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The strategic priority: How initiatives actually get chosen

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.

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 4 horsemen of the professional apocalypse, and how to defeat them

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?”

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

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

Join us on WhatsAppInstagramFacebookX, and LinkedIn to stay connected.

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Indonesia’s McEasy secures US$9M to move fleet management from tracking to prediction

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.

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Health, wealth, and legacy planning converge as new wave of SEA insurtechs emerges

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.

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The end of manual finance? AI agents are coming for startup payments

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.

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AI will not cut costs or grow revenue until you redesign how work gets done

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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Why Southeast Asia cannot build sovereign AI on borrowed choices

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.

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

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

Join us on WhatsAppInstagramFacebookX, and LinkedIn to stay connected.

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AI uncertainty is pushing companies from long leases to flexible offices

The office is not disappearing. But the long lease, once treated as a badge of corporate stability, is starting to look like a liability.

A new study by International Workplace Group (IWG), the flexible workspace operator behind brands such as Regus and Spaces, suggests that artificial intelligence is becoming a fresh source of uncertainty for corporate real estate planning. The research found that six in 10 CEOs and CFOs believe the rise of AI has made it harder to predict how much office space their organisations will need over the next two years.

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

That uncertainty is changing how business leaders think about the workplace. According to the study, 73 per cent of respondents said technological change, including AI, has made their organisation less willing to commit to long-term office leases or traditional real estate solutions. Nearly all, at 99.8 per cent, said their organisation is actively trying to move real estate costs from fixed commitments to more variable spending.

For founders and operators in Southeast Asia, the findings land at a moment when the region is still settling into its post-pandemic work patterns. Hybrid work is no longer a temporary workaround, yet return-to-office mandates have not fully restored the old rhythm of five-day office attendance. At the same time, AI is beginning to reshape hiring plans, team structures and the pace at which companies scale or contract.

The result is a more fluid view of space: less headquarters-first, more networked, and increasingly tied to where employees actually live.

AI makes headcount harder to forecast

Corporate real estate decisions have always involved a degree of guesswork. Companies sign leases based on expected hiring, expansion plans and market conditions. AI adds another layer of volatility because it can change both how many people a company needs and where those people work.

The IWG study found that 88 per cent of CEOs believe the rise of AI means organisations need greater flexibility in workspace and real estate decisions. When asked how technology, including AI, is influencing office location strategy, 42 per cent said it enables remote work and reduces the need for a central office. Another 39 per cent said it encourages decentralised or flexible office models, while 37 per cent said it expands access to global and distributed talent.

This does not mean companies are simply cutting space. It means they are becoming less comfortable with locking themselves into a single, expensive footprint when the underlying assumptions can shift quickly.

The same tension is visible across Southeast Asia’s startup and technology ecosystem. AI tools may allow leaner teams to do more with fewer people in areas such as customer support, software development, marketing and analytics. But they can also create new roles, new product lines and new collaboration needs. A startup that looks like a 50-person company today may need 120 people in 18 months, or may remain small while expanding revenue through automation.

For CFOs, that uncertainty makes a 10-year lease harder to justify.

Singapore’s office map is already shifting

In Singapore, the debate is particularly sharp because office costs remain high. CBD rents have continued rising, while tight supply has pushed some demand into decentralised areas such as Alexandra and Paya Lebar. For companies watching costs, the question is no longer only whether to be in the CBD, but how much of the workforce really needs to be there every day.

IWG’s study found that cost reduction is now almost universal in office location decisions, with 99 per cent of CEOs and CFOs saying it is a factor. More than a quarter, or 27 per cent, said it is their main driver.

Also Read: Market access, redrawn: Why Southeast Asia is becoming the world’s strategic advantage

The shift also aligns with Singapore’s broader urban planning direction. The Urban Redevelopment Authority’s Draft Master Plan 2025 emphasised the development of brownfield precincts such as Bishan and Woodlands into business and residential nodes, bringing housing, community facilities and work infrastructure closer together. Technology hubs are also forming outside the traditional CBD, including the Punggol Digital District and Jurong Innovation District.

More recently, one-north was named as the site for Kampong AI, Singapore’s first dedicated AI park. The project is designed to bring startups, companies, practitioners and experts into a live-work environment focused on AI development.

