Human rights are getting diluted or ignored in the oft-neglected “S” (social) of ESG, even though the rules for protecting them have been clear for a decade and a half.

The 2011 UN Guiding Principles on Business and Human Rights make every company responsible for respecting human rights and for finding, preventing, and repairing the harm its business causes.

In ESG terms, human rights are the basic protections owed to everyone a business affects, such as safe work, fair pay, freedom from forced labour, and a community’s say over its land. Many companies still fill the social pillar with donations, wellness programmes, and diversity campaigns, and leave out the people their operations touch most directly.

The World Benchmarking Alliance assessed the world’s 2,000 most influential companies in 2024 and found that more than 90 per cent fall short on human rights, working conditions, and ethics. Only one in five checks its supply chain partners for human rights risks, and just 4 per cent commit to paying a living wage.

I’m seeing some of the clearest answers to that failure coming from Southeast Asia, where some companies and governments are protecting the workers and communities their growth depends on, often before any law forced them to, and simply because these are inalienable rights.

Carbon’s easy to count. So what about the delivery rider in the flood?

Emissions come in tonnes, with standard units and reporting rules. A worker’s exhaustion, family’s lost land, or rider’s fear of the next flood has no standard unit, so it rarely makes it into the sustainability report, and the “E” gets most of the attention.

That imbalance falls especially hard on three groups in Southeast Asia: gig workers, communities living on land that companies want, and the people doing dangerous industrial work for the green economy.

Ride-hailing and delivery apps run on drivers/riders classed as independent contractors, outside most labour protections. Researchers from Fairwork, a project based at the University of Oxford, scored platforms in the Philippines out of 10 for fair pay, conditions, contracts, management, and representation. The best scored 3, and none could show that their workers earned the minimum wage after costs. One rider they interviewed worked an 11-hour day for PHP 750 and kept PHP 460 after fuel, phone credit, maintenance, and his vehicle loan. The platforms take a commission of at least 20 per cent on each job.

Also Read: Passing 1.5°C global warming: Performative ESG is no longer acceptable

Those workers keep riding through heatwaves and typhoons, because the app pays only when they move. They carry the risk, repair bills, and lost income when a storm hits, while demand for deliveries goes up.

Grab began giving drivers and passengers free personal accident insurance across all six of its markets in 2016, years before any law required it. GrabCar was the only Philippine platform that Fairwork found to adequately protect its workers from the risks of the job. Singapore has since put that principle into law: Its Platform Workers Act now gives ride-hailing and delivery workers employee-level injury compensation, retirement contributions, and the right to form associations that represent them.

The right to say no to a project

Plantations, mines, and even solar and wind farms need land, and some of it belongs to Indigenous and rural communities who have farmed it for generations. International standards say those communities must give their free, prior, and informed consent before a project goes ahead, which means they also have the right to refuse.

Families lose the ground that feeds them when companies ignore that right. In 2022, P&G, Nestlé, Hershey’s, and Colgate-Palmolive suspended business with a large Indonesian palm oil producer after farmers in Central and West Sulawesi said their land had been taken and that those who objected faced criminal charges.

The clean energy transition can repeat the same harm. Only four of the 12 renewable energy companies in a Business & Human Rights Resource Centre survey of Southeast Asia had substantial human rights commitments, and only two publicly committed to respecting Indigenous Peoples’ rights. The frontrunners were ACEN in the Philippines and B.Grimm Power in Thailand. B.Grimm assesses every one of its sites and direct suppliers for human rights risks every three years, along with its joint ventures.

Green metals, human cost

Indonesia’s nickel boom feeds the batteries that power electric vehicles around the world. On Christmas Eve 2023, a furnace exploded at a smelter in the Morowali industrial park and killed 21 workers, 13 Indonesian and eight Chinese, and injured 38 more.

Also Read: Why investors and customers are betting on ESG-aligned startups

Those were 21 people who went to work and didn’t come home. Carmakers can build an electric car’s battery on that kind of loss, and no carbon disclosure will show it. Companies that take safety seriously treat it as a human rights duty across the whole supply chain, contractors included, and B.Grimm’s site and supplier assessments show one way to do it.

Why this is existential

Climate change and human rights abuses land on the same people. Excessive heat on the job affects 74.7 per cent of workers in Asia and the Pacific, above the global average of 71 per cent. Delivery riders and farmers work outdoors in that heat, and furnace crews work beside it. My concurrent work with an NGO on climate policy keeps showing me that overlap. A climate strategy that ignores these workers only moves the damage from the atmosphere onto human beings.

ESG exists to make business answer for its effects on the world, and it stops doing that job when companies reduce the “S” to charity and slogans. Southeast Asia is building the factories, data centres, plantations, and power grids it will rely on for the next half-century, and the choices companies make now will decide whether the people doing that work share in the region’s future or pay for it.

Where to start

Thailand’s Ministry of Justice is drafting a mandatory human rights and environmental due diligence law that would cover manufacturers with annual revenue above THB 500 million and service businesses above THB 300 million, with fines of up to THB 5 million. Companies that write a serious policy now will be ready for it.

While helping companies in Asia to develop their policies, the strongest I see are specific. One forward-thinking Japanese client’s applies a policy that rests on the Universal Declaration of Human Rights, ILO’s core labour principles, and UN Guiding Principles. It rules out forced labour, human trafficking, and child labour, bars discrimination and harassment, and protects the right to form unions. It also commits the company to due diligence, remedy for harm, a confidential reporting channel to its compliance committee, and training, and the policy itself is subject to revision.

Any company in the region can start the same way: Write down the risks to people that your business creates, check your own sites and suppliers against them, and give workers and communities a way to raise concerns that doesn’t run through the managers they’re complaining about.

The people who build, deliver, and grow what our economies run on belong at the centre of the “S”. Putting them back there is the least business can do, and it may decide whether this region’s growth is worth having.

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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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Sit in the architecture review of almost any platform that has reached four ASEAN markets and the same diagram appears. One product on top. Underneath, four data stores, four payment integrations, four know-your-customer flows (the identity checks that onboard each user), and four sets of legal entities and directors. Adding Malaysia means adding a fifth column and a fifth team.

Nobody designed it that way. Each market was added while the opportunity was live, by the fastest route to compliance. It worked four times. That is the problem.

