Artificial intelligence is no longer sitting at the edge of enterprise experimentation. Across the Asia Pacific, AI assistants and autonomous agents are moving into live business environments, embedded across email, customer support, internal messaging, cloud applications and collaboration workflows.

That shift is creating an enormous opportunity. AI can help organisations move faster, automate routine work, improve customer experience and support better decision-making. But it is also changing the security equation. As AI becomes part of how work gets done, it is expanding where risk appears, how quickly incidents move, and how difficult it is for security teams to investigate what happened.

Proofpoint’s 2026 AI and Human Risk Landscape report shows that AI adoption in Singapore has already moved well beyond the pilot stage. 87 per cent of organisations in Singapore have deployed AI assistants beyond the pilot stage, and 70 per cent are actively piloting or rolling out autonomous agents. Yet security readiness has not kept pace. Close to three-fifths of these organisations describe their AI security posture as catching up, inconsistent or reactive. 38 per cent have already experienced a suspicious or confirmed AI-related incident.

This is the gap that should concern security leaders. AI is not waiting for governance frameworks to mature. Security leaders in Asia are under more pressure to address key areas of concern.

AI has expanded the attack surface

For many years, cybersecurity strategies were built around familiar control points: email, endpoints, cloud applications, identities and data repositories. Those still matter. But AI is now connecting these environments in new ways, allowing risk to move across workflows at machine speed.

In Singapore, email remains the most common AI-related threat vector, affecting 58 per cent of organisations. But exposure now extends much further: SaaS and cloud applications at 44 per cent, AI assistants or agents at 41 per cent, and collaboration tools such as Teams or Slack at 44 per cent. Among organisations that experienced an AI-related incident, exposure rises across every channel, including 61 per cent in file sharing platforms and 58 per cent involving collaboration tools.

Also Read: Singapore’s data analysts trust AI to work, not to think

This matters because enterprise work no longer happens in a single channel. A sensitive document may move from email into a collaboration platform, be summarised by an AI assistant, stored in a cloud application, and referenced by an autonomous workflow. Each step creates another point where data, identity and intent need to be understood.

Many organisations already have some forms of AI security controls, for example, monitoring shadow AI applications. However, the critical visibility is whether those controls can see across the connected environment how AI is actually being used.

Data security and AI security are the same problem

One of the most common structural errors in how organisations approach AI security is treating it as a separate workstream from data security. It is not. They are facets of the same problem, and solving one without addressing the other creates compounding exposure.

The earliest AI security challenge was clear: employees were using consumer AI tools to process sensitive business information. In 2025, 63 per cent of employees who used AI applications uploaded confidential company data, such as source code and customer records, to personal chatbot accounts. According to IBM’s Cost of a Data Breach Report, shadow AI breaches cost an average of US$670,000 more than standard security incidents, driven by delayed detection and difficulty determining the scope of exposure.

The second wave is more complex. As organisations moved to enterprise AI platforms — Microsoft Copilot, Salesforce Einstein, and others — the question became not whether data was leaving the organisation, but whether AI tools were accessing only the data they were supposed to. That is a data security problem expressed through an AI lens.

The third wave is real-time and agentic. Autonomous agents do not just respond to prompts. Similar to humans, they connect to external tools and MCP servers, acquire new capabilities, and act on data across connected systems. Understanding what an AI agent is doing requires capturing not just the prompt and response, but every tool call and downstream action in between. When security teams do not have visibility into what AI is connecting to and acquiring, they cannot tell the board they have it under control.

Also Read: AI-powered business automation: How SMEs are transforming operations in Southeast Asia

Gartner projects that by the end of 2026, up to 40 per cent of enterprise applications will integrate with AI agents, up from less than five per cent in 2025. It also predicts that by 2028, 25 per cent of all enterprise GenAI applications will experience at least five minor security incidents per year, up from nine per cent in 2025. The risk is scaling faster than governance.

Security and data governance teams need a shared view: what data exists, who and what has access to it, and how AI agents are actually using it. Having a clear view of all your data is not fictional, and it should be the foundation of building robust AI security for any organisation.

Tool sprawl is holding security teams back

Fragmented security stacks are compounding the challenge. Almost all organisations in Singapore say managing multiple security tools is at least moderately challenging, and 61 per cent describe it as very or extremely difficult. Respondents cite operational cost pressures, integration challenges and difficulty correlating threats.

When controls sit in separate systems, security teams lose time moving between dashboards, reconciling alerts and trying to connect activity across email, cloud, collaboration and AI systems. That delay matters when incidents can spread across workflows quickly.

As AI scales, security architecture becomes a strategic priority. More than half of Singapore organisations are actively pursuing vendor and tool consolidation, and 58 per cent believe a unified platform is more effective than point solutions. This reflects a broader shift. Organisations are recognising that AI security cannot be solved with isolated controls. It requires an architecture that can protect people, data and AI systems across the channels.

AI adoption in Singapore and Asia Pacific is not slowing down. The boards and CEOs driving it are right that falling behind carries a real competitive cost. The security leaders are now in a perfect position to enable this AI innovation with the visibility to secure it, govern it, and defend it. That is what setting the pace looks like.

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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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Vietnam’s electric vehicle (EV) market is entering a more practical phase. After years of attention on vehicle launches, subsidies, and consumer adoption, the next question is becoming harder to ignore: where will all these cars and motorbikes charge, and who will pay to build the network?

That question sits at the centre of Grab’s latest move in Vietnam. On July 10, the Singaporean superapp company announced a direct investment in EBOOST, a Vietnamese electric vehicle charging platform and infrastructure provider.

Also Read: Why rising fuel costs are pushing drivers towards EVs

The investment size was not disclosed, but the deal is notable for what it says about the next stage of EV adoption in Vietnam: charging is no longer just a real estate or infrastructure problem. It is becoming a platform business tied to mobility, payments, driver economics, and fleet utilisation.

The investment follows a memorandum of understanding signed by the two companies last November. At that point, the partnership was framed around giving Grab’s driver-partners easier access to EBOOST’s charging network. The new investment deepens that relationship and gives EBOOST additional financial capacity to expand its footprint across Vietnam.

