asia, fraud groups have become more sophisticated in blending social engineering with real consumer habits. Delivery scams, fake toll or tax notices, investment fraud and phishing links often travel through the same apps people use to speak with family, sellers, banks and colleagues. The more commerce moves into chat, the more attractive these channels become.

New codes put pressure on platforms

The iMessage disruption comes shortly after Singapore issued new Codes of Practice under the Online Criminal Harms Act. Announced on August 17, the codes apply to seven services assessed as posing the highest scam risk: WhatsApp, Telegram, WeChat, Apple iMessage, Apple FaceTime, Google Message and Google Meet.

The services must comply by January 31, 2027, with anti-impersonation measures due earlier, by September 30, 2026.

Also Read: Inside the dark economy of crypto scams: 2024’s most lucrative fraud tactics

Messaging platforms are a major part of the scam landscape. Police said services such as WhatsApp and Telegram accounted for about 23 per cent of scam cases in 2025. That figure is significant because messaging apps are no longer just communications tools. They are customer service channels, sales channels, community spaces and, increasingly, the first point of contact between businesses and users.

For regulators, the challenge is to impose safeguards without breaking the usefulness of these platforms. Identity checks, faster takedowns and impersonation controls may help, but scammers adapt quickly. If one route becomes harder, they often move to another, whether that is an ad, a marketplace listing, a fake account or a compromised device.

This is why Singapore’s approach is widening beyond a single channel. Earlier in the week, police announced a separate Social Media Code covering Facebook, Instagram and TikTok. Together, the three platforms accounted for about 30 per cent of scam cases in 2025, with Facebook alone making up about 18 per cent.

The Social Media Code focuses on scam advertisements, a common gateway for fraud. Platforms will be required to block and promptly remove suspected scam ads, verify advertisers’ identities against government records, and prevent advertisements offering financial products or services unless the advertiser is licensed by the Monetary Authority of Singapore.

That last requirement is particularly relevant in a region where fake investment schemes remain a persistent threat. Scammers often use paid ads to create the impression of legitimacy, sometimes borrowing the faces of public figures, media brands or financial institutions. By the time an ad is reported and removed, victims may already have been funnelled into private chats or fraudulent websites.

Marketplaces also under scrutiny

Singapore is also tightening rules for e-commerce platforms. An enhanced E-Commerce Code covering Carousell, Facebook Marketplace and Facebook Business Pages will introduce stronger controls on logins from unrecognised devices.

Marketplaces have long been vulnerable because they combine informal peer-to-peer transactions with a high volume of listings. Scams can range from fake concert tickets and rental listings to non-delivery of goods and phishing links disguised as payment or delivery pages. Stronger login controls may help limit account takeovers, where criminals use legitimate-looking profiles to trick buyers or sellers.

Also Read: AI phishing is turning trust into APAC cybersecurity’s weakest link

Police said scam cases on services already covered by earlier codes fell by about 37 per cent between 2024 and 2025. That suggests platform rules can have an impact, although the latest iMessage case also shows that fraudsters keep searching for gaps.

The stakes are set to rise further. The government has proposed increasing the maximum penalty for non-compliance to S$10 million (US$7.8 million) per breach. More details are expected when the Scams (Countermeasures) and Other Matters Bill is debated in Parliament in September.

For startups and digital platforms in Southeast Asia, Singapore’s direction of travel is worth watching. The city-state often acts as a regulatory reference point for the region, especially in fintech, digital identity and online safety. Measures introduced there can influence how other markets think about platform responsibility.

For consumers, however, the immediate lesson is more basic: the channel does not guarantee the sender. A message arriving through iMessage, WhatsApp, Telegram, Facebook or TikTok may still lead to the same spoofed payment page. In the current scam economy, trust is no longer attached to the app. It has to be earned at every click.

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J&T Global Express has delivered the kind of first-half numbers that usually make public-market investors sit up.

The Hong Kong-listed logistics company reported revenue of US$7.67 billion for the first half of 2026, up 39.5 per cent year-on-year, while express delivery revenue rose 39.6 per cent to US$7.46 billion. Adjusted net profit more than doubled to US$350.6 million, and adjusted EBIT climbed 121.7 per cent to US$433.6 million.

The headline story is clear enough: J&T is still expanding quickly, still riding the growth of e-commerce, and now important enough to have joined the Hang Seng Index in June. For Southeast Asia, where the company has held the largest express delivery market share for six consecutive years, its performance also speaks to the region’s growing weight inside global logistics.

Also Read: More parcels, less profit: Logistics’ big squeeze

But beneath the strong top-line figures, J&T’s interim results tell a more complicated story. Its fastest growth is no longer coming from China, the company’s largest market by parcel volume. Its improvement in revenue per parcel appears to be driven partly by a shift in geographic mix rather than clear pricing power. And its enlarged HK$2 billion (~US$255 million) share repurchase programme raises questions about how the company is balancing investor returns against long-term network investment.

China is still huge, but growth is slowing

J&T handled 17.50 billion parcels globally in the first half, up 25.1 per cent from a year earlier. Yet that growth was far from evenly spread.

