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When Singapore’s offices emptied out in 2020, flexible work was sold as the cure for an overworked city. Employees would get their commutes back, set their own hours and finally see their families at dinner.

Six years on, the cure appears to have come with side effects. According to the Jobstreet by SEEK Singapore Workplace Culture Study 2026, 52 per cent of employees say workplace stress has increased since 2019. Only 27 per cent say they have gained more flexibility and work-life balance.

Also Read: Building burnout-resistant teams

Employers broadly agree. Some 48 per cent of them say stress has risen, while 30 per cent think the balance has improved. Nearly half of both groups (46 per cent of employees and 45 per cent of employers) say job scopes have become more demanding.

In short, Singaporeans gained control over where they work, not over how much they work.

The flexibility paradox

The study’s central finding is a contradiction that many remote workers will recognise. Flexible arrangements changed the location and timing of work, but not its volume or complexity. Once the office walls disappeared, so did many of the natural boundaries that used to end the working day.

The commute that once marked the start and finish of work is gone. So is the shared understanding that leaving the building means being off duty. What replaced them, for many, is a state of constant, low-grade availability: the laptop on the dining table, the Slack ping at 9.30 pm, the “quick call” during school pickup.

This happened even as Singapore formalised flexible work. The government’s Tripartite Guidelines on Flexible Work Arrangement Requests, which took effect in December 2024, require employers to properly consider workers’ requests for flexible arrangements. The guidelines govern access to flexibility. They say nothing about how much work fills those flexible hours.

The finding fits a wider pattern e27 has tracked: burnout is no longer just a personal problem but an operations problem.

Same storm, different worries

The study suggests both sides see the same forces reshaping work, but feel them in very different places.

AI and automation are cited as a primary driver of workplace change by 56 per cent of employees and 53 per cent of employers. Economic uncertainty, inflation and the cost of living are named by 53 per cent of employees and 46 per cent of employers.

Also Read: The Philippines uses AI as a burnout shield; Singapore uses it for deep work

From there, the concerns split along predictable lines. For employees, the pressure is personal. Sixty per cent cite salary growth and the cost of living as their main worry, 48 per cent are focused on job security, 37 per cent struggle to protect work-life balance, and 33 per cent feel squeezed by competition for roles. It is the anxiety e27 contributors have described as global uncertainty turning into workplace anxiety.

For employers, the pressure shows up on the balance sheet and the org chart. Fifty-two per cent are preoccupied with how AI and automation will change workflows and team structures. Forty-nine per cent are wrestling with salary expectations in an inflationary environment, and 24 per cent cite restructuring and running leaner teams.

That last figure is the hinge of the whole story. When nearly a quarter of employers are running leaner teams, the work does not vanish with the departed headcount. It gets spread across whoever is left. That would explain why job scopes have grown, and why stress has risen alongside flexibility. Working from anywhere has become a convenient way to absorb the extra workload.

It is a local version of what e27 has called Big Tech’s efficiency paradox of record profits and record layoffs. There is also a regional warning attached: the AI layoff trap points straight at Southeast Asia.

Where the money is going, and where it isn’t

The most revealing data in the study concerns employers’ spending plans. The money is overwhelmingly flowing into technology and skills: 34 per cent of employers plan to invest in AI and digital skills, 25 per cent in new workplace technology tools, and 23 per cent in upskilling and reskilling.

The measures most likely to relieve the pressure employees describe are far lower on the list. Only 14 per cent plan wellbeing and mental health initiatives, 13 per cent plan workload management improvements, and just 12 per cent plan to hire more people to ease the workload.

Put bluntly, companies are trying to solve a capacity problem with software. Training a stretched employee to use an AI assistant may make them faster. It does not give them fewer things to do. Without changes to workload or headcount, technology investment risks simply raising the speed at which already exhausted people are expected to work.

That may be a rational short-term bet for employers watching costs. But the bill tends to arrive later, in the form of burnout, declining work quality and the quiet departure of the high performers who have the most options elsewhere.

Fixing the design, not the location

The study’s recommendations are less about where people work than about how the work is designed. They are practical rather than revolutionary.

• First, companies should run regular workload and priority audits that compare targets against actual headcount, so that scope creep is caught before it becomes chronic stress.
• Second, when business goals change, managers should state explicitly what will be paused or dropped, rather than stacking new priorities on top of old ones.
• Third, middle managers need proper training to run distributed teams and protect boundaries. Other research has found that the manager has an outsized effect on wellbeing at work. Fourth, performance should be judged on outcomes rather than responsiveness or how visibly “online” someone appears. That matters in a city where two in five employees already feel watched at work.

None of this is new to HR professionals. The hard part is that each step costs management time and, in the case of hiring, real money, which is exactly what most employers are not budgeting for.

A caveat on the numbers

The figures presented here do not come with sample sizes, survey dates or a methodology, so they are best read as directional rather than definitive. The comparisons with 2019 are also based on what respondents remember, not on data tracked over time.

Also Read: SEA’s CEOs want innovation, but employees feel stuck in execution mode

Still, the overall shape is hard to dismiss when both employees and employers report it. Neighbouring markets have treated flexibility as a competitive advantage; in the Philippines, flexible work is no longer a perk but the price of talent. Singapore’s experience suggests flexibility alone is not enough. Unless companies also limit how much work it carries, the freedom to work from anywhere can turn into the obligation to work from everywhere.

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Southeast Asian companies are not short of money for AI. Most of them simply can’t get their projects past their own IT and security teams.

That is the uncomfortable picture painted by the Southeast Asia findings of Adobe’s 2026 State of AI Readiness Report, released today. The study surveyed 461 senior executives in the region and found that 73 per cent of companies are increasing their AI investment year-on-year. Yet 48 per cent are still working on their first AI use case, or have given up after a failed attempt.

Also Read: Everyone has adopted AI. Almost nobody is ready for it

In other words, budgets are going up while results are standing still.

About 57 per cent of organisations are stuck in what the report calls the “Frozen Middle”. They are piloting or deploying AI without the coordination needed to scale it. For agentic AI, meaning systems that can carry out multi-step tasks on their own rather than just answer prompts, the figure is 40 per cent. Some 79 per cent of the region’s companies sit at an early stage of AI readiness, the highest share anywhere in Asia Pacific.

The findings echo a theme that keeps resurfacing on these pages: Southeast Asia doesn’t have an AI adoption problem, it has a scaling problem.

Where the good ideas go to die

The most striking number in the report is the attrition rate. Only 15 of every 100 AI ideas make it into production.

The biggest losses do not happen in the lab or the boardroom. About 36 per cent of AI proposals fail to get past IT evaluation. A further 18 per cent are rejected at security review, and that rejection rate nearly doubles in highly regulated industries such as banking and healthcare.

Read one way, that is a story about bureaucracy strangling innovation. Read another, it is a story about gatekeepers doing their jobs. Many AI proposals arrive at IT with no clear data plan, no security model and no owner. e27 has previously reported on the hidden risk of unchecked agent privileges, and a security team that waves every agentic pilot through would be failing at its own job.

The more useful question is why so many proposals reach IT in a state that deserves rejection. The report’s answer is that nobody owns them.

The ownership vacuum

Only 12 per cent of Southeast Asian companies have an AI owner with budget authority, meaning someone who can both make decisions and pay for them. Among the companies Adobe classifies as Leaders, that figure is 89 per cent.