Before the Grab partnership, EBOOST had already built one of the country’s larger independent charging networks, with more than 2,500 charging points and over 10,000 EV users nationwide. Its charging locations cover office buildings, residential developments, public destinations, parking facilities, and other everyday sites where vehicle downtime can be turned into charging time.

For Vietnam, this kind of distributed network matters. Unlike markets where charging infrastructure is concentrated along highways or in suburban homes, Southeast Asian cities are denser, more fragmented, and heavily reliant on two-wheelers. A successful EV charging strategy must serve office workers, apartment residents, ride-hailing drivers, delivery riders, and taxi fleets, often in the same neighbourhood but with very different charging habits.

From MoU to in-app charging

The clearest sign of the partnership’s commercial value is the integration of EBOOST’s network into the Grab Driver app.

Since April, Grab-Car driver-partners have been able to use the app’s EV Charging feature to find nearby EBOOST stations, start charging sessions, and complete payments without switching platforms. Hundreds of charging points have already been connected to the system.

Also Read: Electrifying Southeast Asia: Unleashing the radical potential of electric vehicles

That may sound like a product detail, but it addresses a real barrier for drivers. Charging is not just about the price per kilowatt-hour. It is also about route planning, waiting time, payment friction, reliability, and confidence that a charger will be available when needed. For ride-hailing drivers, every extra minute spent hunting for a charger is potential income lost.

Early usage data suggests the service is finding a repeat audience. More than 70 per cent of driver-partners continue using the service within the first seven days. For EBOOST, that points to stronger charger utilisation and recurring revenue. For Grab, it helps make EV use more practical for drivers whose daily income depends on predictable vehicle uptime.

The next phase will extend the same charging experience to electric motorbike driver-partners. That could be more consequential than the car segment alone. Vietnam remains one of the world’s largest motorbike markets, and the electrification of two-wheelers will be central to any meaningful shift in urban transport emissions.

Integrating thousands of additional charging points for motorbike users could give Grab a stronger role in shaping driver behaviour at scale.

Why Grab needs charging partners

Grab’s interest in EV infrastructure is not surprising. Across Southeast Asia, ride-hailing and delivery platforms face growing pressure to reduce emissions, while drivers remain highly sensitive to operating costs. EVs can lower fuel and maintenance expenses, but only if charging is convenient, affordable, and reliable.

That is where charging operators such as EBOOST become strategically important. A platform can encourage drivers to switch to EVs, but it cannot afford a poor charging experience that disrupts earnings. By embedding charging access into its driver app, Grab can reduce friction for drivers while gathering data on demand patterns, station performance, and charging behaviour.

Also Read: Grab’s US$600M deal could save Taiwan from a delivery monopoly

The model also reflects a broader shift in EV infrastructure. In early markets, charging networks were often built as standalone assets. In more mature ecosystems, they are increasingly tied to software layers: booking, payments, fleet management, energy optimisation, loyalty, and data analytics. EBOOST’s proprietary software platform is designed to support both electric cars and motorbikes, giving it room to serve mixed fleets and different user groups.

For Vietnam, that flexibility is important. The country’s EV market is not moving in a straight line. Private car adoption, taxi electrification, delivery fleets, e-motorbikes, and public charging demand are developing at different speeds. Charging companies that can serve multiple vehicle types may be better placed than those built around a single use case.

A crowded but still-open market

EBOOST is not building in an empty field. Vietnam’s EV charging landscape is shaped heavily by VinFast and its related infrastructure ecosystem, particularly V-Green, which has been expanding charging access to support the country’s largest domestic EV manufacturer. Regional players are also watching the market closely, including Singapore-based Charge+, which has been building cross-border charging ambitions in Southeast Asia, and other energy and mobility companies exploring EV infrastructure across the region.

The competitive question is whether independent networks can create enough utilisation outside manufacturer-led systems. EBOOST’s partnership with Grab gives it one potential answer: aggregate demand through a large mobility platform rather than relying only on walk-in consumer charging. If Grab’s EV driver base grows, EBOOST could benefit from more predictable charging volumes, while Grab gains a charging layer without having to build and operate the entire network itself.

That matters because EV charging is a capital-intensive business. Hardware deployment can be expensive, site acquisition is complex, and payback periods depend heavily on utilisation. A charging station in the wrong location can sit underused; one tied to a reliable flow of commercial drivers can become a recurring revenue asset.

The Southeast Asian test case

Vietnam is emerging as one of Southeast Asia’s most closely watched EV markets. Its combination of urban density, motorbike dependence, local manufacturing ambition, and fast-growing digital services makes it a useful test case for the region. If companies can solve charging access for ride-hailing cars and motorbikes in Vietnam, similar models could be adapted in Indonesia, Thailand, and the Philippines.

Also Read: Grab’s US$425M Stash acquisition is about AI coaching, not America

Still, execution will decide the outcome. EBOOST will need to expand without sacrificing reliability, while Grab must ensure the economics work for drivers, not only for platform targets. Charging access has to be priced and located in ways that make daily use practical.

The investment gives EBOOST stronger backing at a time when Vietnam’s EV ecosystem is moving from headline ambition to operational detail. The next contest will not be won only by who installs the most chargers. It will be won by the companies that make charging invisible enough for drivers to build their working day around it.

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Deep tech in a fractured world needs something very different.

For much of the last 30 years, the working assumption behind technology and capital was simple: the world was converging into one increasingly integrated market. If you could build a product that scaled, global demand and global capital would be there to meet you.

That assumption is now clearly breaking down. Supply chains are being rewired, export controls are spreading, and critical technologies are being treated as instruments of statecraft rather than just engines of growth. The question for Southeast Asia is whether it wants to be a spectator to this shift, or a protagonist.

Deep tech sits right in the middle of this story. It is capital intensive, politically sensitive, and deeply entangled with physical infrastructure and long-term industrial policy. Yet many of the funding models we rely on were designed for asset‑light software, not for advanced manufacturing, new energy systems, or frontier materials.

If Southeast Asia wants a meaningful role in this new order, it cannot rely solely on importing technology and exporting talent. It has to build its own deep tech platforms – and it has to do so with an investment model that acknowledges fragmentation rather than assuming frictionless global markets.

The deep tech paradox

There is a paradox at the heart of deep tech today.