China remained the group’s biggest market by some distance, contributing 11.62 billion parcels, or about two-thirds of total volume. But parcel volume in China grew only 9.6 per cent year-on-year. That is modest compared with the company’s performance elsewhere: Southeast Asia parcel volume jumped 71.2 per cent to 5.52 billion, while “other markets”, mainly Latin America and the Middle East, rose 119.9 per cent to 365 million parcels.

The gap matters because China has long been the world’s most competitive express delivery market. Years of price wars among players such as SF Express, ZTO Express, YTO Express and others have pushed down delivery tariffs and made scale essential. J&T’s market share in China did edge up by 0.5 percentage points to 11.6 per cent, but the single-digit volume growth suggests the company may be bumping into a tougher ceiling in its largest market.

Management has framed this as a move towards better-quality growth. Group Vice President Charles Hou said J&T remains focused on “strengthening operating quality and efficiency”. That is a reasonable priority in a low-margin business. Still, for investors and regional operators, the question is whether China is becoming a cash-heavy but slower-growth base while Southeast Asia and newer markets are asked to carry the expansion story.

The revenue-per-parcel question

One of the more striking parts of J&T’s results is that revenue grew much faster than parcel volume. Overall revenue rose 39.5 per cent, while parcel volume increased 25.1 per cent. On a simple calculation, the company’s average revenue per parcel increased from about US$0.393 in the first half of 2025 to US$0.438 in the first half of 2026.

At first glance, that looks like stronger pricing power. For a logistics company, being able to earn more per parcel while still growing volume is a strong signal. But in J&T’s case, the explanation may be more about geography.

In the first half of 2025, China accounted for roughly three-quarters of J&T’s parcels. By the first half of 2026, its share had fallen to 66.37 per cent. Southeast Asia’s share, meanwhile, rose from 23.05 per cent to 31.54 per cent. Because delivery rates in Southeast Asia and other emerging markets are generally higher than in China’s fiercely competitive domestic market, a larger share of non-China parcels can lift group average revenue per parcel even without a major pricing breakthrough.

Also Read: The rise of logistics startups in Southeast Asia: How AI powers supply-chain revolution

This does not make the improvement meaningless. A healthier geographic mix can support margins, and Southeast Asia’s e-commerce market still has room to grow as online shopping penetrates smaller cities and cross-border sellers seek faster fulfilment. But it does mean the ARPU gain should be read with care. If Southeast Asian markets become more crowded, or if platform-owned logistics arms intensify competition, J&T may face the same pressure on delivery fees that has shaped China’s market.

Southeast Asia is the prize and the battleground

J&T’s Southeast Asian performance remains its strongest argument. The region delivered 5.52 billion parcels in the first half, helped by rising e-commerce adoption, social commerce, and the demand for low-cost delivery across archipelagic and emerging markets such as Indonesia, the Philippines and Vietnam.

The company has also built a dense regional network, including 127 sorting centres in Southeast Asia. That infrastructure is hard to replicate quickly and gives J&T an advantage in markets where delivery reliability can determine whether consumers continue buying online.

But it is not alone. In Southeast Asia, J&T competes with Ninja Van, Flash Express, SPX Express, Lazada Logistics, DHL eCommerce and country-specific postal and courier players. Some rivals are backed by major e-commerce platforms, giving them captive parcel flows. Others are pushing aggressively into small merchants and cash-on-delivery-heavy markets. Globally, J&T’s expansion into Latin America and the Middle East also puts it closer to established logistics groups and regional specialists with deep local networks.

That competitive backdrop makes capital allocation especially important.

A large buyback at a sensitive moment

J&T said it had completed the repurchase of 99.32 million shares and increased the size of its share repurchase plan to US$256.4 million. The company also reported total cash resources of US$2.91 billion, giving it financial room to manoeuvre.

Buybacks are not inherently problematic. They can signal management confidence, improve earnings per share, and return excess cash to shareholders. But for a logistics company still expanding across multiple regions, a repurchase plan of this size deserves scrutiny. The US$256.4 million programme is equivalent to about 73 per cent of the company’s adjusted net profit for the half-year.

The timing is also notable. J&T’s inclusion in the Hang Seng Index brings greater visibility, but also greater pressure from institutional investors and index-tracking funds. A buyback can help support market confidence during that transition. The trade-off is that every dollar used to repurchase shares is a dollar not used to strengthen sorting centres, last-mile capacity, automation, fleet efficiency, or market entry in expensive new geographies.

There is another layer to the numbers. J&T’s announcement highlights adjusted net profit, adjusted EBIT and adjusted EBIT per parcel, but does not foreground statutory net income in the same way. Adjusted metrics are useful for understanding operating performance, especially in businesses affected by non-cash charges or one-off items. Still, investors need the unadjusted picture too, because costs excluded from adjusted earnings can remain economically real.

J&T also promoted a milestone in the second quarter: global average daily parcel volume exceeded 100 million for the first time. That is operationally significant. Yet across the full first half, 17.50 billion parcels over 181 days works out to about 96.7 million parcels a day. The company is clearly operating at immense scale, but the distinction shows how selective framing can make performance appear cleaner than it is.

Also Read: Lazada unveils US$100M affiliate push to power creator-led growth in SEA

For Southeast Asia, the lesson is not that J&T is weakening. It remains a formidable logistics player with regional scale that few competitors can match. The more important point is that its future growth story now depends heavily on this region continuing to expand profitably.