Measurement is just as thin. Only 8 per cent of companies measure AI return on investment comprehensively, and most track usage (how many people logged in) rather than quality (whether the output was any good). Meanwhile, 81 per cent of businesses say their AI skills shortfall stayed the same or got worse over the past year. That is consistent with what we have  seen in Malaysia’s AI skills gap and elsewhere in the region.

Also Read: Scaling beyond AI pilots: Six-move Capability Cycle

“Organisations that establish clear ownership, shared measures of success and stronger collaboration between business and IT will be best placed to move beyond isolated pilots and achieve sustainable impact,” said Jeremy Willmott, Senior Director, Digital Strategy Group Asia Pacific at Adobe.

It is a sensible prescription, if not a new one. Many consultants have offered similar advice, and e27 contributors have argued that Singapore’s AI adoption problem is not worker resistance but weak execution.

What the Leaders do differently

The Leaders are a very small club: 6.3 per cent of Southeast Asian respondents, or just 27 organisations. They have scaled AI across multiple workflows and built an operating model to sustain it. According to Adobe, they convert pilots to production at nearly four times the rate of other companies.

They also outscore the regional average by 67 per cent on ownership and accountability, and by 59 per cent on embedding AI into everyday processes. They keep their “destructive failure” rate, failures that cause real damage rather than lessons, below 1 per cent. By contrast, more than a third of companies repeat the same AI mistakes. And 41 per cent of Leaders say their single most effective move was funding the employees who were already making AI work.

That last finding is perhaps the most practical in the report. The fastest route to scale often runs through the staff member who quietly built a working tool in a spreadsheet, not through the vendor’s pitch deck. It fits other regional research showing that workers are sprinting ahead of their bosses in AI adoption.

Read the fine print

Before boards rewrite their AI strategies around these numbers, a few caveats are worth noting.

First, the sample. The research was conducted by Incisiv on Adobe’s behalf. It covered only organisations with at least US$100 million in annual revenue, 60 per cent of which earn more than US$1 billion. Startups and the SMEs that make up most of Southeast Asia’s economy are not in the picture.

Second, the Leaders’ statistics rest on just 27 companies in the region. When the report says 89 per cent of Leaders have a budget-holding AI owner, that comes to roughly 24 companies. That is enough to suggest a pattern, but not enough to prove it.

Third, the headline conclusion is partly built into the method. The report says organisational readiness, not technology or spend, is the strongest predictor of whether AI scales. However, Leaders were defined in the first place by combining deployment speed with “operating model maturity”. Finding that Leaders have mature operating models is therefore close to circular. The more interesting finding is the negative one: globally, company size, industry and reported AI spend did not predict Leader status.

Finally, Adobe sells AI tools to enterprises. It has an obvious interest in persuading companies that the obstacle to AI value is organisational, not technological. The company’s own earlier research found GenAI adoption rising in Asia while ROI remained elusive. This year’s report reads like the sequel.

Also Read: Why most enterprise AI in APAC is still stuck in the proof-of-concept room

None of that makes the diagnosis wrong. The pattern of rising spend, stalled pilots and nobody in charge matches what founders selling into Southeast Asian enterprises describe privately. Telcos and universities are now setting up joint efforts, such as Singtel’s RE:AI tie-up with the SIT-NVIDIA centre, specifically to close the deployment gap.

For B2B AI startups, the lesson is blunt. The buyer who signs the pilot is often not the person who can take it to production. Until someone with a budget owns the outcome, the region’s AI spending will keep rising faster than its returns.

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India’s MakeO has raised US$10 million in a round led by Singularity AMC, with unnamed funds and family offices also participating, eight months after the Mumbai-headquartered dental aligner company agreed to absorb Singapore’s Zenyum.

The cheque, disclosed in a statement on October 13, is modest by the standards of the clear aligner category’s go-go years. But it lands on a business that now claims something few consumer healthtech companies in the region can: more than SGD70 million (US$54.7 million) in annual recurring revenue (ARR) and sustained profitability on an EBITDA (earnings before interest, taxes, depreciation, and amortisation) basis.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

The money has three jobs. It will fund Zenyum’s dentist network expansion across Southeast and North Asia, strengthen the group’s technology, and expand manufacturing capacity. Zenyum also said it continues to evaluate acquisitions across India and other parts of Asia, a signal that the shopping basket is not yet full.

The combined group says it works with roughly 1,100 partner dentists across India, Singapore, Malaysia, Vietnam, Taiwan, Hong Kong, and Japan.

The deal behind the round

That dentist figure is, for now, largely the arithmetic of a merger rather than fresh growth. When the two firms announced their tie-up, Toothsi had about 500 partner doctors and more than 50 dental centres in India, while Zenyum had 600 doctor partners.

MakeO agreed to acquire Zenyum in February. Zenyum’s own framing at the time was more diplomatic, describing it as a “merger of equals” because both businesses had similar top lines and complementary geographies. Financial terms were never disclosed, and Zenyum CEO Julian Artopé declined to comment on valuation or the post-merger shareholding split.

The structure tells its own story. MakeO co-founder Arpi Mehta remains group CEO and Artopé continues to run Zenyum, with Mumbai as the group headquarters and Singapore retaining control of Southeast and North Asia operations. Crucially, Toothsi’s vertically integrated, US FDA-certified aligner platform now designs, manufactures and exports aligners for the whole business across Asia. Put simply: India is the factory, Singapore is the shopfront.

For Zenyum, it marks a notable turn. Founded in 2018 by Artopé and Frederik Krass, the startup raised a US$13.6 million Series A in 2019 and followed it with a US$40 million Series B led by L Catterton in 2021, backed by Sequoia Capital India (now Peak XV), RTP Global, Partech and others. Back then, Artopé was on a mission to help Asia smile more and Zenyum reported a fourfold revenue jump in 2020.

Five years later, a Series B-stage Singapore brand folding into an Indian peer is another data point for a regional exit market that increasingly favours trade sales over IPOs.

Also Read: ADA acquires India’s Algonomy in push beyond customer data into AI-led action

MakeO, for its part, has been on the acquisition trail. The Zenyum deal was its second acquisition in six months, and the company had earlier merged its Toothsi and Skinnsi brands under the MakeO umbrella in 2022. It raised a US$16 million bridge round in 2024 backed by Ashish Kacholia and 360 One Asset.

Reading the fine print

The headline numbers deserve a closer look. ARR is a natural metric for software subscriptions, but aligner treatment is largely a one-off purchase, topped up with consumables such as toothbrushes and retainers. The group did not explain how it calculates the figure, whether it is an annualised run rate, or how much comes from repeat customers.

The profitability claim also comes with an asterisk. EBITDA strips out depreciation and amortisation, which are precisely the costs that matter for a company expanding manufacturing capacity and carrying acquisition-related intangibles on its books. Being EBITDA-positive is a real achievement in a sector that has burned cash freely, as Carsome’s run of profitable quarters in used cars has shown is possible elsewhere. But it is not the same as net profit.

Then there is the size of the round. At under a fifth of the stated ARR, US$10 million looks like targeted growth capital rather than a war chest, which makes the continued talk of acquisitions intriguing. Either deals will be small, or more capital will follow. The valuation, and the identity of the participating family offices, remain undisclosed.