On one hand, governments and corporates worldwide describe it as strategically important. Climate solutions, AI for science, semiconductors, and advanced manufacturing all sit near the top of policy agendas. In Southeast Asia, reports like DealStreetAsia’s The State of Deep Tech in SE Asia 2025  note that deep tech’s share of overall funding is rising, even as absolute capital fell during the recent funding winter.

On the other hand, a lot of the IP that could underpin these sectors still struggles to leave the lab. High‑value patents and prototypes often stall in what investors like to call the “valley of death”: that messy, expensive space between proof of concept and commercial scale.

Traditional venture capital evolved around “optionality”: spread small cheques across many companies, keep ownership light, and hope a handful of outliers carry the fund. That logic made sense when the product was software you could ship globally at marginal cost. It is misaligned with deep tech, where outcomes depend on engineering discipline, regulatory engagement, and long-term offtake contracts.

Also Read: Deeptech’s secret: Ignore the market, master the engineering, and let opportunity find you

In a fracturing world, that misalignment becomes more dangerous. Export controls, national security reviews, and shifting sustainability rules can redraw a company’s viable markets overnight. Treating deep tech as a spray‑and‑pray portfolio of lottery tickets is no longer just inefficient; it increases the risk that strategically important IP never reaches scale at all.

From exposure to control

Fragmentation doesn’t just increase risk. It also changes what “good” looks like for investors and builders.

In a flat world, the main question was often, “How do I maximise exposure to a theme?” In a fractured one, the more relevant question becomes, “Where do I need real control – over governance, capital structure, supply chains, and commercialisation?”

In Southeast Asia, a new pattern is emerging in that “messy middle” between traditional venture capital and private equity. Instead of spreading capital thinly, some platforms are taking significant stakes in a small number of ventures, combining capital with operating control, and standardising parts of the commercialisation process.

A key design choice is to start at higher Technology Readiness Levels – TRL 7 to 9 – where core scientific risk has already been resolved through public–private research ecosystems. In Singapore, for example, institutes such as ASTAR and university labs have built a deep pipeline of such IP, and recent work by McKinsey, the Singapore Economic Development Board (EDB) and Tech in Asia in AI in Southeast Asia: An era of opportunity shows how AI and related technologies are moving beyond pilots into scaled deployment.

By entering at this stage, investors and operators can focus on market design, go‑to‑market architecture, and capital efficiency rather than basic feasibility. Just as importantly, they can design governance and cap tables from the outset, which matters when regulatory and geopolitical risks are as material as technological ones.

Deep tech as a “non-aligned” asset class

In this environment, it’s helpful to think of deep tech as a potential “non‑aligned” asset class.

The most valuable technologies of the next decade – from advanced manufacturing and energy systems to critical materials – are likely to be contested by multiple blocs, rather than dominated by a single geography. Companies structurally tethered to one jurisdiction or standard can find their freedom to operate constrained as policies shift.

By contrast, platforms that anchor IP and governance in trusted hubs, while diversifying markets and manufacturing across regions, can become shared infrastructure rather than instruments of any one industrial strategy.

Southeast Asia, and Singapore in particular, is unusually well positioned to build such platforms. The region sits at the intersection of US, Chinese, and regional supply chains. Singapore offers a credible legal and regulatory environment, and its AI and tech ecosystems are maturing quickly. The AI in Southeast Asia: An era of opportunity report, for example, finds that nearly half of companies surveyed in the region have moved beyond AI pilots, putting Southeast Asia ahead of the global average. A Business Times summary notes that more than 80 per cent of companies are already piloting and scaling AI projects.

Anchoring IP in Singapore while designing ventures that can route production and customers across Asia, Europe, and beyond is one way to turn fragmentation into optionality. In practice, that means thinking early about export controls, dual‑use risks, data localisation, and AI governance frameworks such as ASEAN’s AI governance guide and Singapore’s Model AI governance guidelines.

Also Read: Why traditional marketing fails for complex B2B and deeptech products

From discovering to industrialising

The deeper shift, though, is recognising where the real bottleneck lies.

We are no longer constrained primarily by a lack of scientific discovery. Labs around the world – including those in Southeast Asia – are full of promising high‑TRL IP. The real constraint is institutional: our ability to take that IP and industrialise it, turning it into companies with credible revenue, governance, and liquidity paths.

Some emerging platforms treat company building explicitly as an engineering problem. They standardise finance and governance templates, regulatory pathways, and operational playbooks, and apply these across a concentrated portfolio where they hold meaningful ownership from inception.

Rather than backing dozens of experiments, they co‑build a smaller number of high‑conviction ventures, often with the aim of reaching public markets within a defined timeframe. Exchanges such as SGX, HKEX, and NASDAQ are already home to advanced manufacturing and deep tech listings. EDB’s Destination Southeast Asia 2024 report shows how the region’s tech hubs are attracting more sophisticated capital, while DealStreetAsia’s deep tech reviews highlight a growing share of deep tech deals in the overall venture mix, even after a pullback in funding.

In a fracturing world, this approach has two advantages. It keeps cap tables and governance relatively clean, which simplifies regulatory engagement and cross‑border partnerships. And it gives investors a clearer line of sight to liquidity, which matters when global IPO windows are more volatile, and capital is becoming more selective.

Where Southeast Asia fits

All of this brings us back to the original question: where does Southeast Asia fit in a fracturing world?

On one level, the region is another theatre in a global competition for capital, talent, and supply chains. On another, more interesting level, it can be a builder of the deep tech platforms that fragmentation actually requires: resilient, multi‑market, and anchored in trusted institutions.

If Southeast Asia can consistently take high‑quality IP from its research institutions and partners, industrialise it, and bring it to market with credible governance and liquidity, it becomes more than a manufacturing base or testbed. It becomes a generator of infrastructure‑grade deep tech – platforms that multiple blocs can depend on, but none can easily dominate.

That is ultimately how the region’s voice becomes part of the conversation, rather than reacting after the fact: not by recreating Silicon Valley’s venture playbook, but by building the kinds of deep tech institutions that a fracturing world will increasingly need.

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Bitcoin sits at US$66,188.15 this morning, down 0.65 per cent over the past 24 hours, and the number tells only a fraction of the story. The cryptocurrency pulled back from a one-month high near US$67,000 as WTI crude oil surged above US$85 and then climbed further toward US$94 per barrel amid an escalating US-Iran conflict now in its second week.

