For many small businesses in Malaysia, the challenge is not finding demand. It is finding working capital quickly enough to buy inventory, take on larger contracts, pay suppliers, or bridge the gap between completing a job and getting paid.

That financing gap is the problem Funding Societies is trying to address through a new working capital financing facility from Malaysia Debt Ventures (MDV), a subsidiary of Malaysia’s Minister of Finance. The facility will be deployed through Funding Societies’s platform to support technology-driven and underserved small and medium-sized enterprises (SMEs) in Malaysia.

The two organisations did not disclose the size of the facility. They described it as a multi-year arrangement that builds on a relationship dating back to 2022, when MDV first participated on Funding Societies’ platform to support technology-based SMEs.

Also Read: The SME finance reset: 3 steps to fix what’s breaking your growth

The latest facility is notable not because it introduces a new model, but because it deepens an existing public-private financing channel at a time when Malaysia is trying to move more SMEs up the value chain. Under the country’s New Industrial Master Plan 2030, one priority is to help businesses grow into stronger mid-tier companies, especially in technology-based and high-impact sectors.

MDV’s role is to provide flexible and specialised financing for technology companies. Funding Societies, meanwhile, brings a digital lending platform that uses alternative data to assess SMEs that may not have the long credit histories, collateral, or banking relationships required by conventional lenders.

Why digital SME lending matters

SMEs dominate Malaysia’s business landscape. They account for 96.1 per cent of business establishments, close to 39 per cent of gross domestic product, and roughly half of national employment. Yet many continue to face a familiar constraint: access to timely and appropriately sized financing.

Traditional banks remain central to SME credit, but their processes can be slow and documentation-heavy, especially for smaller businesses with fast-moving capital needs. Digital financing platforms aim to reduce that friction by using non-traditional data points, faster credit checks and more automated workflows.

In practical terms, this can mean assessing cash flow, transaction behaviour, invoices, platform activity, or other operating data alongside standard financial documents. The promise is not that every SME becomes creditworthy overnight, but that more viable businesses can be assessed with greater speed and lower servicing costs.

That distinction is important in Southeast Asia, where SME financing gaps remain stubborn despite the region’s rapid digitalisation. Many small businesses sell online, use e-wallets, manage procurement through digital tools, or transact through marketplaces, but their financing options have not always kept pace with how they operate.

Malaysia has a relatively developed financial sector compared with some of its neighbours, but underserved SMEs still fall through the cracks. These include young firms, small contractors, businesses with irregular cash flows, and companies in sectors where growth requires upfront spending before revenue is collected.

Funding Societies’ model sits in this gap. To date, it has disbursed close to MYR 7 billion (about US$1.71 billion) in financing to more than 10,000 businesses in Malaysia. MDV, established in 2002, has approved more than MYR 14 billion (US$3.42 billion) in financing for over 1,184 technology projects across high-impact sectors.

A multiplier for development finance

The MDV facility is designed to use Funding Societies as a distribution channel for developmental capital. Instead of financing one company at a time through a purely direct lending model, MDV can extend its reach by funding a platform that already has SME borrowers, underwriting systems and digital servicing capabilities.

“Financing a platform is a multiplier. One facility from MDV reaches thousands of businesses instead of one at a time,” said Chai Kien Poon, Country Head of Funding Societies Malaysia. “For MDV, that is development financing doing what it is meant to do at the scale and speed Malaysia’s SME economy actually needs.”

Also Read: Funding Societies raises strategic equity investment from Gobi Partners

That framing gets to the heart of why state-backed capital is increasingly working with fintech platforms across Southeast Asia. Governments and development finance institutions want to support SMEs, but direct lending can be operationally expensive when ticket sizes are small and demand is fragmented. Digital lenders, for their part, need reliable sources of capital to grow their loan books responsibly.

Sharul Sazman Samaan, Chief Business Officer of MDV, said the continued partnership reflects MDV’s confidence in fintech platforms as a way to widen financing access for technology-based SMEs.

“By supporting an established platform with strong reach and digital financing capabilities, MDV is able to channel developmental capital more efficiently to businesses with smaller, faster-moving financing needs,” he said.

The risk, as with any SME lending model, lies in credit quality. Faster approval and wider reach must be balanced against repayment discipline, especially in a higher-cost operating environment where SMEs face pressure from wages, supply chains and shifting consumer demand. The test for Funding Societies will be whether it can scale access while maintaining prudent underwriting.

Regional competition and Malaysia’s fintech lending field

Funding Societies operates in a competitive alternative financing market. In Southeast Asia, its closest regional peers include Validus, which also focuses on SME financing, and regional digital lenders and embedded finance players that work with marketplaces, corporates and supply-chain networks. In Malaysia, platforms such as CapBay and Fundaztic also serve SME or peer-to-peer financing needs, while banks are increasingly digitising their own SME lending processes.

Funding Societies’s advantage in Malaysia will depend less on being first and more on access to institutional capital, local credit data, repayment performance and its ability to serve SMEs that banks find too costly or complex to underwrite at scale.

What this means for Malaysia’s SME ambitions

The facility also highlights a broader shift in how SME development is being financed. Rather than treating fintech lenders as challengers sitting outside the financial system, institutions such as MDV are increasingly using them as partners to reach segments that conventional channels struggle to serve efficiently.

This is particularly relevant to Malaysia’s ambition to build more technology-based firms and stronger mid-tier companies. Businesses rarely move up the value chain through grants or equity alone. They also need working capital for machinery, software, hiring, receivables and expansion into new contracts.
If well deployed, the MDV facility could help more SMEs access financing at the point where growth is possible but cash flow is tight. That may not sound dramatic, but it is often the difference between a company staying small and being able to take on the next stage of growth.

Also Read: Funding Societies raises US$25M to further expand payments business in SEA

For Funding Societies, the arrangement strengthens its Malaysian lending base and reinforces the importance of institutional partnerships in fintech lending. For MDV, it extends the reach of development finance into a broader pool of smaller, faster-moving businesses.

The impact will ultimately be measured not by the announcement of the facility, but by how many SMEs receive capital, how effectively they use it, and whether repayment performance supports continued funding. In Malaysia’s SME economy, scale matters, but sustainable scale matters more.

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For all the attention paid to Southeast Asia’s digital economy, one of the region’s harder problems is far more physical: how to build enough clean power, quickly enough, for economies that are still growing, urbanising and industrialising.

PCG Global, a Singapore-based clean energy infrastructure platform, is trying to answer that question with a model it says has already been tested at scale in China. The company has closed a pre-Series A financing round led by GenZero, the Temasek-owned investment platform focused on decarbonisation, in its first external capital raise.

Also Read: New JV to power Southeast Asia with 500MW of renewable energy projects

The size of the round was not disclosed. PCG Global said the proceeds will be used to accelerate project origination and execution across Southeast Asia, Oceania and the Middle East — three regions where rising electricity demand, corporate net-zero targets and energy security concerns are pushing governments and businesses to add more renewable capacity.

The company already has its first operational project in Indonesia and is advancing utility-scale opportunities in the region. It currently has about 1.8GW of projects in various stages of development, covering distributed solar, utility-scale renewable plants, behind-the-meter storage and smart energy management.

A China playbook, adapted for international markets

PCG Global was founded in Singapore by the team behind PCG Power, which the company describes as one of China’s major distributed energy operators, with more than 2GW of operational assets.

Its international platform is built around a full-cycle model: develop projects, construct them, operate the assets, securitise them where possible, and reinvest the proceeds into new infrastructure. In practical terms, this means PCG Global is not positioning itself merely as a developer that exits once a project is built. It wants to manage the entire asset lifecycle, from financing and development to operations, carbon management and eventually recycling capital into new projects.

That distinction matters in Southeast Asia. Renewable energy projects often face bottlenecks not because demand is absent, but because execution is difficult. Developers must navigate land acquisition, grid access, offtake agreements, local permitting, currency risk and long development timelines. Smaller commercial and industrial solar projects can move faster, but they still require disciplined construction and asset management to deliver predictable returns.