If China keeps slowing and Southeast Asia absorbs more of the growth burden, J&T will have to prove that its regional dominance can translate into durable margins — not just higher group averages created by geographic mix. Its first-half results are impressive. They are also a reminder that in logistics, scale is only half the story. The harder test is whether that scale keeps producing real profits once the easy volume growth fades.

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I was recently invited to evaluate the performance of a leadership team inside a growing organisation.

The company had already gone through multiple rounds of evaluations before I arrived. Capability gaps had been mapped. Consultants had been brought in. AI adoption initiatives had been launched. Leadership workshops had been conducted. Internal reviews had been repeated.

Yet despite all the activity, nothing seemed to move the needle. The same tensions kept resurfacing.

Meetings became longer but less decisive. Teams aligned quickly but execution quality remained inconsistent. AI usage increased, yet clarity did not. Different departments blamed one another for bottlenecks. Senior leaders questioned whether employees lacked initiative. Employees quietly questioned leadership judgement.

On the surface, it looked like a capability problem. But as I facilitated several rounds of workshops and observed the patterns emerging inside the room, I saw something familiar.

The issue was not primarily incompetence. Nor resistance. Nor even the technology itself.

The organisation had slowly created an environment where certain ways of thinking became psychologically easier than others.

Agreement travelled faster than exploration. Confidence carried more social weight than uncertainty. Speed was rewarded more than reflection. And over time, the team became highly efficient at reinforcing itself.

This is becoming increasingly common inside organisations attempting large-scale transformation. Especially those accelerating AI adoption.

The shift most organisations still do not see

Many leaders assume AI exposes capability gaps. But often, AI exposes environmental weaknesses that were already there. Because before people decide, something has already shaped what they are able to see.

The modern workplace is no longer simply a collection of people making independent judgements. It is a living cognitive environment shaped by incentives, visibility pressures, organisational fear, performance systems, operational velocity, AI interfaces, and social signalling.

Inside these environments, even highly intelligent teams can become fragile. Not because they lack intelligence. But because they become too synchronised. Too internally coherent. Too efficient at confirming themselves.

This is where transformation efforts quietly begin to drift. Not at the level of strategy decks or implementation roadmaps. But at the level of perception itself.

When environments reward agreement over exploration, organisations slowly lose their ability to detect weak signals, challenge assumptions, or see emerging risks clearly. And because modern organisations increasingly mistake speed for intelligence, this drift often remains invisible until performance deterioration becomes undeniable.

Also Read: Four lessons from GITEX Global 2025: What Dubai’s AI playbook means for Southeast Asia

The myth of the high-performance team

Research increasingly supports this tension. A 2024 study published in PLOS Computational Biology found that moderate confirmation bias can improve group learning under certain conditions. But once confirmation bias crosses a critical threshold, especially in smaller groups, performance begins to deteriorate and polarisation emerges. The study found that small groups lacked sufficient buffering against dominant assumptions and became more vulnerable to suboptimal collective outcomes.

This directly challenges one of the most celebrated myths in modern business culture: the mythology of the elite small team.

Lean teams. Tiger teams. Founder-mode teams. AI-native task forces.

The assumption is simple: smaller equals sharper.

But small high-performing teams can also create ideal conditions for hidden distortion: compressed dissent, shared blind spots, social conformity, unquestioned assumptions, and escalating certainty.

The danger is not low intelligence. The danger is interpretive convergence.

Everyone slowly begins seeing through similar lenses while believing they are thinking independently. The organisation becomes operationally faster while perceptually narrower.

AI is accelerating interpretive convergence

AI intensifies this dynamic further. Because AI does not merely accelerate productivity. It accelerates convergence.

When teams increasingly rely on the same models, same summaries, same prompts, and same machine-generated framings, cognitive diversity quietly collapses beneath the appearance of intelligence. People begin inheriting similar interpretations before genuine discussion even starts.

A recent Harvard Business Review experiment demonstrated this clearly. Executives who consulted ChatGPT during forecasting exercises became more optimistic, more confident, and less accurate than groups relying on peer discussion alone. AI-generated confidence altered judgement quality itself. Participants became more certain while becoming less correct.

This is not simply an AI problem. It is an environmental amplification problem. AI magnifies the conditions already embedded inside the system.

If the environment rewards speed over reflection, AI accelerates impulsivity. If the environment suppresses dissent, AI amplifies consensus. If the environment mistakes confidence for clarity, AI industrialises overconfidence.

This is why many organisations now appear more optimised yet less adaptive. More informed yet less perceptive. More connected yet less cognitively resilient.

Also Read: Not every cheque keeps every door open: Why Southeast Asian founders must rethink smart capital

The real competitive advantage is changing

Many organisations still operate using an outdated model of intelligence. They believe better outcomes come primarily from better individuals.

But increasingly, intelligence behaves environmentally. The quality of judgement emerging from a team depends heavily on the conditions surrounding perception itself.

This is why some highly credentialed organisations repeatedly fail under pressure while less celebrated teams adapt remarkably well despite fewer resources.

The difference is often not raw intelligence alone. It is the architecture surrounding judgement.

The organisations that will thrive in the AI era are unlikely to be the ones that simply deploy the most advanced tools. They will be the ones capable of protecting judgement itself.

