Singapore’s payments industry is getting a new conduct playbook at a time when digital wallets, cross-border transfers and instant payments have become part of daily life for consumers and businesses.

The Singapore FinTech Association (SFA), together with industry players, today launched the Payments Industry Code of Conduct, a voluntary framework that sets out common standards for payment service providers in the city-state. The Code covers areas including pricing transparency, advertising, fraud prevention, card dispute liability, data protection, operational resilience, and anti-money laundering and countering the financing of terrorism controls.

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

The move comes as Singapore’s payments market matures beyond basic digital adoption. Consumers now expect payment services to be fast, cheap and available across borders. At the same time, regulators and industry bodies are facing a more difficult question: how to preserve trust when payment products are increasingly embedded into apps, platforms and regional business flows.

Unlike a regulation issued by the Monetary Authority of Singapore (MAS), the Code is not mandatory. Instead, payment service providers may assess their own policies, systems and processes against the standards and publicly declare themselves as “Code Adherents”. These declarations are valid for one year and must state the year in which the self-assessment was conducted.

That voluntary structure is important. It gives the industry room to adopt a common baseline without creating a new licensing regime. But it also means the Code’s impact will depend heavily on how many providers sign up, how seriously they conduct their assessments, and whether customers begin to treat adherence as a marker of trust.

What the Code covers

The Code applies to holders of major payment institution licences, standard payment institution licences and money-changing licences, as well as exempt payment service providers, in relation to regulated fiat currency payment services under the Payment Services Act 2019. It does not cover digital payment token services, even where those services are offered by the same provider.

At its core, the Code is an attempt to make payment costs easier to understand before a customer commits to a transaction. Code Adherents are expected to show the full cost upfront, including the principal amount, transaction fees, applicable exchange rate, any mark-up, and the final amount to be transacted.

This is especially relevant for cross-border payments, where consumers and small businesses often compare providers based on advertised fees, only to discover that part of the cost is built into the foreign exchange spread. The Code explicitly discourages “free” or “zero fee” claims where the provider still earns through an exchange rate mark-up, unless that cost is clearly disclosed.

It also takes aim at drip pricing, where mandatory charges are added partway through a transaction. For consumers, this means fewer surprises. For providers, it raises the bar for how pricing must be presented in user flows, advertisements and competitor comparisons.

The Code also states that marketing must not create a false or misleading impression about the cost of a service. Comparisons with competitors must be fair, accurate and capable of being substantiated. Providers should not selectively omit their own costs while highlighting rivals’ fees to suggest savings that may not exist.

Also Read: Singapore’s next payments chapter will be written by AI and tokenised money

SK Saraogi, CEO of Wise Asia Pacific and outgoing Co-Chair of the SFA Payments Subcommittee, said the Code sends a clear message that customers should understand the total cost before making a payment. “A mark-up hidden in the exchange rate is still a cost to the customer and should be displayed transparently,” he said.

Fraud, data and resilience move up the agenda

Pricing may be the most visible part of the Code, but its wider significance lies in how it frames consumer protection as a shared industry responsibility.

Code Adherents are expected to maintain a documented fraud prevention framework. This includes regular risk assessments, transaction monitoring, clear escalation procedures and user education on common scams. They are also expected to participate in or support structured industry-wide initiatives led by SFA, MAS or other bodies where relevant and proportionate to their business model, size and risk profile.

That caveat on proportionality matters. Singapore’s payments sector includes large regional players, specialist remittance firms, card issuers, money changers and smaller fintech companies. A one-size-fits-all compliance model could be costly and impractical. The Code instead tries to establish common expectations while recognising that providers face different levels of risk and operational complexity.

The framework also addresses card dispute liability. For card-based payment services, Code Adherents are expected to adopt liability standards aligned with those applying to banks under the Association of Banks in Singapore Code of Practice. This includes caps on customer liability for unauthorised transactions and clear procedures for reporting lost or stolen cards.

On data privacy and security, the Code requires internal controls, data minimisation and compliance with the Personal Data Protection Act. In the event of a notifiable data breach, providers must notify affected users and the Personal Data Protection Commission as soon as practicable, and within three calendar days of assessing the breach.

Operational resilience is another major pillar. Code Adherents are expected to identify and stress-test critical systems such as ledger and wallet systems, payment gateways, customer-facing application programming interfaces and authentication services. In plain terms, these are the systems that keep money moving, users verified and balances accurate. When they fail, the impact can ripple quickly across merchants, consumers and platforms.

Why it matters beyond Singapore

Singapore has long positioned itself as a trusted fintech hub for Southeast Asia, and payments sit at the centre of that strategy. The city-state is a regional base for global fintech companies, a launchpad for cross-border services, and a testbed for regulatory frameworks that often influence conversations elsewhere in the region.

Across Southeast Asia, payments remain one of fintech’s most competitive and strategically important segments. Digital wallets, real-time payment rails, QR payments and remittance platforms have expanded rapidly, but customer experiences remain uneven. Fees can be opaque, fraud risks are rising, and cross-border payment costs are still a pain point for consumers, migrant workers and small businesses.

Singapore’s new Code does not solve these issues across the region. It is domestic in scope and voluntary by design. Still, it may become a useful reference point for markets trying to balance innovation with consumer protection, especially as payment providers increasingly operate across borders.

Also Read: What stands in the way of fintech growth in Asia?

For Singapore-based providers with regional ambitions, adherence could also become part of their trust narrative when dealing with partners, regulators and enterprise customers in neighbouring markets. In payments, credibility is not just about speed or price; it is about whether users believe the provider will behave fairly when something goes wrong.

Holly Fang, President of the SFA, said payments now touch almost every part of daily life in Singapore, making transparency and protection central to public trust. “For consumers, that means fewer surprises and clearer recourse when something goes wrong. For the industry, it raises the baseline of trust that good businesses are built on,” she said.

The Code will be reviewed and updated regularly as the payments industry evolves. SFA has also said it will welcome new market participants and providers over time.

The key test will come after the launch. A voluntary code can clarify expectations, but it only becomes meaningful if providers adopt it, customers notice it, and the industry treats self-assessment as more than a box-ticking exercise. For now, Singapore’s payments sector has a clearer benchmark for what fairer, more transparent payment services should look like.

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TikTok Shop is moving beyond the basic pitch of helping merchants sell through short videos and livestreams. In Singapore, it now wants to train a larger slice of the people who make that model work: the sellers in front of the products and the creators who turn browsing into buying.

At its inaugural TikTok Shop Singapore Summit on 30 July 2026, the company announced a fresh set of capability-building programmes aimed at local merchants and content creators. It plans to support and train at least 1,000 Singapore-based sellers by the end of 2027 and more than 3,000 creators by the end of 2026.

Also Read: TikTok Shop is eating Vietnam’s e-commerce market alive

The summit, held as a full-day hybrid event, drew more than 1,500 sellers, creators and ecosystem partners. It also gave TikTok Shop a chance to underline the momentum it says it is seeing locally: in the past year, its Singapore business recorded 1.7 times year-on-year growth in gross merchandise value, a measure of total sales transacted on the platform, and 1.6 times growth in monthly buyers.

The numbers point to a broader shift in Southeast Asian e-commerce. For years, platforms competed largely on assortment, discounts and logistics. Increasingly, the battle is moving to content: short videos, creator recommendations and live selling sessions where hosts demonstrate products, answer questions and close purchases in real time.

From listing products to performing commerce

TikTok Shop’s new seller initiatives are designed for different stages of merchant maturity. The GO LIVE Academy targets new sellers learning the basics of running their own livestream sessions. The Growth Accelerator Programme is aimed at sellers ready to scale through content and live commerce, with incubation and strategic guidance.