Now the region has agreed to write some of its rules once. ASEAN’s senior economic officials said in May that negotiations on the Digital Economy Framework Agreement, DEFA, were concluded, with signing targeted for November. It covers digital trade, payments, digital identity, data governance and cross-border e-commerce. Studies cited in their statement put the region’s digital economy at about US$1 trillion by 2030 on current trends, and as much as US$2 trillion with implementation. The market’s reading is simple: the cap on cross-border digital scale just came off.

The cap did not come off, it moved inside

I read it differently. Scaling is the ability to add markets without a proportional rise in cost, complexity and founder attention. DEFA lowers the cost of entering a market from the outside. It does nothing about the cost of being rebuilt on the inside. So here is the law I keep returning to: when the outside cost of entering a market falls, scale is capped by how many times the business has to be rebuilt inside.

Walk it through. Market one costs what it costs: a data store, a payments connection, an onboarding flow, an entity. On a country-by-country build, market two costs almost the same again, because little of market one transfers. Market three costs the same again, and now someone must reconcile three versions of the customer. Cost per market stays flat while coordination load, the management attention spent keeping the copies consistent, climbs with every addition. That is not a scaling curve. It is a multiplication table.

A firm that built one operating layer sees a different curve. One data architecture, one payments abstraction, one identity and compliance model, with each country’s rules held as configuration rather than as a separate system. For that firm, shared external rules make market two cheaper, then three, then four, because the hard work was done once. The same agreement bends the curve for one company and leaves the other flat. The difference is not the treaty. It is the floor plan.

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

The ceiling sits differently at each size. For a micro business it is the home market plus one existing channel, and it moves when another country’s system recognises its business identity. For an SME it is the number of lanes one person can track, and it moves only when the lanes collapse into one. For a regional operator it is the number of parallel stacks, and it moves when they are consolidated. For a nation, it is political: whether a regulator trusts that data leaving the country does not mean control leaving with it.

Corridors built in pairs

Here is the detail most people skip. We have already run a version of this experiment. By December 2025, ASEAN’s finance ministers and central bank governors reported 29 QR and person-to-person instant payment links, within the region and with outside partners. QR payments across them reached 36.2 million transactions worth US$716.4 million in 2025. That is real, and still small against domestic volumes.

Why small? My read is that each link was built as a pair. Two systems agree a standard and a corridor opens. The next corridor needs its own agreement, and every operator carries its own settlement, currency conversion and merchant onboarding for it. Shared standards made each handshake cheaper. They did not make the twentieth corridor cheaper than the second, because the work sat inside each participant, not between them.

Singapore’s Deputy Prime Minister Gan Kim Yong has said seven ASEAN countries have signed up to Project Nexus, a multilateral link meant to replace the pair-by-pair model. A shared rail bends the curve only for the company that connects to it once. Connect it to four stacks and you have built four connections.

One thing nobody can tell you yet. At the time of writing, the full text and the country-specific exceptions have not been published, so no one knows how much national rule will survive. That uncertainty is the argument for one operating layer. A firm that holds exceptions as configuration can absorb whatever remains. A firm with four stacks cannot.

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

Two moves, depending on your exposure

If DEFA touches your business directly, the move is architectural before it is legal. Consolidate toward one data layer, one payments abstraction and one identity model. That pulls the replicability lever, because what copies across borders is what you build once. Stop funding the fifth country team and fund the shared core, which lowers the capital needed per unit of growth. Use local partners for the last-mile exceptions instead of a full rebuild, and keep those terms fair and written down.

When one dependency sets the speed of the whole system, a partner you have squeezed becomes your slowest link. Most of the businesses that use my scaling framework are no longer asking when DEFA is signed. They are asking how many times they are carrying the same company.

If you are less exposed, a single-market firm or one whose core is not digital, use the quiet period. Hire or acquire people who understand both the regional rules and the national exceptions. That pulls talent density, because that capability will stay trapped in a few heads until someone builds it into a playbook. Then position for the harder markets, finance, health and logistics, where baseline rules plus local exceptions create a barrier that early movers can price.

India shows the other order of operations. It built one domestic instant-payments layer, UPI, for a very large home market, then connected it outward to other countries’ systems instead of rebuilding it for each. The core came first and the corridors were extensions. ASEAN’s QR links show what happens when the corridors come first.

Sign the agreement. Ratify it. Watch the exceptions. Then do the unglamorous work of making sure you only have to build the company once. Outside rules will keep getting cheaper. The inside is the only cost you set yourself.

When entry gets cheaper, scale is capped by how many times you have to be rebuilt.

—

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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A lot of startup advice about predictive AI starts too early. It starts with the model, the tool, the data stack, or the dashboard. Those things matter, but they are not the first question.

The first question is simpler: what decision should this prediction change? If a startup cannot answer that clearly, it is usually too early to talk about predictive AI.

This sounds obvious, but it is where many teams drift. They say they want to predict churn, forecast demand, score leads, or spot revenue risk. Those are reasonable ideas. But a prediction by itself does not help much. It only becomes useful when someone knows what should happen next.

Take churn as an example. If a model flags that a customer may be at risk of leaving, what changes? Does someone from customer success reach out? Does the account get reviewed? Does the product team check whether activation is stuck? Does the system trigger a reminder? Or does the prediction sit in a dashboard where nobody acts on it?

That is the real test.

The same problem shows up in sales. A startup may say it wants to predict which leads are most likely to convert. Fine. But what decision changes after that? Do those leads get faster follow-up? Do they get routed to a more experienced salesperson? Do they receive different onboarding or pricing information? If the workflow stays the same, the prediction is just another score that people may ignore.

This is why defining the decision first is a useful habit. It forces the team to think in workflow terms, not just data terms.

Also Read: Why SEA’s retailers need to protect AI-powered customer experience with observability 

A practical way to do this is to write the decision as one sentence.

• If this prediction is high, we will do X.
• If this prediction is low, we will do Y.
• If the model is uncertain, we will ask a human to review it.

That simple exercise clears up a lot. Sometimes it shows the startup is not ready for prediction yet. Sometimes the team realises it does not need a model at all. A basic rule or threshold may be enough. Sometimes it reveals that the real problem is not prediction, but missing process. The company may not even have a clear follow-up path once a risky account or warm lead is identified.