The broader crypto market cap slipped 0.47 per cent alongside Bitcoin, volume contracted 6.14 per cent, and Bitcoin dominance held near 59 per cent with no meaningful capital rotation into altcoins. On the surface, this looks like a gentle consolidation after a recovery rally from July lows.

But when I step back and look at the full macro picture unfolding on July 23, 2026, I struggle to share the optimism that we have found a floor. The global factors stacking up right now suggest Bitcoin has more downside to endure before any sustainable recovery takes shape.

The oil story dominates everything else at this moment. Crude prices above US$94 per barrel represent the highest levels since June, and they carry direct implications for inflation expectations and Federal Reserve policy. The US-Iran conflict shows no signs of de-escalation as it enters its second week, which means supply disruption risks remain firmly on the table. Higher energy costs feed into transport, manufacturing, and consumer prices across the board. Airlines already face margin compression.

Treasury yields hover near 2026 peaks as bond markets price in the possibility that the Fed keeps rates higher for longer. The July 28 FOMC meeting looms as the next critical catalyst, and if the statement hints at delayed rate cuts or, worse, another hike, risk assets, including Bitcoin, will absorb the blow directly. Bitcoin in this environment behaves exactly like a leveraged tech stock rather than a decoupled store of value, and that correlation works against holders when macro conditions deteriorate.

Also Read: Bitcoin just broke US$66,000: Is this the start of the next bull run or a trap for late investors?

The equity backdrop reinforces my caution. Wall Street benchmarks finished lower overnight, with S&P 500 and Nasdaq futures slipping as megacap tech earnings delivered mixed signals. Tesla fell around four per cent after missing both revenue and margin estimates. Alphabet reported strong Q2 cloud growth but slid in extended trading as investors baulked at plans to increase capital expenditures. Yes, Super Micro Computer surged 20 per cent on strong AI server margin forecasts, but that single bright spot does not offset the broader disappointment.

The AI narrative that has propped up markets for over one year now faces scrutiny on whether spending translates into returns. When growth stocks wobble, Bitcoin wobbles harder. The 6.14 per cent drop in crypto trading volume suggests buyers have stepped back and lack the conviction to defend current levels. This is not the behaviour of a market that has found its bottom.

Technically, Bitcoin tests its daily pivot near US$66,103 right now. The immediate Fibonacci support sits at US$64,750, representing the 23.6 per cent retracement level. Below that, the US$63,000 to US$63,400 zone serves as the next meaningful floor. Overhead, the 100-day EMA near US$68,000 caps any rally attempt. The structure looks neutral on paper, but I read it as fragile.

A close below US$64,750 opens the trapdoor toward US$63,000, and given the macro headwinds I just described, I think that break becomes more probable with each passing day of elevated oil prices and unresolved geopolitical tension. The recovery channel from July lows remains intact for now, but channels break, and they tend to break in the direction of the prevailing macro wind.

Also Read: Bitcoin reclaims key technical levels, Ethereum leads broader market gains

Grayscale research head Zach Pandl offers a more constructive view, suggesting Bitcoin’s recent price low might hold if the Federal Reserve ends interest rate hikes and economic growth remains stable. He treats Bitcoin as a mature asset influenced by growth and Fed policy, rather than by the traditional four-year cycle model, which would predict a longer bear market and deeper declines.

Grayscale also points to the CLARITY Act and Strategy’s improved financial position, including a US$216 million Bitcoin sale that strengthened cash reserves and reduced forced selling risks, as structural positives. I respect that framework, but it relies on the Fed cooperating and growth holding steady. With oil above US$94 and inflation concerns resurfacing, the Fed has every reason to stay hawkish. The conditions Pandl requires for a bottom simply do not exist right now.

SkyBridge founder Anthony Scaramucci argues that Bitcoin will grind higher from here and cannot get much worse. I appreciate the sentiment, but the global picture tells a different story. Mixed Asian markets preparing for a cautious open, a steady US Dollar against the Yen and Euro that signals continued risk aversion, and geopolitical tensions with no resolution timeline all point toward sustained pressure. The AI boom cushions some of the blow in equities, but it does not immunise crypto from a liquidity squeeze if yields push higher.

Here is where I land. Bitcoin at US$66,188.15 reflects a market in pause, not a market in recovery. The 0.65 per cent daily decline understates the vulnerability beneath the surface. Oil above US$94, an active US-Iran conflict, disappointing tech earnings, yields at 2026 peaks, and an FOMC meeting five days away create a cocktail of risk that has not fully priced into crypto.

The 6.14 per cent volume decline confirms that participants are waiting on the sidelines rather than accumulating. Bitcoin dominance at 59 per cent shows no rotation, no excitement, no fresh capital entering the ecosystem. I do not think we have bottomed.

The US$64,750 level will face a serious test before July ends, and if the FOMC disappoints or oil pushes toward US$100, the US$63,000 zone becomes the realistic near-term target. Patience, not optimism, serves holders best in this environment. The macro picture has not given us permission to call a bottom, and until it does, every rally toward US$68,000 looks like a selling opportunity rather than a breakout.

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Most leaders entering a new market think first about demand. They ask whether the problem is large enough, whether the timing is right, whether regulation is favourable, whether distribution can be acquired at acceptable cost, and whether the economics can support scale. Those are sensible questions, but they often arrive too early. Before a market can scale, before it can standardise, before it can attract sustained capital and serious institutional participation, it has to solve something more basic. It has to establish a trust primitive.

By trust primitive, I do not mean brand warmth or a vague sense of confidence. I mean the foundational mechanism that allows strangers, institutions, and counterparties to participate despite uncertainty. It is the smallest reliable unit of belief that makes the market usable. In some categories, that primitive is escrow. In others, it is identity verification, a guarantee, a clearing mechanism, transparent pricing, dispute resolution, regulatory oversight, or auditability. The exact form changes by sector, but the strategic truth does not. Every new market becomes real only when participants know what they can rely on, what happens when something goes wrong, and who absorbs the consequences.

That is why so many markets look promising in theory and fragile in practice. 

New markets do not fail because of weak demand

A surprising number of early market failures are misdiagnosed. We often say customers were not ready, adoption was too slow, or the proposition was not compelling enough. Sometimes that is true. But in many cases, the real problem is that the market asks people to take too much on faith.