These initiatives point to a future in which work is distributed across multiple nodes rather than concentrated in one central business district. For a small country such as Singapore, this is partly about land use and transport efficiency. For businesses, it is about giving employees access to professional workspaces without insisting that everyone commute to the same central office.

Flexible space becomes a financial tool

Flexible workspace was once seen mainly as a short-term option for freelancers, small teams or companies between leases. That perception has changed. For many firms, it has become a financial tool that allows them to match office costs more closely with headcount and business demand.

According to IWG, 57 per cent of CEOs and CFOs are actively investing in hybrid workspace arrangements. Another 55 per cent are looking at networks of locations closer to where employees live, while 52 per cent are considering decentralised workspace models.

The appeal is not only lower rent. Flexible offices convert part of a company’s property bill into a variable cost, freeing up capital that might otherwise be tied to deposits, fit-outs, underused desks and long-term commitments. That matters to startups and scaleups, especially in a funding environment where investors are paying closer attention to burn rates and operating discipline.

In Southeast Asia, where teams often span several markets before a company has the scale to justify full offices in each, this model can be useful. A company headquartered in Singapore may have staff in Jakarta, Manila, Ho Chi Minh City or Bangkok, but may not want to sign conventional leases in every market. Flexible workspace gives such teams a halfway point between remote work and a permanent office.

The model also supports talent strategy. IWG cited research suggesting that hybrid working models, particularly those that let staff use flexible workspaces closer to home, can deliver an 11 per cent productivity uplift over five years. That claim should be read with caution, as productivity is difficult to measure across different roles and industries. Still, it reflects a broader point: employees increasingly value time saved from commuting, while employers want work arrangements that do not erode collaboration.

The office still matters

Perhaps the most interesting finding is that business leaders are not writing off the office. More than three quarters of CEOs, or 76 per cent, said the role of the office will become more important over the next two years. Only 0.8 per cent said it will become less important.

That suggests the argument has moved beyond “office versus remote”. Companies still need physical spaces for collaboration, onboarding, culture-building and complex problem-solving. AI may even increase that need if teams must rethink workflows, train employees and make judgement-heavy decisions about how automation is used.

The question is what kind of office they need. CapitaLand Investment’s research, cited in the IWG report, found that AI-ready workspaces are likely to require collaboration areas, infrastructure for AI-enabled work, and layouts that support both remote participation and flexible work patterns. In other words, the office may become less of a place for routine desk work and more of a space for coordination, learning and decision-making.

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

Christian Schmitz, CEO of IWG, framed the shift around uncertainty. “Nobody knows exactly what their organisation will look like in two years’ time, but they do know they need the agility to respond,” he said.

That is the core challenge for companies across Southeast Asia. AI is not only changing software budgets or job descriptions. It is making business planning less linear. In that environment, a fixed office footprint can quickly become either too much space or too little.

The office is not dead. But in the AI era, it is becoming less like a monument and more like infrastructure: useful when it is adaptable, costly when it is rigid.

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Ecosystem Roundup: Why VC-backed startups are more likely to commit fraud

A new body of research is forcing the venture capital industry to confront an uncomfortable truth: the startups it funds are statistically more likely to commit fraud than their non-VC-backed counterparts.

The findings, published in a peer-reviewed study and reported by TechCrunch, point to structural incentives baked into the VC model itself. Pressure to show hyper-growth, hit milestone-based funding triggers, and satisfy investor expectations creates an environment where founders may distort metrics, misrepresent traction, or outright falsify financials.

Researchers found that VC-backed firms were significantly more prone to fraudulent behaviour precisely because of, not despite, the capital and scrutiny they attract. The dynamic is paradoxical: the more a startup is funded, the more it is expected to perform, and the higher the temptation to manufacture that performance.

For Southeast Asia’s ecosystem, the implications are direct. The region has seen a wave of high-profile startup collapses tied to governance failures and inflated numbers. As the funding environment tightens and investors demand more rigorous due diligence, this research adds academic weight to calls for stronger founder accountability, independent board oversight, and standardised financial reporting, reforms that remain inconsistently applied across the region.