“This round reflects institutional confidence in our ability to translate proven distributed energy capabilities into high-quality outcomes beyond China. We look forward to delivering lasting impact across our target markets,” said Li Wenxuan, Chairman and Chief Executive Officer of PCG Power.

For PCG Global, the question is whether a model refined in China’s vast renewables market can be localised across fragmented international markets. Southeast Asia, in particular, is not one market but a patchwork of regulatory regimes, power utilities, grid constraints and financing norms.

Why Southeast Asia is a difficult but attractive market

The region’s clean energy opportunity is large, but uneven. Indonesia, Vietnam, the Philippines, Malaysia, Thailand and Singapore all have different power market structures and different levels of openness to private renewable energy investment.

Also Read: Geopolitical uncertainty drives China’s export resurgence as clean energy finds new demand

Vietnam has already seen both the promise and the pain of fast solar deployment, with earlier feed-in tariff policies triggering a boom before grid bottlenecks and policy uncertainty slowed momentum.

Indonesia has enormous solar potential but remains heavily reliant on coal, while the Philippines has become one of the more active markets for private renewable energy developers.

At the same time, the commercial logic for renewables is getting stronger. Multinational manufacturers are under pressure to decarbonise supply chains, data centres are driving new electricity demand, and governments are trying to reduce exposure to volatile fossil fuel prices. For Southeast Asian countries competing for advanced manufacturing and digital infrastructure investment, access to reliable low-carbon power is becoming part of the investment pitch.

This is where distributed energy and behind-the-meter systems can be important. Instead of waiting for large grid-scale projects to be completed, companies can install solar and storage directly at factories, warehouses or commercial sites. Such systems typically sit “behind the meter”, meaning they supply power directly to the customer’s premises and can reduce reliance on grid electricity. Smart energy management software can then optimise usage, storage and costs.

PCG Global’s portfolio mix suggests it is targeting both ends of the market: smaller distributed assets that can serve commercial users, and utility-scale projects that can feed power systems at a larger scale.

GenZero’s bet on infrastructure execution

GenZero’s participation gives the round strategic weight beyond the capital itself. The Temasek-owned platform was set up to back solutions that can accelerate decarbonisation, including nature-based solutions, technology-based solutions and carbon ecosystem enablers. A renewable energy infrastructure platform with operational ambitions fits into that broader mandate, particularly if it can turn project pipelines into bankable assets.

Kimberly Tan, Head of Investments at GenZero, said the PCG team has demonstrated capabilities across capital management, project development and operational execution. “We believe they are well-positioned to expand globally, particularly in regions with significant demand for clean and resilient energy infrastructure,” she added.

For investors, clean energy infrastructure is attractive because operating assets can generate relatively stable long-term cash flows. But early-stage development is riskier. Many projects announced across emerging markets never reach financial close, and those that do can face delays in construction, interconnection or offtake. PCG Global’s ability to convert its 1.8GW pipeline into operational assets will therefore be the real test of the platform.

The competitive landscape

PCG Global is entering a crowded field. In Southeast Asia, renewable energy developers and operators include EDPR APAC, formerly Sunseap, which has a strong base in Singapore and regional solar projects; Cleantech Solar, which focuses on commercial and industrial solar across Asia; NEFIN, active in distributed solar; and larger regional players such as ACEN and Vena Energy, which develop utility-scale renewables across Asia Pacific. Global energy groups, infrastructure funds and Japanese trading houses are also competing for projects, offtake agreements and acquisition opportunities.

PCG Global’s point of differentiation will likely hinge on whether it can combine Chinese distributed energy operating experience with local execution in each market. Scale alone is not enough. In Southeast Asia, the winners are often the companies that can build local partnerships, manage regulatory complexity and offer customers financing structures that reduce upfront costs.

From capital raise to construction

PCG Global is headquartered in Singapore and operates under independent governance, with development teams across its target markets. Singapore is a natural base for such a platform: it has limited domestic space for large-scale renewables, but it is a regional hub for climate finance, infrastructure investors and corporate clean energy procurement.

Also Read: The hard truth about Asia’s energy future: Why we need a new class of sovereign alternatives

The company’s first external funding round comes as Southeast Asia’s energy transition moves from ambition to implementation. Governments have set targets, companies have made pledges, and investors have raised climate capital. The harder work now lies in turning pipelines into projects that actually produce power.

For PCG Global, the GenZero-led round is an opening move. The larger story will be written in permits secured, megawatts connected, customers signed and assets operated over time. In a region where clean power demand is rising faster than many grids can adapt, execution will matter more than announcements.

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Inside most organisations, the phrase strategic priority is treated as though it describes an objective fact. It sounds neutral, disciplined, and almost beyond debate. Leaders say an initiative is a strategic priority as if they are simply recognising reality. In practice, that phrase usually hides a far messier process.

Initiatives are not chosen only because they are the most important. They are chosen because enough powerful people can support them, defend them, fund them, explain them, and absorb the consequences of backing them. That is a very different test.

This matters because many capable operators misread how companies make big decisions. They believe the best idea should rise through evidence, logic, and business value. Sometimes it does. More often, initiatives rise because they fit the organisation’s current mood, protect leadership from regret, align with visible narratives, and feel governable enough to survive internal scrutiny. The work is not just to prove merit. The work is to become choosable.

Strategic priority is not a ranking of importance

One of the first mistakes people make is assuming strategic priority means the organisation has identified the most economically valuable or mission critical work. That is a comforting idea, but it rarely survives contact with real decision making.

In reality, strategic priority usually reflects a blend of factors. Some are commercial. Some are political. Some are operational. Some are reputational. Some are deeply human. The chosen initiative may indeed matter, but it is often not selected because it is the single best use of capital in an abstract sense. It is selected because it sits at the intersection of urgency, sponsor strength, organisational readiness, executive incentives, and narrative fit.

The organisation is not choosing ideas, it is choosing consequences

A more realistic way to understand strategic choice is this. Organisations do not choose initiatives in the abstract. They choose the consequences that come with them.

Every proposed initiative carries an entire package around it. It brings budget implications, visibility, implementation burden, executive ownership, dependency risk, delivery uncertainty, and political exposure. Even the strongest business case has to travel with those realities.

That is why some initiatives with obvious value still struggle to become priorities. Their consequences feel difficult. They require cross-functional coordination that nobody wants to own. They surface uncomfortable trade-offs. They create visible disruption before results appear. They require leaders to admit previous decisions were insufficient. They may be strategically correct and still remain institutionally unattractive.

Also Read: Architecting the future: A strategic guide to building an internal AI academy

By contrast, some weaker initiatives move forward because their consequences are easier to manage. They fit existing reporting structures. They can be launched without major conflict. They create the appearance of momentum. They align neatly with what the leadership team already wants to say externally or internally. They are easier to package as progress.

Executive attention is not allocated rationally

Much of what becomes strategic is shaped by a simple constraint that is often underplayed in planning conversations. Executive attention is scarce, and it is not allocated like a clean portfolio model.

Leaders are drawn towards some initiatives and away from others for reasons that are rarely written in formal documents. Some issues feel timely because investors, regulators, customers, or the Board are already asking about them. Some feel attractive because they offer visible progress within a leadership cycle. Some feel safe because they have precedent. Some feel energising because they allow executives to project confidence and direction. Others feel heavy, ambiguous, slow, or difficult to explain, so they drift even when their long-term value is clear.

This is one reason why timing can matter as much as quality. The same initiative can be ignored one quarter and embraced the next, not because the underlying economics changed dramatically, but because the surrounding political conditions did. A regulatory incident, a public breach, a missed target, a new executive arrival, or a shift in cost pressure can suddenly make an old idea feel strategically urgent.

The best initiative does not always win. The best sponsored one often does

There is a tendency to talk about sponsorship as if it were just a helpful accelerator for a good idea. In reality, sponsorship is often part of what makes an initiative viable in the first place.

A serious initiative needs someone with enough credibility and institutional weight to carry it through resistance. That means handling objections, negotiating trade-offs, absorbing criticism when execution stumbles, and ensuring the work continues to matter once the initial announcement has passed. Without that sponsorship, even strong initiatives can stall in the gap between approval and sustained commitment.

