Insurance has spent the past year experimenting with generative AI in the most obvious corners of the enterprise: summarising emails, extracting details from claims forms, drafting responses for call-centre agents, and speeding up document-heavy back-office work.

Those are useful gains. But a new whitepaper by Google Cloud and CoverGo argues that they may also be a distraction.

Also Read: OneByZero raises US$20M Series A to help enterprises move AI from pilots to production

The paper, authored by Maxim Afanasyev and Tomas Holub, frames the industry’s current AI moment as a potential “Kodak moment”: a point at which incumbents optimise an existing business model while the market quietly moves somewhere else. As management thinker Oren Harari put it, “The electric light did not come from continuous improvement of candles.” For insurers, the warning is that better workflow automation will not be enough if the fundamentals of claims, underwriting and distribution are being rewritten by autonomous software agents.

The paper points to studies from MIT and BCG suggesting that 95 per cent of organisations struggle to generate tangible returns from agentic AI. Its diagnosis is blunt: many institutions still treat AI as a series of isolated productivity projects, rather than a new operating layer for the business.

For Southeast Asia’s insurance sector, where mobile-first customers, digital wallets, embedded finance and platform ecosystems are already reshaping financial services, that distinction matters.

When every market participant has an AI agent

Agentic AI refers to systems that do more than answer prompts. These agents can plan tasks, call tools, retrieve data, make decisions within defined limits, and interact with other agents or systems. In insurance, that could mean an underwriting agent checking data sources, a claims agent assessing evidence, or a broker agent negotiating terms.

The bigger shift begins when insurers are no longer the only ones deploying these tools.

Customers may use personal AI agents to read policy wording and optimise claims submissions. Healthcare providers, repair workshops, brokers and even regulators may use agents to submit, verify, challenge or audit information in near real time. At that point, insurance stops being a slow exchange of forms between humans and becomes a machine-speed negotiation between multiple software actors.

The whitepaper gives a simple example: if a dental policy reimburses treatment linked to acute tooth pain, a customer’s AI agent may learn to frame a routine visit around that symptom. In underwriting, agents could identify weak spots in pricing models and generate applications that exploit them. In distribution, broker agents may flood the market with automated outreach, while customer agents filter or block them.

The result is not just more volume. It is a change in market behaviour.

Also Read: Health, wealth, and legacy planning converge as new wave of SEA insurtechs emerges

Economists have long discussed signalling: the idea that reliable information is costly to fake. AI weakens that assumption. If polished applications, claims narratives and supporting documents can be generated cheaply, insurers need to think beyond traditional risk scoring. They need mechanisms that encourage truthful behaviour, detect strategic manipulation, and decide when a human should intervene.

Why Southeast Asia cannot treat this as a distant problem

In mature Western markets, insurers often carry decades of legacy infrastructure and rigid internal rulebooks. Southeast Asia has its own constraints, including fragmented regulation, uneven data quality and protection gaps, but it also has a different adoption curve.

The region’s consumers are young, mobile-first and used to digital financial services moving quickly. In markets such as Singapore, Indonesia, Vietnam, Thailand and the Philippines, customers increasingly expect insurance to be embedded into payments, e-commerce, lending, travel, logistics and healthcare journeys. The next insurance interaction may not start on an insurer’s website. It may start inside a superapp, a hospital system, a broker platform or a small business finance tool.

That makes the agentic shift especially relevant. If every partner in the value chain deploys AI, insurers cannot rely on isolated internal pilots. A claims chatbot here and an underwriting assistant there will not be enough. The core systems must be able to coordinate agents, enforce authority limits, maintain audit trails and connect with external systems safely.

This is where the paper introduces the idea of an Agentic Core Operating System.

From AI tools to an agentic operating layer

The argument is not that insurers should replace humans with autonomous machines. It is that they need a technology foundation where human experts and AI agents work as coordinated teams.

In practical terms, this means moving away from monolithic, hardcoded systems towards modular architecture. One building block highlighted in the paper is the Model Context Protocol (MCP), often described as a “USB-C for AI”. The idea is to give agents a standardised way to access context from systems such as customer databases, policy administration platforms and claims records, without building a custom integration every time.

Other emerging layers include Agent-to-Agent protocols, which allow different agents to exchange information and negotiate tasks, and agentic payment protocols that could enable transactions under strict controls. Workflow engines based on directed acyclic graphs can help manage the recurring loop of plan, act, observe and reflect that agentic systems use.

Also Read: Why Southeast Asian enterprises need AI governance before scaling generative AI

For many Southeast Asian insurers, the appeal is clear. Legacy replacement projects are expensive and risky. A protocol-driven architecture could allow incumbents to build around old systems first, rather than rip everything out at once. But the governance challenge is just as important as the technology.

The new failure modes

A traditional AI tool can make a bad recommendation. A network of agents can create system-level failures.

The whitepaper identifies several risks. One is an agent loop, where two or more agents keep triggering each other without resolution. Another is agent disagreement, where different agents reach conflicting conclusions on the same case. A third is authority violation, where an agent acts beyond its approved mandate.

These risks matter in insurance because decisions affect money, compliance and trust. A wrongly denied claim can damage a customer relationship. A mispriced portfolio can create financial losses. A poorly governed automated process can draw regulatory scrutiny.

The proposed answer is a schema-driven agent model, where business rules, authority limits and escalation triggers are defined in configurable schemas rather than buried in code. Human-in-the-loop controls remain central: confidence scoring, mandatory escalation for sensitive cases, audit logs, and clear accountability for decisions.

For regulators in Southeast Asia, this could become a key question. The issue will not simply be whether an insurer uses AI, but whether it can explain how autonomous decisions are made, challenged and reversed.

The road beyond pilots

The paper’s modernisation roadmap is deliberately incremental. It begins with intelligent document processing to extract logic from legacy documentation, then separates solution design from execution so business and technology teams can validate workflows before coding. New agentic platforms can be built in parallel with legacy systems, master data can be loaded first, and policies can gradually move to the new core before old systems are retired.