For merchants that want to reduce dependence on external hosts, the Merchant Self-LIVE Programme focuses on building in-house live selling teams. A separate Brand IP Accelerator Programme will help sellers with an existing content presence sharpen their founder or brand identity on the platform.

The emphasis is telling. TikTok Shop is not merely asking Singapore merchants to list inventory online; it is asking them to behave more like media operators. Sellers need to script product stories, analyse audience behaviour, manage creators and hosts, and build repeatable content formats.

“We’re seeing a clear shift in how businesses grow on TikTok Shop. Success today is no longer defined simply by having a presence online or treating LIVE selling as another sales channel,” said Leon Koh, Fashion Cluster Lead and Head of Seller Management at TikTok Shop Singapore. He added that stronger businesses are investing in content creation, in-house live expertise and more strategic creator partnerships.

That is particularly relevant in Singapore, where the retail market is small but digitally mature. Local brands often face a ceiling at home, while cross-border platforms and overseas brands compete aggressively for consumer attention. For smaller merchants, live commerce offers a way to differentiate through personality and community rather than price alone, but only if they can execute consistently.

Creators become part of the commerce stack

TikTok Shop is also expanding programmes for creators, reflecting how central they have become to social commerce. Its LIVE Host Academy will train aspiring professional livestream hosts and connect them with sellers. The Affiliate Accelerator will support creators building affiliate-led businesses through live selling and short-form content. The Short Video Programme will help emerging creators develop product storytelling skills and collaborate with TikTok Shop sellers.

The company says live viewership on TikTok Shop in Singapore has increased by 150 per cent year-on-year. That matters because live commerce requires a supply of people who can hold attention and convert it into sales. In practice, this creates new types of digital work: hosts, affiliate creators, video specialists and community builders.

Also Read: TikTok Shop beats Tokopedia to become SEA’s second-largest e-commerce platform

Across Southeast Asia, this creator-commerce layer has become a key competitive front. Indonesia, Thailand, Vietnam and the Philippines have already seen consumers adopt livestream shopping at scale, helped by mobile-first behaviour and high social media usage. Singapore’s market is smaller, but it can serve as a useful testbed for higher-value categories, brand-led campaigns and professionalised creator operations.

The challenge is quality. A flood of low-effort product pushes can quickly erode trust. For TikTok Shop, training creators is partly about expanding supply, but also about making recommendations feel credible enough for consumers to keep watching and buying.

Local brands test a new playbook

Singapore fashion label Young Hungry Free is one example of how merchants are adapting. The homegrown brand, known for bold collections and a community-driven identity, has used TikTok Shop as a channel for content-led selling and live commerce.

Founder and Creative Director Winnie Ong said TikTok Shop had changed how the brand connects with customers. More interesting than the endorsement is the operational shift behind it. According to Ong, Young Hungry Free began with one full-time employee managing live selling sessions alongside other duties. It has since built a dedicated live commerce team of more than 10 people.

That reflects a bigger change in how e-commerce teams are structured. Merchandising and performance marketing are no longer enough. For brands leaning into discovery commerce, content production and live hosting become core capabilities, not side experiments.

TikTok Shop is also positioning itself as a bridge for more traditional businesses. Kim’s Duet, a Singapore brand by coffee company Kim Guan Guan, has used the platform to reach younger consumers while selling traditional local coffee. Nevin Soon, the second-generation coffee manufacturer and management associate, said the company faced a steep learning curve entering live commerce. After joining TikTok Shop’s onboarding and Growth Accelerator efforts, the brand achieved 42 per cent month-on-month gross merchandise value growth in June 2026.

Such examples help explain why social commerce has appeal in Southeast Asia. Many small and family-run businesses are digitally aware but do not have deep e-commerce teams. Platforms that provide training, traffic and creator access can become important growth infrastructure — though they also increase merchants’ dependence on one ecosystem.

A crowded and contested market

TikTok Shop’s push in Singapore comes amid stiff competition. Shopee, owned by Sea Group, remains the dominant e-commerce marketplace across much of Southeast Asia and has invested heavily in livestreaming and affiliate tools. Lazada, backed by Alibaba, continues to court brands and merchants through its regional marketplace infrastructure. Amazon Singapore competes in selected categories with its logistics strength, while Shein and Temu pressure fashion and lifestyle sellers on price and supply-chain speed. On the content side, Meta, YouTube and other social platforms are also trying to capture creator-led product discovery.

This rivalry means TikTok Shop cannot rely on entertainment alone. Its advantage lies in the tight loop between content, recommendation algorithms and checkout. But to sustain that edge, it needs sellers who can produce better content and creators who understand commerce without turning every video into a hard sell.

Also Read: TikTok teams up with Vietnam to power the country’s digital growth

The Singapore summit suggests TikTok Shop is now treating training as part of its market strategy. In a region where e-commerce growth is increasingly shaped by trust, attention and creator influence, the platforms that win may not simply be those with the most products. They may be the ones that teach the most merchants how to sell in a world where shopping starts with discovery.

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One of my jobs in what I came to think of as a corridor business did not look like one at the time. It was in the years before COVID-19, at China Business Network, a small but meaningful consultancy in Beijing, where my role was to help governments attract Chinese visitors, which gave me an actual front-row seat to what I think of as modern commerce.

This was the era when Chinese travellers took 155 million trips abroad in a single year, and spent US$255 billion doing it, reported McKinsey back then. It was amazing to witness, because the world was hungry for a share of it, and governments from Qatar, Bahrain, Luxembourg, the Ivory Coast, Sudan, American tourism boards and destinations, and many more came to our door, all with the same request: help us make the Chinese visitor comfortable in our country, comfortable enough to stay and spend, and show us how to, before our neighbours figure it out.

Our job was to move them to the front of that queue by means of marketing, promotion, media, and consulting advisory services. We secured them top billing at Beijing’s flagship tourism events, had their destinations promoted to the Chinese public, media appearances, and partnerships with local businesses. Needless to say, they routinely stole the show, and the partnerships we grew lasted many, many years.

In hindsight, those governments rehearsed a relationship with a rising economic power, learning its holidays, payment applications, its hospitality codes, and its way of building trust. A tour bus was a prototype to what would pave the way to something greater, a bond I saw take place with every market we served: people flows predict capital flows. Governments signed agreements constantly with China, but only some became actual corridors; now, to know which ones will shape the future, watch the arrivals hall at airports.

The sequence has a shape

Credit: Embassy of the People’s Republic of China in Singapore

Signatures on paper come first; for instance, ASEAN and China signed their framework trade agreement in 2002, ASEAN reported, with Beijing granting approved-destination status country by country, and Chinese delegations initiated agreements on nearly every state visit then. But we all know early paper is only permission, not proof of anything, and a lot of signed agreements never made it past the initial stage. What made the others succeed was how they moved, the number of their visitors, the small adaptations, Mandarin signage, familiar payment rails, staff trained to understand what Chinese guests actually wanted, and with that, familiarity was quietly building between parties.

Familiarity allowed deep architecture to consolidate, and from that, trade under the agreement grew more than fivefold. ASEAN overtook the EU as China’s largest trading partner in 2020, Nation Thailand reported, with two-way trade reaching a record US$984 billion in 2024, while the region has been the top FDI recipient among developing regions for four consecutive years, with manufacturing FDI climbing nearly 150 per cent to US$44 billion, which led to CAFTA 3.0 arriving only in late 2025, a quarter century after the framework.

Also Read: Southeast Asia in the 2026-2030 world order: Trade, chips, AI, and capital

So when we say it’s signatures first, people, second, and they’re the real test. Then comes capital, and only then deep architecture that allows existing flows to run. Nothing de-risks a market like millions of ordinary encounters involving tourists, students, and pilgrims are a distribution of due-diligence networks that predict which signed agreement truly comes alive, while others vanish in the halls they were signed in.