Read the full article here:

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REGIONAL

Grab posts US$252M Q2 profit, raises full-year guidance: The superapp’s return to sustained profitability prompted an upward revision to full-year guidance, reinforcing confidence in its path to long-term financial health after years of heavy losses.

Vertex Growth leads X Miles’ US$21.4M Series C: The round will fund product expansion and headcount growth across Asia for the Singapore-based AI platform serving non-desk workers, a largely underserved segment in enterprise software.

SoftBank, SC Zeus, and Robust HPC sign SEA AI data centre pact: The three-way partnership spans Japan, Singapore, and Malaysia, reflecting accelerating cross-border investment in AI infrastructure as demand for compute capacity outpaces existing supply across the region.

ADA acquires India’s Algonomy to deepen AI-led marketing: The deal moves ADA from data aggregation into real-time AI decisioning, giving the Malaysian martech firm a stronger foothold in India while broadening its product stack across Asian markets.

Malaysia is the only Asian market with 100% AI investment commitment from AlibabaCloud: The designation cements Kuala Lumpur’s position as a regional cloud and AI hub, with Alibaba Cloud’s full commitment signalling confidence in Malaysia’s infrastructure readiness and policy environment.

VNG posts 86% Q2 profit jump as Zalo AI users hit 24M: The surge in Zalo AI adoption is emerging as a key growth driver for VNG, suggesting Vietnam’s super-app ecosystem is maturing faster than many investors had anticipated.

Indonesian fleet startup raises US$9M Series A: The capital will scale its logistics technology platform across Indonesia’s fragmented transport sector, where digitisation of fleet management remains at an early stage despite strong commercial demand.

Philippine fintech Skyro turns H1 profitable: The milestone is significant for a buy-now-pay-later firm operating in one of SEA’s most under penetrated consumer credit markets, where profitability has proved elusive for most regional peers.

VinFast opens 21 e-motorcycle showrooms in Philippines: The expansion targets mass-market urban commuters in one of Southeast Asia’s largest two-wheeler markets, as VinFast pushes to build a retail presence beyond Vietnam ahead of regional rivals.

Singapore’s new payments code targets hidden mark-ups: The revised code tightens consumer protection rules for payment service providers, requiring greater transparency on foreign exchange fees and prohibiting misleading “zero-fee” marketing claims.

Thinking Machines joins Temu’s group in regional AI push: The partnership marks a notable cross-border enterprise collaboration, with the Philippine AI firm bringing localisation expertise to Temu’s ambitions for production-grade AI deployment acrossSoutheast Asia.

Malaysia shuts down Balaji Srinivasan’s Network School: The closure ends an experiment that had drawn significant attention from the global tech community, with authorities citing regulatory concerns over the crypto advocate’s education venture.

Malaysia’s Cradle Fund opens Startup Summit registration: The annual summit targets early-stage founders seeking investor access and market insights, as Cradle positions itself as a key bridge between Malaysian startups and regional capital.

TikTok Shop pushes Singapore merchants to think like content teams: The platform’s content-first commerce push is reshaping how Singapore sellers approach customer acquisition, blurring the line between entertainment and retail as TikTok deepens its grip on the city-state’s e-commerce market.

Construction startup cuts site reporting to 30 seconds: Wenti Lab’s tool automates on-site documentation for an industry still burdened by manual compliance paperwork, targeting a productivity gap that costs contractors significant time and money across project cycles.


INTERVIEWS & FEATURES

Connecting SEA founders is now the whole job, not a side task: Ecosystem builders describe how cross-border founder connectivity has shifted from a peripheral activity to the central function of community work, as fragmentation across SEA’s markets deepens.

SEA’s CEOs want innovation but employees feel stuck in execution: The execution gap between leadership ambition and ground-level reality persists across Southeast Asian companies, with staff reporting they lack the autonomy, tools, and psychological safety to move beyond day-to-day delivery.

Gulf tourists first, trade later: SEA’s next boom explained: The piece argues that Gulf capital and tourism will arrive before trade partnerships deepen, and that SEA governments and startups should position now rather than wait for formal economic frameworks to materialise.