Organisations capable of designing environments where reality remains visible even under acceleration. Where disagreement remains psychologically survivable. Where dissent is structurally protected rather than socially punished. Where multiple interpretations can coexist long enough for better thinking to emerge. Where AI supports cognition without becoming cognitive authority. And where reflection is not mistaken for inefficiency.

The next phase of organisational design

This requires a fundamentally different approach to transformation. Not just capability building. Not just AI implementation. But deliberate design of the environments shaping judgement itself.

Organisations may soon need to treat cognitive environments the way previous generations treated operational systems: something that must be designed, audited, stress-tested, and continuously recalibrated.

This means creating structures that intentionally slow premature consensus. Designing meetings where dissent is expected rather than awkward. Separating exploration from decision pressure. Ensuring AI outputs are challenged rather than absorbed passively. Rewarding signal detection, not merely execution speed. And teaching leaders to recognise when organisational coherence is slowly becoming distortion.

Because the greatest risk facing organisations today is no longer simply making bad decisions. The greater risk is creating environments where bad decisions increasingly feel unquestionably correct.

And once that happens, organisations do not merely lose accuracy. They lose the ability to see that they are drifting at all.

The organisations that will win next

The future advantage will not belong to organisations that move the fastest. It will belong to organisations that can still think clearly while moving fast.

Organisations capable of preserving judgement under acceleration. Organisations capable of protecting cognitive diversity while scaling AI. Organisations capable of designing environments where reality can still interrupt consensus before consensus becomes collapse. That capability will become increasingly rare.

Because most organisations are still investing heavily in intelligence amplification while neglecting judgement preservation. But in the AI era, amplification without calibration becomes dangerous. And transformation without ecological awareness eventually creates fragility disguised as performance.

The organisations that thrive next will understand something others do not: Before transformation succeeds externally, the environment shaping perception internally must first become visible. Because before decisions fail, environments drift. And the organisations that learn to detect that drift early may become the few still capable of seeing clearly while everyone else mistakes acceleration for intelligence.

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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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Millions of travellers already have phones that can help them stay connected abroad. They book flights with apps, keep boarding passes in digital wallets, use maps to plan, rely on ride-hailing after landing, and message throughout their trips. But many travellers still do not know if their phone supports eSIM.

This is the main contradiction in travel connectivity. The technology is easier to get, the benefits are clear, and travel eSIM use is growing fast. Still, many travellers are not used to eSIM yet.

That is why the next big opportunity in the eSIM market is not just about cheaper data or wider coverage. It is about closing the awareness gap.

A YouGov survey for Proximus Global/BICS found that only 33 per cent of people in the US, UK, and China know what an eSIM is. Another 42 per cent are not sure if their phone has one. Still, 49 per cent said they would consider using an eSIM while travelling if they understood the benefits.​

This gap is important. The problem is not a lack of demand for connectivity, but uncertainty. Many travellers are not sure how eSIM works, if it is right for them, or if they can count on it when they need it.

For years, international mobile connectivity focused on cost. Travellers worried about roaming bills, airport SIM counters, and confusing fees. Travel eSIMs offered a simple solution: buy data before the trip, install it digitally, and connect abroad without changing SIM cards.

Also Read: The unsexy side of SEA traveltech: eSIMs, visas and hourly hotels win big

That value still matters, but the market is moving into a new phase.

More importantly, the way eSIM is explained needs to change. Travellers do not want technical details. They want simple answers: what to do before the trip, what to expect when they land, and how to stay connected without problems.

GSMA Intelligence says that about five per cent of smartphones worldwide had eSIM by the end of 2025. This is expected to reach 10 per cent by the end of 2026 and double again in 2027. By 2030, there will likely be more eSIM smartphones than traditional SIM ones.

This shows that eSIM is moving from something only early adopters used to a mainstream standard. More phones will support it, and more travellers will have access without needing new devices. But just because devices are ready does not mean consumers are.

A traveller might have an eSIM-ready phone but still hesitate. They might worry if installing an eSIM will affect their main number, if apps like WhatsApp will work, or when to install it. They could also be concerned about activation failing at the airport or losing access to maps. These are everyday worries, not technical problems.

For most travellers, staying connected is part of the trip. It affects how they get around, communicate, book things, and handle daily needs. If setup is confusing, they will likely stick to what they know, even if it costs more. That is why education is now key to the market opportunity.

The travel eSIM market is set to grow fast. Juniper Research says global travel eSIM users will rise from 40 million in 2024 to over 215 million by 2028. Meanwhile, ACI World expects 10.2 billion travellers in 2026. These trends show a clear path: more travellers, more eSIM-ready devices, and more people open to using them. What is missing is clarity.

Also Read: From arrival anxiety to instant connectivity: How eSIM changes the first hour of travel

For travel eSIM providers, this changes what it takes to compete. It is not enough to just say eSIM is cheaper than roaming. Travellers need to feel confident. They want to know if their phone works with eSIM, how to activate it, what to expect when they arrive, and what help is available if something goes wrong.

The best brands will not just sell data. They will remove doubt.

This is important because eSIM changes how people travel. For years, travellers used physical SIM cards or roaming plans. Downloading a data plan before flying and connecting right away is still new for many. This makes the awareness gap a chance for brands to grow.

Companies that explain eSIM clearly can become trusted guides. They can make compatibility, setup, and everyday use simple, making eSIM a normal part of getting ready for a trip.