Now, watch the same tape run one more time

Credit: Saudi Embassy in Singapore

Apply the rule today, and something jumps out: Asia is emerging as a top choice for Gulf travellers, with destinations from Singapore, Bangkok to Phuket, Penang, and Bali adapting through halal-friendly services and expanding air links with the Gulf. In the Global Muslim Travel Index 2025, Singapore leads every non-OIC destination while Malaysia holds the top position among OIC countries: Southeast Asia is where the Gulf goes on holiday, and notice the symmetry here: some of the same Gulf states that once courted Chinese travellers have become the source market everyone else now adapts for.

This said, unlike the China corridor, this one has a cultural floor, with Indonesia holding the world’s largest Muslim population, halal is natively ingrained, not an accommodation, and a Gulf family lands in Southeast Asia already halfway to comfortable, so a corridor there that serves this public should run the sequence faster.

Now, look one step behind the travellers

Middle Eastern sovereign wealth funds manage more than US$5.6 trillion in assets, a figure projected to reach US$8.8 trillion by 2030, wrote Foreign Policy, increasingly run from offices in Asia rather than London now. This is one of the reasons why the Gulf is converting a finite oil endowment into permanent stakes in the world’s fastest-growing consumer markets while hedging deliberately against the unpredictability of Washington’s tariffs, thrown here, there and everywhere.

Asia House expects GCC trade with emerging Asia to climb from US$450 billion in 2023 to US$680 billion by 2030, and the whole deployment has already begun with Abu Dhabi’s ADIA partnering with the Indonesia Investment Authority on toll roads, joining the consortium that acquired Malaysian Airports. While in May 2025, Kuala Lumpur hosted the first-ever ASEAN-GCC-China trilateral summit, here are three blocs representing a combined GDP of US$24.87 trillion and some 2.15 billion people.

Also Read: As global trade fragments, Southeast Asia must build leverage, not just attract investment

I must add, if the China sequence I spoke about took two decades, endless tour buses, and trade partners to take shape, the Gulf-Southeast Asia way is being paved; it is still in its early days, and that’s exactly when positioning is still cheap and highly rewarding, while other regions are still figuring things out.

Northeast Asia is already running the experiment

Credit: Saudi Gazette

Adaptation is a choice, not an accident of geography, and Korea and Japan are running the experiment on behalf of Southeast Asia. It needs to learn from the following two neighbours, and how their diverging results preview what happens when a lead is tended, and another is left alone.

South Korea’s tourism authorities have run a systematic Muslim-friendly program for over a decade, with four-tier restaurant classification, prayer kits for hotels, guide training, and incentives for prayer rooms, and what makes the program remarkable is the anti-Muslim sentiment wind that has been sweeping the country.

South Koreans had years-long conflict over a mosque in Daegu, reported the Korea Herald, where opponents to the project placed pig heads outside the construction site, and a 2018 petition against Yemeni asylum seekers that drew some 700,000 signatures. South Korea’s tourism professionals looked at where travellers are going, and built the pavement anyway.

Japan is the sharper, and quite unconventional, lesson. It made a move very early, and reached an all-time high of 3rd place among non-OIC destinations in the 2019 index, but the country’s efforts remained fragmented. Japan’s tourism authority acknowledges that there is no central agency that handles halal accreditation, and by 2023, the country slipped to the 6th position while Singapore held the non-OIC top spot in 2025. In short, Japan’s early adaptation decayed as soon as it stopped moving forward.

Why people always come first

I learned the trust half in person. I am from northern France, bordering Germany, where business means punctuality, agendas, and follow-through, and I’ve witnessed that in some parts of the world, trust translated into dinners, over months, through relationships preceding any transaction. I began closing deals only when I stopped selling and started understanding and adapting.

That gap now sits in the middle of the Gulf-Southeast Asia corridor mentioned, not only through language alone, but the deeper kind that displays how trust is established before anyone shows a number, and what silence means in Riyadh versus Jakarta. Every Gulf family in Langkawi, every Emirati student in Kuala Lumpur, every new direct flight quietly closes that gap, and the visitors are the corridor’s earliest infrastructure that shapes the future of the bond.

Also Read: The hidden engine driving Bitcoin price action that most retail traders ignore: Open interest

The caveat and how founders can use the rule

All in all, the GCC remains only ASEAN’s seventh-largest trading partner, and the bloc-level ASEAN-GCC free trade agreement is still a feasibility study, not a signature. The good news is Malaysia went the farthest into the relationship by launching its own FTA negotiations with the GCC, and this is where founders can leverage existing opportunities. 

Treat travel data as market intelligence: arrival numbers, flight routes, and Muslim-friendly rankings are public leading indicators almost nobody in technology reads. Build cultural awareness and readiness before the wave, as the businesses that adapted early to Chinese visitors did, because the same window is open now for Gulf customers across hospitality, retail, health, education, and digital services, and sell to the Gulf rather than merely raising from it.

Where Southeast Asia fits

Back at China Business Network, the governments that won Chinese visitors were never the ones with the biggest budgets; they were the ones willing to learn how the other side built trust. They were the ones who kept coming back to Beijing, learned what comfort meant to a Chinese guest, from the food on the table to the pace of a negotiation, and built it before their rivals did.

Malaysia’s Anwar Ibrahim has long maintained that neutrality is the source of ASEAN’s centrality, and the arrival halls suggest the market agrees. In a world where great powers force everyone to choose, the scarcest asset is a place that forces no one, and that is what keeps Southeast Asia’s terminals full. The pattern has run through the region twice, first with China, now the Gulf region, which is currently after Singapore, Malaysia, and Thailand.

Great treaties follow the flows, and corridors get built in a way that no summit can legislate, with halal kitchens a Bangkok hotel adds, the Arabic-speaking staff at a Kuala Lumpur clinic, the direct flight that turns a Gulf family’s holiday into a habit. Millions of small welcomes like these are the region’s real endowment, so when the ASEAN-GCC agreement moves from speeches to signatures, the advantage will belong to those who spent years studying their markets, language, and habits.

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

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Ask any project manager on a Southeast Asian construction site what eats up their evenings, and the answer rarely involves engineering. It’s paperwork: chasing updates, compiling reports, making sure the right photo is attached to the right ticket.

Ethan Ow knows this frustration intimately. He lived it as a junior executive in 2012, left the industry for tech in 2017, and returned years later to find nothing had changed. Group chats were still buzzing with site updates, but somebody, somewhere, still had to convert that chaos into a proper record,  usually late at night, usually by hand.

Also Read: AI takes centre stage in Singapore’s push for Zero-SIF construction sites

That gap is what Ow set out to close with Wenti Labs, a Singapore-based startup building AI agents that turn WhatsApp messages, photos and voice notes from construction sites into structured reports — no new app, no retraining an entire workforce.

Today, more than 20 paying enterprise customers, including Woh Hup, PentaOcean, Obayashi, and Jacobs, run their reporting through Wenti Labs. The company processes around 20,000 API calls a day and claims to cut a task like a site walk report from an hour down to 30 seconds.

Building inside the chat, not around it

The decision to embed Wenti Labs inside WhatsApp rather than launch a standalone platform wasn’t a branding choice; it was a survival strategy.

“WhatsApp was the obvious starting point because of its ubiquity across Southeast Asia,” Ow explains. The real problem wasn’t a lack of data; it was what happened to it afterwards. Project managers were still pulling information out of chat threads and manually re-entering it into spreadsheets before a report could be filed. Wenti Labs was built to eliminate that step entirely.