That may sound unglamorous, but it addresses a real problem in insurance technology: transformation often fails when companies try to jump straight from legacy complexity to a grand new platform.

Also Read: What I’m learning about the second wave of insurance digital transformation in Indonesia

The deeper message for founders, investors and operators is that AI strategy in insurance can no longer be measured only by productivity gains. The question is whether the business is prepared for a market where customers, intermediaries and service providers also have intelligent software acting on their behalf.

Peter Drucker’s warning remains apt: “There is nothing so useless as doing effectively that which should not be done at all.”

For Southeast Asia’s insurers, the risk is not that they fail to automate enough tasks. It is that they automate yesterday’s business model while the market shifts to a new one. The next phase of insurtech will belong to those that build not just AI use cases, but the operating system for an agentic economy.

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Over the past year, the question I hear from operators shipping into three or four ASEAN markets has quietly changed. It used to be about the tariff. Now it is about the paperwork. Which certificate does this lane need? Who in our supply chain can prove where this component was made? Why does the same product clear in two days in one market and sit for a week in the next?

That question just got sharper. On 19 September in Manila, ASEAN’s 10 signatories adopted a declaration allowing the upgraded ASEAN Trade in Goods Agreement, the bloc’s core treaty on goods trade, to apply provisionally ahead of its formal entry into force on 1 June 2027. The economic ministers described it as bringing the upgrade into force early, within 2026. The upgrade itself is substantial: relaxed rules of origin, broader self-certification, stronger digital documentation and a new chapter for small businesses.

Read the mechanism, though, and the picture changes. Provisional application is voluntary and reciprocal. It applies only between member states that have each notified the ASEAN Secretary-General, from the dates each one specifies. The upgrade does not switch on across the region. It switches on lane by lane.

One rulebook, many lanes

For the next eight months at least, there is no single upgraded ASEAN. There is a matrix of upgraded lanes, legacy lanes, and lanes where one side has moved and the other has not. A business shipping across four markets will scale at the speed of its slowest notifying partner.

And the tariff was never the real barrier. ASEAN reported as far back as 2019 that 98.6 per cent of import duties across the bloc had already been eliminated. Yet the ministers’ statement this month put ATIGA’s utilisation rate, the share of eligible trade that actually claims the preferential tariff, at 62.7 per cent for 2025 across the nine members that reported, up from 51.7 per cent the year before. Real progress. It also means more than a third of qualifying trade still does not claim the benefit it is entitled to. The gap is not access. It is the capacity to prove eligibility, shipment after shipment.

That gap behaves differently at each scale. A micro exporter will not notice the upgrade at all and will keep filing the old forms, or ride on a larger buyer’s paperwork. An SME selling into two or three markets faces a dual regime that taxes management attention, and will either overpay duty to stay safe or claim wrongly and invite post-clearance audits. A regional operator that can see which lanes are live gets a temporary moat, pricing and positioning inventory ahead of competitors who still treat ASEAN as one market. At national level, notification becomes a competitiveness decision. A government that notifies late quietly taxes its own exporters relative to its neighbours.

Also Read: Your safest market is a crowded trade: Why I moved to Dubai six weeks after the ceasefire

The fast lane is already reserved

The detail that matters most is who benefits first. Since 30 June 2026, ASEAN’s mutual recognition arrangement for Authorised Economic Operators has been implemented by every member state except Timor-Leste. An AEO is a trader certified by its own customs authority as low risk, and under the arrangement that status earns expedited clearance and priority treatment across the region. Certification demands audited processes, security controls and a clean record. Few SMEs hold it. The upgrade stacks new facilitation on top of a club most of them have not joined.

The second trap sits upstream. Proving a product is ASEAN-made usually means showing regional value content, the share of its value created inside the region, of at least 40 per cent, or a qualifying change in tariff classification. That evidence lives in your suppliers’ invoices and bills of materials, not in your own files. If a tier-two supplier in another market cannot produce it, your claim fails, regardless of how the treaty reads.

The digital rail is not the problem. Ministers reported that 1,346,971 electronic certificates of origin moved through the ASEAN Single Window in 2025. The rail works. What travels on it is only as good as the record behind it.

Two paths through the patchwork

For businesses directly exposed, the move is architectural. Build a lane map: every origin and destination pair you ship, its notification status, and the origin rule for your top products. Treat it as a live operating document owned by someone senior, not a compliance memo filed and forgotten. Start AEO or approved exporter preparation now, because the window is short and certification is slow. Bring suppliers into the record. Origin evidence is shared data, and paying fairly for clean documentation costs less than a failed claim. Most of the businesses that use my scaling framework have stopped asking which market to enter next. They are asking which lanes their records can carry.

Also Read: Tourists first, trade later: Why Southeast Asia’s next boom is coming from the Gulf

For businesses less exposed, services-heavy or still largely single-market, this is a window to buy capability rather than build it. Distributors with good lanes but weak systems will struggle through the patchwork, and some will be open to partnership or acquisition on fair terms. Slower-notifying markets will see competitors hold back inventory. That is where patient positioning pays.

Korea already ran this experiment

South Korea learned early that signing trade agreements is not the same as using them. Its own officials, presenting to APEC, named complicated rules of origin and a shortage of trained staff as the main barriers for SMEs. The response was an institutional layer: the Korea International Trade Association’s FTA and Trade Support Center, a public-private one-stop service for smaller exporters, a free consulting hotline, visiting advisers, and from 2018 a trade ministry programme that linked exporters’ ERP systems directly with their suppliers’ data, supporting parent companies and their partners as a group.

That last detail is the lesson. Korea treated origin as a supply chain data problem, not a customs form problem. ASEAN now has the treaty and the digital rail. It does not yet have that layer coordinated across 10 markets during the patchwork. Operators will have to build their own.