This matters even more as the market gets crowded. As more providers join, price and coverage will not set brands apart as much. Many companies will offer similar plans. But not every company will make things easy for travellers.

The next stage of competition will focus on simplicity, transparency, and support. Clear pricing, easy setup, reliable help, and practical advice will matter more than technical features.

This also means looking beyond just the sale. The customer journey starts before the trip, when travellers are planning and deciding what to set up ahead of time. Brands that help travellers at this stage can build trust early.

This could mean explaining when to install an eSIM, how to check if a phone is compatible, how to keep a main number active, or how much data different activities use. These details matter because travel often feels uncertain. People are not only buying data. They are buying peace of mind. That is the real chance behind the eSIM awareness gap.

The market does not need to convince travellers that staying connected is important. They already know that. What matters now is making eSIM feel simple, reliable, and easy to use. The next phase of growth will not come from technology alone. It will come from education, building confidence, and good experiences.

Brands that understand this will have an edge. They will not just sell data plans. They will help travellers feel ready before they leave and connected when they arrive.

In a market where millions already have the right device but still do not know what an eSIM is, this could be the biggest opportunity.

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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.

The post The eSIM awareness gap is the market’s biggest opportunity appeared first on e27.

Singapore’s Cyber Command has disrupted more than 30,000 Apple iMessage accounts tied to a fraud campaign that has cost victims roughly SGD2.2 million (US$1.7 million) since June, a figure that jumped nearly US$800,000 in the weeks since police last updated the tally on August 5.

The scam itself was mundane: spoofed messages impersonating Ninja Van, J&T Express and government agencies, directing victims to fake payment pages. What makes it notable is the platform. Unlike SMS, which sits inside Singapore’s telecom-level filters and sender ID registry, iMessage runs entirely on Apple’s own infrastructure, untouched by the safeguards that have already cut scam cases on covered services by 37% between 2024 and 2025.

The timing matters. This bust follows new Codes of Practice under the Online Criminal Harms Act covering seven high-risk messaging and calling services, a parallel Social Media Code targeting scam ads on Facebook, Instagram and TikTok, and tighter e-commerce login rules for Carousell and Facebook Marketplace. Non-compliance penalties could soon rise to SGD10 million per breach.

For founders building anything that touches payments, messaging or marketplaces in Southeast Asia, Singapore’s enforcement posture is the clearest signal yet of where platform liability is heading, and it’s moving faster than most product roadmaps.

REGIONAL

Timah Partners secures US$46.5M to buy ageing Singapore SMEs: A wave of Singapore SME owners is approaching retirement with no succession plan, and Timah Partners is raising capital to buy up profitable, founder-run businesses in logistics, healthcare support and compliance before they simply close.

Shopee Pay to enable cross-border QR payments in China via Tencent: Singapore and Thailand users will be able to scan and pay at Chinese merchants through a Tencent Global partnership, deepening fintech interoperability between SEA and mainland China.

Foxconn to invest US$265M more in Vietnam, US$57M in Singapore: Taiwan’s contract manufacturer is deepening its Southeast Asian footprint as firms accelerate supply chain diversification away from China amid ongoing trade tensions.

KCP raises US$725M anchored by sovereign fund: Singapore-based KCP closed one of the city-state’s largest recent private fund raises, backed by a sovereign wealth fund in a sign of continued institutional appetite for regional alternatives.

MAS introduces measures to strengthen Singapore’s asset management hub: New regulatory measures from the Monetary Authority of Singapore target enhanced fund structuring flexibility and incentives to cement the city-state’s position against rival financial centres.

IMDA opens nearly 2,000 tech jobs for Singapore workforce: The Infocomm MediaDevelopment Authority is expanding job placements across cybersecurity, AI, and software engineering, partnering industry players to address a growing digital talent gap.

38% of Malaysian businesses use AI but most stuck on basics: A new survey finds Malaysian firms have adopted AI at surface level, with limited integration into core operations, signalling a widening gap between adoption rates and meaningful deployment.

Singapore’s DynaAI to test auto insurance AI in Japan: The Singapore-based startup is piloting its AI-driven underwriting model in Japan’s motor insurance market, marking an early cross-border expansion for a SEA insurtech into a notoriously closed financial sector.

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INTERVIEWS & FEATURES

SEA traveltech’s next winners are eSIMs, visas and hourly hotels: Southeast Asia’s travel rebound is shifting away from flight-and-hotel search engines toward the unglamorous plumbing around it — eSIMs, visa processing and hourly bookings — as founders bet friction, not glamour, is where the real margins sit.

It’s not just tariffs: Why Chinese capital is flowing into ASEAN: KPMG’s China Lead Partner Lisa Li tells e27 that boardroom capital allocation into Southeast Asia is driven by far more than supply-chain diversification, unpacking the real calculus behind the shift.

Four lessons Southeast Asia can borrow from Dubai’s AI playbook: From Sam Altman’s fireside chat to candid ministerial exchanges, GITEX Global 2025 offered lessons on state-backed AI ambition that SEA ecosystem builders can adapt.

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INTERNATIONAL

Buddy Bites raises US$4.2M Series A to expand beyond dog food: Hong Kong-founded pet brand Buddy Bites is betting that habit-forming repeat purchases, not discounting, can crack Asia’s pet care market.