A crowded mouth

The rival landscape is formidable. Align Technology’s Invisalign remains the global category leader and sells through orthodontists and dentists across Asia. Swiss implant giant Straumann pushes its ClearCorrect brand, while China’s Angelalign has scaled aggressively in its home market and beyond.

The cautionary tale is SmileDirectClub, the US direct-to-consumer pioneer that collapsed into bankruptcy and shut down in late 2023, partly because cutting dentists out of the loop invited regulatory and clinical pushback. Zenyum and Toothsi have both leaned the other way, routing patients through in-person dentist consultations, which is why the partner network figure sits at the centre of this announcement.

The wider funding climate adds pressure. Southeast Asian healthtech endured a funding drought in 2024, and the investors still writing cheques increasingly want businesses built around healthcare workflows rather than flashy apps. Consolidation is picking up too, with Zeya Health acquiring ConnectLah to build booking rails for clinics.

What comes next

The clearest clue lies in what MakeO said in February. It flagged selective M&A and partnerships with local dental chains and clinical practices in markets including India, Singapore, Taiwan and Hong Kong.

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

That would mark a strategic shift. Owning clinics, rather than partnering with them, turns an asset-light network into an asset-heavy one, with more control over patient experience but more capital locked in bricks, chairs and staff. It would also put the group in direct competition with some of the very dentists it relies on for referrals.

For now, MakeO has a profitable (on its own definition) pan-Asian platform, an Indian manufacturing base and fresh capital. Whether investors keep smiling will depend on how much of that S$70 million turns into cash that stays.

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A founder I know recently received two term sheets in the same week.

The first was from a regional VC firm with a recognisable brand. The cheque was for US$1.5 million. The terms were reasonable. The partner had introduced himself at a conference six months earlier and had been pleasant in every interaction since.

The second was from an operator who had built and exited a company in the same space ten years earlier. The cheque was for US$400,000. The terms were similar. The operator had spent two hours on Zoom with the founder going through the customer acquisition strategy line by line, and had ended the call by listing six potential customer introductions he could make in the first month.

The founder took the smaller cheque.

When I asked why, his answer was direct. The bigger cheque came with a logo and a partner who would attend his board meetings. The smaller cheque came with someone who could shorten his customer acquisition cycle by six months. Six months of runway was worth more than the US$1.1 million difference.

That decision is becoming more common in 2026. It reflects a quiet but significant shift in how founders are evaluating capital.

The cheque is becoming the easiest part

For most of the past two decades, the cheque was the product. Investors raised funds, deployed capital, and provided light-touch support around the edges. The relationship was simple. The founder got the money. The investor got equity. Everything else was a bonus.

That model worked when capital was scarce and the value of the cheque was higher than anything an investor could provide around it.

Capital is no longer scarce for a growing share of early-stage founders. Solo GPs, family offices, corporate venture arms, angel syndicates, and a hundred new fund structures have flooded the market with capital options. The cheque has become the easiest part of the deal to get.

What is harder to get is everything around the cheque. Customer introductions that close in the first month. Engineering hires who would not have taken the call otherwise. Distribution partnerships that take three years to build from cold. GTM expertise from someone who has scaled the same playbook five times. Cross-border access into markets the founder cannot enter alone.

These are the assets that still have scarcity value. The investors who can deliver them are increasingly the ones founders are choosing. The investors who can only deliver capital are increasingly the ones founders are walking away from.

Also Read: Entering Taiwan: A practical resource map for foreign founders

The six things founders are actually buying

Ask a founder raising in 2026 what they want from an investor and the cheque will rarely be the first answer. Here are the six things that come up most often.

• Distribution. Can this investor get my product in front of customers I cannot reach myself. A US enterprise SaaS founder will pay a meaningful premium for an investor who can introduce them to Fortune 500 CIOs. A Southeast Asian consumer brand will pay the same premium for an investor who can open doors at major regional retailers.
• Customer introductions in the first ninety days. Not eventually. Not someday. Specific names, with introductions made within the first three months of the cheque clearing. Every customer an investor delivers in the first quarter is worth more than every customer the founder would have closed without that introduction.
• Hiring leverage. Senior engineering, product, and go-to-market hires are scarce everywhere. An investor who can pull a senior hire from their network in the first six months is delivering value worth multiples of the cheque size. Most founders underestimate how much faster the right hire arrives when the investor has personal relationships with the candidates.
• GTM expertise from someone who has done it. Strategy decks from a consultant are widely available. Operating advice from someone who built and scaled the same model is rare. Founders are increasingly choosing investors based on whether they have run the exact playbook the company needs to execute, not on whether they advise in general.
• Cross-border access. For founders whose markets sit in multiple countries, the value of an investor who can open regulatory, distribution, and partnership doors in the second and third market is enormous. A Singapore founder expanding into Indonesia, Vietnam, and the Philippines needs three different sets of local relationships. The right investor brings two of those sets pre-built.
• Strategic advice when things break. Companies do not run smoothly. Pivots happen. Growth slows. Customers churn. Co-founders leave. The investors who matter are the ones who pick up the phone when the company is in trouble, not the ones who only show up when the metrics look clean.

The investors who can deliver all six are rare. The investors who can deliver four or five are valuable. The investors who can only deliver capital are increasingly being filtered out by founders who have other options.

What this means for investors

The implication for investors is straightforward. The brand of the firm is no longer enough. The value delivered around the cheque is becoming the actual product. Investors who have not built a deliberate value-add capability are about to find themselves losing the deals they used to win on brand alone.

The investors who will keep winning are the ones who have built genuine assets around their capital. A real customer network. A real hiring pipeline. A real distribution rolodex. A real operating playbook in a specific category. Not slogans. Not platform teams. Not marketing claims about being founder-friendly. Actual delivered value that the founder can name and quantify.

Also Read: The 90-second pitch that helps foreign founders crack Tokyo’s networking scene

The brand premium that used to justify a US$1.5 million cheque over a US$400,000 cheque is shrinking. In some categories, it has already disappeared.

What this means for founders raising in 2026 and 2027

Three actions for founders evaluating capital this year.

Before accepting any cheque, write down what you actually need beyond the money. Be specific. Three named customers you cannot reach. Two senior hires you cannot recruit. One distribution channel you cannot build alone. Then evaluate every investor against that list. The right investor is the one who can deliver the most items on that list, not the one with the biggest cheque.

Ask every investor for three specific examples of value they delivered to a recent portfolio company. Not a general claim. A specific story. Which founder. What was the problem. What did the investor do. What was the outcome. The investors who can answer this question crisply are usually the ones who deliver. The ones who answer vaguely usually do not.

Be willing to take less money to get the right partner. The compounding value of an investor who delivers around the cheque can be ten times the differential in cheque size over three years. A founder who optimises for cheque size in 2026 is optimising for the wrong variable. The variable that matters is what the cheque comes with.

The investor-founder relationship is being rewritten. The investors who recognise that the cheque has become the easiest part of the deal will keep winning. The founders who recognise that the cheque is no longer the most valuable part of an investor’s offer will spend the next decade building with the right capital partners around them.

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The post The new value test for investors: What founders are actually buying when they take your cheque appeared first on e27.