When a market is new, uncertainty exists at every layer. Buyers do not know whether quality claims are real. Sellers do not know whether they will be paid fairly or on time. Partners do not know whether standards will hold. Regulators do not know whether risks are visible early enough. Investors do not know whether apparent growth is durable or merely subsidised experimentation. In that environment, even a strong product can struggle because the surrounding conditions are too ambiguous for meaningful commitment.

This is where strategy often gets superficial. Teams focus on proposition design, pricing, or acquisition before addressing the deeper question of assurance. What would make a rational participant comfortable enough to depend on this market, not just sample it? That question sounds softer than it is. In reality, it is structural. 

Trust is not a brand outcome; it is market infrastructure

Consumers may say they trust a brand, but what they often mean is something more concrete. They believe payments will settle correctly. They believe data will be handled properly. They believe there is recourse if something goes wrong. They believe quality has been checked by someone other than the seller. They believe abuse will be contained. They believe the rules will be applied consistently. In other words, what looks like trust is often confidence in invisible infrastructure.

Also Read: How creativity, commerce and AI collide in mid-2026 marketing mix

This matters because markets do not stabilise through aspiration alone. They stabilise through mechanisms that reduce the cost of belief. That may include insurance, certification, guarantees, identity systems, standard contracts, transparent governance, independent oversight, or rules around loss allocation. Once these mechanisms are in place, the market no longer depends on every participant making a heroic judgment call every time they engage. The system does more of the work.

The first trust primitive is rarely the final one

Another mistake is assuming trust is solved once a market gets early traction. In reality, trust evolves in stages, and the primitive that unlocks early adoption is often different from the one required for institutional maturity.

Early consumer platforms, for example, often rely on visible signals such as reviews, ratings, social proof, and simple guarantees. Those mechanisms can be enough to establish initial confidence among retail users. But once the market seeks enterprise adoption, regulatory approval, or critical mass across a more complex value chain, those same mechanisms become insufficient. Institutions do not make decisions on the basis of community sentiment. They want process controls, audit trails, contractual clarity, governance standards, measurable accountability, and credible remediation.

Every serious market solves the question of loss

If I had to reduce the trust primitive to a single test, it would be this. When something fails, who carries the loss, and how quickly is that answer known?

This is where abstract conversations about trust become concrete. Markets that scale are not markets without failure. They are markets where failure is legible, containable, and allocable. Participants know the boundary conditions. They know whether a transaction can be reversed, whether liability sits with the platform or provider, whether disputes can be adjudicated, whether fraud is insured, whether records are accepted as evidence, and whether harm can be corrected without destroying participation.

This is one reason payments matured through rules, networks, chargeback mechanisms, and settlement disciplines. It is why financial services depend so heavily on supervision, capital requirements, complaints handling, and conduct frameworks. It is why digital identity remains such a hard problem in many emerging categories. It is why AI markets will increasingly be judged not only by capability, but by traceability, explainability, and responsibility when decisions cause harm.

The best growth strategy is often trust architecture

A well-designed trust primitive compresses adoption friction. It shortens decision cycles. It reduces the burden on frontline sales teams to overexplain risk. It lowers compliance anxiety. It improves repeat behaviour because participants are not renegotiating uncertainty every time they return. Most importantly, it changes the shape of the market itself. More counterparties become willing to join, more workflows can move from exception handling into standard process, and more capital becomes comfortable backing long-term participation.

Also Read: The future of marketing isn’t about AI, it’s about judgment

This is why the most consequential strategic moves in a new market often look unglamorous from the outside. They involve rule setting, standard creation, liability design, governance forums, audit models, certification systems, customer protections, and interoperable controls. These are not usually celebrated as growth stories in the early narrative. But they are precisely what separates a category that remains interesting from one that becomes durable.

The paradox is that trust architecture can feel like friction in the short term while creating expansion in the long term. Weak leaders avoid it because it slows the initial story. Strong leaders invest in it because it changes the ending.

The first question should not be market size

When evaluating a new market, the smartest first question is not how large it could become. It is what participants need in order to trust it enough to rely on it.

That framing changes the quality of strategic thinking. It moves the conversation away from enthusiasm and towards structure. It forces clarity on institutions, incentives, safeguards, and failure management. It also reveals whether the company is actually building a market or merely exploiting a temporary gap before trust catches up with reality.

This matters especially for leaders trying to build new businesses in complex sectors. The closer a market is to money, identity, data, safety, or operational continuity, the less room there is for trust to remain informal. In these domains, trust must be engineered, evidenced, and governed. Without that, scale tends to arrive before legitimacy, and that is usually when the real problems begin.

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

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

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

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Indonesia is one of the world’s fastest-growing digital economies: young, mobile-first, and home to more than 270 million people. It is also one of the region’s most dynamic and layered markets, full of opportunity for companies that take the time to understand how it really works.

That opportunity was the backdrop for Scale Up to Global 2026: Gateway to ASEAN, a closed-door session hosted by e27 in partnership with Thailand’s National Innovation Agency (NIA) at the Mandarin Oriental Jakarta on 23 July. The premise was straightforward: as Southeast Asia’s largest economy, Indonesia is a natural gateway for regional expansion, and the event was designed to help a group of Thai scale-ups explore that market by meeting the local partners, enterprises and investors who could help them land.

Rather than a run of stage pitches, the afternoon was built around conversation, bringing together Indonesian enterprise leaders, founders, investors and government representatives for a panel discussion, followed by roundtables with each visiting company and open networking. The emphasis throughout was practical: partnerships, pilots and distribution.

The five Thai scale-ups

The showcase featured five Thai companies, each already deployment-ready at home and each addressing a sector that matters to Indonesia:

• MUI-Robotics, an NIA-backed deeptech company whose AI-Nose platform digitises smell and taste for quality control, safety and environmental monitoring in industries such as food and beverage, cosmetics and chemicals.
• ViaBus, a transit-technology company that digitises public transport end to end, from real-time passenger information to driver tools and fleet-management systems, already operating across Malaysia, Thailand, Laos and the Philippines.
• Precision Dietz (Dietz.asia), a telemedicine platform for chronic-disease and non-communicable-disease care that connects hospitals, clinics and home-based monitoring into a more continuous model.
• ALIVELOOP, a circular-materials platform that turns hard-to-recycle packaging waste, such as multi-layer foil, into industrial-grade material, with the goal of building circular supply chains across the region.
• PraIn FinTech (ChillPay), a Bank of Thailand-regulated payment-gateway provider expanding from domestic payments into cross-border commerce, connecting Thai merchants with customers across the region.