What Europe still doesn’t understand about Asian entrepreneurship: Drawing on experience across Taipei, London, and Bangkok, the author argues European startup thinking systematically underestimates the pragmatism, speed, and relationship-driven culture that defines business success in Asia.

What it really takes to scale a startup beyond Series A: Experienced operators share hard-won lessons on the structural and cultural shifts required post-Series A, from building management layers to redefining a founder’s own role as the company grows beyond its founding team.


INTERNATIONAL

VC-backed startups commit more fraud, researchers find: Academic research links the pattern to milestone-based funding pressure and weak governance, raising urgent questions for SEA investors tightening due diligence after a wave of high-profile regional startup collapses.

India starts paying for apps, not just downloading them: The shift in monetisation behaviour among Indian consumers signals a maturing digital market and offers a near-term roadmap for how Southeast Asian app ecosystems could evolve as disposable incomes rise.

Apple challenges UK demand for iCloud backdoor access: The legal battle over encrypted iCloud data could set a precedent for how tech firms respond to government surveillance demands in other markets, including Southeast Asia’s increasingly assertive regulators.

TikTok settles three teen social media lawsuits before trial: The pre-trial settlements limit TikTok’s immediate legal exposure in the US but keep regulatory and reputational pressure firmly on the platform as it navigates scrutiny across multiple markets simultaneously.

OpenAI agents reportedly ran amok in internal tests: New evidence of unintended agent behaviour adds to concerns about deploying autonomous AI systems without sufficient guardrails, a debate with direct relevance for enterprises across SEA adopting agentic workflows.

OpenAI influencers draw backlash over luxury retreat: The controversy reignites debate about conflicts of interest when AI companies cultivate media proxies, raising questions about editorial independence in an era of heavily funded AI narrative management.

Sam Altman makes the case for parenting via ChatGPT: OpenAI’s CEO publicly advocated for using ChatGPT as a child development tool, drawing criticism from experts who warnagainst outsourcing early childhood guidance to a large language model with knownlimitations.


CYBERSECURITY

Horizon3 raises US$250M as demand for autonomous pentesting grows: The fundinground reflects surging enterprise appetite for automated penetration testing tools, a category gaining traction among large Asian firms seeking to stress-test defences without expanding internal security headcount.

Singapore’s security teams are losing a race they don’t know they’re in: Corporate security functions are structurally under-resourced against the speed and sophistication of modern threats, leaving firms exposed to risks they have yet to fully map, let alone defend against.

Cybersecurity becomes a gatekeeper for Malaysia’s semiconductor suppliers: Global chipmakers are tightening vendor standards, making cybersecurity compliance a condition of supply chain entry for Malaysian firms a shift that raises barriers but also rewards suppliers who invest early in robust security postures.


SEMICONDUCTOR

DeepX surges to US$2.2B valuation on edge AI chip demand: The South Korean firm’s latest funding round reflects intensifying investor appetite for on-device AI inference chips,with DeepX targeting robotics, automotive, and industrial applications where cloud-dependent processing is impractical.


AI

Singapore ranks first globally in workforce AI maturity: A Notion-commissioned study placed Singapore ahead of all other markets on tool adoption and employer investment in AI upskilling, though critics have flagged methodology limitations that may flatter the city-state’s standing.

Most Singapore firms are not ready for AI audits: High AI adoption rates mask serious gaps in documentation, governance frameworks, and accountability structures, gaps regulators are beginning to scrutinise as Singapore moves toward formal AI oversight requirements.

AI is closing education gaps across the Global South: From teacher shortages to language barriers, AI tools are addressing structural gaps in low-income markets across SoutheastAsia, Africa, and South Asia, though equitable access to infrastructure remains a binding constraint.

Building AI products for SEA means ignoring most of the frontier hype: The piece examines the practical constraints infrastructure gaps, data scarcity, and talent shortages that force SEA founders to make very different product decisions than their counterparts in theUS or China.


THOUGHT LEADERSHIP

Southeast Asia is becoming the world’s strategic advantage: The essay argues that SEA’s geopolitical neutrality, demographic dividend, and digital infrastructure combine to make it the most strategically valuable region of the next decade, if it can maintain its balancing act.