Southeast Asian companies are spending more on AI and getting little out of it. Adobe’s 2026 State of AI Readiness Report, which surveyed 461 senior executives in the region, finds 73% raising AI budgets year on year, while 57% remain stuck in AI’s Frozen Middle, piloting without the coordination to scale.

Only 15 of every 100 AI ideas reach production. IT evaluation kills 36% of proposals and security review another 18%, gatekeeping that partly reflects the risk of unchecked agent privileges. The deeper problem is ownership: just 12% of firms have an AI owner with budget authority, against 89% among the 27 Leaders, and only 8% measure ROI comprehensively. The findings reinforce e27’s argument that the region has a scaling problem, not an adoption one.

Leaders’ most effective move was funding employees already making AI work, echoing research on workers sprinting ahead of bosses. Caveats apply: the sample covers only firms with at least US$100M in revenue, the Leader cohort is tiny, and Adobe, an AI vendor, has an interest in blaming organisation over technology. The report reads like a sequel to Adobe’s earlier GenAI ROI findings.

Efforts such as Singtel’s RE:AI tie-up with SIT-NVIDIA aim to close that deployment gap.

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REGIONAL

Malaysia’s Budget 2027 earmarks US$110.4M for startups: National fund of funds Jelawang Capital and KWAP’s Dana Perintis will deploy the money, Prime Minister Anwar Ibrahim told parliament, though e27’s analysis shows the bigger cheques go elsewhere in the budget.

TCGX closes US$600M Asia biotech fund, opens Hong Kong office: The US healthcare investor’s first Asia-focused vehicle was oversubscribed by international institutional investors and will back drug developers across stages. TCGX is also opening in Shanghai, but has not named LPs, cheque sizes or portfolio companies.

BII commits US$1.6B to SEA climate finance over five years: The UK development financier will put up about half and mobilise the rest privately, largely through British Climate Partners, targeting renewables, transmission, EV infrastructure and water in Vietnam, Indonesia, the Philippines, Malaysia and Thailand.

Zenyum parent MakeO raises US$10M to grow dentist network in Asia: Singularity AMC led the round. The group, with 1,100 partner dentists across seven markets, has passed US$54.7M in ARR, is profitable on an EBITDA basis and is weighing more acquisitions in India and Asia.

NEU Battery Materials raises US$7.6M to scale LFP recycling: Shift4Good and SGInnovate co-led the round after NEU commissioned a 1,300-tonne-per-year Singapore factory. Both backed its 2023 seed, and NEU’s emissions claims still lack an independent life-cycle assessment.

Vietnamese-founded Drylab raises US$3M for autonomous AI lab agents: Vertex Ventures Southeast Asia & India led the seed round, with PatSnap joining. The Singapore-based startup’s agents run entire computational research workflows across 800-plus tools, though it has yet to name customers.

UOB and AWS team up on hybrid cloud and AI banking across ASEAN: The MoU creates a dedicated Singapore cloud environment, AWS’s first hybrid-cloud bank tie-up in ASEAN, designed to meet data residency rules while testing conversational banking and personalised services across UOB’s five markets.

VinFast takes 36% of Vietnam’s car market in first nine months: The Vingroup EV maker’s 177,697 domestic deliveries already exceed its full-year 2025 total, and September marked its 25th straight month on top. Toyota trailed in second place with 53,978 units.

INTERVIEWS AND FEATURES

17 SEA startups that can actually prove their social impact: In a funding winter, founders stretch the social impact label to cover anything with a farmer or a recycling bin in the deck. e27 picks 17 startups taking the harder route of proving it.

Singapore workers won flexibility, then lost their evenings: Jobstreet’s study finds 52% of employees report higher stress since 2019, against 27% citing better balance. Leaner teams spread the workload, yet only 12% plan to hire to ease the pressure.

Vietnamese shoppers ask AI first, then check Google before buying: Ho Chi Minh City-based Q&Me finds 62% of buyers use AI, including generative tools, to research purchases, but Google search remains the final checkpoint before most shoppers commit.

INTERNATIONAL

Moonshot execs seek settlement with Recurrent.ai ahead of HK IPO: In Hong Kong arbitration since 2024, Recurrent.ai accuses the Kimi maker’s CEO and CTO of misleading investors and moving or deleting AI data, seeking 10.5% of Moonshot’s original shares, potentially worth over US$700M. Yang denies wrongdoing.

Firmus pulls US$5B Australian IPO, will turn to private capital: Investors balked at a roughly US$31B valuation, three times its August round, for an operator running 42MW of a 1GW target. Firmus is building AI factories in Singapore, Indonesia and Malaysia, with OpenAI anchoring two Malaysian sites.

IMF tips Vietnam to lead SEA growth at 8.4% as global GDP slows: The fund projects 3.1% world growth in 2026, with Malaysia at 5.2% and Singapore at 5%. SEA gains from the AI cycle mainly through trade and manufacturing, not by building AI infrastructure.

CYBERSECURITY

TikTok CEO Chew Shou Zi warns of AI agents’ cybersecurity risks: Speaking at DBS Asia Future Forward in Singapore, Chew said users may grant AI agents broad permissions, including access to credit cards, without grasping the consequences, and urged firms to stop staff sharing trade secrets with public chatbots.

Igloo exports cyber insurance to Turkey in first move beyond APAC: Through Aktif Bank’s Sigortayeri, the Singapore insurtech will sell cover for cyberbullying and fraud to the N Kolay app’s eight million users for about US$43. Mitsubishi Corporation brokered the deal.

Microsoft’s Brad Smith wants breach alerts for rogue AI agents: Calling AI a weapon and a tool that threatens global cybersecurity, Smith argued the UK, not the US or EU, should lead safety rules, and said voluntary pledges only bind responsible companies.

SEMICONDUCTOR

TSMC posts record US$46.7B Q3 revenue on AI chip demand: Sales rose 50% year on year and topped TSMC’s own guidance of US$44.6B to US$45.8B, as well as analyst forecasts. Full earnings, with margins and outlook, land on 15 October.

Sumitomo Bakelite adds chip packaging lines in Singapore and Suzhou: The Japanese firm expects to roughly double underfill material capacity for AI and satellite chips, with Singapore output from early 2029. It also completes its US$190M purchase of Kyocera’s chemical business this month.

FedEx report finds AI boom pushing chipmakers across Asia Pacific: The AI race now hinges on where chips are made, how far they travel and whether supply chains survive the next geopolitical shock, putting logistics resilience on the agenda for SEA manufacturers.

a16z sends infrastructure team to South Korea to court chip firms: Partner Raghu Raghuram will meet Korean AI hardware firms and officials weeks after the US$1.1B Machine Age Fund launch, eyeing semiconductor and robotics partnerships as the country cements its role as AI’s memory backbone.

AI

Anthropic test agent sent false murder tip to Philadelphia police: The city’s spam filter caught the July tip, and Anthropic took over two months to spot it. Police called the delay unacceptable, and Anthropic is now cutting its internal evaluations off from the live internet.

Satya Nadella calls for an ’emergency brake’ on AI models: Microsoft’s CEO wants every meaningful model action logged with tamper-proof evidence and an authorised person able to pause a model mid-task. Firms should assume a model is compromised from the start, he argued.

Singapore SMEs save time with AI, but messy data stalls scale-up: Small businesses cite time savings as AI’s main payoff, but systems that are not ready stop pilots from running more of the business, mirroring the scaling gap larger firms face.