Neocrete raises US$3.5M to make low-carbon concrete cheaper: New Zealand-based Neocrete has raised funding to prove lower-carbon concrete mixes can survive tight construction-site economics, a problem that’s always been about margins, not chemistry.

Bangladesh launches US$33M fund-of-funds for its startup scene: State-backed Startup Bangladesh has begun deploying government capital through professional VC managers rather than direct investments, a model regional peers may watch.

BYD targets Japan with compact EV built for local tastes: China’s largest EV maker is entering one of the world’smost resistant auto markets with a Japan-specific small vehicle, a move that could shape EV competitive dynamics across Asia.

OpenAI eyes 2027 IPO window: A senior OpenAI executive confirmed the company is targeting a public listing in 2027,a timeline that will be closely watched by SEA investors and AI startups benchmarking against the sector’s dominant player.

Meta launches AI tools for small businesses: Meta’s new suite of AI-powered marketing and customer engagement tools targets SMEs globally, with direct implications for the millions of small businesses across SEA reliant on its platforms.

Ant International launches AI model to forecast FX risk: Ant Group’s international arm has deployed an AI-driven foreign exchange risk forecasting model, a significant move for cross-border payment players and treasury teams operating across SEA’s fragmented currency landscape.

OpenAI launches ChatGPT for teens amid scrutiny: ChatGPT’s new teen-focused tier arrives as regulators and parents globally question AI safety for minors, a debate increasingly relevant in SEA markets with large youth populations.

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CYBERSECURITY

Singapore Polytechnic launches CASTLE to shore up SME cyber defences: Singapore Polytechnic is turning student training into practical protection for small businesses through its new CASTLE initiative, aiming to close a gap where cybersecurity remains too costly for most SMEs to manage alone.

Why India needs Singapore’s playbook against digital-arrest scams: A contributor recounts nearly falling victim to a “digital arrest” scam impersonating Mumbai police, arguing India should adopt Singapore’s platform-level countermeasures before such fraud scales further.

Building cybersecurity sovereignty by design: A TechNode analysis examines how governments and enterprises can embed transparency, control, and resilience into digital infrastructure rather than treating security as an afterthought.

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SEMICONDUCTOR

Nvidia to ship AI chip to China by year-end: Reuters reports that Nvidia is preparing to export a downgraded AI chip to China before year-end, navigating US export controls, a development with major implications for the regional AI hardware supply chain.

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AI

AI lifts one half of global economy as trade strife drags the other: A Moody’s-cited analysis finds AI-driven productivity gains are creating a bifurcated global economy, with geopolitical and trade pressures suppressing growth in the other half, a direct concern for SEA’s export-reliant tech sectors.

Deepgram expands to Singapore to crack APAC’s multilingual voice AI: Voice AI provider Deepgram is betting its Singapore expansion can solve what has held back regional voice tech: callers who code-switch between English, Mandarin, Malay and Tamil mid-conversation.

Why Malaysia’s AI Nation 2030 plan matters for B2B startups: Malaysia’s National AI Action Plan signals that speed alone won’t carry SEA’s AI-era vendors as AI moves into banking, healthcare and public infrastructure, raising the bar on governance and accountability.

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THOUGHT LEADERSHIP

Why top SEA startups are quietly building R&D hubs in Vietnam: As a US$5M Series A now buys just three senior engineers in Singapore, growth-stage founders are relocating core engineering to Vietnam, trading growth-at-all-costs for cost-efficient scaling.

Investors aren’t ghosting founders, they’re reading them: Vague, deck-less outreach rarely gets replies because investors are quietly evaluating founder judgement before the first call; silence, the writer argues, is diagnostic, not dismissive.

Your founder brand could swing your valuation by up to US$1M: Founders rarely realise investors form snap judgements before any pitch begins; this piece argues personal brand, not follower count, can shift early-stage valuation by hundreds of thousands.

What ASEAN banks still aren’t pricing into climate risk: A polished quarterly risk presentation at an Indonesian bank omitted the physical climate exposures that matter most, a gap the writer argues ASEAN lenders can no longer defer.

The hidden cost of treating AI as software, not capability: AI doesn’t create advantage on its own; it amplifies whatever organisational capability already surrounds it, shifting the leadership question from where to deploy AI to what kind of company can absorb it.

The post Ecosystem Roundup: Singapore’s iMessage scam bust exposes a regulatory blind spot appeared first on e27.

Every fintech founder in this region has drawn the same slide at some point: the underbanked adult, the missing credit file, the product that will finally reach them. Fewer have noticed that the person on the slide already owns a savings product, and has for centuries. It is called arisan in Indonesia, paluwagan in the Philippines, hui in Vietnam, chit fund in India, tanda in Mexico, gam’eya in Egypt, stokvel in South Africa, susu in West Africa. An estimated two billion people use some version of it, moving on the order of a trillion dollars a year entirely outside formal banking.

The mechanism is almost insultingly simple. Ten people agree to put in US$100 a month. Each month the group hands the full US$1,000 to one member. After ten months everyone has paid in US$1,000 and everyone has taken out US$1,000. No interest, no lender, no credit file.