The upgraded ATIGA will be fully in force by mid-2027. By then, the lanes worth having will already belong to operators who treated this year as an architecture problem. The treaty is regional. The advantage is architectural.

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Healthcare digitisation usually becomes visible through the applications people interact with: patient portals, telehealth platforms, digital prescriptions and, increasingly, digital health wallets. But the interface is rarely the difficult part.

In March 2026, the World Health Organisation (WHO), the Alliance for Health Policy and Systems Research and Temasek Foundation launched a three-year initiative to help ASEAN Member States move from paper-based health records towards secure, interoperable digital health wallets. The programme plans to use global standards including HL7 FHIR and the International Patient Summary so essential health information can move more consistently between systems. That initiative represents something bigger than another healthcare app.

It raises a question that Southeast Asia’s healthtech ecosystem will increasingly have to answer: Can health information remain useful when it moves beyond the system, provider or country where it was originally created?

Digital health is becoming infrastructure

Digital health is no longer confined to individual hospital transformation projects. Globally, WHO reported in 2026 that 129 Member States have national digital health strategies. Its Global Digital Health Certification Network already serves 1.8 billion people across 80 countries.

The broader South-East Asia region is moving in the same direction. WHO says at least 80 per cent of countries in its South-East Asia Region have drafted or completed national digital health strategies, with interoperability among the areas these strategies are intended to strengthen.

As this infrastructure expands, simply digitising records becomes less of a differentiator. The harder problem becomes making those records usable outside the system that created them.

Imagine a traveller receives treatment in one country and later needs care elsewhere. A digital wallet may be capable of presenting a medication, allergy, diagnosis or laboratory result to the second provider. Technically delivering that information is one problem. Making sure the second system understands it correctly is another.

Moving data is not the same as understanding it

Healthcare interoperability is often discussed as an API problem. Connect System A to System B. Exchange information using FHIR. Validate the payload. The integration works.

Except healthcare data carries meaning as well as structure. One hospital may represent a laboratory observation using an international terminology. Another may use a local code created years ago. Medication names can differ. Units can vary. Clinical concepts can be recorded at different levels of specificity.

The receiving platform might therefore successfully receive the data while still being unable to use it reliably. That distinction becomes especially important when health information starts travelling between organisations and eventually across borders.

Also Read: Why Southeast Asia’s next healthtech winners will be built around healthcare workflows, not just AI

FHIR provides a standard way to structure and exchange healthcare information, but consistent exchange does not automatically guarantee consistent interpretation. That is why capabilities such as healthcare terminology services become relevant: clinical vocabularies, mappings and value sets help different systems interpret the same underlying concepts consistently. For regional digital health infrastructure, semantic consistency may prove just as important as connectivity.

Standards are a foundation, not a finished solution

The ASEAN digital health wallet initiative’s use of HL7 FHIR and the International Patient Summary is significant because common standards reduce the number of proprietary formats each participant needs to understand. But adopting a standard does not remove every interoperability challenge.

Healthcare systems still have to resolve questions around patient identity, consent, terminology, data quality, security, access permissions and the clinical context surrounding a record.

There is also a regional dimension. Southeast Asia is not one healthcare market with a common technology stack. Countries differ in digital maturity, healthcare delivery models, public and private sector participation, regulation and existing infrastructure. Even within one country, a modern cloud platform may need to exchange information with hospital systems that were designed years earlier for very different purposes.

This means regional interoperability cannot simply mean making every participant use identical software. A more realistic goal is enabling different systems to communicate through shared standards while preserving the meaning, trust and context of the information being exchanged.

This changes how healthtech products should be designed

For healthtech founders, interoperability is easy to postpone. An early product may integrate with one hospital, one laboratory or one device ecosystem. At that stage, a tightly coupled integration can appear sufficient.

The architecture starts to matter differently once the product expands. A second healthcare provider may use another EHR. A new market may require different consent rules. Clinical terminology may change. A partner may expose FHIR APIs while another still depends on older interfaces.

If these differences are handled individually inside the core product, every expansion adds another layer of dependency. That is why interoperability increasingly needs to be treated as product architecture rather than a later integration task.

Also Read: Vietnam’s healthtech boom has a talent problem nobody is talking about

A scalable healthtech platform should be able to separate its core product logic from the systems around it. Identity, consent, terminology mapping, API management, auditability and data transformation need clear boundaries.

The objective is not to predict every future integration. It is to avoid designing a product where every new integration requires rebuilding the foundation.

Portability also creates a trust problem

Technical interoperability alone will not determine whether portable health records work. Healthcare data is unusually sensitive. Giving individuals greater control over their information also creates questions about authentication, consent, revocation, data provenance and who should be permitted to access what.

A clinically useful record needs to be more than portable. A receiving healthcare organisation needs confidence about where the information came from, whether it has changed and whether the person presenting it is authorised to do so. That makes trust architecture inseparable from interoperability architecture.

This is particularly important for systems intended to operate across multiple organisations or jurisdictions. The further health data travels from its original environment, the more important identity, provenance and governance become.

The opportunity is larger than the wallet

Digital health wallets may eventually become one visible layer of Southeast Asia’s healthcare infrastructure. Their deeper significance is what must exist underneath them.

For a patient’s essential health information to travel with them, hospitals, government platforms, healthtech products and other healthcare systems need ways to exchange data without losing its meaning or trustworthiness. That will not happen because every organisation adopts the same application. It will happen through standards, governance and architectures that allow different applications to participate in the same healthcare ecosystem.

For Southeast Asian healthtech companies, this changes the strategic question. The next generation of regional healthcare products will not only need to ask, “Can our software work in another market?” They will increasingly need to ask: “Can our product become part of a healthcare system where information needs to move beyond us?”

That may ultimately be the more important interoperability test.

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

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

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Endeavor Catalyst has closed an oversubscribed US$320 million fifth fund, lifting its assets under management to more than US$850 million and giving the global investment platform fresh capital at a time when startup markets outside Silicon Valley are beginning to stir again.