This also helps with data collection. Once the decision is clear, the useful data becomes clearer too. If the goal is to predict which trial users are likely to activate, the team should care about setup behaviour, time-to-first-action, drop-off points, and usage patterns. If the goal is to predict which inbound leads deserve faster follow-up, the team should care about source, page visits, company profile, response speed, and prior engagement. Without the decision, teams often collect too much and still learn too little.

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

The same rule helps with pilots. A startup does not need to roll out a grand predictive AI system on day one. It can start with one narrow decision that already matters. Which users should get a check-in email? Which leads should be escalated first? Which accounts should be reviewed before renewal? A smaller pilot is easier to judge because the team can see whether the decision actually improved.

This matters because prediction sounds smart even when it is not useful. A chart, a score, or a risk label can create the feeling that the company has become data-driven. But if nobody can explain what the business does differently because of that prediction, then the model may be adding complexity without adding leverage.

So before asking which tool to buy, which model to train, or which platform to use, a startup should pause and ask one operational question: what decision should this prediction change?

That question does not make predictive AI less ambitious. It makes it more usable. And for a small team, usable usually matters more than impressive.

—

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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Thirty-seven per cent. Eleven per cent. Those two numbers, tucked deep inside Jobstreet by SEEK’s Singapore Workplace Culture Study 2026, may say more about the region’s productivity problem than any headline about AI or inflation.

The first is the share of employees who try to manage workplace pressure by quietly drawing their own boundaries: muting the group chat, blocking the calendar, putting the phone face down after dinner. The second is the share who feel comfortable raising that pressure with their manager.

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

In other words, Singapore’s workers are negotiating their working hours with themselves. The one person who could actually change the terms is rarely part of the conversation.

The study, which polled 600 employees and jobseekers and 200 hiring decision-makers, has a headline finding that is already well documented: bosses say they want initiative, workers try to prove themselves through availability.

Some 49 per cent of employers rank proactive problem-solving as their top expectation, yet only 32 per cent of employees prioritise it. Meanwhile, 63 per cent of employees lean on rapid replies and 52 per cent on after-hours availability, behaviours just 39 per cent and 18 per cent of employers respectively expect.

The harder question is why the mismatch survives when both sides can see it. Some 85 per cent of employers concede their organisations expect staff to stay reachable, and 81 per cent of those acknowledge the long-term risks, chiefly burnout. Read across the data, the answer keeps landing on the same layer of the org chart: the line manager.

The signal nobody sends

Hybrid work removed the oldest, crudest performance metric of all: being seen at your desk. Nothing replaced it. In that vacuum, employees reach for what is legible. A reply at 11pm is visible. Three hours spent untangling a broken process is not.

The report calls this visibility bias, and leadership is part of the feedback loop. Among employees under always-on pressure, 35 per cent blame explicit manager or leadership expectations, a view echoed by 41 per cent of employers who point at senior leadership. And 46 per cent on both sides describe constant availability as an unwritten cultural baseline.

“Unwritten” is the operative word. Nobody instructs staff to answer at midnight. Nobody tells them not to, either. In workplaces across Southeast Asia, where hierarchy still shapes who speaks first in a meeting, asking a boss whether a message really needs an answer tonight can feel uncomfortably close to insubordination. So people don’t ask, and the 11 per cent figure starts to make sense.

The youngest are paying the most

The burden is not evenly spread. Some 64 per cent of workers aged 25 to 34 face frequent always-on pressure, and much of it is self-generated: 33 per cent of this cohort cite self-imposed pressure, compared with 21 per cent of those aged 35 to 44 and 16 per cent of those aged 45 to 54. Career-progression anxiety follows the same curve, at 35 per cent for early-career workers against 27 per cent for the oldest group surveyed.

Also Read: Gen Z isn’t hard to manage, you just need to rethink how you lead

Older employees feel the pressure almost as often (62 per cent for ages 35 to 44, 61 per cent for 45 to 54) but for different reasons: structural workload and embedded culture rather than fear. Put crudely, the young are performing, while the middle-aged are carrying.

The cruel part is that the performance does not pay. Some 62 per cent of employees say always-on behaviour has had no impact on their career progression, and only 6 per cent believe it is how people actually get ahead. Young workers keep doing it anyway, which is less a sign of irrationality than of the absence of any clearer signal. For companies still rethinking early-career hiring for a remote world, that silence is a design flaw, not a personality trait.

Support that never reaches the desk

Employers will argue they have tools in place. The data suggests those tools rarely survive the journey from the HR handbook to the individual worker. Half of employees say they receive no support to manage workplace pressure, while only 22 per cent of employers believe they offer none. Workload conversations are offered by 40 per cent of employers but experienced by 22 per cent of employees. Boundary protections show a 36 per cent to 19 per cent split, and confidential feedback channels a 33 per cent to 13 per cent split.

Notice what those programmes have in common. Almost every one of them depends on a manager to start the conversation, honour the boundary or act on the feedback. Yet manager development sits near the bottom of employers’ investment plans at 12 per cent, alongside workload-management improvements at 13 per cent. AI and digital skills (34 per cent) and technology tools (25 per cent) lead the list.

AI could widen the gap

That ordering carries a real risk. Some 24 per cent of employers are restructuring towards leaner teams, and 40 per cent already admit workloads cannot be finished within standard hours. Layer faster AI-driven workflows on top, without teaching managers to ration capacity, and the likely result is more tasks arriving more quickly, not fewer late-night messages.

As one contributor recently argued, AI will change what work has to be managed; it does not remove the need to manage it. If anything, it may expose weak leadership faster than any engagement survey.

What a useful manager would do

The report’s prescriptions are sensible, if a little consultancy-tidy. Strip response speed and digital status indicators out of appraisals. Write down what genuinely counts as urgent. Run weekly capacity reviews in which managers and teams agree what to prioritise, what to delay and what to stop altogether. It even supplies a script for employees to propose their priorities and agree offline hours in advance.

The weak spot is authority. A “stop” list only works if the manager drawing it up is allowed to stop anything. Many middle managers in lean regional startups are themselves squeezed between founders chasing growth and teams drowning in status updates, and the skills a monitored workplace demands are rarely the ones they were promoted for.