This is where many organisations quietly reveal how decisions are really made. The initiative that wins is not always the one with the clearest long-term logic. It is often the one with the strongest coalition behind it. Someone important wants it. Enough people can align around it. The narrative around it is coherent. The owner is seen as capable of making it real. The internal politics are survivable.

Strategic priority often goes to what can be narrated cleanly

One of the least discussed features of initiative selection is narrative clarity. Leaders back what they can explain.

Also Read: ESG as strategic value: Why Asian boards must move beyond disclosure

An initiative that can be described in simple, defensible terms has a major advantage over one that is genuinely important but harder to package. If the proposition is easy to translate into Board language, investor language, customer language, or staff language, it travels better. It acquires momentum faster because fewer people have to interpret it from scratch.

This is why some broad programmes gain priority even when their delivery model is vague. Their story is strong. They stand for something that leadership wants associated with the company. Efficiency. Resilience. AI adoption. Customer trust. Simplification. Platform modernisation. Cost discipline. Each of these can become a strategic umbrella under which many different motives sit.

What gets chosen is often what looks governable

An initiative may be highly attractive in principle and still lose if it feels too sprawling, too cross-functional, too dependent on uncertain external factors, or too difficult to measure. Leaders are not only asking whether the initiative matters. They are asking whether they can monitor it, steer it, explain delays, and intervene when things go wrong.

This is where many ambitious ideas fail. They are directionally right but operationally loose. Nobody can tell where ownership truly sits. Dependencies are large and unclear. Benefits depend on behavioural change across teams that have other incentives. Milestones are fuzzy. The initiative looks like a good aspiration but a poor management object.

The portfolio is shaped by who bears the pain

Every priority creates winners and losers. Some teams gain budget, status, and visibility. Others inherit more work, more scrutiny, and more dependency. Some leaders get credit for ambition while others absorb delivery burden. This distribution is rarely discussed openly, but it heavily influences which initiatives become acceptable.

If the pain is concentrated in parts of the business with weak political voice, approval is often easier. If the pain lands on powerful functions, strategic resistance rises quickly. That resistance may be expressed in rational terms about sequencing, readiness, or capacity. Often those concerns are real. They are also part of the politics.

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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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Singapore companies are moving quickly from experimenting with artificial intelligence to letting it perform multi-step tasks with limited human intervention. But a new study by Sumsub and the Singapore Fintech Association suggests many businesses still cannot answer a basic question: what exactly did the AI decide, and can they prove it?

According to the Sumsub APAC State of Digital Trust: AI Governance Benchmark report, 94 per cent of Singapore businesses are using or piloting multi-step AI systems, often described as agentic AI. Unlike simple chatbots or copilots, agentic AI can plan, take actions across systems, trigger workflows, and make decisions with varying degrees of autonomy.

Also Read: Razer and NUS launch Singapore AI lab to rethink how games respond to players

That shift matters because AI is no longer just helping employees draft emails, summarise documents, or analyse data. In some organisations, it is moving into operational workflows, compliance checks, fraud monitoring, risk screening, customer service, and other areas where mistakes can carry financial, legal, or reputational consequences.

Yet only 29 per cent of organisations can produce an audit trail for AI-driven decisions, according to the study. Sumsub calls this gap “Accountability Asymmetry”: companies may own the consequences of AI decisions, but many cannot reconstruct or explain how those decisions were made.

“Everyone is focused on how quickly AI is advancing, but the bigger question is whether governance is keeping pace,” said Holly Fang, President of the Singapore Fintech Association. “As AI moves beyond copilots into autonomous agents handling increasingly critical workflows, the focus now should be on building the traceability, accountability and governance needed to deploy AI at scale.”

A cautious market, not a slow one

The findings complicate the usual narrative that Southeast Asian businesses are racing into AI with little restraint. Singapore, in particular, appears to be moving deliberately.

Only 16 per cent of Singapore businesses significantly increased the scope or autonomy of their AI systems over the past year, the most measured deployment rate among the APAC markets surveyed. The report frames this not as hesitation, but as caution in a market where regulators, banks, fintechs, and enterprise buyers are asking harder questions about risk.

Singapore scored 65.6 on the report’s overall AI governance benchmark, slightly below the APAC average of 67.1. At first glance, that might suggest the country is lagging. But the report argues the opposite: Singapore’s more mature regulatory environment has given companies a clearer yardstick, making them more conservative in judging their own readiness.

Earlier in 2026, Singapore launched governance guidance for AI agent use through its Model AI Governance Framework for Agentic AI. This means local firms are being pushed beyond broad policy statements and towards more technical questions: Who authorised an AI agent? What systems did it access? Which data did it use? What action did it take? Who is accountable if something goes wrong?

In other words, Singapore businesses may be less willing to claim readiness unless they can back it up.

“Prudence, rather than a lack of strategic intent, defines how the enterprises are scaling AI agents,” said Penny Chai, Vice President for APAC at Sumsub. “When financial liabilities are on the line, immature traceability systems create an unacceptable operational risk.”

Governance is becoming an infrastructure problem

The study evaluates businesses across three dimensions: autonomy, responsibility, and traceability. Autonomy measures how far AI systems are already acting independently. Responsibility looks at whether ownership of outcomes is clearly assigned. Traceability examines whether decisions can be reconstructed and explained.

Singapore performs relatively well on responsibility. Seventy per cent of businesses maintain explicit guidelines assigning direct responsibility for AI outcomes, split between a specific person at 40 per cent and a team at 30 per cent. That matches the APAC average.

Also Read: What AI safety researchers actually worry about

The weakness lies in evidence. Having a policy that names an accountable person is not the same as having system logs, identity verification, access records, model activity histories, and decision pathways that can stand up to scrutiny from regulators, customers, or internal risk teams.

This is where agentic AI creates a new problem. Traditional enterprise software usually follows predictable rules. Human users click buttons, systems record actions, and responsibility can often be traced through access controls and approvals. Agentic systems are more fluid. They can chain tasks together, call external tools, act on outputs from other models, and operate across platforms. Without proper monitoring, the decision path can become blurred.

For Singapore’s financial services and fintech sectors, this is not an abstract concern. AI is already being applied to fraud detection, anti-money laundering checks, customer due diligence, credit workflows, and risk monitoring. The report found that Singapore businesses see the greatest real-world impact from AI in data-related tasks at 29 per cent, operations and workflow processing at 21 per cent, and security applications such as fraud detection, AML, and risk monitoring at 15 per cent.

These are precisely the areas where an unexplained decision can become costly.

Southeast Asia’s uneven AI governance map

Across APAC, the study shows how regulation shapes business behaviour. Thailand leads the benchmark at 70.3, followed by the Philippines at 69.6, with the report linking their performance to early alignment with strict digital laws and business requirements.

India scored 68.5, China 68.0, Hong Kong and Australia both 66.7, Indonesia 66.0, and Malaysia 62.4. Malaysia’s lower score reflects a market preparing for an incoming AI Governance Bill, rather than one operating under fully settled rules.

For Southeast Asia, the broader lesson is that AI governance will not be solved by adoption alone. The region has a large base of digital-first consumers, fast-growing fintech and e-commerce sectors, and governments keen to use AI to improve productivity. But it also has fragmented regulatory regimes, uneven enterprise infrastructure, and varying levels of technical capacity across markets.

Highly regulated industries appear to be ahead. Financial services topped the sector index at 69.6, supported by rigid compliance standards and 68 per cent audit trail adoption. IT and software services followed at 68.8, although the report warns that rapid deployment could outpace governance.

By contrast, e-commerce scored 65.4, while mobility and delivery platforms came last at 64.4. These sectors often prioritise speed, conversion, routing efficiency, and customer experience. But as AI systems begin making operational decisions at scale, weak oversight could create blind spots in pricing, fraud handling, worker allocation, refunds, or dispute resolution.