The fund, announced from Singapore, comes after a difficult few years for venture-backed companies across Southeast Asia and the wider region. Late-stage funding became harder to secure, public listings slowed, and investors became more selective about growth claims that were once rewarded during the low-interest-rate era.

Also Read: Grab’s US$1.49B Atome deal signals a deeper race for SEA’s credit economy

For funds with exposure to emerging markets, the question has shifted from whether talent exists in these ecosystems to whether companies can find the capital, governance and international networks needed to scale through tougher cycles.

Endeavor Catalyst is betting that they can. Across its five funds, it has backed 437 companies in 44 markets, including 83 companies valued at more than US$1 billion and 39 exits. Its portfolio includes names familiar to Southeast Asian readers, such as Bukalapak, Advance Intelligence Group and Carsome, alongside global companies including Replit, Moove and Preply.

In Asia, the firm has invested US$46.8 million across 44 companies in six markets. The region accounts for only a slice of its global portfolio today, but Endeavor Catalyst says deal activity has picked up after several quiet years, making Asia a stated pipeline priority for Fund V.

That matters because the reset in Asian tech has not killed founder ambition. It has changed what investors are looking for. The era of subsidised growth has given way to a sharper focus on sustainable unit economics, regional execution and paths to liquidity. For founders, especially those building from Southeast Asia, capital alone is rarely enough. Access to follow-on investors, senior operators and customers across borders can be just as important.

A fund built around the Endeavor network

Endeavor Catalyst is the investment arm of Endeavor, the entrepreneur network that selects and supports founders building high-growth companies outside the most established startup hubs. The fund invests only in companies led by Endeavor Entrepreneurs and usually co-invests alongside other venture capital and growth equity firms.

This gives the platform a different shape from a traditional VC fund. Its sourcing depends heavily on Endeavor’s founder network, while its limited partner (LP) base also reflects that community. Endeavor Entrepreneurs make up about 30 per cent of Fund V’s LPs by headcount and account for 20 per cent of the capital raised.

The pitch is what Endeavor calls the “Multiplier Effect”: founders who build large companies return as mentors, investors and connectors for the next generation. In Southeast Asia, that idea is increasingly visible. Entrepreneurs behind companies such as Carro, Carousell and ShopBack have become part of a broader support system for newer founders, whether through angel investing, operator networks or informal advice.

“Asia has never been short of founders with global ambition,” said Nadir Zafar, Managing Director of Endeavor’s Global Hub in Singapore. “Endeavor Entrepreneurs like Carro, Carousell and ShopBack have shown what the region can build, and many of those founders are now mentoring and investing in the next generation.”

The Singapore hub, launched in 2026, is intended to anchor Endeavor’s regional activity. It also reflects Singapore’s continued role as a base for Southeast Asian scaleups, regional headquarters and cross-border capital — even as founders increasingly build teams, revenue and product depth across Indonesia, Vietnam, the Philippines, Malaysia and Thailand.

Asia after the funding winter

For Endeavor Catalyst, the timing is notable. The region’s venture market has been through a funding winter, particularly at later stages. Many startups cut costs, delayed expansion plans or explored consolidation. Some investors who entered Southeast Asia during the pandemic boom pulled back or slowed deployment.

Also Read: When 43 per cent of the world’s funding goes to two firms, where does that leave SEA?

Yet the slowdown has also created a clearer divide between companies built on durable demand and those reliant on cheap capital. Fintech infrastructure, AI applications, logistics, software for small businesses, climate and mobility remain active themes, but investors are asking harder questions about margins, regulation and exit options.

“2025 was a ‘wait and see’ year for investment in Asia, but we’re expecting companies to scale up, merge, list, and exit at a faster pace in the coming years,” said Caela Tanjangco, Head of Asia for Endeavor Catalyst.

The firm has had no liquidity events in Asia since 2025, but globally it has recorded 15 liquidity events since the start of that year. Fund V has already made 32 investments across 30 companies and deployed US$55.7 million. Early investments include Replit, Moove, Preply, Cashea, Respond.io, HappyRobot, Flash and Lunar Outpost.

The current pipeline is strongest in Europe and Latin America, according to Endeavor Catalyst, but Asia is seeing a rise in opportunities. The fund is also tracking deeptech, foundational AI and cloud infrastructure, as well as second-time founders building AI companies at the application layer, areas drawing investor attention globally.

For Southeast Asia, the AI wave presents both opportunity and risk. The region has large digital populations, fast-growing enterprise demand and cost-efficient engineering talent, but it still depends heavily on imported compute infrastructure and global foundation models. The companies most likely to break out may be those solving practical industry problems in finance, commerce, logistics, healthcare or manufacturing, rather than those trying to compete head-on with US and Chinese AI labs.

The competitive landscape

Endeavor Catalyst is not alone in chasing founders outside traditional venture centres. In Southeast Asia, it competes for access and mindshare with regional firms such as East Ventures, Wavemaker Partners, Openspace Ventures, Jungle Ventures, Insignia Ventures Partners and Monk’s Hill Ventures. Global and multi-market investors including 500 Global, Antler, Peak XV Partners, Accel and Lightspeed also maintain exposure to the region in different ways.

What differentiates Endeavor Catalyst is that it does not operate as a broad sourcing fund; it invests through the Endeavor network and into founders already selected by that community. That can narrow the investable universe, but it may also give the fund warmer access to later-stage founders and a built-in support system across markets.

Redrawing the innovation map

Endeavor’s larger argument is that venture capital remains too concentrated in a small number of markets, even as high-quality companies emerge elsewhere. Latin America remains Endeavor Catalyst’s largest region, while Europe accelerated sharply with 12 new investments in the first half of 2026, after 14 in all of 2025. The broader portfolio spans the Middle East, Africa, Asia, the US and Canada.