“This allows them to continue using a familiar channel while the administrative work happens in the background,” he says.

Why an entire industry got stuck

Construction’s resistance to digitisation is often framed as stubbornness. Ow disagrees.

“A lot of project data is logged in group chats, but the actual digital records are often created later, when the person responsible has time to enter everything manually,” he says. “This delay creates gaps, so different teams can end up working from different sets of information.”

His conclusion is blunt: “I do not think that the industry is resistant to technology. The problem is that many digital tools have asked people to do more admin before they save them any time.”

Inside the pipeline

Before writing any code, Wenti Labs maps out each customer’s workflow — what needs tracking, what a correct report looks like, and where information needs to land. Only then does the team build customer-specific agents using OpenAI’s Responses API, tools, and Agent SDK.

Ow describes a typical scenario: a worker spots a missing safety barricade during an inspection and sends a photo into the project’s WhatsApp group. The model extracts the issue, location, category and criticality, and the system generates a ticket automatically. Once a colleague fixes it and shares evidence in the same chat, the agent closes the ticket, no forms involved.

“The difference is that changes are recorded in real time and the worker does not have to transfer the same information from one platform to another,” Ow says. “Because the output is structured and follows the customer’s workflow, it can be used for official reporting.”

One playbook, many markets

Expanding across Singapore, Malaysia, Indonesia and Vietnam means confronting different documentation standards and languages, a challenge Wenti Labs tackles through configuration rather than a rigid template.

“We tailor the final output to each customer’s local requirements by configuring the required fields and connecting the result to their existing systems,” Ow says.

Some Singapore-based contractors communicate in Mandarin; other customers operate entirely in Vietnamese. Site teams message in whatever language they’re comfortable with, and the agent produces the report in whichever language is required. The company has extended the same approach to heat stress management for customers in Singapore, Australia and Japan, each with distinct regulations.

Whose data is it, anyway

Construction data can include safety incidents, contractor performance and project delays, all flowing through a consumer messaging app. Ow is direct about where the company draws the line.

“We stay true to the principle that all project data belongs to the customer,” he says. “Wenti Labs does not use customer uploads to train or fine-tune a shared model, and our agents can access only the information the customer has authorised.”

Also Read: The dawn of housing abundance: Why AI will collapse construction costs by 90 per cent

Deployment isn’t one-size-fits-all either; the company supports granular access controls alongside region-specific and on-premises storage, letting customers control exactly where their data lives.

Winning over the sceptics

Older, less tech-savvy site supervisors are often assumed to be the hardest group to convert. Ow found the opposite, provided the tool asked nothing new of them.

“Workers of all ages can simply keep doing what they already do,” he says. “From the site team’s point of view, Wenti Labs functions just like another user they can message on WhatsApp.”

The numbers back him up. One customer had previously logged around 30 safety issues a month through a form-based system. After adopting Wenti Labs, that jumped to 300 — not because more accidents were happening, but because roughly 90 per cent of existing issues simply weren’t being recorded before.

“Any new tool can feel unfamiliar at first, but seeing is believing,” Ow says.

The reliability bar nobody talks about

Perhaps the most candid part of the conversation is Ow’s math on reliability. At 20,000 API calls a day, even a model with a 90 per cent success rate would generate roughly 2,000 unreliable outputs daily — “a margin that is not feasible in construction.”

Cheaper open-source models were tested along the way, but the extra engineering needed to hit the necessary consistency wasn’t worth the trade-off. “OpenAI’s reliability has generally kept pace with each update, so we eventually decided it was more practical to stop evaluating every new model and focus that time on solving customer problems,” Ow says.

Codex has also become part of the internal toolkit — not just for writing code, but for quality assurance, monitoring agent outputs across customers and surfacing new edge cases. “A typical sprint used to take two weeks; now we can ship a feature in about two days,” Ow says.

Beyond the site walk report

Safety inspections were the starting point, but other bottlenecks are in the crosshairs too. Equipment certificates, for instance, typically arrive as PDFs that someone manually copies expiry dates from into a spreadsheet. Wenti Labs’ agents now extract that information automatically and flag renewals before they lapse — the same logic applied to consolidating concrete supplier and test lab data for real-time project oversight.

A retention play, not just an efficiency play

Singapore’s construction sector has a well-documented labour crunch, and Ow frames the company’s impact in terms broader than pure productivity.

“It does not help when teams find themselves weighed down by repetitive paperwork instead of the engineering and site work they signed up for,” he says. “Over time, that can push people away from the industry.”

Staying in its lane

Despite interest from farming, manufacturing and shipping, Ow has no plans to turn Wenti Labs into a generic tool for every industry.

“Wenti Labs’ domain knowledge lies in construction,” he says, pointing to legal-AI platform Harvey AI as the model he’s chasing: deep, not broad.

Also Read: The vision-based shift: Transforming construction safety with AI

The ambition stretches beyond report generation altogether — Ow wants Wenti Labs to become something closer to an operating system for construction projects, quietly connecting chats, emails, documents and existing software into a single, coherent record of what’s actually happening on the ground.

For an industry that has spent decades logging its most important information in group chats and losing track of it soon after, that would be less a feature than a fundamentally different way of working.

The post The 30-second report: How one startup is killing construction’s paperwork problem appeared first on e27.

For every startup that celebrates a successful Series A funding round, dozens quietly discover that raising capital is the easier part.

The real test begins after the money arrives, when investors expect not just growth but predictable, repeatable execution. It is at this stage that many founders realise they are no longer building a product, but they are building an institution.

India’s startup ecosystem has matured enough to reveal a pattern. Companies that successfully transition from Series A to growth-stage businesses rarely win because they raised the most capital. They become bigger because founders reinvent themselves as the company evolves.

The founder who excels at discovering product-market fit is not automatically equipped to manage a 300-member organisation, multiple business lines, international markets and board expectations. Scaling requires learning a completely different set of skills.

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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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For decades, we’ve taught financial literacy as one of life’s most important skills. Save before you spend. Build an emergency fund. Invest early. Understand compound interest. Avoid unnecessary debt. Those lessons remain just as relevant today as they were a generation ago.

But I believe future generations will need to master another form of literacy alongside them: AI literacy. Not because artificial intelligence is the latest technological trend, but because it is rapidly becoming one of the most powerful forms of economic leverage available.

The conversation around AI has largely centred on productivity. Businesses talk about doing more with less, while employees worry about whether AI will replace their jobs.

I think we’re asking the wrong question.

The real value of AI isn’t simply helping us work faster. It’s giving individuals greater control over how they think, build, earn and create opportunities.

Just as financial literacy changed the way people managed money, AI literacy may change the way people create it.

Financial literacy helps us manage money, AI literacy helps us create leverage

Financial literacy has always been about making better financial decisions.

Should you save or invest? Should you buy a property or continue renting? Should you build a business, start a side hustle or remain employed? Should you diversify your investments or focus on growing your company?

There has never been a single correct answer because everyone’s financial goals, responsibilities and appetite for risk are different. That’s what makes financial literacy so valuable. It equips people to make informed decisions based on their own circumstances.

I believe AI literacy is evolving in much the same way. Many people think AI is about replacing work. I see it as helping people make better decisions.

Imagine having a personalised thinking partner that can help you evaluate a business idea, compare different income strategies, identify blind spots, challenge your assumptions or explore opportunities you hadn’t considered. Not because AI knows the “right” answer. But because it can help you think more broadly and more objectively.

Whether someone chooses to become an entrepreneur, build a side hustle, invest in the stock market or remain in full-time employment, these are deeply personal financial choices.

AI won’t make those decisions for us. But it may help us make better ones.