From AI policy to proof

Singapore businesses are aware of the technical hurdles. The report identifies their top engineering priorities as managing model complexity at 66 per cent, integrating AI systems smoothly across platforms at 50 per cent, and tracking actions taken by third-party or external AI tools at 49 per cent.

That last point is especially important. Many companies do not build every AI tool in-house. They rely on external models, software vendors, cloud platforms, and specialised agents. If those systems act inside a company’s workflow, businesses still need a way to link each action back to an authorised AI agent and a responsible human overseer.

Also Read: Why Southeast Asia cannot build sovereign AI on borrowed choices

The appetite for such infrastructure appears strong. Ninety-eight per cent of Singapore businesses said they are ready to adopt a third-party verification solution that ties autonomous AI actions back to a verified identity network.

For regulators and enterprises, the next phase of AI governance will likely be less about writing principles and more about proving compliance in real time. Singapore’s approach, including initiatives such as MAS’ Safeguards for Agentic Finance at Runtime, points to a future where AI systems need operational guardrails, not just ethics statements.

The report’s message is clear: agentic AI is already entering the enterprise. The harder task now is making sure every automated decision leaves a trail.

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The narrative of the great leader has long been synonymous with the great martyr. We’ve spent decades promoting the masochistic archetype—the leader who stays latest, suffers loudest, and equates their personal exhaustion with professional worth.

But in the modern era of work, this grind is no longer a badge of honour. It is a talent liability. Gallup’s 2024 State of the Global Workplace report reveals that while engagement is stagnant, the cost of replacing disengaged talent is rising to nearly 18 per cent of an employee’s annual salary, making sustainable leadership a financial imperative.

When we lead through personal suffering, we inadvertently invite the four horsemen of the professional apocalypse into our organisational culture.

The four horsemen of the professional apocalypse

• The horseman of martyrdom: This is the belief that commitment is measured by sacrifice. Harvard Business School research on “emotional contagion” shows that a leader’s burnout doesn’t stay personal—it spreads to the team immediately, creating a culture of collective exhaustion.
• The horseman of urgency: When everything is a priority, nothing is. According to a study published in the Journal of Organisational Psychology, leaders trapped in urgency culture experience a 37 per cent decline in decision-making quality as rapid-fire reactions replace thoughtful analysis.
• The horseman of isolation: The “it’s faster if I do it myself” mentality. This hoards stress at the top while depriving the team of psychological safety, the number one predictor of high-performing teams.
• The horseman of endurance: The obsession with input over output. In a knowledge economy, hours worked are a poor proxy for value. Companies that cling to these measures risk losing top talent—especially younger workers—who prioritise flexibility and autonomy.

Also Read: 7 leadership skills every manager needs in a monitored workplace

The talent pivot: Hiring the four agents of growth

To survive in a talent-first landscape, we must systematically fire the Horsemen and replace them with the four agents of growth.

• The agent of white space (strategic rest)

In an age of AI, a leader’s value is in thinking, not just doing. Neuroscience suggests that structured “nothingness” allows the brain’s Default Mode Network (DMN) to connect disparate ideas and form original strategic thoughts. Rest is not a reward; it is a strategic tactic for improved performance.

• The agent of systems (prevention over reaction)

We must stop hero-worshipping the firefighter. High-performing organisations focus on “architects”—those who build systems that prevent turnover and crisis. Trust and stability are now recognised as the primary drivers of organisational effectiveness.

• The agent of delegation (psychological safety)

Masochistic leaders hoard stress to feel essential. Growth leaders distribute responsibility to make the team essential. Moving from being a bottleneck to a catalyst creates an environment where employees feel safe to admit mistakes and innovate without fear of punishment.

• The agent of outcomes (impact over input)

Evidence from reduced working hour trials shows that focusing on outcomes over hours increases well-being and engagement without sacrificing productivity. By valuing results, you attract the 51 per cent of employees willing to switch industries for greater autonomy.

The bottom line

The masochistic archetype is a relic of an industrial age. In the creative age, longevity is the new competitive advantage. The question is no longer “How much can you take?” but “How much can you grow?”

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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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Southeast Asia’s insurance industry has quietly turned into one of the region’s most crowded battlegrounds for digital disruption. Behind traditional insurers’ glossy premiums and paperwork-heavy claims lies a swarm of insurtechs rewriting the rules, some genuinely solving access gaps, others simply repackaging old products with an app and an API.

From Singapore’s bolttech and Igloo pushing embedded insurance into e-commerce checkouts, to Indonesia’s PasarPolis and Qoala betting on microinsurance for underserved consumers, the region’s insurtech map spans at least six countries and just as many business models. Thailand’s Sunday and Roojai lean on AI for pricing; Malaysia’s PolicyStreet, Senang, and Ouch! chase Takaful and MSME niches; Vietnam’s INSO and Papaya digitise claims and corporate benefits.

Also Read: What Southeast Asia can learn from Europe’s insurtech revolution

Then there’s the stranger fringe: “death tech” platforms like Kamboja and Bereev monetising end-of-life planning, wellness-insurance hybrids like Rey.id, and infrastructure plays like Agiliux and Finology quietly powering the back end for everyone else.

The pitch is always the same: insurance is broken, and technology fixes it. Whether that holds up under scrutiny, or whether it’s just VC-funded distribution dressed up as innovation, is the real story worth unpacking.

bolttech (Singapore)

Founded in 2020, bolttech operates a digital, cloud-based embedded insurance platform that connects insurance providers, distributors, and customers across more than 35 markets globally.

Igloo (Singapore)

Established in 2016, Igloo provides AI-powered digital infrastructure, big data, and real-time risk assessment to help businesses embed and distribute affordable insurance products across Southeast Asia.

PasarPolis (Indonesia)

Set up in 2015, PasarPolis acts as a full-stack platform focused on affordable microinsurance, using digital tools to streamline policy purchases and fast-track claims for everyday and underserved consumers

Sunday Insurance (Thailand)

Founded in 2017, Sunday uses artificial intelligence, machine learning, and data science to offer personalised motor, health, and business insurance products.

PolicyStreet (Malaysia)

PolicyStreet, started in 2016, provides digital insurance solutions, financial advisory, and underwriting services partnering with over 40 life, general, and Takaful providers to serve consumers and businesses across Southeast Asia and Australia.

Also Read: PolicyStreet targets gig workers and SMEs after lifting Series C round to US$26M

Roojai.com (Thailand)

Launched in 2016, Roojai provides direct-to-consumer (D2C) non-life insurance products, including car, EV, motorbike, personal accident, cancer, and travel insurance, operating in Thailand and Indonesia.

Bang Jamin (Indonesia)

Bang Jamin offers transparent vehicle, health, and travel coverage, aiming to process policy issuance and claims within 24 hours

Rey.id (Indonesia)

Rey.id operates a subscription-based mobile application that combines life and health insurance, outpatient and inpatient care, and wellness features into an integrated ecosystem for individuals, employers, and insurers.

Blacaz (Singapore)

Blacaz is an active insurance broker and digital insurtech company that focuses on making business insurance simple and accessible for startups and SMEs, offering corporate covers like health benefits, professional indemnity, and director liability.

Senang (Malaysia)

Senang is an embedded digital financial platform founded in 2018. It provides affordable microinsurance, on-demand coverage, and financial services targeted at micro, small, and medium-sized enterprises (MSMEs), the gig economy, and everyday consumers.

Qoala (Indonesia)

Launched in 2018, Qoala uses digital technology, big data, and machine learning to make insurance simple, affordable, and accessible.

Also Read: In Indonesia, the problem is lack of insurance accessibility, not affordability: Qoala CEO

Teleskop Technologies (Singapore)

Started in 2023, Teleskop provides a digital wealth-tracking and legacy-planning platform designed to help individuals, financial advisors, and institutions aggregate, analyse, and manage both financial and non-financial asset portfolios.

GetDoc (Singapore)

GetDoc is designed to connect patients instantly with medical practitioners and clinics, primarily operating across Singapore and Malaysia.

INSO (Vietnam)

Launched in December 2018 and headquartered in Hanoi, INSO lets users buy customised insurance policies, self-assess assets, and process claims automatically through a smartphone app. It operates as a venture under the NextTech Group.