For Asia, the significance of Fund V is less about a single cheque and more about what it signals. Investors are not rushing back indiscriminately. But funds with patient networks, cross-border reach and a sharper view of emerging-market execution are preparing for the next cycle.

Also Read: Southeast Asia’s biggest tech IPO of the year is a landlord

If the last few years forced Southeast Asian startups to prove they could survive without easy money, the next few may test whether the strongest among them can scale, consolidate and eventually return capital. Endeavor Catalyst’s new fund is a bet that more of those companies will come from places still underrepresented in global venture portfolios — and that Asia will be one of them.

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Southeast Asia’s retail sector is racing to embed AI into every layer of the customer journey and nowhere is that race more visible than in Singapore. The government announced a 400 per cent tax deduction on AI expenditure to help small and medium-sized retailers adopt the technology, a clear signal that AI is no longer a nice-to-have but a core national retail strategy.

The appetite from shoppers matches the ambition of policymakers. A Lazada study found that 92 per cent of SEA shoppers already use AI-powered personalised recommendations, and 90 per cent say they trust AI-generated product summaries. 

For an industry built on razor-thin margins and fierce competition, AI has become the linchpin for anticipating demand, auditing inventory in real time, and crafting personalised offers at scale.

Customer experience at stake

But the same systems retailers are racing to deploy can just as easily undermine the customer experience they’re meant to elevate. A broad-strokes rollout during peak sales periods like Singles’ Day or a festive season is a high-stakes gamble. 

If AI misreads data and pushes false discounts mid-campaign, the financial and reputational fallout can be immediate and severe. Irrelevant recommendations quietly erode brand loyalty, one dismissed suggestion at a time.

As AI spreads across more retail functions, the margin for error shrinks toward zero. Retail organisations intent on unlocking AI’s full potential should accelerate adoption while building the safeguards and visibility needed to deploy it responsibly.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

Observability as a strategic guardrail

This is where observability enters as a key strategic tool  holding the whole system together. A New Relic report into the sector reveals that 55 per cent of the industry already embed active monitoring into its workflows, indicating that granular visibility, not blind trust, is the path to digital maturity.

AI-strengthened observability gives retail and engineering teams consolidated visibility into application performance, data quality, and system behaviour. This provides insight into not just what AI is doing, but why. By continuously monitoring the full lifecycle of AI requests and responses, teams can catch anomalies early, validate assumptions, and act before problems ever reach a customer’s checkout screen.

Observability is also a frontline defence against bias, hallucination, and quiet performance decay. Continuous monitoring flags the moment a model starts leaning on incomplete, outdated, or skewed data, long before customers notice degraded recommendations or vanished stock.

An example would be a scenario during a peak season sale. Instead of AI confidently surfacing products that are already sold out, observability finds the mismatch between real-time stock signals and inferred availability the moment it appears. When pricing or personalisation logic starts drifting from actual customer behaviour, anomaly detection flags the deviation before it snowballs into a bad Raya or Black Friday customer experience.

Also Read: The hidden economics of autonomous AI agents

Observability also lets teams pre-validate AI applications in staging environments, reducing the risk of disruption once systems go live. Paired with automated, gradual rollouts, updates can be introduced safely even during the highest-traffic festive periods. If something misbehaves, teams can pause or roll back changes fast. This is an especially critical capability, given that high-business-impact outages in retail can cost as much as US$1 million per hour.

Moving ahead with clarity

AI may be the engine driving retail growth, but observability with other practices is a key tool that keeps that engine on the road. Retailers that have already invested in these safeguards are seeing performance improvements deliver up to 2x ROI. In a market where digital customer experience is the real battleground, from SEA’s tech-forward shoppers to the booming quick-commerce apps, organisations need to ensure they are pairing bold AI innovation with the granular oversight needed to make every touchpoint reliable.

With half of retailers already integrating granular browser and mobile monitoring to close visibility gaps, they are certainly moving in the right direction. The next step is correlating front-end user behaviour directly with AI-driven back-end performance. A mature observability practice will enable engineering teams to track model response times, monitor prompt quality, and manage operational costs, all without losing sight of the mobile and browser experience that ultimately drives revenue.

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

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

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DePIN (Decentralised Physical Infrastructure Networks) crowdsources real-world resources like compute, storage, and connectivity via blockchain incentives. RWA tokenisation brings tangible assets (real estate, commodities, infrastructure) on-chain for fractional ownership, liquidity, and global access. Together, they address SEA’s challenges: high infrastructure costs, limited banking reach, and volatility in local currencies.

Global tokenised RWA (excluding stablecoins) surpassed US$36 billion by late 2025, with forecasts reaching trillions by 2030. DePIN adds real utility: devices generate verifiable data or yield that becomes tokenised RWA.

Global alliances driving convergence

Western alliances set benchmarks. The Superintelligence Alliance (ASI, merging Fetch.ai, SingularityNET, Ocean Protocol) focuses on decentralised AI but connects to RWA through partnerships like SuperWorld. This integrates ASI’s AI stack with virtual real estate, enabling monetisation of physical-digital interactions via tokenised assets, DePIN hardware, and RWA rewards, democratising access to infrastructure yields.

These alliances standardise protocols, attract capital, and reduce fragmentation, turning AI-DePIN data into investable RWA.

SEA’s regulatory momentum and emerging alliances

Vietnam’s 2026 framework marks a shift. The Law on Digital Technology Industry (effective January 1, 2026) recognises digital assets, backed by real-world underlying (excluding securities/fiat). Resolution 05/2025/NQ-CP launches a five-year pilot for issuance/trading, emphasising compliance, cybersecurity, and tokenised assets for practical use.

This creates opportunity for RWA in infrastructure, real estate, and green assets. SEA builders leverage it with alliances tailored to local needs: mobile onboarding, affordable hardware, cross-border utility.