Also Read: Southeast Asia in the 2026-2030 world order: Trade, chips, AI, and capital

AI literacy isn’t about learning prompts

One of the biggest misconceptions I encounter is that AI literacy simply means learning how to write better prompts.

That’s like saying financial literacy is knowing how to use a calculator. The tool isn’t the skill.

Real AI literacy is understanding how to redesign the way you think and work. It’s recognising which tasks require uniquely human judgement and which repetitive processes can be delegated to AI. It’s learning how to collaborate with technology rather than compete against it. It’s thinking in systems instead of individual tasks.

More importantly, it’s about understanding leverage.

Someone who knows how to use AI strategically doesn’t simply finish work faster. They create capacity. Capacity to build another income stream. Capacity to launch a business. Capacity to spend more time with customers. Capacity to solve bigger problems.

That’s a very different outcome from simply becoming more productive.

The confidence gap is often bigger than the technology gap

Through the workshops and programmes I run for entrepreneurs and professionals, I’ve noticed something fascinating. Many participants begin with exactly the same sentence. “I’m not a tech person.”

A few weeks later, those same individuals are building automations, validating business ideas, launching digital products and creating workflows they never imagined possible.

The technology didn’t change. Their confidence did. The biggest barrier wasn’t technical ability. It was the belief that AI was only for developers or engineers.

Once that mental barrier disappears, people begin asking different questions. Instead of asking, “Can I do this?” they start asking, “How could I build this?” That shift in mindset may be one of AI’s greatest contributions.

AI isn’t making us think less. It’s changing where we think.

One criticism I hear regularly is that AI will make people intellectually lazy. Used carelessly, perhaps. But the same argument could have been made about calculators, spreadsheets or search engines. Technology has always changed how we work.

The real question is whether it allows us to think at a higher level. I believe it does.

Instead of spending hours rewriting documents, summarising meetings or performing repetitive administrative tasks, AI allows us to redirect our thinking towards strategy, creativity, relationships and decision-making.

It’s not about using less of our brain. It’s about using more of it where it matters most. AI shouldn’t replace thinking. It should elevate where our thinking creates the greatest value.

Also Read: Bring uncertainty out for lunch: Why leadership may need a new management discipline in a changing world

Why AI education should become mainstream

Financial education exists because society recognises that understanding money leads to better outcomes. I believe AI education deserves the same treatment.

Not everyone will become an AI founder. Not everyone will build a startup. But almost everyone will need to understand how AI affects their career, business or future earning potential.

Future generations shouldn’t only learn how to budget, save and invest. They should also learn how to evaluate AI-generated information, collaborate with AI responsibly, automate repetitive work and use AI to solve meaningful problems.

These aren’t simply technical skills. They’re economic skills. Because increasingly, understanding AI won’t just influence how we work. It will influence how we earn.

The next evolution of financial literacy

Financial literacy isn’t becoming less important. If anything, it matters more than ever. Understanding how to manage money will always be fundamental.

But managing money is only one part of building financial security. Creating opportunities is the other.

AI gives individuals something previous generations rarely had access to: personalised leverage. The ability to explore business ideas, validate opportunities, automate execution, test different strategies and learn at a pace that was previously impossible without significant resources.

For the first time, sophisticated guidance is becoming accessible to almost anyone with an internet connection. That’s why I don’t see AI literacy replacing financial literacy. I see it becoming part of it.

Just as previous generations learned about budgeting, saving and investing, future generations may also learn how to leverage AI to build businesses, create side hustles, automate income-generating systems and make more informed financial decisions.

Not because AI guarantees success. But because it gives more people access to the tools that make success possible.

Also Read: The pitch you never gave: What AI tells buyers about your startup

The next chapter of financial literacy

Every major technological shift changes the skills society values.

The internet made digital literacy essential. Smartphones transformed how we communicate. Cloud computing reshaped how businesses operate.

AI is different because it doesn’t simply change how we access information; it changes how we think, create and execute.

That’s why I believe AI literacy is becoming as important as financial literacy. Not because everyone needs to become an AI engineer or build the next unicorn startup, but because understanding how to leverage AI will increasingly shape a person’s ability to create opportunities, generate income and make better financial decisions.

Financial literacy teaches us how to manage money. AI literacy teaches us how to create leverage.

Together, they give people something far more valuable than either skill alone: the ability to build a future on their own terms. Because in the years ahead, the greatest advantage may not belong to those who work the hardest. It will belong to those who know how to combine human judgement with AI leverage.

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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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Every few months, a new platform promises to hand retail traders the same edge that institutional desks have spent decades building. Plug in the AI, sit back, let it run. The pitch has become so familiar it barely registers anymore.

I’ve been trading for thirty years and made every mistake in the book. What I’ve learned, slowly and sometimes expensively, is that the gap between a good trader and a losing one is rarely about access to better data. It’s about what happens between receiving a signal and deciding what to do with it. That gap is where discipline lives, and it’s where most retail traders come unstuck.

AI cannot close that gap by excluding the human from the equation. Used well, though, it can do something more valuable: make the human in that equation better.

Why the losses keep happening

The numbers on retail trading outcomes have been remarkably consistent for decades. India’s markets regulator, SEBI, spent three years tracking live trading data and found that 93 per cent of retail futures and options participants ended in the red. A 27-year dataset of eight million traders, covering every major market cycle from 1998 to 2025, showed failure rates holding steady between 74 per cent and 89 per cent, regardless of conditions.

What’s striking about that data is not the scale of the losses. It’s the consistency of how they happen. A period of early success, then overconfidence, then the familiar spiral: positions held too long, stops moved in the wrong direction, winners sold off before they’ve run.

These aren’t analytical failures. They’re behavioral ones. Giving a trader faster AI tools doesn’t fix them. If anything, it gives them a faster way to repeat the same mistakes.

Also Read: Finance doesn’t have a math problem, it has an ego problem.

The checklist I kept on my desk

When I started trading, computers were new enough that I kept a physical checklist next to the screen. Five items. Every single one had to be checked before I put on a trade. Looking back, it was the thing that saved me from myself more than any indicator or strategy ever did.

The same principle sits at the heart of how I think about AI in trading. A well-built system should process a large number of combinations and discard most of them. The job is to surface only the moments when multiple independent signals simultaneously point in the same direction. When that happens, the notification fires. When it doesn’t, nothing happens, and that silence is the system doing its job.

A 2025 study in the IUP Journal of Accounting Research found a negative correlation between AI tool adoption and loss aversion: traders who regularly used AI-based platforms were measurably less prone to premature exits and fear-driven decisions. The tools were improving the conditions for decisions, not replacing them.

The problem with more

The instinct when building or using AI for trading is to maximise: more signals, more alerts, more data across more screens. The logic seems sound. More information should mean better decisions. In practice, it usually means the opposite.

Crypto trading is the clearest case study. The market never closes. It reacts to a tweet, a regulatory rumour, a shift in sentiment elsewhere: all without the session structure that gives forex or futures traders natural stopping points to reexamine. Traders who manage structured markets well often find crypto genuinely difficult, not because the basic mechanics are more complex, but because there’s no closing bell to interrupt the emotional momentum of a bad run.

Also Read: The fatwa lag: How AI is overtaking the system designed to govern Islamic finance

Keeping accountability where it belongs

The version of AI-assisted trading that actually works looks like this: the technology handles the analytical work that humans genuinely can’t do well at speed. Scanning across instruments, monitoring correlations, tracking whether the risk profile of an open position has shifted. That work is relentless and emotionless, which makes it well-suited to a machine. Carrying it mentally through a full trading session tires a person, and tired traders make worse decisions.

