Singapore is doubling down on its ambitions to become Asia’s undisputed payments capital, as a new industry report paints the city-state as one of the world’s most advanced digital and cross-border payments hubs.

The Singapore FinTech Association (SFA), together with PwC Singapore, has launched “Payments’ State of Play 2026”, a sweeping review of how the island nation’s payments ecosystem has evolved over the past decade, and where it is headed next.

Also Read: Fintech rebound: Singapore bags US$1.04B, outpaces global peers

The report argues that Singapore’s rise has been driven by a rare combination of progressive regulation, strong foundational infrastructure, high consumer demand for seamless digital experiences, and close public-private collaboration. What began as basic payment rails has now matured into one of the most sophisticated payment markets globally.

Digital payments dominance and record funding momentum

One of the most striking findings is Singapore’s scale of digital adoption. More than 98 per cent of adults are banked, while real-time payments and digital wallets increasingly dominate everyday transactions.

Digital wallets alone are projected to process US$66 billion in online and point-of-sale transactions by 2027, underscoring how cashless behaviour has become deeply embedded in the country’s economy.

Investor confidence has also remained resilient. The report notes that the city-state’s payments sector raised over US$319 million in funding in the first nine months of 2025 — surpassing the combined fintech funding totals of Indonesia, Malaysia, the Philippines, Thailand, and Vietnam.

Real-time rails powering the ecosystem

Singapore’s domestic payments infrastructure continues to scale rapidly, led by systems such as PayNow and FAST.

FAST transaction volumes hit 500 million in 2024, representing a 31 per cent year-on-year increase, as real-time transfers become the default for consumers and businesses alike.

Card payments also grew strongly, with total value rising at a compound annual growth rate (CAGR) of 12.9 per cent from 2020 to 2024. E-money value expanded at a CAGR of 7.3 per cent over the same period, despite a slight decline in transaction volume.

E-money growth and the global wallet boom

Singapore’s digital payments market is expected to accelerate further. Total transaction value reached US$39.37 billion in 2023 and is forecast to climb to US$113.65 billion by 2030.

Also Read: Singapore’s SME fintechs face growth hurdles amid restricted API access

E-money transactions are projected to rise steadily to US$4.28 billion by 2028, supported by AI adoption, embedded finance innovation, stronger stablecoin regulation, and expanding cross-border payment networks.

This trajectory mirrors a wider global shift, with mobile wallet transactions forecast to surge to an estimated US$17 trillion by 2029.

Cross-border connectivity as a regional differentiator

Singapore is also positioning itself as a key settlement and connectivity hub for Asia. Initiatives such as Project Nexus, alongside PayNow linkages with Thailand and Malaysia, are strengthening the city-state’s leadership in cross-border real-time payments.

Total remittance volume reached US$8.05 billion in 2022 and is expected to grow to US$13.34 billion by 2032, representing a CAGR of 5.2 per cent.

Stablecoins, digital assets, and Singapore’s FX strength

The report highlights Singapore’s rising influence in digital assets, particularly stablecoins. The city-state now accounts for over 70 per cent of Southeast Asia’s non-USD stablecoin market pegged to the Singapore dollar, supported by the Monetary Authority of Singapore’s globally recognised regulatory framework.

Singapore is also reinforcing its status as a major foreign exchange hub. The country is now the world’s third-largest FX trading centre, with average daily trading volumes climbing to US$1.485 trillion in April 2025 —  a 60 per cent increase from April 2022.

Holly Fang, President of the Singapore FinTech Association, said, “Over the past decade, Singapore has developed one of the most advanced, resilient, and trusted payments ecosystems in the world.”

She added that progressive regulation and industry collaboration have
positioned Singapore as a leader in real-time and cross-border payments, while also confronting fraud and scams head-on.

PwC Singapore Partner Wong Wanyi echoed this view, noting, “Payments are evolving rapidly, led by technology and emerging realities, while also presenting new risks.”

Also Read: Singapore’s regulatory vision is shaping cross-border payments in Asia: Report

She emphasised that sustaining Singapore’s leadership will require strong risk management frameworks and regulatory clarity that encourage innovation while building trust.

The next wave: AI, embedded finance, and consumer protection

Looking ahead, the report identifies several trends shaping the next phase of payments innovation:

• Embedded finance and super apps, integrating lending, investment, and payments into everyday platforms
• AI-powered payments, enhancing fraud detection and optimising processing
• Tokenised deposits and regulated stablecoins, expanding use cases in domestic and cross-border payments
• Greater interoperability, driven by regional initiatives like Project Nexus
• Stronger consumer protection, amid escalating scam risks

Fraud remains a pressing challenge. As of November 2025, scam-related losses in Singapore reached US$620 million, close to the US$812 million recorded across the whole of 2024 — underscoring the urgency for coordinated action across the ecosystem.