Vouch Insurtech (Singapore)

Set up in 2016, Vouch is a P2P car insurance platform that let safe drivers form groups to earn cash-back rebates up to 15 per cent on annual premiums if no claims were made. It partnered with major insurers like NTUC Income, Sompo, and Tokio Marine

Finology (Malaysia)

Finology specialises in embedded finance, providing API-driven software solutions that allow banks, insurance firms, and non-financial consumer businesses (like property developers and car distributors) to offer instant loan and insurance approvals.

Papaya Insurtech (Vietnam)

Launched in 2018, Papaya digitises health and life insurance administration. It connects insurance companies, healthcare providers, and corporate clients through a cloud-based platform, streamlining employee benefits and automating medical claims.

Kamboja (Indonesia)

Kamboja is an integrated funeral and end-of-life planning insurtech platform. Founded in January 2021, it digitises and coordinates comprehensive death-care services, easing both the emotional and financial burden of funeral arrangements for grieving families.

DearTime (Malaysia)

Founded in 2019 and based in Kuala Lumpur, DearTime provides pure protection life insurance products entirely through a mobile and web application without traditional agents or medical checkups

Ouch! (Malaysia)

Ouch! operates as a digital takaful (Islamic insurance) platform. It aims to make financial protection simple and paperless by removing agents and long forms, offering affordable coverage options starting from RM4.13 per month via its mobile app.

Also Read: Ouch! nets US$1.2M to expand market share, drive insurance innovation

Bereev (Malaysia)

Bereev is a Malaysian “death tech” and legacy planning startup founded in Kuala Lumpur in 2018. It provides an online platform and app designed to help individuals organise their personal, financial, and end-of-life plans, making it easier for families to handle logistics and estate tasks after a loved one passes away.

Checkup (Singapore)

Launched in 2023, Checkup builds proprietary cloud- and AI-powered health tracking APIs that allow large digital platforms and insurance companies to monitor and track user health metrics with or without wearable devices.

Agiliux (Singapore)

Agiliux is an AI-native, cloud-based core platform and system of record designed for modern insurance brokers, managing general agents (MGAs), and insurance providers. It replaces fragmented legacy software and spreadsheets to unify operations including policy administration, submissions, quoting, claims, accounting, and compliance.

Gigacover (Singapore)

Gigacover is a Singapore-founded insurtech platform established in 2017 that provides flexible, digital insurance and financial health benefits tailored for freelancers, gig economy workers, and SMEs in Southeast Asia. It bridges the gap by offering safety nets typically reserved for traditional corporate employment.

The post Health, wealth, and legacy planning converge as new wave of SEA insurtechs emerges appeared first on e27.

Indonesia’s roads are a hard place to run a logistics business. Trucks move across thousands of islands, ports and industrial estates, often through traffic, poor visibility and thin margins. For fleet operators, knowing where a vehicle is today is useful. Knowing when it will break down, which route will waste fuel, or where delays will hit next is more valuable.

That is the problem McEasy is now trying to solve at a larger regional scale. The Indonesian fleet management startup has closed a US$9 million Series B round, combining equity and venture debt, to expand across Southeast Asia and deepen its use of machine learning in commercial vehicle operations.

Also Read: IoT-powered logistics platform McEasy extends Series A round

The round was led by Singapore-based Integra Partners, with venture debt from InnoVen Capital. It follows earlier backing from East Ventures and Granite Asia, two investors with deep exposure to Southeast Asia’s startup ecosystem.

According to reports, the equity tranche stood at US$6 million, giving Integra Partners a 13.4 per cent stake in McEasy and making it the company’s third-largest shareholder after East Ventures and Granite Asia. For a logistics software company operating in a market where many startups still prioritise growth over financial discipline, McEasy’s profitability appears to have been a major part of the attraction.

From vehicle tracking to prediction

Founded in Indonesia, McEasy serves more than 2,000 fleet operators and tracks around 350 million kilometres of commercial vehicle movement every month. That scale gives the company a large operational dataset across trucks, vans and other commercial vehicles, a foundation it now wants to turn into predictive tools.

Fleet management software has traditionally been built around visibility: GPS tracking, driver behaviour, fuel monitoring, route history and digital records. These features help companies move away from manual processes and reduce leakage in day-to-day operations.

McEasy’s next step is to move from recording what happened to predicting what is likely to happen next.

Co-founder and CEO Raymond Sutjiono has said the company wants to transform vehicle data into models that can forecast future events for fleet operators. In practical terms, this could include predicting maintenance needs before a breakdown happens, recommending more efficient routes, identifying risky driving patterns, or spotting operational bottlenecks before they hurt delivery schedules.

For Southeast Asian logistics firms, that shift matters. The region’s supply chains are becoming more complex as e-commerce, manufacturing, cold-chain distribution and cross-border trade continue to grow. At the same time, many transport operators remain small or mid-sized businesses with limited access to advanced technology. A platform that can reduce downtime, fuel waste and route inefficiency could have a direct impact on margins.

Profitability sets it apart

The funding also comes at a time when investors in Southeast Asia have become more selective. After the cheap-capital years of 2020 and 2021, VCs have pushed portfolio companies to show stronger unit economics, clearer paths to profitability and more disciplined spending.

McEasy appears to fit that newer investor preference. The company has reached EBITDA profitability with a double-digit margin and recorded nearly doubled annual recurring revenue over the past year.

For software companies serving enterprises and small businesses, annual recurring revenue is an important signal because it shows how much predictable income comes from subscriptions or repeat contracts. In McEasy’s case, that growth suggests that fleet operators are not only adopting the platform but continuing to pay for it as part of their daily operations.

Also Read: East Ventures injects US$1.5M into vehicle management and tracking startup McEasy

Jennifer Ho, Partner at Integra Partners, said McEasy’s growth had been consistent and reliable, driven by a land-and-expand strategy. That approach typically means winning a customer through one product or use case, then increasing revenue from that account by adding more services, vehicles, features or business units over time.

The fact that McEasy reached profitability before raising this round also gives it a different profile from many venture-backed logistics startups, which often need heavy capital to scale operations. McEasy is not buying trucks or building warehouses; it is selling software into a sector where digitisation is still uneven.

Why Indonesia is a strong launchpad

Indonesia is a demanding but attractive home market for a fleet technology company. It is Southeast Asia’s largest economy, with a population of more than 270 million and a geography that makes logistics both essential and difficult. Goods move across Java’s dense industrial corridors, Sumatra’s plantations, Kalimantan’s mining routes and an archipelago of ports and secondary cities.

That fragmentation creates inefficiency but also opportunity. Fleet operators must manage fuel costs, vehicle maintenance, driver safety, delivery windows and compliance across routes that can be unpredictable. Even modest improvements in vehicle utilisation or maintenance planning can produce meaningful savings.

This is where McEasy’s nine-year dataset could become a competitive advantage. Machine learning systems need large, relevant and clean datasets to become useful. The more vehicles, kilometres and operating conditions a platform sees, the better it can identify patterns. In a region as varied as Southeast Asia, local data matters because road conditions, driver behaviour and logistics networks differ sharply from those in the US or Europe.

If McEasy can train models on Indonesia’s real-world fleet activity and adapt them to neighbouring markets, it may be able to offer more relevant insights than global platforms built primarily for developed markets.

A crowded but underpenetrated market

McEasy is not alone in chasing this opportunity. The fleet management and telematics market includes global players such as Samsara, Geotab and Verizon Connect, which offer connected vehicle software, safety analytics and asset tracking. South Africa-born Cartrack, now part of Nasdaq-listed Karooooo, also has a presence in several Asian markets, while Indonesia has local competitors such as TransTRACK targeting transport digitisation.

The competitive question for McEasy is not simply whether it can track vehicles. Many companies can. The harder challenge is whether it can combine local market knowledge, reliable hardware integration, software usability and predictive analytics in a way that fits Southeast Asian operators’ budgets and workflows.