What the machine shouldn’t own is the outcome. The decision to trade, the parameters around risk, the wider context of why this position makes sense now: those have to stay with the human. The moment that accountability is fully delegated, you’ve also removed the last check on the system’s own blind spots.

A boring trader is a wealthy trader

A boring trade is the one that builds wealth. It is the six-hour trade where all you do is adjust a stop every sixteen minutes to lock in more profit. No story. No social media post. But it compounds.

AI’s role is to make that boring trade easier to hold: removing the emotional pressure to act when the right move is to wait, and providing the structural prompts that keep a trader in a position long enough for it to work. In that way, it supports patience rather than replacing it.

Retail traders now have access to analytical resources that were, until recently, exclusive to institutional desks. That is a meaningful shift. But access to better tools is not the same as better judgment. Judgment, about risk, about context, about when to sit on your hands, remains the trader’s responsibility.

Use AI for the things it does better than you. Keep the decisions that require context and stewardship in your own hands. That division of labour, more than any single signal or strategy, is what makes trading sustainable over time.

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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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The first seven months of 2026 have produced a volume of legislative activity that technology companies in Malaysia will reckon with for years. Lawmakers passed laws on cybercrime to promote online safety and competition, often within days of each other. Other bills in the pipeline include AI governance and amendments to the copyright law to include AI generated contents.

Beyond legislative changes, this mid-year review also examines the ongoing regulatory uncertainty surrounding the Network School in Johor, which has emerged as a critical test case for the nation’s appetite for novel tech ecosystems. For founders and investors building in AI, crypto, or digital platforms, the cumulative effect is a regulatory architecture that touches every layer of the stack.

Online Safety Act (ONSA)

ONSA took effect on 1 January 2026, converting voluntary compliance into a statutory licensing regime. Platforms with eight million or more Malaysian users are automatically deemed licensed; Child Protection and Risk Mitigation Codes followed on 1 June with fines of up to RM10 million (approximately US$2.44 million). For startups distributing through Facebook, TikTok, or WhatsApp, ONSA changes the content-moderation calculus overnight. Singapore’s code-based approach under IMDA is less prescriptive; Malaysia opted for statutory duties comparable to the EU’s Digital Services Act.

Cybercrimes Bill 2026

Passed by the Dewan Rakyat on 1 July, the Bill creates offences for deepfakes and AI-generated intimate images, with extraterritorial reach and penalties of up to seven years and RM500,000 (approximately US$121,951). For AI and blockchain startups, prosecutors must prove criminal intent; AI-generated content is not automatically unlawful. Singapore’s Online Criminal Harms Act 2023 handles the same problem through directions to disrupt criminal content rather than new offences.

Competition and communications reform

On 2 July 2026, the Dewan Rakyat passed the Competition (Amendment) Bill, the most significant antitrust overhaul in Malaysia since 2012. It expands the scope from “commercial activity” to “any economic activity,” effectively capturing digital platforms that previously operated outside the regulatory perimeter. It also arms the Malaysia Competition Commission (MyCC) with enhanced investigative powers, including new incentives for whistleblowers.

This was followed on 15 July 2026 by the passage of two further bills that consolidate state control over digital infrastructure. The Communications and Multimedia (Amendment) Bill 2026 fuses national security mandates into the existing Universal Service Provision framework.

A concurrent amendment to the MCMC Act expands the commission’s remit and lifts the contract-value threshold for projects requiring ministerial sign-off to RM50 million (approximately US$12.20 million). For incumbents and new entrants alike, the message is clear: the state is preparing for a new era of infrastructure consolidation, one where connectivity, competition policy, and national security are increasingly seen as indivisible.

Also Read: 163,000 workers, 37% training: Malaysia’s AI skills gap in focus

AI Governance Bill

The National AI Office released its public consultation paper on 10 July, with submissions due by 31 July. The Bill introduces a risk-classification framework by potential harm, with incident reporting and harm assessment. Its standout feature, treating both AI training data and AI outputs as intellectual property, would be an ASEAN first.

Vietnam’s AI Law (effective 1 March 2026) imposes binding obligations on developers but does not go this far on IP. If enacted, Malaysia may become the region’s most AI-rights-protective jurisdiction, a factor AI/ML founders and their VCs should weigh.

MyIPO Copyright Act Amendments: AI Content

MyIPO opened a public consultation in July 2026 on a three-pillar framework for AI and copyright: transparency, fair terms, and compensation for use of copyrighted works as training data. The orphan works provisions allow startups training LLMs to use older, out-of-commerce works with a clearer legal pathway. The outcome may feed into the AI Governance Bill’s IP provisions, since both tracks address the same question of ownership in generative AI.

Malaysian Media Council Act 2025

The MMC is a self-regulatory body with a mandate to set ethical standards and manage complaints. Its 21-member board covers legacy outlets, independent journalists, and freelancers, relevant to any content platform or digital media startup in Malaysia.

Yet a critical gap remains. Unlike Australia, which enacted the News Media Bargaining Code requiring Google and Meta to pay for journalistic content, Malaysia has no equivalent revenue-sharing mechanism. This affects digital-first media startups whose work is distributed through platforms prioritising AI-generated content over accredited reporting. The MMC has also not articulated an AI news strategy, despite the Reuters Institute finding that 12 per cent of under-35s use AI platforms as primary news sources. Platforms themselves fall under MCMC.

SC Digital Asset Guidelines

The Securities Commission’s revised Guidelines on Recognised Markets took effect on 20 May 2026, tightening client asset safeguards and governance for digital asset exchange operators. Six DAX operators are now approved. For crypto and blockchain startups, a regulated pathway gives Malaysia an edge over jurisdictions still deliberating, but the direction is towards tighter oversight.

The SC has also been active on enforcement, maintaining an Investor Alert List similar to MAS in Singapore. Binance was added for operating without registration, and Bybit, on the list since July 2021, was ordered in December 2024 to shut down its Malaysian operations, which it did. Notwithstanding this, both entities have since secured indirect market exposure by investing in local exchanges.

Also Read: Malaysian SMEs grapple with a growing “confidence gap” in AI adoption

Consumer Credit Act 2025 and BNPL Regulation

The Consumer Credit Act 2025 (CCA), gazetted on 31 December 2025 and in force since 1 March 2026, establishes the Consumer Credit Commission (CCC) as the new regulator for non-bank credit activities in Malaysia. For the first time, Buy Now Pay Later (BNPL) providers, Atome, Grab PayLater, SPayLater, and others, along with leasing firms, factoring companies, debt collection agencies, licensed moneylenders, and pawnbrokers, must obtain a CCC licence to operate.

Licensing opened on 1 June 2026 with a six-month transition period (until approximately 30 November 2026) for existing providers. Banks and entities already regulated by Bank Negara Malaysia are excluded. The CCC has issued BNPL-specific standards covering affordability assessments (required for credit limits above RM1,000), white-labelling arrangements, pricing methodologies, late payment practices, merchant conduct, and digital authentication requirements, including Shariah compliance provisions.

For fintech founders, the CCA ends the regulatory arbitrage that BNPL operators previously enjoyed outside formal credit oversight. Compliance costs are rising, but the licensing framework also creates a moat that could consolidate the market around compliant players.

Other credit business models should take note, including earned wage access providers, whose products could fall within the Act’s definition of a credit facility depending on how repayment structures are designed.

Network School and regulatory grey zones

The controversy at Balaji Srinivasan’s Network School in Forest City, Johor, initiated in 2024, has become a live case study of regulatory uncertainty. While the project was meant to be a flagship drawcard for the Johor-Singapore Special Economic Zone, the tech community is now facing government investigations into immigration and local licensing.