Their sectors map neatly onto Indonesia’s own priorities: AI-driven quality control, digital health, sustainable packaging and cross-border payments.

Inside the panel discussion

The panel, moderated by e27 Co-Founder and CEO Mohan Belani, paired an incoming operator with three players who know the local market well. On the incoming side was Intouch Marsvongpragorn, Co-Founder and CEO of ViaBus. Representing the local view were Abhishek Pansari, COO of distribution company Baskit; Agustine Gunawan, SVP of partnerships at digital-health platform Alodokter; and Bayu Seto, a partner at Living Lab Ventures, the corporate venture arm of Sinarmas Land. Over an hour, the conversation moved from the big-picture opportunity to the practical steps that make a cross-border expansion work.

Also Read: Southeast Asia’s AI future is being written in Vietnamese, Thai, Indonesian

One country, many markets

A recurring theme was that Indonesia is best understood not as one market, but as many. ViaBus, which operates across Malaysia, Thailand, Laos and the Philippines, drew a contrast with its home market: Thailand is highly centralised, with most activity in Bangkok, while Indonesia is an archipelago of islands, each with distinct cultures, regulators and local players.

The company’s most useful takeaway was counter-intuitive: you may not need to start in Jakarta at all. Pansari agreed from the distribution side. Indonesia’s 270 million people are far from homogeneous; tastes, spending power and online behaviour vary widely, and trends move fast, so a single go-to-market model rarely travels well.

Regulation comes first

For companies in regulated sectors, compliance is the first thing to plan for, and it usually takes longer than newcomers expect. Gunawan spent close to a year securing a place in the Ministry of Health’s regulatory sandbox, an investment that now sets Alodokter apart.

Pansari pointed the same way from consumer goods: Halal and BPOM certification can take five to seven months, with more steps than in some neighbouring markets. The lesson for founders is to build regulatory timelines in from the start, because treating certification as an afterthought is one of the most common reasons a launch slows down.

The value of a local partner

If there was one point of consensus, it was the value of a strong local partner, for reasons beyond market knowledge. Pansari described Southeast Asia as a relationship-driven, trust-based environment: even the best product benefits from someone who can open the right doors, both to the market and to the right conversations.

Some consumer brands, he noted, came to Baskit only after trying to go it alone. Marsvongpragorn agreed while staying flexible on structure: a joint venture, vendor relationship or channel partnership can all work, but a local partner, sometimes more than one, is what activates each region.

Localisation as strategy

Gunawan offered one of the sharpest framings: in Indonesia, localisation is not cosmetic adaptation but core business strategy, and pricing is decisive. He pointed to a premium US hospital-information system that, within a month of launching, was matched by cheaper local alternatives that slotted into existing systems.

Indonesian teams are quick and resourceful, so a product priced above what the market will bear can be undercut fast, and because higher costs are passed on to the customer, pricing strongly shapes adoption. Pansari’s beauty-sector example echoed it: a brand that had thrived in China, Taiwan and Thailand found Indonesia far more price-sensitive, a market where a fresh product line may be needed every three to six months to stay ahead.

Also Read: Why agritech is key to securing long-term food resilience in Indonesia

A city as a sandbox

Seto used his time to introduce a different route into the market. Living Lab Ventures is wholly owned by Sinarmas Land and has grown from managing its parent’s balance sheet into a cross-border fund manager with external investors, backing Asia-Pacific companies that are ready to expand into Southeast Asia.

Its distinctive asset is Sinarmas Land’s BSD City: a fully private, self-operated city of around 6,000 hectares (larger than Pattaya, and roughly a tenth the size of Singapore), home to some 500,000 residents and, by Seto’s account, the second-highest GDP per capita in the country after Jakarta.

Because the group manages the city’s roads, transport, water, fibre and hundreds of CCTV cameras feeding a single command centre, BSD can serve as a live sandbox where startups run controlled pilots, backed by investment and a go-to-market programme, before committing to a national roll-out. AI runs through much of it, from traffic management upward, though Seto was candid that not every experiment works: an autonomous-bus pilot paused when regulation was not yet ready.

That kind of controlled test, he suggested, is a signal in itself, showing whether Indonesia is ready for a product, or the product ready for Indonesia. Living Lab, he added, is now actively looking for AI investments. His advice: validate the model in a captive, high-spending-power environment first, then scale with confidence.

Playbooks that have worked

Seto grounded the pitch in examples. In 2023, Living Lab invested in a Melbourne-based loyalty app for its Southeast Asian expansion and connected it into the Sinarmas ecosystem, with venues such as Plaza Indonesia and Aeon adopting it.

In 2025, it partnered with a century-old Japanese technology company to bring around 20 intellectual-property assets, spanning semiconductors, logistics and healthcare, into Indonesia through an accelerator that matched them with local startups; the cohort produced four joint ventures.

The logic is a repeatable, win-win trade: the foreign partner gains market access, while the local startup gains new innovation and handles the localisation. A product does not have to be the best in the world, Seto said. It has to be adapted into the right business model for Indonesia.

How foreign companies can stand out

Companies have been coming to Indonesia to expand and partner for years, Belani noted, prompting the panel to consider where the biggest opportunities now lie. The answers pointed less to the product than to the approach: co-development, genuine IP collaboration, and true partnership rather than going it alone.

Marsvongpragorn closed with a fitting metaphor: in a fragmented market, where Bangkok alone has around 200 bus operators, the goal is not to fight for slices of a small cake, but to work with others to bake a bigger one. For founders weighing a move into Indonesia, the message was encouraging and clear: come with an open mind, the right local partner, realistic timelines, and a willingness to adapt. For those who do, the opportunity is substantial.

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Startup ventures in Asia have been growing over the past few years; however, many young companies think marketing is optional. However, marketing is not a choice, but a must, according to analysis, and without a proper strategy and budget, no amount of effort will make the campaign successful.