India’s Clueso raises US$10M Series A for AI business video: Dharana Capital led the round, lifting total funding to US$12M. Clueso, used by Atlassian and Zendesk among 1,000-plus companies, will spend the money on enterprise sales and stronger agentic editing tools.

THOUGHT LEADERSHIP

US-China trade enters managed rivalry; SEA firms must adapt: Shawn Balakrishnan argues Trump’s May visit to Beijing produced a transactional framework, including new Boards of Trade and Investment, that Southeast Asian businesses must now plan around.

Deeptech founders should split their funding needs by job: Jun Yan argues one round size hides money doing different jobs, from funding an unproven product to buying equipment, carrying inventory or financing receivables, and each calls for a different kind of capital.

Human work is priced by the hour, machine thought by the token: Sayan Ghosh notes that only machine intelligence prices are falling fast, while timesheets and pricing models still assume human hours, forcing organisations to rethink how they value knowledge work.

Widespread AI tool use does not make a firm AI-ready: Elvis Lau argues that staff using ChatGPT and Copilot daily, plus a handful of production pilots, can still leave enterprises short of genuine AI readiness at the organisational level.

SEA’s SMEs should chase AI value, not just automation: Arnold Warner argues the real question is where AI creates measurable value without disproportionate complexity, cost or risk, a distinction that matters for the region’s small businesses.

Stop building new apps; build inside the ones users already open: Citing Sensor Tower, Manit Parikh notes users open around ten apps a day and global downloads grew just 0.8% last year, so founders should build inside existing platforms.

AI video is fast to generate and slow to make usable: Drawing on his work with AI-generated cinematic video, Jay Chua explains why prompt-to-scene speed is no guarantee of producing a usable video, where most of the effort now sits.

SEA retailers need observability to protect AI customer service: With Singapore offering a 400% tax deduction on AI spend for smaller retailers, Simon Rizkalla argues that real-time monitoring of AI systems decides whether the customer experience holds up.

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Artificial intelligence is increasingly moving from an experimental technology to an operational tool for small and medium-sized enterprises across Southeast Asia. Yet the more important question is not whether SMEs will use AI, but where AI can create measurable business value without adding disproportionate complexity, cost, or risk.

This distinction matters because Southeast Asia’s SME landscape is unusually diverse. The region includes highly digitised economies such as Singapore alongside markets where many smaller businesses are still building basic digital infrastructure. As a result, AI adoption is unlikely to follow a single regional trajectory.

According to the Asian Development Bank’s Asia Small and Medium-Sized Enterprise Monitor 2025, MSMEs account for an average 99.8 per cent of enterprises and 67.6 per cent of employment across its 26 developing member economies. For Southeast Asia, therefore, the economic significance of AI depends heavily on whether smaller businesses can convert increasingly accessible AI tools into productivity gains.

AI’s first advantage is operational, not transformational

For many SMEs, the immediate value of AI lies in improving existing processes rather than reinventing the business.

Customer service, finance and accounting, marketing, inventory management, demand forecasting and internal knowledge management are among the functions where AI can reduce repetitive work or accelerate decision-making. Singapore provides a useful illustration. Data from the country’s Infocomm Media Development Authority shows that SME AI adoption increased from 4.2 per cent in 2023 to 14.5 per cent in 2024. SMEs using AI-enabled solutions supported through the Productivity Solutions Grant reported average cost savings of 52 per cent in 2024.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

The significance of this example is not the specific percentage. It demonstrates that AI’s commercial case for smaller companies can begin with relatively narrow applications. A company does not necessarily need to build a proprietary AI system. It can use existing models to automate repetitive workflows, analyse information, assist employees or improve customer interactions.

This lowers the technological barrier, but it does not eliminate the organisational one.

The real constraint is digital readiness

AI systems depend on the quality of the processes and data around them. A company with fragmented spreadsheets, inconsistent customer records and largely manual workflows may find it difficult to obtain reliable results from increasingly sophisticated AI tools.

Recent research published by ADB on digital technology adoption among 681 entrepreneurs across Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam found that selected digital applications and business-model digitalisation were associated with greater experimentation and changes in business models.

This suggests an important sequencing issue. AI is often presented as the next step after digitalisation, but for many SMEs, basic digital infrastructure remains the prerequisite for meaningful AI adoption.

A 2026 analysis published through ADB’s Southeast Asia Development Solutions identifies substantial differences in AI readiness across the region, with infrastructure, skills and regulatory maturity influencing adoption. It also notes that Cambodian manufacturing SMEs, for example, continue to face gaps in basic digital systems.

Consequently, an SME that has not yet digitised its core operations may receive more value from improving its data and workflow infrastructure than immediately implementing advanced AI.

Southeast Asia will not have one AI adoption curve

The region’s diversity also means that national AI strategies and enterprise capabilities will shape adoption differently.

Singapore has developed comparatively mature AI infrastructure and policy support, while other Southeast Asian markets continue to address foundational connectivity, skills and digitalisation challenges. The result is likely to be a multi-speed AI economy, rather than a uniform regional transformation.

Also Read: AI chats are becoming the new starting point for online shopping

This matters for enterprise software providers and policymakers. A sophisticated AI platform designed for a highly digitised Singaporean SME may not address the constraints of a smaller business in Cambodia, Laos or parts of Indonesia. Solutions need to account for differences in connectivity, workforce capabilities, language, affordability and existing software systems.

The Asian Development Bank similarly argues that AI readiness, rather than adoption alone, will determine how effectively economies capture the technology’s benefits.

The SME advantage could be speed

There is, however, a structural reason to expect smaller businesses to remain important participants in AI adoption.

Large organisations often have more resources but also have complex legacy systems, multiple approval layers and established processes. SMEs have fewer resources, but they can sometimes introduce new tools across a smaller organisation with less organisational friction.

AI can therefore reduce some of the disadvantages traditionally associated with company size. A small retailer can use automated customer support. A manufacturer can apply predictive analytics to production data. A professional-services firm can use AI to organise documents and accelerate research. A small exporter can use AI-assisted translation and market research to communicate across markets.

This does not make AI a substitute for scale. Rather, it can lower the cost of performing certain activities that previously required specialised staff or external providers.

Also Read: AI labs are chasing a slice of the corporate pie in Southeast Asia

The productivity question is more complicated

There is a tendency to equate AI adoption with productivity automatically. The evidence suggests a more conditional relationship.

AI can reduce the time required for particular tasks, but the resulting productivity gain depends on what employees do with the time released. Poorly implemented automation can simply move work elsewhere, introduce errors or create additional verification requirements.

This is particularly important for SMEs, where a single employee may perform several functions. Automating one task does not necessarily produce a proportional reduction in costs if the employee remains essential for related activities.

ADB’s broader research on digital transformation highlights both the productivity opportunities and the risks, including digital divides, labour-market disruption, cybersecurity, data privacy and governance challenges.

The economic impact of AI should therefore be measured through business outcomes such as processing time, error rates, customer response times, revenue per employee and operating costs, rather than simply the number of AI tools deployed.

Governance will become an SME issue

As AI moves into finance, recruitment, customer service and other decision-making processes, governance becomes increasingly relevant to smaller companies.

ASEAN’s Guide on AI Governance and Ethics provides a regional framework covering issues including transparency, fairness, security, accountability and human oversight. ASEAN subsequently expanded the guidance to address generative AI specifically.