The AI-native economy is SEA’s once-in-a-generation opportunity: Southeast Asia can leapfrog legacy systems and build AI-native businesses from the ground up,  but the window for doing so ahead of better-resourced competitors from the US, China, and India is narrowing fast.

SEA’s both-sides advantage is real but widely misunderstood: The piece pushes back on the idea that SEA benefits symmetrically from US and China ties, arguing the advantage is structural and contingent, and could erode quickly if the region is forced to choose sides.

Market access, not scale, is the new moat for expanding startups: In a fragmented global economy, the ability to navigate markets not sheer size defines competitive advantage, with implications for how SEA founders should think about cross-border expansion strategy.

SEA is a key node, not a peripheral player, in the new growth map: Global trade fragmentation is redrawing growth corridors, and Southeast Asia is emerging as a critical connector between East and West rather than a secondary beneficiary of someone else’s supply chain shifts.

Data sovereignty is becoming a commercial lever for infrastructure vendors: Sovereignty concerns are shaping enterprise buying decisions across emerging markets, with cloud and infrastructure providers increasingly framing local data residency as a competitive differentiator rather than a compliance burden.

The most valuable employee in 2030 will know how to work with AI: AI fluency not raw intelligence or technical depth will define workforce value by the end of the decade, reshaping how companies hire, train, and retain talent across every sector.

Stakeholder mapping: identifying who can quietly kill your deal: A practical guide for founders on spotting hidden blockers in complex sales or partnership negotiations those with informal veto power who never appear on org charts but can derail deals at the last moment.

The marketing funnel was never neutral — Asia’s markets prove it: Using Asian consumer and market dynamics, the piece exposes cultural assumptions embedded in Western conversion frameworks that systematically misread buyer behaviour outside North America and Europe.

Global uncertainty is now showing up as workplace anxiety: Macroeconomic and geopolitical instability is translating into measurable stress and disengagement among employees a dynamic that high-growth startups navigating volatile conditions are poorly equipped to manage at scale.

Bitcoin holds at US$63,500 as stocks rally — which market is right?: The divergence between crypto and equities raises a pointed question: is Bitcoin’s relative stability a signal of macroeconomic caution, or does it simply reflect a structurally different investor base with different risk horizons?

The case for leaning Ethereum over Bitcoin right now: Despite Bitcoin’s dominant narrative, the author argues Ethereum’s fundamentals DeFi momentum, utility depth, and institutional positioning make it the more compelling asymmetric bet at this point in the cycle.

Japan shows how non-USD stablecoins can complement dollar-pegged assets: Japan’sregulatory approach to yen-backed stablecoins offers a model for Asian markets building local digital currency infrastructure without displacing USDC and USDT a balance SEA regulators are still working to strike.

Ethereum at 11: maturity or another speculative cycle?: The piece examines whether Ethereum’s latest price recovery reflects genuine ecosystem maturity or a familiar speculative pattern, and what the next upgrade cycle means for developers and long-term investors.

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

An investor with more than twenty years in the market asked me recently whether AI was going to replace software. He was not asking from a position of weakness. He has built a reputation, a portfolio, and a network that most people will not match in a lifetime. He just wanted to understand a thing that the people in their twenties around him already seemed to grasp.

A decade ago, that question would not have happened. Twenty years of market experience was a moat. The senior was the one with the answers. The junior was the one with the questions. The org chart, the seating chart, and the pay chart all reflected the same underlying logic.

That logic is breaking. The information that justified the old hierarchy is no longer scarce, and the market has noticed.

What hierarchies were really paying for

For most of the industrial era, organisations were built around an inconvenient fact. Information was hard to access, slow to move, and expensive to interpret. The people who had accumulated it over decades were genuinely more valuable than the people who had not. Seniority was a routing system. Junior staff gathered data. Mid-level managers compiled it. Senior leaders decided what to do with it.

This is the shape of what I call the Information Hierarchy. It is the org chart that most companies still operate on, even though the foundation it sits on has shifted.

Block’s founder Jack Dorsey wrote in a recent shareholder letter that “intelligence tools have changed what it means to build and run a company” as he cut his workforce from 10,000 to under 6,000. Coinbase is capping its structure at five layers between CEO and individual contributor. Moderna folded HR and technology into a single Chief People and Digital Officer role. Gartner projects that 20 per cent of organisations will eliminate more than half of their middle management positions by the end of 2026.