Global platforms may have deeper resources, but local players often understand procurement habits, service expectations and on-the-ground pain points better. In fleet technology, support can matter as much as the dashboard.

Regional ambitions

With the new funding, McEasy plans to expand beyond Indonesia into Southeast Asia. The company has not specified which markets it will enter first, but the regional opportunity is clear.

Thailand and Vietnam have large manufacturing and logistics sectors. Malaysia and Singapore are important trade and distribution hubs. The Philippines, like Indonesia, faces archipelagic logistics challenges. Across these markets, fleet operators are under pressure to improve delivery reliability while controlling costs.

The question is how easily McEasy’s Indonesia playbook can travel. Each market has different regulations, transport structures, customer expectations and competitive dynamics. Expansion will likely require local partnerships, sales teams and product localisation rather than a simple copy-and-paste approach.

Also Read: More parcels, less profit: Logistics’ big squeeze

Still, the timing may be favourable. Southeast Asia’s logistics sector has already gone through one wave of digitisation, driven by e-commerce and on-demand delivery. The next wave is likely to be more operational: better asset utilisation, predictive maintenance, fuel efficiency and data-driven decision-making.

For McEasy, the Series B round is not just growth capital. It is a bet that fleet management in Southeast Asia is moving from visibility to intelligence — and that the companies which own the best operational data will shape how the region’s vehicles move.

The post Indonesia’s McEasy secures US$9M to move fleet management from tracking to prediction appeared first on e27.

Every week, another AI tool launches with the promise of helping businesses save time, reduce costs and grow faster.

Companies subscribe. Employees attend workshops. Leadership teams announce that the organisation is now “AI-enabled”.

Yet months later, many businesses are still facing the same problems.

Founders remain buried in operations. Teams continue chasing approvals through email. Customer enquiries are manually routed. Marketing work is duplicated across platforms. Decisions still depend on one or two key people.

The organisation may have more AI tools, but it does not necessarily have better operations. That distinction matters.

AI can help a business reduce costs and increase revenue, but only when it changes how work moves through the organisation. Simply giving employees access to a chatbot rarely creates meaningful transformation.

The technology is not the problem. The operating model is.

AI tools improve tasks, AI operations improve businesses

Most organisations begin their AI adoption journey at the task level.

Someone uses AI to write an email. A marketer generates social media ideas. A sales executive asks AI to improve a proposal. A manager uses it to summarise a meeting.

These are useful productivity gains. They may save several minutes or even a few hours. But they remain isolated activities.

Once the task is completed, the employee returns to the same workflow, the same approval structure and the same operational bottlenecks. The task became faster. The business did not necessarily become better.

The more important question is not: “How can AI help us complete this task?” It is: “How should this work move through the organisation if AI were part of the operating model from the beginning?”

That shift moves the conversation from productivity to organisational design.

Instead of asking AI to assist occasionally, leaders are beginning to look at how humans, automation, and specialised AI agents can work together across entire business functions. That is where the real gains begin.

From one assistant to an AI operating layer

Across several of my businesses, we stopped treating AI as software that someone opens whenever they need help. Instead, we began designing operations around specialised AI agents.

Different agents support different functions, including administration, operations, class delivery, marketing, retention and customer support.

At the centre is Seraphina, my AI chief of staff and digital twin. I often describe her as my AI co-founder because she does more than respond to a single instruction.

We discuss ideas. She helps structure the execution. She assigns tasks to specialised agents. She reviews their work. She identifies gaps. She analyses the outcome before the work reaches me.

Also Read: ASEAN doesn’t need to win the AI race, it needs to run it together

My role is to oversee Seraphina, who, in turn, oversees the broader AI crew.

This is very different from using a general AI tool to complete one task at a time. A tool waits for an instruction. An agentic operating model can coordinate, execute, review and improve a process across multiple stages.

That does not remove humans from the organisation. It changes where humans create the most value.

The biggest return is not speed, it is a thinking space

Many conversations about AI focus on how many hours it can save. That matters, but I do not believe it is the biggest return.

The bigger return is thinking space.

When founders and leaders spend less time on repetitive execution, they have more capacity to think at a macro level. They can focus on strategy, partnerships, product development, positioning, customer experience and long-term growth.

Without that space, leaders often become trapped at the micro level. They are answering messages, correcting documents, following up with teams, checking small details, and repeatedly solving the same operational issues. They may be working extremely hard, but they are not necessarily moving the business forward.

AI can help shift leaders from micro-execution to macro-direction. But that only happens when the work has been designed properly.

If a founder is still involved in every step, every approval and every exception, AI becomes another tool that the founder personally has to manage. The founder remains the bottleneck.

Founders need to understand both the macro and the micro

There are two levels to every business process.

The macro level describes the overall journey. For example, a customer discovers the business, makes an enquiry, receives information, makes a purchase, goes through onboarding, and eventually receives ongoing support.

The micro level includes every action within that journey.

Who responds to the enquiry? Where is the customer information recorded? What happens when the person does not reply? Who approves a discount? Which message is sent after payment? What happens if the payment fails? When should a human step in?

Leaders need to understand both levels. At the macro level, they need to see how the process supports the wider business objective. At the micro level, they need enough detail to delegate, automate and maintain quality.

This has always been important for scalability, even before the age of AI. A founder who cannot explain how work gets done will struggle to delegate it to a human team. The same is true with AI.

The difference is that AI makes poor process design much more visible.

Also Read: The new border: Why server farms are the battleground of AI sovereignty

AI does not fix chaos, it scales it

One of the biggest misconceptions about AI is that it automatically creates efficiency. It does not.

If a process is unclear, AI cannot magically make it clear. If different team members use different methods, AI may simply reproduce that inconsistency more quickly. If nobody knows who owns a decision, an automated workflow may move the problem around rather than solve it.

Many companies believe they have an AI adoption problem. What they actually have is an operational clarity problem.

AI does not only automate good processes. It can also automate confusion, duplication and unnecessary work.

That is why leaders should not begin by asking which tasks they can automate. They should begin by understanding the process itself.

A practical way to document your processes with AI

Many founders know their business well but struggle to document it. The process exists in their heads.

They know what to do because they have handled the same situation hundreds of times, but the steps, exceptions and decision points may never have been written down.

AI can help extract that knowledge. You do not need to begin with a blank document. You do not even need to type everything. You can speak to the AI and ask it to interview you through the process.

For example, you might say: “Ask me questions one at a time about how we handle a new customer enquiry. Start with the overall process, then go deeper into the detailed steps, decisions, tools, people involved and exceptions. Keep asking until you have enough information to create a complete workflow.”

Then answer naturally, as though you were explaining the process to a new team member.

Start with the macro view. Explain where the process begins, what the intended outcome is and which major stages are involved.

Then move into the micro view. Describe the specific actions, approvals, tools, timelines, handovers and possible problems.

At this stage, do not worry about speaking in perfect order. The goal is to capture the raw data.

Once the AI understands the process, ask it to organise the information into a structured flow. It can help turn your explanation into:

• A standard operating procedure
• A step-by-step checklist
• A workflow diagram
• A responsibility matrix
• An automation plan
• A list of decision points
• A quality-control framework

You can then review the process and identify which parts should remain human-led, which can be automated and which can be handled by AI agents. This is often a much easier starting point than writing an SOP manually.

A simple sequence is: Extract first. Structure second. Review third. Automate last. The order matters. Automation should not begin until the process is understood.

Delegation is becoming a core AI skill

For years, founders have been told that they need to learn how to delegate. That principle has not changed.

What has changed is who, or what, can be delegated. Today, a business process may be divided between:

• A human team member
• A specialised AI agent
• An automated system
• A founder or manager making the final decision

The leader’s job is to decide how these parts work together. That requires more than prompting skills. It requires operational judgement.

Leaders need to know what good output looks like, where the risks are, which decisions require context and where human oversight is essential. AI may perform the execution, but leadership still defines the direction, quality and boundaries.

In that sense, AI does not reduce the need for good management. It raises the standard.