For SMEs, governance does not necessarily require large compliance departments. It can begin with practical questions: What data is being provided to an AI system? Who reviews its outputs? Which decisions can AI make independently? What happens when the system produces an incorrect or biased result?

These questions become more important as AI moves from assisting employees to executing parts of business workflows.

From experimentation to infrastructure

The next phase of AI adoption among Southeast Asian SMEs will be less about experimenting with individual tools and more about integrating AI into core business operations.

The real value will come from connecting AI with customer data, finance, inventory and internal workflows. As adoption grows, success will depend not simply on access to AI, but on digital readiness, skills, governance and practical use cases.

The challenge is no longer accessing AI. It is turning AI into measurable, repeatable productivity.

—

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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Malaysia has put a number on its startup ambitions: MYR450 million (US$110.4 million). That is how much the National Fund of Funds Jelawang Capital and Dana Perintis, the startup vehicle of the Retirement Fund (KWAP), will invest in developing startups, Prime Minister Anwar Ibrahim said in his Budget 2027 speech to parliament on Friday.

“Startups need support at every stage, from ideas to seed capital and scaling up,” said Anwar, who is also Finance Minister.

Also Read: Malaysia’s digital economy’s second wave looks nothing like the first

It is a welcome commitment. It is also, by some distance, not the biggest cheque in the speech. Read the budget end to end and a clear pecking order emerges: mid-tier companies and semiconductors first, research and AI skills next, and early-stage startups a little further down the list.

Follow the money

Anwar said MYR2.1 billion (about US$515 million) from the NIMP fund (linked to the New Industrial Master Plan 2030), KWAP’s Dana Pemacu and Khazanah’s MTC Fund will go towards strengthening mid-tier companies in electrical and electronics (E&E), digital technology, aerospace and medical devices. These firms, he said, “must rapidly become self-reliant to lead in high-value industries.”

Set side by side, the startup allocation is roughly a fifth of the mid-tier pot. Add the MYR1.2 billion (about US$294 million) Khazanah and KWAP will put into local semiconductor companies, and the MYR6 billion (about US$1.47 billion) earmarked for research, development and commercialisation across ministries, and the startup line starts to look like a footnote.

That is not necessarily a mistake. Mid-tier firms hire at scale, export and anchor supply chains, and Malaysia’s growth story rests on climbing value chains rather than minting unicorns. But for founders, the size of the fund matters less than who manages it and how quickly the money actually moves.

KWAP’s presence on both sides of the ledger is worth watching. The pension fund spent part of this year containing the damage from the eFishery fraud shock. Retirement money in venture capital invites political scrutiny, and whether that makes Dana Perintis a more disciplined investor or simply a slower one remains to be seen.

Nudging private capital off the sidelines

The more interesting moves may be the ones designed to pull private money in. Anwar announced MYR270 million (about US$66 million) to support SMEs, mid-tier companies and social enterprises raising through equity crowdfunding (ECF) and peer-to-peer (P2P) lending platforms.

Also Read: AI demand lifts Malaysia’s chip sector, but not every player wins

Income tax exemptions for individual investors on ECF platforms, and for angel investors backing early-stage tech startups, have been extended to December 31, 2030. That four-year runway matters more than it sounds. Angels plan around portfolios and holding periods, not single budget cycles, and a sunset clause that keeps moving is a poor basis for a long-term bet.

Cradle Fund, meanwhile, gets MYR41 million (about US$10 million) to implement the Malaysia Startup Ecosystem Roadmap and to hand out grants for prototype development and commercialisation. For a roadmap meant to reshape the national ecosystem, it is a slim line item. Its impact will depend on how tightly Cradle targets the grants.

Semiconductors, again

If there is a single obsession running through the speech, it is chips. MTDC’s SemiconStart programme will provide follow-on equity for startups in IC design, advanced packaging and semiconductor materials. Khazanah is partnering with the Selangor state government on a fund to develop the state’s semiconductor industry. Khazanah and InvestPenang will also set up a MYR100 million (about US$24.5 million) Strategic Investment Fund for early-stage semiconductor and advanced manufacturing firms.

The follow-on detail is the smart part. In deeptech, first cheques are rarely the problem. Startups stall at the second and third rounds, through long and capital-hungry design cycles. Firms such as GreatAsic, which is trying to pivot Malaysia from chip assembly to indigenous design, are exactly the kind that need patient capital. The risk is the old one: the sector has already faced questions over governance and credibility, and more state money will not settle them on its own.

AI for everyone, on someone else’s model

On AI, the budget leans heavily on access. The free AI subscription initiative will be expanded by another 100,000 subscriptions and extended to new applications such as ChatGPT. Rakyat Digital, meanwhile, offers free digital courses through a partnership with Coursera.

There is an irony here. Paying for subscriptions to an American chatbot sits awkwardly beside Putrajaya’s sovereign AI rhetoric. Local AI startups may wonder why public money is building habits on a foreign platform rather than theirs.

Also Read: Why Malaysia’s AI Nation 2030 plan matters for B2B startups

AI Malaysia Bhd will receive almost MYR15 million (about US$3.7 million) to reinforce a safe and ethical AI ecosystem, towards a target of 200,000 AI-skilled workers. MDEC’s MYR30 million (about US$7.4 million) fund will help 4,000 MSMEs adopt AI and automate their operations, and will also train and certify 5,000 AI professionals. Do the maths and that is at most MYR7,500 (about US$1,840) per business, before the training component takes its share. Given how wide Malaysia’s AI skills gap already is, that is a starter kit rather than a transformation.

Platforms, gig workers and quantum threats

The government and Grab will jointly fund a MYR160 million (about US$39 million) package from 2027 to boost net earnings and improve the welfare of e-hailing and p-hailing (parcel delivery) workers. It is a pragmatic arrangement, though one in which the regulator and the dominant platform now share the bill for gig worker protection. Riders will judge it by what lands in their pockets, not by the press release.

On e-commerce, the government is examining legislative and enforcement measures to shield local MSMEs from unfair competition from foreign platforms and to ensure product safety. An E-commerce Bill will be tabled in the next parliamentary sitting. It continues a broader pattern in which Malaysia regulates while Singapore funds.

For growth-stage companies eyeing an exit, the Securities Commission Malaysia and Hong Kong’s securities regulator have formalised cooperation to help Malaysian companies list in both markets, with matching grants to cover listing costs. Forest City’s Single Family Office scheme offers zero tax on qualifying income, and a Multi-Family Office model will let licensed fund managers serve several families.

Green tech tax incentives have been enhanced and extended to December 31, 2030. They include an Investment Tax Allowance of up to 100 per cent for green and sustainable technology projects, EV charging stations, and green assets bought for own use.

On cybersecurity, Malaysia says it is now recognised at Tier 1 for readiness to transition to post-quantum cryptography. The National Cyber Security Agency (NACSA) and the Malaysian Communications and Multimedia Commission (MCMC) will develop home-grown data protection technologies built to withstand attacks from quantum computers, while CSCDC Bhd’s capabilities are strengthened. “Home-grown” is the operative word. The country’s cyber startups have been battling a funding drought, and they will want to know whether that work flows to them or stays inside government agencies.