As an entrepreneur says, “it takes years to make an impression,” while a partial campaign “simply does not work.” Surveys prove the point: companies that spend less than five per cent of their budget on marketing only grow in half of the cases, while 80 per cent of those that invest 5-10 per cent of their budget grow considerably.

Localise your strategy for diverse markets

There is great diversity among Asian markets, and blanket marketing techniques rarely work. Simply mimicking Silicon Valley approaches is not always helpful; for instance, the approach that works in Singapore will not necessarily work well in rural Southeast Asia. In successful Asian startups, unique playbooks are crafted because the company takes time to learn local culture and behaviours rather than mimicking global trends. Grab and Gojek gained traction by taking advantage of local trends such as cash payments and motorbike taxis as opposed to Uber.

The other key thing is trust, since most consumers in many Asian markets are skeptical and need to be won over before they fully embrace the product or service. In addition to creating demand through the use of attractive content, startups must be able to earn customers’ trust. The use of content such as guides and customer success stories helps to build credibility. PropertyGuru became a trusted name in property listing by offering simple guides in local languages.

Build credibility with content and PR

Mistake: Assuming all marketing is paid advertising. In Asia, earned media and content authority are huge assets. Neglecting public relations and expert commentary is a missed opportunity. Detutu Media notes that founders who shift from shouting into the void to borrowing existing audience through expert quotes and media coverage can land their first press mentions in 60–90 days. In other words, rather than creating content for no followers, startups should contribute expert insights to established publications. Those quick media wins instantly transfer the outlet’s credibility to the startup.

Also Read: The future of marketing isn’t about AI, it’s about judgment

Diversify channels and content

Overreliance on one platform is another mistake entrepreneurs make. 72 per cent of Asian companies say social media is their most used digital channel. Startups spend money on Facebook, TikTok, and Instagram ads. It’s a big mistake because the entrepreneur relies solely on paid advertisements while ignoring other platforms.

Overlooking content marketing and SEO may become even costlier. One marketing company suggests that “over-reliance on paid ads without content authority” is not an efficient strategy. The meaning behind that statement is clear: you spend money on ads but haven’t created any blog posts, videos, or emails for SEO and organic marketing.

It’s a huge mistake to skip influencer and community marketing, too. Consumers in Asia are loyal to the recommendations of influencers. According to Nielsen, approximately 80 per cent of Asian social users are more willing to purchase products recommended by influencers.

However, startups do not make use of that opportunity since fewer than half of the companies in Asia consider influencer marketing crucial. Entrepreneurs should use local influencers who will engage their audience and connect better with them. The same is true about email marketing. In conclusion, diversify your marketing efforts. Use paid ads, owned content, and earned marketing on social platforms.

Measure everything and stay agile

Firstly, do not fly blind. Analytics should not be skipped or neglected. Research proves that in a certain number of Asian companies (for instance, six per cent in Singapore), there is no monitoring of any kind of marketing metrics whatsoever. This is a path to failure. A startup has to measure performance and act according to its results. As a famous marketer says, acting on instincts rather than numbers is often seen but not advisable. 

Also, be prepared to pivot. If something does not work in Asia, change it. According to one marketing manual, a company should stay “agile” while working on its strategy. For example, if a copy of a landing page is confusing to the audience (ShopBack found out that it was true about the word “cashback” in SEA), then change the copy and make it beneficial to users.

Also Read: Driving the future: How AI is rewriting the global marketing roadmap for the Land Rover Defender

Invest wisely (but invest enough)

Finally, don’t starve marketing of resources. Underfunding and expecting magic are traps. As one founder remarks, too many startups launch once and “disappear” because they lack a follow-through plan. A sustained budget and timeline are essential. Hiring an all-star in-house marketing team may be out of reach, but shortcuts like always choosing the cheapest agency or tool often fail. A Singapore marketing guide cautions against “choosing agencies based only on price”. Instead, pick partners for fit and expertise.

Beware shiny-object syndrome (e.g., unvetted AI content) as well. Using generic AI text without human review can hurt authenticity. Always ensure creative quality and local relevance, even on a budget. Finally, embrace the mobile reality: Asia is largely mobile-first.

Currently in APAC, there are over 1.4 billion people (~51 per cent penetration) who use mobile internet. If your website is not optimised for mobile or neglects applications and messaging services that are popular among your local audience, then you’re missing out on customers. The digital presence of a startup needs to be fast-loading and native to how Asians consume media.

Marketing in Asia is much more complicated than in Western countries. Avoiding all these mistakes due to poor localisation, brand-building, and/or data, the underinvested startups can actually transform their marketing into a competitive advantage. Marketing done correctly can be the difference between earning your startup’s first paycheck and celebrating its tenth anniversary.

Conclusion

Learning from failures is crucial. The best thing for any startup from Asia would be planning, adapting to cultural contexts, using paid as well as earned media platforms, and constantly measuring the results. In an area where everything depends on context and trust, the most intelligent startups steer clear of making mistakes and use data-driven advertising campaigns.

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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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In the tech ecosystem, we often talk about the next big thing in terms of code and cloud scalability. But as we move further into 2026, I’ve realised that the most complex system I am currently architecting isn’t a SaaS platform. It is a physical puzzle hunt in the heartlands of Singapore.

I call it The Kampung Files.

If you search for it, you won’t find a landing page or a venture capital pitch deck. That’s because it is currently a live pilot (running June–August 2026), designed as a navigational and cognitive challenge for adults aged 55 to 65. It is my contrarian answer to a fundamental question: How do we apply high-level systems thinking to the real world?

High-tech logic for high-touch reality

While the industry is obsessed with the Metaverse, we are ignoring a massive, systemic friction point: the ageing human mind in an increasingly complex urban environment.

The Kampung Files applies the human rapid loop to social infrastructure. We aren’t just entertaining seniors; we are pressure-testing their cognitive readiness and navigational sovereignty. We use AI to architect the logic of the puzzles, but the vibe is 100 per cent human, rooted in the history of Toa Payoh’s hawker centres and town plazas. It’s a response to the Global Loneliness Epidemic, treating social isolation not as a feeling, but as a failure of neighbourhood architecture.

Solving non-profit fatigue with systems thinking

The social sector is often plagued by symptom management. If seniors are lonely, we give them a tea session; if they are inactive, we give them a gym. But as Stanford Social Innovation Review argues, real change requires moving from fixes that fail to Root-Cause Architecture.