Also Read: AI can increase productivity. It can’t increase human capacity

The usual caveats apply. This is a Singapore-only sample, employer responses are self-reported, and a jobs platform benefits when workers feel restless. Still, 55 per cent of employees and 45 per cent of employers agree that both sides share responsibility for always-on culture, which at least gives them a starting point.

For founders, the cost of ignoring it is measurable: 36 per cent of employees are already exploring other jobs, and across the region keeping good people is proving harder than hiring them. The gap between 37 per cent and 11 per cent is, in the end, just one conversation. Someone senior needs to be the one who starts it.

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Almost every founder I meet reaches the same conclusion at roughly the same moment. You cannot outbid a funded competitor on salary, so you give people a stake in the outcome instead. The logic is sound. Equity turns salary-limited hires into owners and aligns the people doing the work with the value they create.

Then the plan gets written. Usually quickly, usually from a template, usually at the point the company is 10 people in one city and the founder is doing it between two other things. That is the version that survives, unexamined, for the next four years.

What changes underneath it is the company. The first engineer in Bengaluru. The first salesperson in Sydney. A designer who moves to Lisbon and keeps working. Cross-border hiring is now normal inside the first 10 hires, and the plan that was written for one country is quietly doing work it was never designed for.

It is a stack of decisions, not one

What looks like a single call, let us give the team equity, is really a series of choices that each carry legal, tax and reporting consequences.

You are choosing between options and actual shares. You are deciding whether phantom units, which track share value and pay out in cash without handing over ownership, are a better fit. You are setting vesting, and sometimes reverse vesting for founders and early joiners. You are agreeing reserved rights, picking a valuation method, and inheriting statutory reporting obligations in every country where you have someone on the plan.

None of it sits in isolation. Your valuation method affects the tax your employee pays. Your vesting structure affects when that tax is triggered. A reporting rule in a country you hired into once creates an obligation nobody in the company knows exists. The complexity compounds, and it compounds fastest exactly when you are growing.

Two colleagues, the same grant, different outcomes

Where your company is incorporated sets your compliance baseline. A US-incorporated company generally has to value its shares regularly, often annually, to price options defensibly. Every fresh valuation moves the strike price, which changes what your team believes their options are worth. If the numbers climb faster than expectations, the incentive dulls even as the company succeeds.

Also Read: Rethinking ESOP pools in India: Building ownership without losing control

Where your employees sit matters just as much. Some countries put the reporting obligation on the employer, others on the employee. The taxable moment moves as well. It can attach at vesting, at exercise, or only at an eventual exit. Two people with identical grants, doing identical jobs, can end up with materially different outcomes because one of them lives somewhere else.

Singapore is a good example for anyone hiring here. Employers carry reporting obligations on share plan gains, and a foreign employee who ceases Singapore employment can be treated as having exercised outstanding options before they leave. That is a tax charge on a gain they have not received in cash, arriving at the worst possible moment in someone’s working life.

Your team does not even want the same thing. Many people prefer to pay nothing until an exit gives them the cash to pay with. Others would rather pay a small amount now, while the shares are cheap, to shrink a much larger bill later. Both are rational. A good plan makes room for both where the law allows, and most templates make room for neither.

The part that costs you money

Here is the part founders underweight. Institutional investors read your cap table and your incentive plan closely, and they read them together. An overly complex or poorly documented plan is a diligence problem before it is anything else. Unclear ownership. Unquantified tax exposure. Reporting nobody can produce on request. Each of those slows a round, and a slow round is a cheaper round.

Also Read: The best new year resolutions for startup founders: Offering ESOPs that actually work

A clean plan does the opposite. It signals a company that is straightforward to invest in and, later, straightforward to acquire. That is not a compliance benefit. That is a valuation one.

What founders do about it

When a plan has to serve people in several countries, structure starts earning its keep. Some founders hold shares in trust for the team, which keeps the cap table tidy while preserving employees’ economic interest in the upside. Others set up a dedicated special purpose vehicle that holds equity for employees as a group, centralising the reporting and giving everyone a consistent experience regardless of where they log in from.

Neither is free. Each has trade-offs in cost, control and tax treatment, and the right answer depends on where your company sits and where your people do.

Decide early, revisit deliberately

The through-line is that these choices are far cheaper to make well at the start than to unwind later. A plan designed around your entity’s rules, your team’s locations and your investors’ expectations stays effective as you grow. One assembled piecemeal loses its pull precisely when you need it to hold.

The question worth asking is not whether your ESOP is generous. It is whether it still describes the company you have actually become.

—

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 National Bank of Bahrain (NBB) has signed a memorandum of understanding with blockchain settlement network Partior to study whether the Bahraini dinar (BHD) can be settled on the Singaporean firm’s rails, with NBB acting as the currency’s settlement bank.

Read the fine print and the ambition shrinks to its proper size. The MoU commits the two parties to evaluate an operating model, implementation requirements and technical feasibility, and to work out what Bahrain’s regulators would require. There is no launch date, no live service and no disclosed financial terms. It is, for now, a feasibility study with a signing ceremony.

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

Still, the deal says a good deal about where Partior wants to go, and why a piece of Singapore-born financial plumbing keeps turning up in the Gulf.

The plumbing problem

Cross-border bank payments still run largely on correspondent banking: chains of banks holding accounts with one another (the “nostro” and “vostro” accounts of banking jargon), each link adding time, fees and opacity. A transfer can take days, and billions sit idle in pre-funded accounts around the world, waiting for business hours to overlap.

Partior’s answer is a shared ledger on which member banks settle with each other directly and around the clock, using tokenised commercial bank money rather than cryptocurrencies or stablecoins. Each currency is anchored by a settlement bank that holds the real money and issues its digital twin on the network.

The network currently supports three currencies, the US dollar, the euro and the Singapore dollar, through its founding settlement banks, as e27 has explained before.

Adding the dinar would therefore be a first on two counts: a new currency and a new settlement jurisdiction. NBB would do for the dinar what JP Morgan, DBS and Deutsche Bank do for the dollar, the Singapore dollar and the euro.

A Singapore idea, exported

Partior was set up in 2021 by DBS, JP Morgan and Temasek, building on the Monetary Authority of Singapore’s Project Ubin experiments with blockchain-based interbank settlement.

Standard Chartered led its Series A in 2022, and Deutsche Bank later joined as a strategic investor in a Series B that reached US$80 million.