What the circle produces is not yield. It is timing. It converts a slow trickle of savings into a lump sum large enough to do something with — a deposit, a motorbike, a term of school fees — and it does that on social obligation rather than a balance sheet. That is why it has survived every wave of financial inclusion products aimed at replacing it.

It also has exactly one unsolved problem, and it is the only genuinely interesting thing about the format: who goes first?

The ordering problem

The lump sum in month one and the lump sum in month ten are not the same product. The first recipient has effectively borrowed from the group and repays over the remaining rounds. The last has lent to the group for nine months and gets nothing extra for it. Same nominal amount, very different value.

Informal circles resolve this in one of three ways, and each has a well-known failure mode. The organiser decides, which turns the queue into patronage. A lottery decides, which is fair in expectation and unsatisfying in practice — the member with a hospital bill in March does not care about expectation. Or seniority decides, which quietly taxes newcomers to reward the people who least need the money.

All three share a deeper flaw: the position in the queue has real economic value, and nobody is allowed to say what it is. Value that cannot be priced gets settled socially, and settling it socially is where circles collapse. Ask anyone who has run one.

Also Read: Southeast Asia solved distribution: Now fintech has to scale on the balance sheet

The old answer, and why it never scaled

The interesting thing is that this was solved a long time ago, in India. Registered chit funds have run a discount auction for generations, formalised in law since 1982: each round, members bid down the amount they are willing to accept, the lowest bid takes the pool, and the discount is distributed among the rest. A member who needs cash now pays for the privilege. A member who can wait is compensated for waiting.

It works. It also never left its jurisdiction. The auction is administered by a registered foreman, denominated in rupees, tied to Indian regulation, and reachable only by people physically inside that system. The neighbouring arisan in Jakarta, running the same underlying product, still resolves its order by drawing names out of a bowl.

That is the gap worth building into: not the auction — the auction is old and proven — but the fact that it has never been made portable.

What changes when the queue is priced

A disclosure before I go further: I built ROSCASH, so what follows is the perspective of someone with a stake in the answer, not a neutral observer of it.

We run circles where each round is settled by a descending-discount auction. Members bid a discount against their own payout; the lowest bid at the close of a six-hour window wins and receives the pool minus that discount. Seventy per cent of the discount is split across every share in the circle that has not yet been paid out — participation in the bidding is irrelevant to eligibility, and only the single share the winner redeems that round is excluded. The platform keeps the remaining thirty per cent, and nothing else: on auction circles the winner pays no commission on the pool at all, because the discount they bid is already the payment.

Also Read: Southeast Asia’s fintech apps don’t have a literacy problem, they have a fear problem​

Three things follow from that design, and they are worth separating from any marketing claim.

• First, the queue stops being a favour and becomes a good with a market price, set by the people in that specific circle in that specific week. Nobody arbitrates.
• Second, the platform earns only when a member chooses to pay for speed. A round in which nobody bids generates no revenue for us at all. That is an uncomfortable incentive to design into your own business model, and it is the right one — it means we are not paid for the mere existence of a circle.
• Third, and least convenient to say out loud: a savings circle redistributes, it does not create. Aggregate member profit and loss across a full cycle sums to exactly minus the platform’s revenue. There is no yield being generated anywhere. The member who waits is paid by the member who hurries, and any platform in this category that describes both sides as “earning” is selling you something. What a circle offers is not return. It is a priced, voluntary trade between two people with different urgency.

What it does not solve

Custody and regulation remain the hard part, and we would rather state that than be found out. ROSCASH is in public beta. Funds are held and processed by the platform under each circle’s published rules; on-chain custody, where code rather than a company holds the pool, is on the roadmap and has not shipped, there is no contract address and no audit. We hold no licence. Platforms like MoneyFellows in Egypt and Hakbah in Saudi Arabia do hold local licences and settle in fiat, and for a saver who wants a national regulator standing behind the product, that is the honest recommendation.

What settling in USDC buys instead is the thing the chit fund could never do: three members of one circle can sit in three different countries.

The ordering problem is six centuries old and still open in most of the world. It does not need a new savings product. It needs a price.

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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.

The post Southeast Asia’s oldest savings product still has no price for going first appeared first on e27.

From warehouse floor to boardroom

Daniel Yun’s route into supply chain technology did not begin in a lab or a spreadsheet. It began in a logistics warehouse. Before founding Willog, he ran a traditional logistics operation and saw first-hand where shipments broke down and why customers lost faith in their carriers.

One problem kept recurring: when temperature-sensitive cargo, such as fresh food, was damaged in transit, there was no way to work out afterwards where or why it had happened. “The losses recurred, but we could judge the causes only through experience and guesswork rather than data,” Yun says. Most of what happened during a shipment’s journey simply vanished, unrecorded.

That gap is what pushed Yun to redirect his business towards logistics data. Willog built its own IoT sensor devices to capture trustworthy data at the source, then layered AI analytics on top to flag anomalies before they occur.

Also Read: The rise of logistics startups in Southeast Asia: How AI powers supply-chain revolution

The company has since extended that same data foundation into cargo insurance, aiming to connect logistics, AI and insurance on a single data layer. “It wasn’t a problem I observed from the outside, but one I lived through while running the business myself,” Yun says. “It was the problem I understood best, and therefore the one I was most confident I could solve.”