The real test is deployment

Anwar projected growth of 4.2 to 5.2 per cent in 2027, a touch softer than the 4.8 to 5.3 per cent expected this year. In a slower year, capital that sits in committees is capital that does not exist.

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

Budgets are statements of intent. This one is clear about its priorities: build the middle, double down on chips, and give everyone a chatbot. Startups got a seat at the table. Whether they get fed depends on how fast Jelawang, KWAP and Cradle turn announcements into term sheets.

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Before a Vietnamese shopper adds anything to a cart these days, there is a good chance a chatbot has been consulted. As many as 62 per cent of Vietnamese buyers use AI, including generative AI, to research purchases before they buy, according to a new survey by Ho Chi Minh City-based market research firm Q&Me.

Only 3 per cent of respondents said they “never” use AI for pre-purchase research. Frequent use peaks among people in their 30s (68 per cent), while those in their 40s are more likely to say they use it “sometimes” (44 per cent).

Also Read: The invisible shopper rewriting Asia’s e-commerce playbook

On the surface, it is another data point for the thesis that AI chats are becoming the new starting point for online shopping. Read the rest of the survey, though, and a more nuanced picture emerges: AI has joined the shopping journey, but it has not taken it over.

The old guard still runs the funnel

When respondents were asked which research methods they actually use, generative AI came in fifth, cited by 58 per cent. Search engines such as Google led with 86 per cent, followed by e-commerce platforms such as Shopee and Lazada (77 per cent), Facebook (59 per cent) and TikTok (57 per cent).

In other words, the chatbot has become one more tab among many rather than a replacement for them. A typical Vietnamese buyer is likely to ask ChatGPT what to look for, check Google for reviews, compare prices on Shopee, and watch a TikTok creator unbox the product before paying. That fragmentation is a headache for brands, which now have to show up credibly in all five places.

It also matters for the platforms. TikTok, which is investing US$980 million in a logistics project in Ho Chi Minh City, is betting that discovery and checkout happen in the same app. A shopper, who does the thinking inside a chatbot and only arrives at the marketplace to pay, weakens that bet, and hands the most valuable part of the funnel — the moment of consideration — to someone else.

What people actually ask the bots

Shopping is not even the main reason Vietnamese users turn to generative AI. According to Q&Me, 78 per cent use it for information search, 62 per cent for personal advice, 58 per cent for product research and 57 per cent for work tasks.

The information-search figure is the one to watch. Chatbots are increasingly doing the job search engines used to do, a shift regulators elsewhere are starting to notice; Europe’s decision to classify ChatGPT as a search engine is one sign of where that leads. For shoppers, the risk is that a confident-sounding answer replaces a comparison. Product data feeding AI systems is often incomplete or simply wrong, and a chatbot rarely tells a buyer when it is guessing.

Also Read: How Vietnam is emerging as a leading AI builder ecosystem in Southeast Asia

The 62 per cent seeking personal advice deserves its own caution. Advice on what to buy shades quickly into advice on whether to buy, and few of these tools disclose how products end up in their recommendations.

ChatGPT leads, Google closes in

Among generative AI services, ChatGPT is the clear leader, used by 80 per cent of respondents, followed by Gemini (68 per cent) and Meta AI (25 per cent). Claude, Copilot, DeepSeek, Grok and Perplexity trailed behind.

Gemini’s strong second place is no accident. Google has been pushing hard on distribution and local-language capability across Southeast Asia, and it owns both the Android phones most Vietnamese carry and the search engine 86 per cent still rely on.

Meta AI’s 25 per cent, by contrast, looks modest given how deeply Facebook is woven into Vietnamese commerce, which suggests users have not yet learned to treat the assistant inside their social feed as a shopping adviser.

Notably absent from the list of popular services is any homegrown Vietnamese assistant, despite the country’s ambitions to become a regional AI builder and a growing body of work on models trained for Vietnamese, Thai and Indonesian. Vietnamese consumers, for now, are doing their AI-assisted shopping on American platforms.

Read the fine print

The survey’s headline numbers should be handled with care. Q&Me polled just 200 men and women aged 20 to 49 in Ho Chi Minh City and Hanoi. A sample that size carries a margin of error of roughly seven percentage points either way, and the age-group splits are drawn from smaller pools still, so the gap between people in their 30s and 40s may be narrower or wider than it looks.

The sample is also urban, working-age and, almost by definition, online, which is exactly the slice of Vietnam most likely to have embraced AI. It says little about older shoppers or buyers outside the two biggest cities, where much of the next wave of e-commerce growth is expected to come from.

There is a definitional wrinkle too. The survey says 62 per cent use AI for pre-purchase research, yet only 58 per cent name generative AI as one of their research methods, and the same 58 per cent use it for product research. The difference may come down to how respondents interpreted “AI”: some may be counting the AI features already built into search results and shopping apps rather than a standalone chatbot.

What it means for brands and founders

None of this undercuts the direction of travel. Even with a small sample, near-universal exposure among urban buyers is a strong signal, and the 30-something cohort, the one with the most purchasing power, is the heaviest user.

For brands selling into Vietnam, the practical lesson is that being findable now means being legible to machines as well as to people: accurate product data, consistent reviews and clear specifications that a chatbot can parse and repeat. For startups, the gap is in the middle of the funnel, building tools that help merchants show up correctly inside AI answers, and that help shoppers verify what those answers tell them.

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

For now, Vietnamese shoppers are using AI the way they use a well-read friend: worth asking, rarely the final word. Google, Shopee and TikTok still close the sale. The question for the next survey is how long that lasts.

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Ask a Singapore small business owner what AI has done for them, and the answer is time. Ask whether their systems are ready to let AI run more of the business, and the answer gets a lot less certain.

Also Read: Beyond automation: How AI is reshaping the SME landscape in Southeast Asia

That tension runs through “The State of AI in Small Business 2026 – Singapore”, a new report from small business accounting platform Xero. Research firm Pureprofile conducted the survey between July 29 and August 5. It polled 1,018 business owners and senior decision-makers at Singapore companies with fewer than 200 employees and annual revenue of up to SGD100 million (about US$77 million).

The starting point is familiar to anyone who has run a small company. Some 76 per cent of respondents said their leadership and finance teams lose anywhere from three to more than 10 hours a week to manual administrative work. AI, the report argues, is buying that time back.

Where the reclaimed hours go is the more flattering part of the story. Nearly half (46 per cent) of SMEs said they channel them into growth strategies and market expansion, 38 per cent into product and service innovation, and 33 per cent into upskilling existing staff for higher-value tasks.

“The real dividend of AI isn’t simply doing the same work faster. It’s the capacity it gives back, and business owners are clear they’d reinvest that time in growth, innovation and their people,” said Koren Wines, Managing Director of Xero APAC.

It is a tidy narrative, and a convenient one for a company that sells AI-embedded accounting software. It is also worth noting what the survey measures: where SMEs say the hours go, not whether those hours turned into new customers or revenue.

The 82 per cent problem

On sentiment, Singapore’s SMEs are bullish. Some 82 per cent said AI returns have met or exceeded expectations, and 69 per cent plan to increase total AI spending over the next 12 months.

Dig into the operational numbers, though, and the confidence thins out. Only 18 per cent feel fully confident that their current software has enough context to support automated decision-making, such as data history, clean ledgers and connected systems. Nearly a third (31 per cent) named poor data quality as the main reason AI pilots never made it into regular production.