Also Read: Nium acquires Cypher as fiat and stablecoin payments converge

The Gulf has since become its busiest expansion front. Emirates NBD went live with real-time cross-border dollar settlement on the network, with JP Morgan acting as settlement bank in the first phase. When Partior announced its Series B, it named the UAE dirham, the Qatari riyal and the Saudi riyal among currencies it planned to add, alongside the Malaysian ringgit. The dinar was not on that list, which makes NBB a new opportunity rather than a pipeline delivery.

Partior chief executive Humphrey Valenbreder framed the deal as building on the company’s “growing presence across the GCC”. NBB’s Vineet Munot was more guarded, saying the bank would weigh the technical, operational and regulatory considerations before deciding on its value. That caution is the most honest line in the announcement.

Why a small currency matters

The dinar is pegged to the US dollar at roughly US$2.66 per dinar, making it one of the world’s highest-valued currencies but a modest one by volume. For Partior, the prize is less the dinar flow itself than proof that a mid-sized jurisdiction can bring its local currency onto the network, with a local bank as anchor and a local regulator comfortable with the arrangement.

That proof matters for Southeast Asia. Every currency Partior adds beyond its founding trio is a dry run for the ringgit, the baht, the rupiah or the peso. Asia’s cross-border payment flows are enormous, pegged at a US$23.8 trillion opportunity in one contributor’s analysis, yet they remain split across currencies with thin direct liquidity between them.

The Gulf also hosts large communities of Filipino and Indonesian workers, so Gulf-ASEAN corridors are hardly abstract. But a caveat applies: Partior is wholesale, bank-to-bank infrastructure. Faster interbank settlement only reaches a migrant worker’s family if banks and remittance firms pass the savings on, and that has rarely been automatic.

A crowded field

Partior is far from alone. Its own investor JP Morgan runs Kinexys, a blockchain payments network for corporate clients. Swift is building a blockchain-based ledger with Consensys.

Project mBridge, a multi-central-bank platform whose members include the Bank of Thailand and the central banks of the UAE and Saudi Arabia, settles in central bank digital currencies.

Project Nexus, incubated by the Bank for International Settlements, aims to link instant payment systems, including those of Malaysia, the Philippines and Singapore.

Then there are stablecoins, which are chasing the same corridors from the other direction. Circle has agreed to buy Singapore’s Tazapay for US$400 million, while SBI has backed dtcpay’s Japan-SEA stablecoin corridors. Partior’s quiet selling point is that it keeps money inside the regulated banking system, sidestepping the dollarisation worries that stablecoins raise for emerging-market central banks.

MoUs are cheap

Bank-led blockchain consortia have a mixed record. Trade finance networks such as we.trade and Contour were wound down after banks failed to route enough business through them. Partior has done better than most: it is live, it has several settlement banks and it is building round-the-clock liquidity with LSEG’s Digital Settlement House, with production targeted from early 2027.

Also Read: AI agents could help Southeast Asian firms untangle cross-border payment costs

The NBB agreement will be worth more than a press release only when two things happen: the dinar actually goes live, and banks outside the founding circle start sending real volume through it. Until then, Bahrain is a promising line on Partior’s map, not a corridor.

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Every humanoid robot looks brilliant in a launch video. The trouble begins on an ordinary weekday in a warehouse, when a parcel arrives in a shape nobody trained the machine for, a forklift reverses where it shouldn’t, and a worker strolls into its path while scrolling a phone.

That messy gap between the lab and the loading bay is what Hong Kong is now trying to turn into infrastructure. The Hong Kong Productivity Council (HKPC) has launched the Embodied AI International Applications Hub, with the Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR) as its technical lead.

Also Read: Ropedia raises US$22M to build the data layer for robots that understand the real world

HKPC bills it as the city’s first platform built for training, validating and deploying embodied AI. That is the industry’s term for AI housed in a physical body, such as a humanoid, a four-legged robot or an industrial arm, which has to act on the world rather than describe it.

The first phase brings in seven technology partners and eight international “scenario partners”. Scenario partners are the organisations that own the factories, warehouses, shops and public facilities where robots would actually be deployed. The hub targets five areas: manufacturing, smart logistics, retail services, inspection and maintenance, and public services.

A test track, not a showroom

The distinction HKPC is keen to draw is that this is not another glossy demo centre. The pitch is a proving ground. Developers get access to real operating settings, scenario data gets collected, and performance gets measured and fed back into the product.

FLAIR describes a closed loop. It starts with defining the scenario and moves through model adaptation and training, testing, evaluation, data feedback and iteration. The system is then validated again, and the cycle repeats. Systems will be scored on operational accuracy, execution efficiency, reliability, autonomous task completion, safety and how well they adapt to their surroundings.

That list reads like a diagnosis of where robotics keeps stumbling. A robot that behaves perfectly in a controlled lab can come apart when the floor layout changes, the objects differ, the workflow shifts or humans behave like humans. As e27 has reported before, the leap from pilot to production is where many robotics ventures quietly die. Repeated validation across varied conditions is supposed to expose those failure modes before a customer pays to discover them.

The use cases in the launch materials are specific. Logistics robots must handle parcels of all sizes and different warehouse processes. Manufacturing robots must cope with different production lines, equipment standards and safety rules. Inspection and public-facility work may require humanoid or quadruped robots to stay in control in cluttered, unpredictable environments. FLAIR is already showing a flexible embodied-AI production line and industrial robots across manufacturing, logistics, retail and public-service settings.

The quiet prize is data

The deeper value lies less in the hardware than in the data the hub generates. Investors and founders increasingly argue that the real battle in humanoid robotics is about data, not hardware. Real-world scenario data, gathered across many sites and many failures, is scarce and expensive to collect. A hub that systematically harvests it across industries is, in effect, building a dataset as much as a testing facility.

That raises questions the launch materials do not answer. Who owns the scenario data? Can a developer take what it learns in a partner’s warehouse and use it to sell to that partner’s competitor? How are costs split between the government-backed centre, developers and scenario owners? These details will decide whether serious companies bring their best technology or only their safest demos.

Also Read: Why Asia’s Physical AI boom will be decided at the camera, not the model

For scenario owners, the model offers a lower-risk route. A factory or logistics operator can test robots on its own floor without committing to a full rollout. It can also see how a supplier copes with legacy equipment and existing safety procedures before signing a procurement contract. In an industry where purchase decisions are often based on slide decks, evidence collected on site is a useful corrective.

