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

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

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

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

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

bolttech (Singapore)

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

Igloo (Singapore)

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

PasarPolis (Indonesia)

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

Sunday Insurance (Thailand)

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

PolicyStreet (Malaysia)

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

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

Roojai.com (Thailand)

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

Bang Jamin (Indonesia)

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

Rey.id (Indonesia)

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

Blacaz (Singapore)

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

Senang (Malaysia)

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

Qoala (Indonesia)

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

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

Teleskop Technologies (Singapore)

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

GetDoc (Singapore)

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

INSO (Vietnam)

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

Vouch Insurtech (Singapore)

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

Finology (Malaysia)

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

Papaya Insurtech (Vietnam)

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

Kamboja (Indonesia)

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

DearTime (Malaysia)

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

Ouch! (Malaysia)

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

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

Bereev (Malaysia)

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

Checkup (Singapore)

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

Agiliux (Singapore)

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

Gigacover (Singapore)

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

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

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

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

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

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

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

From automation to autonomy

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

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

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

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

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

Why manual coordination is becoming a bottleneck

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

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

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

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

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

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

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

Why Southeast Asia is a likely testing ground

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

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

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

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

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

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

The limits and risks of autonomy

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

Every AI tool comes with an infrastructure decision

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

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

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

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

Sovereignty is not only a concern for governments

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

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

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

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

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

Companies are beginning to separate experimentation from dependence

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

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

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

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

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

Multi-cloud does not automatically mean resilience

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

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

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

Southeast Asia should resist the pressure to choose one permanent side

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

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

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

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

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

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