Making invisible cargo visible

Enterprise systems such as ERP, WMS and TMS are good at tracking what is being shipped, how much, and when. What they largely miss is the physical condition cargo actually travels in — temperature, humidity, light, shock, tilt. Yun describes this as a grey zone that sits outside conventional supply chain software.

Willog’s approach spans four stages. Its own IoT devices, branded Willog Safe, capture physical data at the point of sensing. That data is combined with external context, such as weather and route information, to anticipate problems. The system then prescribes what action should be taken, and finally preserves the entire sequence as verifiable evidence. Rather than simply showing where cargo is, Willog feeds physical-world data back into the enterprise systems that were missing it.

A case with a global e-commerce client illustrates what this looks like in practice. Digital-twin mapping was used to identify thermal weak spots inside a fulfilment centre, turning a problem the client had only vaguely sensed into concrete, location-specific data.

“Information at the level of ‘this warehouse has unstable temperature control’ isn’t enough to act on,” Yun explains. “But once it becomes clear which zone deviates from standards, under which conditions, and how repeatedly; that’s when it leads to real action.”

He describes the lesson as being less about proving a risk exists and more about making the data specific enough to drive a decision.

The zero churn structure

Willog reports zero per cent churn and 100 per cent contract renewal across 2024 and 2025, figures that stand out even against strong SaaS benchmarks. Yun attributes this to how deeply the system is embedded in a customer’s operations rather than sitting alongside them.

“If we were simply providing one more dashboard, a customer could switch away at any time,” he says. “But Willog is embedded in the customer’s own processes — inbound, outbound, quality control, and regulatory compliance.”

Also Read: AI in motion: How automation is reshaping Southeast Asia’s logistics landscape

Once a team has experienced catching problems before they happen, he argues, reverting to intuition-based decisions feels like a step backward. Leaving becomes difficult not because of contractual lock-in, but because the system has become part of how the organisation works.

Where the real moat lies

Real-time telemetry is becoming increasingly commoditised, with multiple providers now able to supply sensor data. Yun places Willog’s differentiation elsewhere, in the accumulated context around that data and the products built on top of it.

Over five years and across six industries, Willog has built up domain-specific knowledge of how different cargo types respond to particular conditions and where losses tend to occur.

The value, he says, comes from interpretation: “The same temperature reading only becomes valuable when you can interpret what it means for a specific pharmaceutical, and what it translates to as an insurance premium.” That combination of operational data and the ability to convert it into financial value, built up over time, is what he considers the company’s real barrier to entry.

Growing through references, not persuasion

Willog’s new contracts grew several-fold last year while customer acquisition cost fell, a shift Yun credits to reference-based expansion rather than any change in sales tactics. Early on, without a track record, approaching large enterprises and government agencies was difficult. The company instead built credibility steadily with small and mid-sized customers, and focused on passing the certifications demanded by its most exacting clients on quality and security.

That track record became a trust signal in its own right, generating inbound interest from companies that had seen it. “We shifted from a model where we approached and persuaded customers, to one where companies that had seen our references reached out to us first,” Yun says.

Trust in sectors that cannot afford mistakes

Willog’s deployments include biopharma cold chains and overseas military logistics, sectors where a single failure carries serious consequences and decision-makers are naturally cautious about new technology. Yun says conservative buyers are less interested in how advanced a system is than in whether failures can be explained afterwards. Willog’s AI judgments are kept traceable, with the underlying data preserved as evidence rather than treated as a black box.

Also Read: 5 smart ways to decarbonise supply chains and logistics with AI

Sequencing mattered too. Rather than asking customers to trust an unproven AI system outright, Willog first cleared some of the strictest verification standards available — international transport for Corning, global knock-down transport quality management with Hyundai Glovis, and cold-chain transport for the ROK Army General Supply Depot. Passing military supply logistics vetting, in particular, gave the company more credibility with subsequent conservative clients than any pitch could.

From monitoring to insurability

Yun says the realisation that shipment data could underpin insurance came from recognising that proof of what actually happened in transit could be used to price risk by measurement rather than estimation. In this model, AI prediction and prevention reduce the probability of incidents occurring at all, while insurance, priced on measured data, covers whatever residual risk remains. “If prediction and prevention are the domain of reducing risk, insurance is the domain of taking responsibility for the risk that still remains,” he says.

What comes next

Willog’s roadmap includes further expansion into Europe and Southeast Asia, alongside a longer-term ambition to go public. Yun frames the IPO as a byproduct rather than the goal itself, contingent on sustaining reference-based growth internationally and establishing insurance as a genuine revenue line rather than a stated plan.

On Southeast Asia specifically, Yun pushes back on the idea that Willog is simply a sensor vendor. With regulation varying by country and cold-chain infrastructure maturity uneven across the region, he argues that knowing where cargo is isn’t sufficient — the value comes from pinpointing where and under what conditions losses occur, something Willog’s five years of cross-industry data is built to do.

Also Read: IoT-powered logistics platform McEasy extends Series A round

Looking further ahead, Yun describes the company’s ambition in structural terms: an infrastructure answering what physically happened, what is likely to happen next, and what that risk is worth, with data, AI and insurance interlocking on a single foundation. “We want to change the very grammar of the industry, from after-the-fact response to advance prediction and prevention,” he says.

The post Sensors, predictions, premiums: How Willog turned shipment data into an insurance biz appeared first on e27.