Hong Kong’s bridge play, again

Strategically, the hub fits a familiar Hong Kong script: the city as a bridge between mainland China and the rest of the world. HKPC argues that Hong Kong’s access to the mainland market, combined with its alignment with international standards, makes it a natural cross-regional testing base.

That pitch has a logic. Mainland China is building robot armies at a pace the West is struggling to match. Chinese makers looking to sell abroad, however, face buyers who want proof of safety and compliance on terms they recognise. A Hong Kong validation stamp could serve as that passport. It also extends a broader push in which the city has positioned itself as the place that solves tech’s final mile to market, and has openly courted Southeast Asia as a “super partner”.

At the launch, Secretary for Innovation, Technology and Industry Sun Dong said the government would keep backing AI through research investment, funding and infrastructure, with an emphasis on safety and compliance. HKPC chairman Sunny Tan framed the hub as taking FLAIR’s work in perception, autonomous decision-making, motion control and industrial applications out of the lab and into real operating environments.

What it means for Southeast Asia

Southeast Asia is not short of robotics ambition. Singapore leads the region in robot density, though e27 has argued that its robotics dominance is a warning sign dressed up as good news. Vietnam’s VinRobotics has taken its humanoids to the global stage, while Thai and Malaysian startups are raising rounds to move physical AI into malls and factories. What the region largely lacks is a shared, neutral venue where a startup can prove its machine works somewhere other than its own test bench.

If the Hong Kong hub opens up to regional players on fair terms, it could become a useful credential for SEA robotics firms that want to sell into China or use Chinese supply chains. If it turns mainly into a finishing school for mainland companies heading overseas, it becomes a competitor’s advantage instead.

Also Read: Hivebotics nets US$6M to take restroom-cleaning robot Abluo into volume production

Some scepticism is warranted. Fifteen partners is a modest start. “Alignment with international standards” is a phrase that sounds reassuring and commits to very little. The China Daily Hong Kong coverage echoed the official line that the centre will speed up the move from research to industry, but that is a stated goal, not an outcome.

The real test is simpler and harder. Will the trials produce benchmarks other people trust and can repeat, and deployments that outlive the ribbon-cutting? Robots are good at repeating the same task. The hub now has to show it can repeatedly turn pilots into real deployments.

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Singapore’s office workers answer a 10.47pm message within 90 seconds to prove their commitment. Their bosses barely notice.

The Jobstreet by SEEK Singapore Workplace Culture Study 2026, which surveyed 600 employees and jobseekers and 200 hiring decision makers, finds that 63% of staff signal dedication by replying fast, a behaviour only 39% of employers value. Some 52% stay reachable after hours; just 18% of employers rate it. What employers actually want is initiative and outcomes (49%).

The anxiety behind this is imported. AI and automation, followed by inflation and the cost of living, top both sides’ lists of workplace pressures. Employees fear for pay and job security; employers worry about AI’s impact on teams and restructuring towards leaner headcounts. That loop turns permanent availability into cheap insurance against the next round of cuts.

The payout is close to nil. Some 62% say always-on working has done nothing for their career, and 63% of those under pressure report lower energy. Yet 48% of employers who expect it have no plans to add support, and budgets favour AI training (34%) over wellbeing (14%) and manager development (12%). Until employers decide when staff may switch off, the late-night reply keeps costing workers dearly.

REGIONAL

SEA fintech funding triples to US$2.3B as deal count nearly halves: Four US$100M-plus rounds took 86% of capital, led by Ant International’s US$1.2B Series A. Seed funding fell 46% to US$53.3M, Singapore firms raised US$1.9B, and the region logged no fintech IPOs, per Tracxn.

Salary Hero raises US$7.6M Series A to take on Malaysia’s loan sharks: Cento Ventures led the round for the Bangkok-based financial health platform, joined by Kairous Capital and M Venture Partners. Salary Hero has also expanded its credit facility to US$10M with regional banks and lenders.

Parallel Loop nets US$6.6M to feed enterprise AI the context it lacks: Founders Yujun Chean and Prateek Jogani previously built Vouch Insurtech, parent of Thai insurtech FairDee, which raised a US$755,000 seed round in 2018. Eight years on, they return with a bigger cheque in a far more crowded field.

Antares Ventures passes US$50M for debut deep-tech fund, eyes US$100M: Family offices and corporate investors from Southeast Asia, Japan and Korea backed the second close. Fund I has already deployed about US$27M across nine companies, including Singapore’s VFlowTech, spanning energy, compute, industry and urban infrastructure.

TalentHero, DeeMoney form Thai JV to link hiring, payroll and payments: The Singapore-headquartered firm, co-founded by Oliver Lewis and James Miles, targets the post-hire grind of local entities, compliance and payroll, plus the cross-border money transfers that slow companies expanding into Thailand.

85% of Singapore employers expect staff to be always on: Yet half of employees say they receive no organisational support, against 22% of employers who admit offering none. Always-on working rarely pays off: 62% of staff see no difference to their career progression.

Malaysia’s VentureTECH backs biosimilar maker and Sabah agritech firm: Hanan Medicare is moving into biosimilars, including insulin, while AgriData Portal runs a seed-to-market model for smallholders. The AgriData deal is the government-backed investor’s first Sabah bet, aligned with PuTERA35.

Cashku adds Muamalat Invest Shariah funds, including private credit: The Malaysian platform offers sukuk, equities, gold and money market funds with no sales charges. Malaysia’s Islamic capital market grew 4.31% to US$660B in 2025, about 63% of the domestic capital market.

Partior and Bahrain’s NBB study putting the dinar on its rails: The Singapore blockchain settlement network could gain NBB as a dinar settlement bank, but the MoU commits only to feasibility work. Neither side disclosed a live service, a launch date or financial terms.

Beenext, Lotus lead US$2M pre-seed for AI search startup Miracly: Miracly, from Swedish home decor operator The Wild Ventures, tracks brand visibility on AI platforms. Acclime and Pixi Beauty are early customers, and it faces rivals such as Profound and AthenaHQ in AI search optimisation.