Also Read: Bitcoin dominance at 58.5% and the 55% line that still blocks altseason

Examples include Depin Alliance (depinalliance.xyz), uniting DePIN builders for standards and adoption. It hosts events like the Yacht Party in Ho Chi Minh City, a luxurious networking gathering with global VCs, DePIN leaders (IoTeX, Aethir, Witness Chain), and founders, fostering collaborations on RWA convergence.

Insights from leading alliances in Asia

I was previously CMO at U2U Network, a DePIN-focused Layer-1, which was involved in organising Depin Alliance conferences and side events across Asia. From that position, onboarding and fragmentation were the problems that came up most often when builders, investors and regulators met at these events. Alliances in this space tend to be judged on whether they reduce those two frictions, so it is worth asking what they have actually changed for members.

Niche approaches like “opening association as a service” (templates, events, support for sector alliances) help scale fast. In SEA, where RWA attention grows (tokenised commodities, private credit), alliances capture momentum by linking physical networks to liquid markets.

Challenges and path forward

Fragmentation across chains causes pricing gaps. Hardware costs and volatility deter contributors. Regulation varies (Vietnam pilots cautiously, Indonesia securities oversight) but clarity boosts confidence.

In 2026, alliances will mature DePIN-RWA. Global ones provide standards; SEA ones localise for inclusion. The convergence promises practical benefits: tokenised infrastructure shares, verifiable yields from physical devices. Builders who connect communities early will lead the wave.

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

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

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There’s a fundamental question that most leaders are avoiding right now, not because it’s too hard, but because the answer is uncomfortable.

If you don’t understand where scarcity lives in the current economy, and you keep focusing on supplying more into markets with less demand or that are already overcrowded, you’ve forgotten Economics 101.

Scarcity drives value. That’s not a theory, it’s the foundation. When an item is scarce, it commands a higher price as its value is derived from its rarity. As supply falls short of demand, consumers are willing to pay more to acquire it. When you flood supply into a saturated market, you don’t create value. You erode it. Price compression. Commoditisation. A race to the bottom that nobody wins.

So the question every leader should be asking isn’t what can we build? It’s where is the genuine scarcity?

Technology is becoming abundant. People aren’t

The numbers are striking. The cost of machine intelligence has fallen by roughly 1,000x in just three years, from US$60 per million tokens in 2021 to US$0.06 with Meta’s Llama 3.2. Intelligence, once one of humanity’s most scarce resources, is becoming ubiquitous, abundant, and essentially free.

AI tokens have seen steeper cost reductions than streaming services and legacy technologies combined, driven by advancements in computational efficiency and competitive market forces. Everyone has access. The barriers are collapsing fast. Technology, for all its power, is no longer the scarce resource.

What’s actually scarce right now? According to Deloitte’s 2026 Global Human Capital Trends report, demographic shifts and disappearing workforces are making human capacity itself a scarce resource, elevating the need to invest where humans create unique and irreplaceable value.

Trust. Judgement. Relationships. And yet most leaders are doubling down on the tool, not the gap.

Audit point one: Where are you adding supply, and is there still demand?

Before you ship the next feature, launch the next campaign, or hire for the next role, map it. Is the market you’re entering hungry or saturated?

The data on tech saturation is clear. While AI spending reached US$200 billion in 2024, nearly 65 per cent of organisations report their AI initiatives have fallen short of expectations. And according to Gartner, 30 per cent of generative AI projects will be abandoned after proof of concept by end of 2025, not because the pilots failed technically, but because the human and organisational foundations weren’t built.

Most leaders can answer the technology question cold. Almost none can answer the demand question with real data. If you can’t point to a specific, unmet human need, you’re not leading. You’re just producing.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

The trap most leaders fall into

Tech-first leaders ask: what can this do? People-first leaders ask: what do people actually need?

One is supply-side thinking. The other is strategy.

To truly thrive with AI, leaders must recognise that its power lies in augmenting, not replacing, human connection. The creativity, emotional intelligence, and judgement that only humans can provide will always be in demand.

The leaders who consistently win are the ones who start with where people are underserved, frustrated, or stuck, and then decide whether technology is even the right answer. They’re reading the map before they start driving. Most leaders right now are driving fast, with no map, and calling it innovation.

Audit point two: What would break if you removed the tech?

This is the revealing question. Strip out the AI, the platform, the automation. Does the value disappear, or does it hold?

If the answer is it disappears, you don’t have a leadership strategy. You have a dependency.

Real value lives in the human layer. Trust is the foundation of most successful organisations. In high-trust organisations, employees feel safe to take risks, express themselves freely, and innovate. In contrast, employees at low-trust organisations are often bogged down by office politics and infighting, more likely to withhold information and hoard resources because they don’t feel safe sharing them.

If your entire proposition collapses the moment the tool goes away, you haven’t built anything. You’ve borrowed someone else’s infrastructure and called it a business.

Also Read: Why Singapore firms fear data sovereignty failures but remain underprepared

Direction over efficiency

There’s a version of tech adoption that looks like progress but isn’t. Only 48 per cent of AI projects make it into production, according to Gartner research, meaning more than half of all AI initiatives die somewhere between proof-of-concept and actual deployment. The research is consistent: this isn’t a technology problem. It’s a direction problem.

Efficiency without direction is just faster mediocrity. You can automate your way through an entire quarter and end up further from where you needed to be, just with better dashboards to prove it.

Audit point three: Are you solving for scarcity or comfort?

Most tech adoption happens because it feels easier, not because it closes a genuine gap. The proof is in the trust data: globally, 68 per cent of respondents say they are worried that business leaders purposely mislead people, a 12-point increase from 2021. Most leaders are starting with a trust deficit and will have to earn trust with consistent communication and transparency.

Ask yourself honestly: am I using this tool because it moves me toward something scarce and valuable, or because it feels like progress?

People worldwide seek hope and trust as foundational leadership traits. Understanding these needs is what helps leaders build relationships and inspire others through changes in the world of work. The leaders who can anchor to that, and act on it, are the ones building where others aren’t looking.