These are not aesthetic decisions. They are companies recognising that they no longer need as many humans to route information.

Also Read: How AI and blockchain could make commerce decisions more accountable

The information hierarchy is repricing

The clearest signal of the shift is what the labour market is paying for. PwC’s 2025 Global AI Jobs Barometer, drawn from close to a billion job advertisements across six continents, found that workers with AI skills now command a 56 per cent wage premium over peers in the same roles without those skills. A year earlier, the premium was 25 per cent. The market more than doubled its valuation of AI fluency in twelve months.

The premium is not uniform across seniority. At entry level, the gap is six per cent. At staff engineer level, it is 18.7 per cent. At senior engineer level in firms like Intuit and Google DeepMind, it exceeds 70 per cent. The premium widens with seniority because the supply of AI-fluent seniors is genuinely thin.

But the more telling data point is the inversion at the edges. The AI Accelerator Institute found that junior AI professionals in North America averaged US$173,500 in total compensation in 2025, exceeding director-level averages of US$152,600 at some organisations. A senior title can now pay less than a junior one if the junior has the right skills and the senior does not.

This is not a story about youth winning and age losing. Some of the highest-paid AI specialists are in their forties and fifties. It is a story about what the market is actually paying for. Position on an org chart is no longer the unit. Capability is.

Reverse mentoring, where junior employees teach senior leaders, has shifted from a curiosity to an institutional practice. International Workplace Group research finds Gen Z employees actively coaching senior colleagues on AI fluency at companies including British Airways, PwC, and Estee Lauder. The arrow of mentorship now runs in both directions because the information advantage runs in both directions.

What replaces it is not flatter

The reflex reading of this trend is that hierarchies are flattening. The data supports that on one axis. But the deeper read is that the Information Hierarchy is being replaced by something else, and the something else is not flat at all. It is just invisible.

When information was scarce, hierarchy was visible in the org chart. When information is abundant, hierarchy moves to the things that remain scarce. Four of them stand out.

Judgment. AI can produce a hundred plausible answers in a minute. Knowing which one is correct, which one will work, and which one will quietly fail in production is a skill that does not improve with prompt access. It improves with reps, with mistakes, and with consequences that the person carries.

Taste. The ability to distinguish good output from technically correct output. AI is excellent at “correct.” It is not excellent at “good.” That gap is where senior judgment now lives.

Customer trust. A buyer signing a meaningful contract is not buying the AI model. They are buying the human who stakes their reputation on what the AI produces. Trust accumulates over years. It does not transfer through a Slack handover.

Also Read: Indonesia’s AI hiring gap is real, just not 28×

Accountability. The willingness to own the outcome when it goes wrong. AI agents do not write resignation letters. They do not lose sleep. They do not get sued. Someone still has to.

These four are the new senior skills, and they share a property. None of them is easy to measure on a resume. The market knows they matter, but it does not yet have a clean way to price them.

The trap

The risk for companies right now is that they are cutting seniority because they have decided that information is no longer the moat, but they are accidentally cutting judgment with it. The two have been wrapped together for so long that most leaders cannot tell them apart.

The pipeline question, raised by Gartner and echoed by IBM’s decision to triple its entry-level hiring in 2026, is the same question in a different form. If you remove the layer where judgment was trained, where does the next generation of judgment come from? You cannot prompt your way to taste. You cannot agentic-workflow your way to accountability. Those grow in environments that the Information Hierarchy used to provide and that the new shape of the company has not yet figured out how to replace.

A 20-year investor asking how AI works is not a sign that experience has lost its value. It is a sign that experience has to be rebuilt around what AI cannot do, which is most of what mattered in the first place.

Three questions for leaders rethinking seniority

When you remove a senior role, are you removing information overhead or are you removing judgment?

When you promote based on AI fluency, are you promoting capability or are you promoting confidence?

If your most senior people left tomorrow and AI tools were unchanged, what specifically would your company no longer be able to do?

If the answer to the third question is nothing, you are not running a team. You are running a query interface with payroll attached.

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 Why seniority is repricing in AI-augmented teams, and what gets valued instead appeared first on e27.