INTERVIEWS & FEATURES

Manus’s US$500M round puts the AI startup back in Chinese hands: Boyu Capital and IDG Capital led the raise, months after Beijing ordered Meta’s US$2B acquisition unwound. The lesson for SEA investors: regulatory exposure follows founders and code, not the registered address.

JFrog’s Sunny Rao: tokens are the wrong yardstick for AI agents: Rao once sold the IVR and chatbot automation wave that left customers trapped in looping phone trees. He warns that enterprises measuring AI agents by token use risk repeating that mistake.

Six wallets are replacing cash-on-delivery across Southeast Asia: Cash-on-delivery, long the default where card ownership was thin, once made up about half of e-commerce. Shoppers are finally abandoning it, but each market is choosing differently, fragmenting checkout for merchants.

Employers buy AI tools while their managers are left behind: Jobstreet by SEEK finds 37% of Singapore employees quietly draw their own boundaries to cope with pressure. Employers, meanwhile, rank manager development near the bottom of their investment list, at 12%.

Auto finance loses buyers in gaps that AI agents could close: Up to 80% of SEA cars are bought on credit, yet only about 10% of prospects complete financing, X Star’s Shawn Yang told AIMCONNECT 2026. Delegates ranked connectivity across institutions first as the fix.

INTERNATIONAL

Netflix cuts about 1,000 jobs as YouTube and TikTok eat into viewing: The roughly 6% cut falls mostly on product, tech, legal and HR teams. It follows Netflix losing the Warner Bros. Discovery bidding war to Paramount Skydance earlier this year.

US EV sales grow for a third straight quarter after tax credits end: EVs took 6.1% of the US market, per Cox Automotive, led by Toyota, Rivian and Subaru. Tesla’s sales fell 20% year-on-year, though it still sold over half of all EVs.

Amazon’s Panos Panay tells founders to fail, then harvest the lessons: Panay cited Microsoft’s Surface RT, which triggered a nearly US$1B write-down, as proof that failures stem from decisions made years earlier. His advice: adjust course by 30 degrees, not 180.

CYBERSECURITY

Singapore wants Google’s AI to stop scams inside the phone: Google unveiled two initiatives with the Singapore Government, shifting part of the anti-scam fight onto users’ devices, away from police operations centres, bank back offices and platform blocking rules.

Spyware makers turned online ads into a zero-click hacking pipeline: Citizen Lab says Israeli firm Discover-Tech let Candiru and Bindecy target victims through ad networks. One campaign hit at least 45 computers at Al Jazeera; exposed bidding records show ads reached military devices.

FBI arrests OpenAI staffer over hidden device in executive boardroom: Prosecutors charged Saudi national Abdulrahman Alfozan with intercepting oral communications after a device turned up in a TV’s USB port. OpenAI says it has no sign of stolen weights or customer data.

Ping An Digital Bank reports 96% deepfake detection in HKMA sandbox: Built with OneConnect, the platform checks customer selfies for forged faces during verification. The bank published no methodology or false-positive rates, and gave no deployment date beyond the trial.

SEMICONDUCTOR

SK Hynix’s Solidigm ties US fab plans to China equipment approvals: Reuters reports Solidigm wants more US tools for its Dalian plant while weighing a NAND factory in upstate New York. The unit is reportedly eyeing a US$10B IPO in 2027.

Applied Materials opens AI chip R&D hub with TSMC, Intel, Samsung: The Sunnyvale centre houses nearly four acres of cleanroom, and Applied expects capital spending of about US$5B over time. Micron, SK hynix and five US universities also joined, but the company gave no timeline or spending breakdown.

Cerebras CEO says AI doomsayers are drowning out the benefits: Andrew Feldman told TechCrunch Disrupt the industry also failed to engage local communities over US data centres, fuelling NIMBY pushback, while countries such as Brazil, Finland and Mexico welcome the buildouts.

Japan to invest up to US$21B in two GlobalFoundries fabs in New York: GlobalFoundries will operate the plants, jointly owned by Washington and Japanese interests. The deal forms part of Japan’s US$550B US investment pledge, made in return for US tariffs on Japanese imports falling to 15% from at least 25%.

AI

Anthropic’s Singapore MOU puts AI safety testing ahead of reach: Opening its fifth APAC office, Anthropic counts GovTech, DBS, GIC, Grab and Sea as users. The MOU carries no investment figure or headcount, and Washington still holds the off switch on US frontier models.

Sea Labs gets Google’s frontier models before February pilots report: Google AI Futures Fund calls Sea Labs its first external lab partner, but no money, equity or milestones were disclosed. Sea cut fourth-quarter 2025 R&D spending 7.8% as revenue rose 38.4%.

Singapore bets AI agent assurance can become its next industry: IMDA unveiled new assurance measures for AI agents, reflecting regulators’ shift in focus from software that chats to software that acts, where a wrong refund or misdirected email becomes a liability.

Singtel, IMDA offer 10,000 SMEs free AI-ready document management: SMEs get six months of free access to Singtel’s SPACE plus workshops, aimed at organising messy business data first. Neither party disclosed sign-ups or measured productivity gains.

Disney offers US$20M for tech Google licensed for US$2.7B: Both deals cover a licence to Character.ai‘s technology, marking a fall of over 99% in just over two years. The chatbot startup has since filed for Chapter 11 bankruptcy.

Hong Kong opens embodied AI hub to test robots in real settings: HKPC and FLAIR have signed seven technology partners and eight international scenario partners across manufacturing, logistics and retail. Hong Kong wants the hub to bridge mainland robotics and global markets.

THOUGHT LEADERSHIP

SEA’s funding winter is real, but its builders have not stopped: Faheem Aizat Kamsan notes that zero regional PE funds closed in the first half of 2026. Yet he argues capital fleeing the region is not the same as founders downing tools.

Indonesian fintechs paid more for ASEAN expansion than they planned: Bobby Yulandika Putra revisits the 2018–2022 cohort, from Investree to Modalku, that pushed beyond Indonesia and hit a structural cost few had priced in.

DEFA lowers the cost of entry, but architecture decides cost of scale: Fathhi Mohamed warns that platforms in four ASEAN markets often run four data stores, four KYC flows and four sets of legal entities, so every new market multiplies complexity.

Singapore’s growth upgrade helps some sectors far more than others: MTI lifted its 2026 forecast from 2–4% to 4.5–5.5% in August. Boris Dzhingarov urges founders to read the sector breakdown, not the headline, before planning.