Also Read: Why Singapore SMEs should treat cybersecurity as a sales issue

Cost and talent remain sticking points too. Integration costs (38 per cent) and upskilling staff (36 per cent) are the top hurdles to expanding adoption.

Read together, the figures suggest SMEs are pleased with what AI does today, which is largely task-level admin relief. They are far less sure it can be trusted with anything more consequential, and high satisfaction may simply reflect modest expectations.

The harder question is what happens after the pilot phase, when AI is expected to make decisions rather than draft emails. As one e27 analysis put it, the region’s small businesses arguably have a record problem before they have an AI problem.

Guardrails, at least on paper

Xero frames Singapore’s SMEs as leaders in responsible AI use. Some 67 per cent have established formal internal usage frameworks, and 90 per cent say vendor data privacy is a priority.

The split within that 90 per cent is telling. Only 36 per cent will work exclusively with vendors that explicitly guarantee customer data will not be used to train external models. The remaining 54 per cent prefer vendors that offer a clear opt-out. That quietly shifts the burden onto the business owner to find, and remember to flip, the right setting.

A framework on file is also not the same as one that is enforced. Larger Singapore firms adopting agentic AI have been found to keep thin audit trails, and many fear data sovereignty failures while remaining underprepared for them. There is little reason to assume a 30-person trading company is more rigorous.

The grant question

For Xero at least, the report’s most commercially useful finding is that 52 per cent of SMEs now use AI built directly into core software they already rely on. That includes cloud accounting, inventory tracking and customer management tools. It is precisely the pitch embedded-software vendors make, so the figure is best read through that lens.

Also Read: The Philippines uses AI as a burnout shield; Singapore uses it for deep work

The other number that deserves attention is 63 per cent: the share of SMEs using government digital grants to fund technology deployment and training. Singapore has made AI adoption a national priority, most visibly in its AI-first Budget 2026, and the grants clearly lower the barrier to entry.

They also raise a fair question. How much of that planned increase in AI spending, reported by 69 per cent of SMEs, is driven by their own conviction, and how much by the availability of subsidies? The answer will matter if grant priorities shift.

Ahead of the region, still short of the finish line

By regional standards, Singapore’s small businesses are in a strong position. A similar Xero study found Malaysian SMEs grappling with a growing confidence gap in AI adoption. Globally, SMEs have been adopting AI four times slower than large enterprises.

But being ahead of the pack is not the same as being ready. For founders building software for Southeast Asia’s SMEs, the report contains a clear signal: the bottleneck is plumbing, not models. Clean books, connected systems and trustworthy data handling are what stand between an SME saving a few hours a week and an SME letting AI make real decisions. It is also, not coincidentally, exactly where Xero wants to sell.

“What’s encouraging is that AI is reshaping roles rather than replacing them,” Wines said. “For Singapore’s SMEs, it’s no longer about adoption. It’s about what happens next.”

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

She is right about that. The first phase of SME AI was measured in hours saved. The next will be measured in revenue earned, and on that score, Singapore’s small businesses have yet to show their workings.

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Generative AI has made video creation remarkably fast. A scene that once required a camera crew, actors, locations and hours of post-production can now begin with a prompt and an idea.

But after working with AI-generated cinematic videos, I have learned that generating a video quickly and producing a video that is actually usable are two very different things.

An AI model can create an impressive scene on the first attempt. At first glance, everything may look great: cinematic lighting, realistic environments, convincing characters and smooth camera movement.

Then you look closer. A car is moving in the wrong direction. A character suddenly looks different. An important object disappears. The environment does not accurately represent the intended location. A person appears twice. The camera moves differently from what the story requires.

The video may look impressive, but it is not necessarily usable.

Good-looking is not the same as correct

One of the most interesting things about AI video is how easily visual quality can hide mistakes. A generated scene can look highly realistic while containing small problems that make the entire shot unusable.

This becomes particularly important when creating content based on real locations, products or brands. If a scene is supposed to represent Singapore, for example, recognisable landmarks, roads, architecture and the overall environment need to make sense. A beautiful futuristic city is not enough if it no longer feels like Singapore.

The same applies to branded content. A logo that changes shape, text that becomes unreadable or a product that changes appearance can quickly turn a visually impressive generation into something that cannot be published.

This means creators need to evaluate AI-generated footage beyond visual quality. Accuracy matters too.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

Consistency is one of the biggest challenges

Creating one impressive AI-generated shot is becoming increasingly easy. Creating 10 shots that feel like they belong to the same film is much harder.

A character may look correct in one scene and slightly different in the next. Clothing can change. Buildings can move. Objects can disappear. Lighting can suddenly shift.

Individually, each generation might look good. Together, however, they may not feel like one continuous story.

This has changed the way I approach AI video creation. Instead of thinking only about individual prompts, I increasingly think about continuity: What must remain the same? What can change? Where should the character be? What direction should objects move? What should the camera be doing? What details from the previous scene need to remain visible?

Consistency becomes part of the creative process.

Prompting is becoming more like directing

Early AI prompts could be relatively simple. “Create a futuristic city.” Today, producing more controlled cinematic content often requires much more thought.

A creator may need to define the subject, action, environment, camera movement, framing, lighting, visual style, timing and restrictions. Instead of simply asking AI to create something beautiful, you begin directing what should happen. The difference is important.

A prompt might specify that the camera slowly pushes forward while the main character remains visible, vehicles continue moving naturally in the correct direction, the environment stays consistent and the shot ends at a particular visual moment. At that point, prompting starts to feel less like giving a computer a sentence and more like communicating instructions to a virtual production team.

The ability to describe an idea clearly becomes increasingly valuable.

Also Read: AI chats are becoming the new starting point for online shopping

Human judgement still decides what is usable

AI can generate alternatives very quickly, but it does not always understand why one result works better than another. A human still needs to notice when something feels wrong.

Is the movement natural? Does the location look accurate? Is the character consistent? Does the camera movement support the story? Is the branding correct? Does the scene communicate the intended message?

These decisions require judgement rather than generation. This is why I do not see AI video simply as a replacement for traditional creative work. It changes where some of the work happens.

Instead of manually producing every visual element, creators increasingly spend time defining ideas, setting constraints, reviewing generations, correcting problems and deciding which results deserve to become part of the final production.

Businesses should look beyond generation speed

For businesses considering AI-generated video, speed is understandably attractive. But “How quickly can AI generate a video?” may not be the most useful question. A better set of questions might be:

• How many generations are required before the result is usable?
• Can characters, products and brand elements remain consistent?
• How much human review is required?
• Can the technology accurately communicate the intended message?
• Does the final content actually feel appropriate for the brand?

AI can dramatically reduce some production barriers, particularly for ideas that would otherwise require expensive locations, visual effects or complex production. But businesses should still treat AI-generated content as creative production rather than a one-click solution.

The role of the creator is changing

AI video technology will continue to improve. Many problems that are difficult today will probably become easier.

Characters will become more consistent. Physical movement will improve. Models will understand environments better. Creators will gain more precise control.

But better technology does not eliminate the need for creative decisions. Someone still needs to decide what the audience should see, how the story should unfold, what feels authentic and whether the final result communicates the right idea.

The tools are changing rapidly, but storytelling, direction and judgement still matter.

AI can generate the footage.

Someone still has to direct it.

—

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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