Also Read: Tokenised assets have moved on-chain. The liquidity has not followed

This is the same logic driving my upcoming masterclass at The Foundry Singapore, where we will be applying Radical Essentialism to social impact. We aren’t teaching theory; we are performing a Systemic Audit of how organisations operate. By turning a neighbourhood into a game board, or a social hub into a high-agency ecosystem, we reveal the leverage points where human connection actually happens.

The build engine for the streets

Designing a community puzzle hunt requires a level of originality and intent that no AI can simulate. It requires an understanding of a specific neighbourhood’s pulse. However, the execution, the logistical maps, the logic gates of the puzzles, and the pilot timelines are accelerated by an Agentic Workflow.

By separating the architecture of intent from the mechanics of execution, we allow the human-in-the-loop to focus on what matters: the cultural nuance and the user experience. This allows us to move from an idea to a street-ready pilot in weeks, not years.

The conclusion: Architect for the soul

The ultimate hack for 2026 is realising that innovation that doesn’t reach the street level is just noise. Whether it is redesigning the leadership intelligence of a company through systems like the Octiverse or helping a 60-year-old navigate Toa Payoh with more confidence, the goal is identical: to use technology to lower the friction of being human.

Stop building for the machine. Start architecting for the Kampung.

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Three weeks ago, I had coffee with the chief risk officer of an Indonesian insurer who has spent the last eighteen months building an AI-driven pricing model for one of their health products. He told me the model now produces better predictions than any of their senior actuaries can defend. He also told me he was not entirely sure what to do about that.

That conversation — repeated across enough insurance C-suites — is the part of the industry’s AI story the actuarial profession is still processing in private.

I spent three years as a financial risk country manager at one of Indonesia’s largest life insurers, and have sat through enough pricing committees and product launch reviews to recognise what is happening. The discipline that holds insurance together — actuarial science — is older, more rigorous, and more sober than the rest of finance gives it credit for. It is also, more than any other part of financial services, built on a single foundational assumption: that risks can be pooled across populations and priced once.

AI is breaking the pool.

What insurance was built on

For three centuries, insurance has operated on a deceptively simple model. Gather a large population of similar risks. Estimate the expected loss across that pool. Charge each participant a premium covering their share of the expected loss plus an insurer margin. The healthier subsidise the sicker. The lower-risk subsidise the higher-risk. The pool, at scale, is more stable than any individual within it.

This is not just a pricing technique. It is the social contract of insurance. Risk pooling is what makes insurance economically valuable for the individuals who buy it.

How AI breaks the pool

Three mechanisms are reshaping the foundation at the same time.

• Granularity. AI models can now distinguish risk at the individual level with precision that classical actuarial models cannot match. Driving telematics, wearable data, health records, and social media activity — all feed into models that price each policyholder as a pool of one. The risk pool, conceptually, dissolves.
• Continuity. The premium that used to be set once a year, based on an underwriting moment, can now be repriced continuously against real-time data. The “policy period” becomes an accounting convenience rather than a risk reality. Adverse changes in behaviour are caught — and priced — within days.
• Selection. With granularity and continuity comes the most consequential change. The healthiest, lowest-risk segments of the market discover they are overpaying inside the traditional pool and migrate to insurers that price them individually. The pool that remains is, by definition, riskier than the original. Premiums rise. The cycle repeats. Adverse selection — which insurance regulation has spent decades preventing — becomes structural.

Also Read: Investors bet on algorithms and insurance to tame Asia’s climate-health crisis

Why ASEAN is more exposed than the markets writing about this

Most of the public conversation about AI and insurance is happening in the United States and Europe, where regulatory frameworks for AI pricing have begun to take shape. ASEAN is running behind those discussions, not for lack of capability but because the region is running behind in everything to do with AI risk.

The exposure here is sharper for three reasons.

The data is more available. Indonesia, the Philippines, Vietnam, and Thailand have generated enormous volumes of digital behavioural data through super-apps, mobile banking, ride-hailing, and digital health. That data is less regulated and more granular than what insurers in mature markets can legally use. The capability to price individually is further ahead than the regulatory ceiling that constrains it.

The actuarial profession is thinner. The major actuarial associations in Indonesia, Malaysia, and the Philippines have credentialled practitioners in the low thousands per market, not the tens of thousands. A shock to the profession from AI displacement lands harder when the bench is smaller.

The supervisor’s capacity is uneven. Insurance regulators have been building AI competence steadily, but the depth of model risk expertise inside ASEAN supervisors is still less than what assessing AI-driven pricing genuinely requires.

What is at stake

The pool, once broken, is difficult to reassemble. The traditional life and health products that have served Indonesian households for forty years rely on cross-subsidy across populations. If the lowest-risk customers leave for individually priced products, the pool that remains is more expensive to insure — and the premium increases force the next layer out, until the product becomes unaffordable for the population it was designed to serve.

This is not theoretical. The same dynamic is already visible in segments of the US auto insurance market, where telematics-based pricing has fragmented what used to be a pooled product. It will appear in ASEAN health and life insurance next.

What regulators should be doing

Three policy responses would meaningfully shape how this lands.

Define the floor. Regulators should specify a minimum level of pooling — categories of customers that must be priced together regardless of what AI models could distinguish. Certain health conditions, genetic information, age beyond a band — these can be enforced as non-disaggregable by rule.

Require disclosure on continuous pricing. Customers whose premiums are being recalculated based on ongoing data should be told how, at what frequency, and against what data. The opacity of continuous pricing is the most consequential consumer protection question of the decade.

Also Read: The AI stack trap: Why more AI tools aren’t translating into more growth

Build supervisor capacity now. AI insurance pricing will be deployed faster than supervisors can train to assess it. The window to bring model risk expertise into OJK, MAS, BNM and their equivalents is short.

The macro stakes

Insurance was built on the assumption that risks could be pooled. AI is making it possible to price individually at scale. The institutions that succeed in the next decade will be the ones that figure out how to use the new capability without dismantling the social contract the old model rested on. The regulators that succeed will be the ones who defined what counts as pooling before the market decided for them.

The actuarial profession will adapt. It always has. What will not adapt easily is the public’s trust in insurance as a mechanism that distributes risk fairly. That trust is harder to rebuild than the pricing model is to replace.

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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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