The audit

Run these three questions quarterly. Not annually. Not when things slow down. Quarterly.

Where are you adding supply, and is there still demand? What would break if you removed the tech? Are you solving for scarcity or comfort?

Demand. Dependency. Direction.

That’s the audit. And right now, most leaders are failing it, not because they lack intelligence, but because they’re leading with the tool instead of leading with the people the tool is supposed to serve.

The map comes first. Always.

—

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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The post AI labs are chasing a slice of the corporate pie in Southeast Asia appeared first on e27.

Jenni is an academic research and writing platform used by more than six million researchers, who have written over 15 million papers on it. The US-headquartered company says it passed US$10 million in annual recurring revenue (ARR) this year and is profitable, having grown almost entirely from revenue after raising only a small angel round.

While ChatGPT remains the first port of call for many researchers, Jenni has deliberately stayed narrow, focusing specifically on academic work. Its citations link to real papers rather than invented ones, users can trace every AI-generated claim back to the source documents behind it, and its AI Declaration feature, built for researchers submitting to publishers, lets them disclose exactly how AI was used in a paper.

Also Read: India is showing the world what AI will do to jobs and education

The platform can also work directly from a researcher’s own uploaded sources, which is crucial for local or non-English research, where general AI tools still default heavily to English-language results.

In Asia, Jenni has growing university partnerships and user bases across Singapore, Malaysia, India, Thailand and the Philippines, with China emerging as its most ambitious, and most complex, expansion target.

Justin Wong, Head of Commercial Operations, helped take Jenni from US$1 million to US$10 million ARR and now leads its B2B sales and international expansion. He spoke to e27 about the decisions that shaped that journey.

A bet on careers, not semesters

For Wong, there was no single dramatic inflection point in Jenni’s growth from US$1 million to US$10 million ARR. The change was a strategic repositioning, away from general writers and students and towards higher education and academic researchers.

The logic was straightforward. A student might use a writing tool for a single semester before moving on; a researcher writes papers across an entire career. Once Jenni reoriented itself around that longer relationship, building a product a researcher could rely on “from their first manuscript to their fiftieth”, retention, willingness to pay and word-of-mouth referrals inside academic labs and departments all improved.

That shift also reshaped operations. Once Jenni crossed the US$10 million threshold, the company went international“with intent”, localising for markets across Asia, Latin America, the Middle East and Europe. At the same time it built an institutional sales motion alongside its self-serve consumer business, largely because faculty were already bringing Jenni into their departments on their own.

Also Read: Why Dropbox refuses to pick a side in the ChatGPT-Claude-Gemini fight

The bootstrapped path wasn’t originally a strategic choice; it became one. Jenni was one of the earlier players in AI-assisted academic writing, and revenue arrived early enough that raising a large venture round never felt necessary. That came with trade-offs. Every acquisition channel had to pay for itself, the team stayed lean with employees wearing multiple hats, and growth was never bought beyond what the business could sustain. The upside, Wong argues, was strategic independence. Without investor pressure to chase whichever segment was commercially hot, Jenni could commit fully to serving researchers over the long term.

Competing with the tab already open

Every researcher already has ChatGPT open somewhere, and Wong is candid that most conversations with universities start from that reality. The pitch isn’t that general models are useless. It’s that they invent citations, produce unsupported claims and generate prose with no identifiable source.

Jenni’s workflow is built to close those gaps:

• AskJenni lets researchers interrogate both their own uploaded PDFs and more than 250 million external papers.
• Autocomplete suggests one sentence at a time rather than generating full passages, keeping the researcher in control.
• Citations link to real sources across more than 10,000 citation styles.
• A four-layer review checks peer-review readiness, claim confidence, proofreading and tone before submission.

Jenni also does not train on user data in any feature.

Institutionally, Jenni avoids asking for a campus-wide commitment upfront. Instead, a department or lab runs a one-to-two-month pilot, tracking hours saved and manuscript activity, and the deployment expands based on the evidence. India-based Lovely Professional University’s pilot with more than 60 faculty members reported over 375 hours saved; Chulalongkorn University’s six-department rollout reached more than 300 users and over 750 hours saved.

Also Read: The localisation gap: Why multilingual AI isn’t enough for APAC markets

Asked what actually closes institutional deals, Wong points to Jenni’s Reviews feature. It scans a full paper for contradicted, unverified or misrepresented claims, whether a human or AI wrote them, and suggests peer-reviewed references to support them.

He, however, is careful not to reduce Jenni to a citation tool. Many users describe it, he says, as “a combination of ChatGPT, Microsoft Word, and Zotero in a single tab”: one workspace for organising sources, drafting and reviewing. That breadth, not any single feature, is what ultimately sells.

Publishers, not a publisher partnership

Wong is quick to correct a common misreading. Jenni didn’t build a feature with publishers. It built its AI Declaration feature for researchers submitting to publishers and journals, as a simple way to disclose exactly how AI was used in a paper, whether for citations, drafting or grammar correction. The declaration has now appeared in over 100 published papers, including work published by Nature, Elsevier, IEEE and MDPI. As journals formalise their AI disclosure policies, Wong sees this as part of making AI-assisted research broadly acceptable rather than suspect.

Localisation, and the China exception

Across Southeast Asia and India, Jenni’s self-serve-then-pilot motion travels well. China is the outlier. Traditional marketing and sales channels don’t work there. Researchers live on Xiaohongshu, WeChat and Douyin, behind entirely different infrastructure, which makes China the most heavily localised market Jenni has entered.

The payoff, Wong argues, justifies the effort: China publishes more research papers than any other country, and many of its universities lead global research output.

Language matters beyond China, too. Because Jenni grounds its AI in a researcher’s own uploaded sources, including local studies, theses and reports a general model wouldn’t surface, it can support hyper-local work in ways general chatbots cannot, since those often default to English-language searches. Jenni supports drafting in more than 30 languages.