Also Read: Why seniority is repricing in AI-augmented teams, and what gets valued instead

Reducing cost does not simply mean reducing headcount

When organisations discuss AI and cost reduction, the conversation often jumps immediately to replacing jobs. That is too narrow.

Cost is also created through delays, duplication, poor handovers, unnecessary meetings, manual data entry, missed follow-ups and founders spending time on low-value execution. AI can reduce these costs without removing the human contribution.

It can make customer support faster. It can shorten campaign production cycles. It can improve retention follow-ups. It can help sales teams respond more consistently. It can reduce the amount of time managers spend gathering information before making a decision.

These improvements can lead to both lower operating costs and higher revenue. A faster response may improve conversion. Better onboarding may reduce refunds. More consistent follow-up may improve retention. Better use of customer data may create more relevant offers.

The revenue impact does not come from AI alone. It comes from improving the process around the customer.

AI Crew is an operating model, not a collection of bots

This thinking has shaped what we are building through AI Crew.

The idea is not simply to give every department another chatbot. It is to create specialised AI agents with clearly defined responsibilities, working together within a coordinated operating structure and under human oversight.

An AI marketing agent should understand the marketing workflow. An AI customer support agent should understand escalation rules. An AI operations agent should understand how tasks move across the organisation. And there should be a coordinating layer that ensures these agents are not operating as disconnected tools.

That is the role Seraphina plays across my businesses. She acts as the chief of staff, connecting the different functions, while I remain responsible for the direction, decisions and final oversight.

This is still evolving, but I believe the broader model will become increasingly common.

The future may not be one employee with ten AI tools open in separate tabs. It may be a coordinated team of humans and AI agents working within a single, clear operating system.

The real AI advantage is organisational design

The companies that gain the greatest advantage from AI will not necessarily be those with access to the most advanced models. Most organisations will eventually have access to similar technology.

The competitive advantage will come from how well that technology is embedded into the business.

Can the organisation clearly explain how work moves? Can leaders separate macro strategy from micro execution? Can processes be delegated without losing quality? Can AI agents operate within defined roles and boundaries? Can humans focus on judgement, creativity, relationships and direction?

These are not primarily technology questions. They are leadership and operations questions.

AI is already here. The next phase is not simply about adopting more tools. It is about building organisations that know how to work with them.

AI will not cut costs or grow revenue simply because a company purchased it. Those outcomes happen when the business itself is redesigned.

—

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

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

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The post AI will not cut costs or grow revenue until you redesign how work gets done appeared first on e27.

For years, the promise of fintech in Southeast Asia was to take paperwork out of finance. Cash became wallet balances. Invoices moved from filing cabinets into enterprise resource planning systems. Payment approvals shifted from email chains to dashboards. The broad direction was clear: digitise what used to be manual.

But a new report by Sunrate and Mastercard argues that this phase is no longer enough. In “Beyond Automation: Defining Agentic Global Payments”, the companies describe a shift from automation to autonomy, a model where AI systems do not merely assist finance teams, but carry out tasks across the payment lifecycle within set boundaries.

Also Read: Airwallex doubles down on agentic commerce with US$320M funding round

The phrase used in the report is “Agentic Global Payments”. In plain terms, it refers to AI agents that can observe what is happening in a financial workflow, assess the best course of action, make decisions, and execute them. Rather than waiting for a human to approve every operational step, these systems can route payments, manage liquidity, flag exceptions, and optimise timing based on real-time conditions.

For Southeast Asian startups, particularly those expanding across borders, this could mark a significant change in how finance operations are built. The question is no longer whether payments can be digitised. It is whether they can increasingly run themselves.

From automation to autonomy

The report frames the evolution of payments in three broad phases.

The first phase was digitisation and rules-based automation. This is where much of the region’s fintech infrastructure has focused over the past decade: moving transactions online, automating batch processing, and replacing repetitive administrative work with software. A system could be told that if a condition was met, a payment should be triggered or a notification sent.

The second phase brought machine learning into financial operations. Instead of following only fixed rules, systems could identify patterns and make predictions. Fraud scoring, credit risk models, and customer segmentation tools all fit into this stage. These systems improved decision-making, but usually still required people to interpret outputs and act on them.

The third phase, according to the report, is agentic execution. This is where systems are designed to do more than recommend. They can act. An AI agent might monitor cash positions across currencies, compare payment rails, consider fees and settlement times, and execute the most suitable transaction path without waiting for a staff member to manually coordinate each step.

That distinction matters. Many companies already use automation, but most workflows still depend on human intervention when conditions change. If a supplier asks to be paid in another currency, if a payment rail becomes slow, or if foreign exchange volatility affects the timing of a transfer, someone in finance typically has to step in. Agentic systems aim to reduce that dependence.

Why manual coordination is becoming a bottleneck

The case for agentic payments is being driven by a widening gap between the speed of commerce and the capacity of finance teams.

Startups in Southeast Asia often scale across markets before they have large back-office teams. A Singapore-based company may sell into Indonesia, hire in Vietnam, source from China, bill clients in US dollars, and pay partners in multiple local currencies. Each market brings its own banking practices, tax rules, compliance expectations, payment rails, and settlement timelines.

Also Read: When the buyer is a machine: Why agentic commerce threatens the trillion-dollar advertising model

The result is operational complexity. Finance teams must reconcile accounts, manage cash flows, track foreign exchange exposure, approve payments, and respond to exceptions across markets. Even with modern software, much of this work still sits between systems. A treasury tool may not speak neatly to an enterprise resource planning platform. A payment gateway may not give enough visibility into liquidity. A banking portal may require separate manual checks.

This fragmentation is common in fast-growing companies. Tools are often added as needs arise, creating a patchwork of systems that solve individual problems but do not always provide a clear view of the whole financial operation.

The Sunrate and Mastercard report describes agentic AI as a move towards a “one brain” model for finance infrastructure. Instead of separate systems handing off partial information, an intelligent layer could coordinate decisions across payments, treasury, compliance, and reporting.

In practice, that could mean a system that understands not only that a payment must be made, but also how it should be made, when it should be sent, which route is cheapest, whether there is enough liquidity in the right currency, and whether any regulatory checks need to be completed first.

Why Southeast Asia is a likely testing ground

Southeast Asia is a natural market for this shift because cross-border complexity is built into the region’s startup economy. Unlike the US or China, where companies can scale across a large domestic market, Southeast Asian startups often face international operations early.

A company expanding from Singapore into Indonesia, Thailand, the Philippines, Malaysia, and Vietnam is dealing not just with new customers, but with different currencies, banking systems, consumer payment habits, and regulatory frameworks. Even within digital commerce, fragmentation remains a defining feature of the region.

This makes payment orchestration: the process of choosing and managing the best payment method, provider, currency, and route, especially important. For startups with thin margins, small differences in fees, settlement delays, or foreign exchange timing can affect working capital. For companies handling high volumes of transactions, manual decisions do not scale well.

An AI agent, in this context, could automatically determine the most efficient rail for a supplier payout, adjust timing based on currency movements, or identify a liquidity shortfall before it becomes an operational issue. It could also help finance teams focus on higher-value judgement calls instead of repetitive coordination.

The report cites research suggesting that by 2025, 85 per cent of enterprises say they will use AI agents across various use cases, with 78 per cent of those being small and medium-sized businesses. It also notes that 33 per cent of industrial B2B firms are expected to have deployed AI-powered buyer agents to make purchasing decisions.

These figures point to a broader shift: AI agents are moving from experimental tools into operational systems. Payments may be one of the areas where the impact becomes visible quickly, because the pain points are measurable — failed transactions, delayed settlements, high fees, trapped liquidity, and compliance friction.

The limits and risks of autonomy

Still, the move towards agentic payments raises questions that cannot be ignored.

Finance is not a low-stakes environment. A poorly configured AI agent could route money incorrectly, miss a compliance red flag, or optimise for cost while creating new operational risk. In Southeast Asia, where regulatory requirements vary widely by market, businesses will need strong guardrails before handing more authority to autonomous systems.

Trust will be central. Companies will want to know how decisions are made, what data is used, who is accountable when something goes wrong, and how quickly human teams can intervene. For regulated sectors such as fintech, lending, and remittances, these questions become even more important.