Founders now judge investors on what comes beyond the cheque: Chris Chen opens with a founder weighing two term sheets in one week, including a reasonable US$1.5M offer from a well-known regional VC, to show what founders actually buy.

Decide which decision predictive AI should change before buying tools: Daniel Tan argues startups begin too early with models, data stacks and dashboards. The first question is what decision a prediction changes; without that answer, he suggests, the rest is premature.

AI saves hours, but teams simply fill them with more work: Rubyni Karun warns that as organisations accelerate AI spending, time saved by automation simply gets refilled with tasks, leaving human capacity as stretched as before.

Audiences warm to ads as marketers lose faith in their own metrics: Prof. Aleks Farseev draws on Kantar’s Media Reactions study, built on interviews with 800-plus senior marketers, to ask why green dashboards cannot show teams what worked ahead of 2027 planning.

Human rights are getting lost in the ‘S’ of ESG: Adam Goulston notes the 2011 UN Guiding Principles have long required companies to find, prevent and repair human rights harms, yet the social pillar still dilutes or ignores them.

The West buys longevity scans while SEA still pays cash for basics: Americans pay up to US$2,499 for full-body MRIs, Maria Gaitanidou notes. In the Philippines, many families delay check-ups until symptoms turn serious, and public hospital beds are often full.

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For two decades, marketers obsessed over a single question: where do we rank on Google? A growing number now ask a stranger one: what does ChatGPT say about us when we are not in the room?

The Wild Ventures, the Swedish-founded company behind home decor brand Wild Palace, is betting that question will turn into a budget line. On October 14, it launched Miracly, an AI marketing platform, after raising a US$2 million pre-seed round.

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

The round was led by Singapore-based BEENEXT and Lotus, with participation from a Swedish family office. Existing backers include Stefan Lindeberg, founder of Nordic VC firm Creandum, and Alex Bouaziz, co-founder and CEO of payroll and HR unicorn Deel. The company did not disclose a valuation.

From wallpaper to software

The launch marks a notable turn for a company that, until recently, described itself as a house of brands. When The Wild Ventures announced itself in April 2025, it closed a US$750,000 angel round at a post-money valuation of around US$7 million. The plan then was to use AI and sharp branding to shake up “stale but big” categories such as wallpaper, pet accessories and paper packaging, through brands including Wild Palace, Poositive Pets and Future Paper.

Miracly is a different animal. It is business software, sold to marketing teams rather than to shoppers.

The founding team helps explain the pivot, or at least the extension. Co-founders Arvid Lithander and Shannon Cullum come from advertising agencies including Saatchi & Saatchi, Wieden+Kennedy and MullenLowe, and are joined by Bjorn Bergstrom and Malin Renström. People who have spent careers fighting for share of voice tend to notice early when the arena moves.

The obvious sceptic’s question is whether a startup can build consumer brands and an enterprise software product at the same time on a pre-seed budget. The generous reading is that Wild Palace doubles as Miracly’s test lab: a live brand on which to try out AI visibility tactics before selling them to others. The company will need to show which reading is closer to the truth.

What Miracly actually does

Miracly said its software helps marketing teams track how brands appear on AI platforms and manage work across channels. In plain terms, it watches how chatbots and AI assistants describe and recommend a brand, then helps the team act on what it finds.

That second half matters. Monitoring alone is quickly becoming a commodity. The harder problem is turning “the chatbot thinks our rival is cheaper” into a coordinated response across a website, PR, social media and paid campaigns, without adding another tool to an already bloated.

Also Read: How earned media drives AI search visibility in ASEAN

Early customers include Acclime, an Asia-focused corporate services firm, and Pixi Beauty. The pairing is telling: one sells complex professional services, where buyers increasingly ask AI tools to compare providers; the other sells skincare, where an AI-generated product recommendation can make or break a purchase.

A crowded new channel

AI search is changing how brands are discovered online, handing marketers yet another channel to monitor and influence. When a user asks an assistant for the best accounting firm in Singapore or a moisturiser for humid weather, the answer usually arrives as a single synthesised reply rather than a page of links. A brand that is absent from that reply barely exists for that customer. It is why marketers have started asking what ChatGPT is saying behind their backs.

For Southeast Asia, the stakes are arguably higher. Discovery in the region is fragmented across languages, super apps and social commerce, and most brands already juggle multiple channels on thin teams. Adding AI answer engines to that mix is not optional, but it is not cheap either.

Miracly is far from alone. New York-based Profound is the category’s heavyweight: it raised a US$96 million Series C at a US$1 billion valuation in February, led by Lightspeed Venture Partners with Sequoia Capital and Kleiner Perkins participating, and serves more than 700 enterprise customers. Industry reports say it followed that with a US$180 million Series D at a US$1.8 billion valuation in September. Y Combinator-backed AthenaHQ, founded by former Google Search engineer Andrew Yan and ex-ServiceNow engineer Alan Yao, came out of stealth in 2025 with US$2.2 million in seed funding and pitches a cheaper, self-serve alternative.

Against that backdrop, US$2 million is a modest war chest. Profound has already moved from tracking into agents that execute marketing tasks, which is precisely the “manage work across channels” territory Miracly is claiming. Competing on features with a company that had raised more than 75 times as much even before its latest round is a losing game. Miracly’s better bet may be geography and price: mid-market brands and Asian firms that Silicon Valley’s enterprise-first players are not prioritising.

That may also be what BEENEXT, which has a long record of backing early-stage startups across Asia, sees in the deal.

The question nobody has answered

There is a deeper uncertainty hanging over the whole category. Search engine optimisation worked because Google published rules, however vague, and the results could be measured. AI models are less predictable: answers shift with model updates, phrasing and even the user’s location. Selling the ability to “influence” those answers requires proof that customers can repeat, not just a dashboard of anecdotes.

Also Read: AI didn’t replace Southeast Asia’s marketing agencies, it repriced them

And as AI platforms experiment with paid placements, the organic window that tools such as Miracly promise to optimise could narrow. If AI search becomes another pay-to-play channel, the winners may be ad buyers, not optimisers.

For now, marketers are paying to find out what the machines think of them. As AI agents rewrite the growth marketing playbook, Miracly’s challenge is to prove that knowing the answer is only half the job, and that it can deliver the other half.

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