This localisation sits alongside two sales cycles that feed each other. Individual researchers buy through free, monthly and annual plans; this is the self-serve business that passed US$10 million ARR in 2026. Institutions follow the slower pilot-then-expand model. Self-serve adoption is often how Jenni gets into an institution in the first place, because faculty and students are frequently already using it before a university formally engages. As universities increasingly centralise AI purchasing decisions, that dynamic turns individual subscriptions into campus-wide deployments.

The moat against bigger players

What stops a well-funded general AI company from simply adding academic features? Wong frames the defence around incentives rather than functionality. General tools optimise for fast, fluent answers sourced from anywhere on the internet, including unverifiable sources. Academic work demands citations a researcher can defend in front of reviewers. Jenni’s workflow is built around accountability, not just output speed: autocomplete, verifiable citations, four-layer review, no training on user data, and AI declarations. “Adding a citation button to a chatbot,” Wong says, “doesn’t replace that workflow.”

Also Read: The EU called ChatGPT a search engine. SEA’s AI startups should worry about what comes next

With 15 million papers written and more than six million researchers on the platform, Wong believes Jenni’s next phase lies in depth rather than breadth: owning more of the research lifecycle rather than chasing new user segments. “If we’re the tool a researcher trusts for their whole career,” he says, “the new segments follow.”

The post Why Jenni thinks researchers need more than ChatGPT for academic writing appeared first on e27.

Across 231 Canadian publishers, the outlets that accept restricted categories charge less than half as much for ordinary articles. That discount measures who has already left the room, and founders in crypto, lending and gaming are shopping in that half without knowing it.

The cheapest quote in a media plan is usually the one worth worrying about. Founders in regulated categories learn the price side of that quickly, because the numbers arrive in an email. The access side takes a year, and by then the budget is spent.

This month ESBO Ltd, the link building and digital PR agency I run, published Canadian link building prices in 2026, built from 231 Canadian publishers filtered to a Domain Rating of 30 and above with at least 2,000 monthly organic visits. Canada is a test case here rather than a market most readers buy in. It legalised two major categories and then restricted how they may be promoted, which makes the effect on media access easy to measure. The median publisher quotes US$810 for a sponsored article. The interesting part is who quotes it.

Two markets wearing one label

Of those 231 publishers, 49 per cent quote a price for at least one restricted category. The remaining 51 per cent quote none at all.

Those two halves also price ordinary, unrestricted articles differently. Publishers that accept something quote a median US$610. Publishers that accept nothing quote US$1,110. Same product, 1.82 times the price, and the gap holds across every category in the file.

So a founder in a restricted category is not getting a discount on the market. They are shopping in a different, cheaper market, and the cheapness is the signal.

Count readers, not publishers

Site counts make the restricted market look workable. Roughly 41 per cent of Canadian publishers accept gambling content.

Now count audience instead. Add up the monthly Canadian visits behind the publishers that accept each category, as a share of all Canadian audience in the file, and gambling reaches 24 per cent of it. Cannabis reaches 34 per cent. Adult reaches 22 per cent. Anything restricted at all reaches 48 per cent.

Also Read: The most expensive links aren’t really links: What 16,625 publisher price lists tell SEA startups

That is the number to plan against. The cheap gambling placement at a median US$355 is not a bargain. It is the price of what remains after three quarters of the audience has removed itself from your list.

The line is compliance, not taste

Compare the categories and the pattern is not about respectability.

Gambling and cannabis produce the widest gaps between accepters and refusers, at 4.27 and 4.11 times. Both are legal in Canada and both carry strict rules on how they may be promoted: the Cannabis Act prohibited promotion capable of appealing to young people when it took effect in 2018, and Ontario’s regulator has since barred athletes from iGaming advertising altogether. Crypto and forex, which carry no comparable promotion rules, produce the narrowest gap at 1.51 times.

The dividing line is how much compliance exposure a publisher takes on by carrying you. A publication with written advertising standards, a disclosure policy and a named person who approves advertisers excludes legally restricted categories as a class, because each one creates obligations somebody has to own. A publication with none of that takes everything.

Disclosure behaviour confirms it from the other side. Stated sponsorship labelling runs from 11 per cent of publishers at the bottom of the authority range to 64 per cent at the top, and publishers that label charge US$1,420 against US$405 for those that do not. Formal terms, disclosure and a closed door to restricted niches travel together, and they travel with a higher price.

Why this matters in Southeast Asia

A category does not need to be illegal to lose its media access. It needs rules that make carrying it a liability.

That mechanism is already here. In January 2022 MAS issued guidelines stating that digital payment token providers should not promote their services to the general public in Singapore, including through third-party websites and social media influencers, leaving them their own sites, apps and official accounts. Whatever you think of the policy, look at what it does to a marketing plan: the third-party channels come off the table, and the startup is left talking to people who already found it.

Also Read: Startups keep scaling ops before they scale data — Here’s why it backfires

Given how much of this region’s startup activity sits in digital assets, lending, remittances and gaming, a large number of founders here are operating in the restricted half of their media market and have priced it as a bargain. This sharpens the argument I made in this column in June about needing public proof before you scale. When the channels that would carry your proof are closed to your category, the ones that remain have to be earned rather than bought.

Four adjustments

Count audience reach, not publisher counts, before approving any plan in a restricted category.

Stop negotiating the cheap half. Publishers that accept everything quote US$260 to US$390 for standard content. They are already at the floor, and there is nothing to win there.

Put the effort into the publishers who currently refuse. That is an editorial conversation about what the content says, which claims it makes and how it is labelled, not a rate conversation.

Read disclosure as quality rather than cost. The publishers that label charge three and a half times more, reach more readers, and have somebody inside whose job is to vet advertisers.

The instinct when a category gets expensive is to shop for a better price. In regulated categories, price was never the constraint. Access was.

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

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

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