There is also the issue of readiness. Many startups still struggle with basic financial data hygiene. If invoices, customer records, bank feeds, and compliance data are incomplete or inconsistent, an AI agent may simply make faster decisions on flawed information. Autonomy depends on infrastructure, and not every company has that foundation in place.

Also Read: Agentic commerce’s dirty secret: the data powering AI purchases is often wrong

For founders, the practical lesson is not to replace finance teams overnight. It is to understand where manual coordination is creating the biggest drag, and where intelligent systems can safely take on more responsibility.

What this means for the next generation of fintech
The first wave of Southeast Asian fintech helped consumers and businesses move money digitally. The next wave may be about making financial operations less visible, not because they matter less, but because they happen with fewer manual steps.

If agentic payments mature, finance teams may spend less time checking dashboards and more time setting strategy, defining risk limits, and managing exceptions. Payment infrastructure companies, meanwhile, will compete not only on coverage or fees, but on how intelligently their systems can act across markets.

For the e27 community, the implication is clear. The next major opportunity in fintech may not be building another tool that helps humans move money more efficiently. It may be building the autonomous layer that decides how money should move in the first place.

The post The end of manual finance? AI agents are coming for startup payments appeared first on e27.

Over the past year, I have noticed a subtle change in the way companies discuss artificial intelligence. The first question used to be: Which tool works best? Now, increasingly, it is followed by several less exciting but more consequential questions. Where will our data go? Who can access it? Will our prompts be retained? Can we use the same platform across Singapore, Malaysia and Indonesia? What happens if the provider changes its terms, raises its prices or restricts access to a particular market?

None of these questions makes for a particularly thrilling product demonstration. Yet they may ultimately matter more than whether one model produces a slightly better marketing plan, customer service response or meeting summary than another.

For the startups, SMEs and communications teams I work with, AI adoption rarely begins as an infrastructure strategy. It begins with a practical need. Someone wants to respond to customers faster, reduce repetitive work, generate content more efficiently or search internal documents without spending hours opening files. The team tests a tool, likes the result and gradually starts building it into everyday operations. That is usually when the simple software decision stops being simple.

Every AI tool comes with an infrastructure decision

The application a company sees is only the top layer. Beneath it sits a much larger stack of models, cloud providers, data centres, processors, jurisdictions and commercial relationships. By choosing an AI platform, a business may also be choosing where its information is processed, which country’s laws may affect that information and how dependent its workflows become on a particular technology ecosystem. This matters because data and AI infrastructure are becoming strategic assets for governments, not merely commercial services.

Singapore’s Economic Development Board has described data and AI sovereignty as increasingly important amid geopolitical tensions, noting the growing focus on storing, processing and securing critical data locally. Singapore has also introduced advisory guidelines encouraging cloud providers and data centre operators to improve the security and resilience of services on which businesses and society increasingly depend.

At the regional level, ASEAN’s expanded guide on AI governance recognises that generative AI introduces new questions around data, accountability, security and the models on which organisations rely. For business leaders, this means the provider behind a technology product can no longer be treated as invisible. A software subscription may look like a procurement decision. In reality, it can also be a decision about jurisdiction, dependence and future freedom.

Also Read: AI uncertainty is pushing companies from long leases to flexible offices

Sovereignty is not only a concern for governments

Much of the sovereign AI conversation focuses on whether countries should build national models, secure domestic computing capacity or retain strategically important datasets within their borders. These are important questions, but they can make sovereignty sound like something only governments, hyperscalers and large technology companies need to consider. Ordinary businesses face their own version of the same problem.

A company may never build a foundation model or operate a data centre. It still needs to know whether it can retrieve its data, move to another provider and continue serving customers if its preferred platform becomes unavailable or unsuitable. Consider an SME using an external AI system to answer customer questions. Over time, the tool may become connected to its product catalogue, customer records, service scripts and internal knowledge base.

The company has not simply adopted a chatbot. It has placed part of its customer experience inside another organisation’s infrastructure. That may be perfectly reasonable. Few SMEs have the money or expertise to build such systems independently. The danger begins when convenience turns into dependence without anyone noticing.

The same concern applies to content, HR, finance and internal productivity tools. A business may upload confidential plans, employee information or client material before deciding which types of data should ever leave its own systems.

The issue is not that global platforms are inherently unsafe or that local platforms are automatically better. The issue is whether the organisation understands the trade-off it is making.

Companies are beginning to separate experimentation from dependence

The most sensible response is not to reject foreign technology or attempt to build every capability locally. For most Southeast Asian businesses, that would be expensive, impractical and potentially counterproductive. Global platforms offer technical capabilities, security investment and scale that smaller providers may struggle to match.

Instead, organisations need to become more deliberate about where experimentation ends and operational dependence begins. A team may freely test several AI tools using public or non-sensitive information. It should apply a much higher standard before connecting one of those tools to customer data, proprietary documents or a business-critical process.

This requires companies to classify their information properly. Not every document needs the same protection. A public press release does not carry the same risk as an employee record, unreleased financial result or confidential client strategy. It also means looking beyond headline features when selecting vendors.

Businesses increasingly need to ask whether a provider offers clear data residency options, meaningful security controls, transparent policies on model training and a practical way to export information. Singapore’s government technology standards, for example, explicitly recognise that failure to enforce appropriate data residency can create legal, regulatory, privacy and security risks. These considerations should not be treated as legal fine print to examine after the contract is signed. They are part of the product.

Also Read: The AI-native economy: Southeast Asia’s once-in-a-generation opportunity

Multi-cloud does not automatically mean resilience

One popular response to infrastructure uncertainty is diversification. Companies assume that using several cloud or AI providers will protect them from becoming too dependent on one. In principle, this makes sense. In practice, adding vendors can also add complexity without creating genuine portability. A business may use three platforms but still depend on proprietary data formats, tightly integrated workflows or skills that apply to only one ecosystem.

Real resilience is not measured by the number of logos on an architecture diagram. It is measured by whether the company can continue operating when one component changes. Can it retrieve its information in a usable format? Can another system take over a critical function? Do employees understand the workflow without relying entirely on one vendor? Does the contract explain what happens when the relationship ends? A business that cannot answer these questions is not diversified. It has simply accumulated several forms of lock-in.

Better architecture may therefore be one unexpected benefit of geopolitical uncertainty. It is forcing organisations to confront questions they should arguably have asked even in a more stable world. Which systems are critical? Which data is sensitive? Which dependencies are acceptable? What must remain portable? Where should human judgement remain in the process? These are not only sovereignty questions. They are good management questions.

Southeast Asia should resist the pressure to choose one permanent side

Southeast Asia occupies a complicated position in the global technology landscape. The region benefits from investment, platforms and partnerships originating from several major technology ecosystems. Its markets also differ significantly in regulation, infrastructure maturity, languages and commercial needs. Choosing one permanent technological bloc may offer short-term simplicity, but it could reduce the region’s long-term room to manoeuvre.

At the same time, trying to remain neutral by accepting every platform without examining its dependencies is not a strategy either. The better approach is informed optionality. Countries need sufficient local talent, governance capacity, digital infrastructure and negotiating power to make meaningful choices. Companies need enough internal understanding to evaluate providers rather than outsourcing their entire technology strategy to them.

Singapore’s National AI Strategy 2.0 and the National AI Impact Programme reflect this broader emphasis on building domestic capabilities among enterprises and workers, rather than treating AI purely as technology to be imported and consumed.

That distinction matters. Technology sovereignty does not require a country or company to own every server, model and application it uses. Complete self-sufficiency is neither realistic nor necessarily desirable. It requires the ability to understand critical dependencies, protect sensitive assets and change direction without breaking the organisation.

For Southeast Asian businesses, the most important AI question is therefore no longer simply which platform produces the best result today. It is whether choosing that platform preserves the company’s ability to make a different choice tomorrow. Sovereignty will not come from selecting the supposedly correct side of the global technology divide. It will come from retaining the power to choose again.

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