Digital Minister Gobind Singh Deo recently stated that applications for the school followed proper procedures, despite the ongoing scrutiny. Previously, an immigration probe triggered by allegations that Israeli nationals used second passports to attend the tech community prompted PM Anwar Ibrahim to order the deportation of any Israeli citizens found.

While the Immigration Department found no evidence of Israeli participation, the episode exposed a deeper problem: no agency could say whether Network School was an educational institution, a co-living space, a tech incubator, or a tourism programme. The school has invested RM100 million (approximately US$24.39 million) and planned a RM500 million (approximately US$121.95 million) expansion, now shelved indefinitely.

For a tech founder or VC evaluating Malaysia, the question is whether this signals a political environment prepared to accommodate novel tech communities, or one where regulatory grey zones chill investment. It’s a stark contrast to Malaysia’s neighbours. Thailand, Indonesia, and other ASEAN countries are actively attracting remote talent with dedicated visas and clear rules, the very clarity the Network School has yet to receive in Malaysia.

PDPA and regional context

The PDPA amendments are now in force, with breach notification, DPOs, data portability, and a cross-border transfer framework in force. The penalty ceiling now reaches RM1 million (approximately US$243,902.44) or three years’ imprisonment.

Malaysia’s legislative sprint puts it behind Singapore in regulatory maturity and Vietnam on AI speed, but ahead of Indonesia and Thailand. Indonesia’s PDP law remains partially operational; Singapore and Thailand are more advanced on enforcement. For founders and investors, compliance with one ASEAN member’s rules does not guarantee compliance with another’s; the fragmentation is the story.

Also Read: The agentic shift: Why AI agents are rewriting the rules of ERP software in Singapore and Malaysia

Gig Workers Act 2025

In force since 31 March 2026, the Act covers 1.64 million workers and applies to any platform operator engaging gig workers, from delivery and ride-hailing to freelance services. Key highlights: mandatory SOCSO contributions (1.25 per cent deducted per job), written service contracts, protection against termination without just cause, and a right to human review when algorithmic decisions affect work opportunities or conditions.

The Act also establishes the Malaysian Gig Economy Council (MyGiG) to set minimum income rates by sector and region, but the Council has not yet been constituted, the Gig Workers Tribunal’s procedures are still pending, and minimum earnings remain undecided. For an investor, the existing compliance obligations are enforceable now; the coming minimum-earnings framework is the variable to watch.

Final thoughts

Malaysia is passing new regulations at a rapid pace, creating a landscape of escalating compliance costs. This rush to create new rules means the government may be missing the feedback needed to make them work effectively. For investors, this creates an environment where vigilance and pre-investment legal counsel are essential for any new business.

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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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Huawei Cloud has launched its agentic AI infrastructure in Thailand and opened beta testing for CodeArts Agent, a software development tool that uses AI agents to generate code, answer engineering questions and automate parts of the development process.

The announcements were made at Huawei Cloud Summit Thailand 2026 in Bangkok, where the company gathered government officials, enterprise leaders, partners and developers to discuss how cloud and artificial intelligence can support the country’s digital economy.

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

For Thailand, the timing is significant. Like many Southeast Asian markets, the country is trying to move AI from policy statements and pilot projects into everyday use across government services, banking, education and industry. That shift depends less on chatbots alone and more on the underlying infrastructure: local cloud capacity, secure data environments, developer tools and computing systems that can run AI workloads at scale.

Huawei Cloud is positioning its latest launch around that need. The company said its Agentic Infrastructure, now available in Thailand, is designed for building and deploying AI agents — software systems that can plan, reason, use tools and perform tasks with varying levels of autonomy.

Building for the next AI layer

The term “agentic AI” has quickly become one of the industry’s most used phrases, but its practical meaning is straightforward. Instead of simply responding to a prompt, an AI agent can break a task into steps, remember context, interact with other systems and keep working towards an objective. In an enterprise setting, that could mean helping a bank generate and test software, assisting government officers with document workflows, or supporting an HR platform that creates personalised learning plans.

Huawei Cloud’s new infrastructure is aimed at making those use cases easier to deploy. According to the company, Agentic Infrastructure supports efficient token generation, unified scheduling of general-purpose and AI computing resources, continuous learning, and secure autonomous operations.

Surasak Wanichwatphibun, CTO of Huawei Cloud Thailand, introduced four main components under the infrastructure. These include UnifiedBus-based AI Cluster Service, or AICS, which is designed to improve token generation efficiency; Agentic Memory Storage Service, or AMS, which offers petabyte-scale memory storage for long-horizon agent tasks; AgentSphere, a secure runtime environment for AI agents; and CCE VolcanoNext, which allows general computing and AI computing resources to be scheduled together.

These details matter because enterprise AI does not run on enthusiasm alone. AI agents need access to memory, compute, orchestration and security controls. If those layers are weak, companies may end up with impressive demos that are difficult to move into production.

In Southeast Asia, where many businesses still operate with fragmented legacy systems, the challenge is even sharper. Enterprises want AI tools that can connect to existing workflows without creating new security risks or runaway infrastructure costs. Cloud providers are therefore competing not only on model access, but also on whether they can provide the full stack needed to run AI reliably.

CodeArts Agent enters open beta

The second major announcement was the Thailand open beta testing launch of Huawei Cloud CodeArts Agent. The tool combines an integrated development environment, coding models and autonomous development capabilities.

For developers and enterprise technology teams, CodeArts Agent supports project-level code generation, code completion, research and development knowledge Q&A, and unit test case generation. Huawei Cloud said the product also applies Specification-Driven Development, a method that links software requirements more closely to the coding and delivery process in order to maintain quality from the earliest stage of a project.

The release also introduces an “Agent Team” mode, where multiple AI agents can be assembled to work together on development tasks at the same time. In theory, this could help software teams reduce repetitive work and accelerate product delivery.

Also Read: 4 ways of agentic AI applications in marketing

That is a message likely to resonate in Thailand and across the region. Southeast Asian companies are digitising quickly, but technical talent remains unevenly distributed. Banks, retailers, logistics firms and public agencies are all under pressure to ship digital products faster, yet many teams face shortages of experienced engineers. AI coding tools are unlikely to replace developers wholesale, but they can change how engineering teams allocate time — shifting routine tasks such as boilerplate code, documentation and test generation to AI assistants.

Still, adoption will depend on trust. Developers need to know whether generated code is secure, maintainable and compatible with internal standards. Enterprises also need clarity on where source code is processed and how proprietary information is protected. That makes Huawei Cloud’s emphasis on security a central part of the launch, not a side note.

Security becomes part of the AI pitch

Huawei Cloud said it has upgraded its security services in two areas: protecting AI systems and using AI to strengthen cyber defence.

Its model lifecycle security solution covers AI infrastructure, training data, model inference and agent applications. The company also said it offers dedicated security zones for enterprises concerned about data sovereignty and privacy. These zones allow customers to manage their own encryption keys while preventing platform administrators from accessing customer data.

Huawei Cloud added that it uses software-hardware integration and hardware acceleration to maintain encryption performance. It has also introduced Data Capsule technology, which ensures that data can only be used within authorised environments and becomes invalid if moved outside a designated security zone.

This reflects a broader concern in the region. Governments and regulated industries in Southeast Asia increasingly want the efficiency of public cloud and AI, but not at the expense of control over sensitive data. Thailand, Indonesia, Malaysia and Vietnam have all been paying closer attention to data localisation, cybersecurity and digital sovereignty as cloud adoption deepens.

At the summit, Thailand’s Ministry of Digital Economy and Society was cited as emphasising the need to accelerate digital transformation through national AI policies, public-sector adoption and stronger public-private collaboration. NECTEC also presented a government AI case study covering AI infrastructure, AI platforms, chatbots and intelligent assistants to support officials and improve citizen services.

A crowded cloud race

Huawei Cloud is not alone in trying to capture Thailand’s AI and cloud demand. Global hyperscalers such as Amazon Web Services, Microsoft Azure and Google Cloud are expanding their Southeast Asian footprints, while Alibaba Cloud and Tencent Cloud have long targeted Chinese-linked enterprises and digital businesses in the region. Local and regional infrastructure players, including data centre and connectivity providers, also remain relevant for customers with strict latency, compliance or hybrid-cloud requirements.

This competition is pushing cloud vendors to localise more deeply. It is no longer enough to sell compute and storage from afar. Providers are building local data centres, forming public-sector partnerships, courting developers and packaging AI tools for specific industries.

Huawei Cloud says it was the first international public cloud vendor to establish local data centres in Thailand and now operates three Availability Zones in the country. The company also says it serves more than 40 government agencies and thousands of enterprises in Thailand. Citing Gartner, Huawei Cloud said it ranks third by revenue in Thailand’s Infrastructure-as-a-Service market.

The summit showcased use cases from Thai organisations, including AI platforms and intelligent assistants for the public sector, AI coding and large language models in banking, and AI-powered learning and skills development platforms for HR.

Also Read: The coming identity crisis of agentic AI

For Huawei Cloud, the Thailand launch is part of a wider attempt to move up the cloud value chain, from infrastructure provider to AI operating layer for enterprises. For Thai organisations, the bigger question is how quickly agentic AI can move from a conference-stage concept to a dependable tool inside real workflows.

That transition will require more than powerful models. It will require local infrastructure, stronger governance, developers who understand the tools, and enterprises willing to rethink how work is done. Huawei Cloud is betting that Thailand is ready for that next step.

The post Huawei Cloud bets on Thailand as enterprises move from AI pilots to production appeared first on e27.

One of the biggest errors in commercial strategy is believing that deals are won or lost in the rooms where the product is actually discussed. That is comforting because it keeps the field of vision manageable. It allows teams to focus on the sponsor, the decision maker, the user, the procurement lead, and perhaps one or two visible executives. It creates the sense that if enough meetings go well, the deal is progressing.

They often die quietly, outside the main conversation, through people who never openly oppose the purchase but make it harder to approve, harder to defend, or harder to prioritise. They ask for one more review. They express concern without escalating it into a formal objection. They delay a dependency. They withhold internal enthusiasm. They flag an unresolved risk at the wrong moment. They do not always say no. Often they simply prevent the organisation from saying yes.

Most stakeholder maps are too neat to be useful

Traditional stakeholder maps are often built around formal hierarchy and declared roles. They identify the budget owner, the executive sponsor, the user lead, the procurement contact, and the technical evaluator. This creates a tidy picture, but not a realistic one.

Real organisations do not work only through formal authority. They work through credibility, proximity to risk, control over process, and the power to raise a problem that nobody else wants to own. A senior architect may not sign the deal, but one comment about integration fragility can slow momentum immediately. A privacy lead may never speak in a steering meeting, but an unresolved data handling question can quietly freeze progress. A finance controller may not be the budget holder, but a remark about cost classification or future run rate can alter the internal appetite for the purchase. An operations leader may not have approval rights, but their concern about implementation burden can turn an enthusiastic sponsor into a cautious one.

Quiet veto power is often stronger than visible authority

There is a reason quiet deal killers are so dangerous. They rarely need to win an argument. They only need to make certainty weaker.

In institutional buying, most decisions do not collapse because somebody delivers a dramatic rejection. They collapse because the burden of proof rises, confidence thins, timing shifts, or the internal sponsor decides the fight is no longer worth the political cost. This makes quiet veto power more potent than many teams realise. A visible executive can sometimes be persuaded, challenged, or escalated past. A quiet sceptic embedded in risk, operations, architecture, legal, or finance can often create just enough resistance to alter the internal calculus without ever becoming the face of opposition.

Also Read: Mid-year 2026 look at Malaysia’s digital and technology regulatory updates

That pattern matters because it changes how leaders should read momentum. A sequence of positive meetings does not necessarily mean the organisation is aligning. It may simply mean the visible participants are supportive while the hidden ones remain unconvinced. Teams often mistake politeness for progress, interest for commitment, and attendance for buy-in. Then they are surprised when the deal slows late, after months of apparently constructive engagement.

The problem was usually not lack of support. It was incomplete map reading.

Every deal has a hidden kill chain

A useful way to think about this is not as a buying committee, but as a kill chain. That may sound severe, but it is closer to reality in high-consequence markets.

A kill chain is the sequence of concerns, functions, and informal interventions through which a deal can be weakened until it loses momentum. It may begin with technical concern, move into security review, surface a legal ambiguity, trigger a finance question, and end with executive hesitation. No single step kills the deal on its own. The cumulative effect does.

This is why teams that focus only on the named decision maker often find themselves outmanoeuvred by the organisation itself. The decision maker is not making a purchase in isolation. They are navigating a network of people whose job is not necessarily to support growth, but to prevent regret. The more regulated the environment, the more politically exposed the spend, and the more operationally sensitive the product, the more this hidden kill chain matters.

The people who kill deals quietly 

Across sectors, quiet deal killers often have four things in common.

First, they own risk without owning the upside. They are accountable for what goes wrong, but they do not personally benefit if the deal succeeds. That creates a naturally asymmetric posture.

Second, they are trusted interpreters inside the institution. Others may not fully understand the technical, legal, operational, or financial detail, so their opinion carries disproportionate weight.

Third, they can delay without appearing obstructive. Their intervention looks responsible rather than political. Asking for more diligence is rarely punished.

Fourth, they operate late enough in the process that reversing course becomes hard, but not impossible. This is the point where internal enthusiasm is most vulnerable because the sponsor has already spent time and credibility pushing the deal forward.

Also Read: Why building a people-first work culture in HR tech matters more than ever in Southeast Asia

The best teams map negative energy early

One mark of senior strategic thinking is the ability to look for negative energy before it becomes visible resistance.

That means asking harder questions early. Who has not yet been engaged but will matter later? Which function is most likely to inherit the downside if implementation struggles? Where does this purchase challenge an existing standard, policy, or internal preference? Which leader is likely to ask whether this is the right use of budget right now? Who may feel that this decision sets a precedent they are not ready to support?

These are not pessimistic questions. They are reality questions.

The strongest teams do not wait for objections to arise formally. They anticipate the domains where discomfort is likely to sit and build the necessary proof before those concerns turn into friction. They know that late-stage alignment work is more expensive than early-stage stakeholder design. They also understand that what looks like objection handling is often actually confidence building for people who were never part of the original enthusiasm.

Strategy leaders need a veto map, not just a stakeholder list

If there is one practical shift worth making, it is this. Stop asking only who is involved in the deal. Start asking who could make the deal feel unsafe, unjustified, mistimed, or too difficult to defend.

That is the veto map.

A veto map does not assume every stakeholder has equal weight. It identifies where silent resistance could emerge, what form it is likely to take, and what evidence would neutralise it before it hardens. It recognises that there are different types of veto. Some are formal. Some are cultural. Some are financial. Some are procedural. Some are reputational. The quietest ones are often the most dangerous because they do not arrive with a clear argument that can be answered in the room.

In many organisations, nobody wants to be the person who blocked the deal unnecessarily. It is much safer to be the person who raised a prudent concern that made others hesitate. Once leaders understand that dynamic, stakeholder cartography becomes much more sophisticated. It stops being a map of supporters and becomes a map of latent doubt.

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The post Stakeholder: Mapping who can kill the deal quietly appeared first on e27.