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Strategic collaborations between research institutions and startups are reshaping the innovation landscape, unlocking new opportunities for growth and delivering meaningful societal impact. These partnerships allow scientific and academic entities to access commercialisation channels and adopt more agile development approaches, while startups benefit from resources and industry expertise needed to scale their innovations effectively.

Many early-stage startups look for the first business partners among corporate players. Yet, challenges remain—according to a Boston Consulting Group survey, 45 per cent of corporations and 55 per cent of startups express dissatisfaction with their partnership experiences, highlighting a gap that science organisations are uniquely positioned to bridge—connecting groundbreaking research with viable business models.

To appreciate the scale of innovation in Southeast Asia, consider this: the region is home to 63 unicorns—companies valued at US$1 billion or more—with over 124,450 startups in total based there as of May 2025.

Around the world, innovation ecosystems are expanding rapidly, with millions of new startups launching annually across regions in North America, Europe, and Asia. Despite this growth, the disconnect between startups and research organisations remains a common obstacle, and the tangible benefits to businesses remain modest. All stakeholders within the innovation ecosystem stand to gain by strengthening these partnerships to better fulfil their promise for society and the economy.

Below are three key benefits to explore.

Enhancing research and development (R&D)

For startups looking to strengthen their R&D efforts by partnering with scientific institutions, there are three key areas to focus on: aligning innovation goals at the project level, establishing clear and open communication channels, and setting precise collaboration expectations within agreements.

Getting everyone aligned on innovation goals at the project level is absolutely crucial. In my experience mentoring startups, many partnerships start with broad, high-level objectives but don’t drill down into specific outcomes for each project. The most successful collaborations are those that sync goals not just strategically, but also at the day-to-day operational level. Using digital tools and collaborative platforms can make this much easier, helping teams coordinate in real time and maintain shared visibility.

Also Read: New research report: The nexus between elite university education and startup funding

Effective communication forms the backbone of any successful partnership, yet transparency often falls short. Issues such as siloed information systems and conflicting priorities can quickly lead to misaligned expectations and wasted resources.

To prevent this, partners should prioritise full visibility into project progress, ensuring that everyone involved has access to accurate, detailed updates—whether by project phase, team, or milestone. Centralising collaboration workflows and clearly understanding associated costs further build trust and accountability.

Equally important is tailoring incentives specifically to joint efforts. Too frequently, research institutions and startups focus on broad research milestones instead of concrete, shared deliverables. This misalignment can cause partners to pursue individual goals rather than common objectives, resulting in resource imbalances where some areas are overstretched while others remain underutilised. Clear, outcome-focused incentives help maintain commitment to the partnership’s overall success.

The Natural Resources Institute Finland (Luke) offers an example of a European research organisation focused on sustainable development through renewable natural resources. Luke conducts extensive research and development across forestry and bioeconomy, supporting both national and international projects.

It provides access to advanced research infrastructures such as greenhouses, research fields, and laboratories, enabling high-quality experimental work. Luke also coordinates the European research infrastructure AnaEE (Analysis and Experimentation on Ecosystems), fostering collaboration and knowledge sharing across countries. Through its involvement in numerous partnerships, Luke plays a key role in turning scientific insights into practical solutions that promote sustainability and well-being.

Fast-tracking commercialisation

Accelerating commercialisation is often the missing piece when startups and research institutions join forces. While both sides excel at innovation, the actual process of getting new ideas to market can get lost in the shuffle. By working together more closely—sharing resources, knowledge, and a unified vision—the journey from discovery to product becomes more efficient and streamlined. This collaboration helps prevent common setbacks such as conflicting priorities, wasted efforts, and delays that can hinder promising technologies.

A concrete example of such effective collaboration is Turion Labs, which recently opened in Singapore as the region’s first comprehensive biotech innovation platform. This joint venture, supported by Korea’s S&S LAB and Indonesia’s Future Lestari, offers modular lab spaces, contract research services, and regulatory assistance within a unified framework.

Turion Labs aims to connect promising scientific research with practical paths to commercialisation. It supports startups and biomedical companies by providing access to advanced laboratory facilities alongside Korean research expertise and Southeast Asian markets. This initiative reflects the growing trend in Southeast Asia to develop collaborative innovation centers that bring together research and industry to help advance biotech development in the region.

Also Read: Nagoya University: Asia’s extensive network of innovation, research, and education

What makes these partnerships work is flexibility. The most successful collaborations aren’t rigid—they adapt to the needs of each project and each team. Startups and research institutions that prioritise both innovation and business efficiency find ways to share risk and align goals, while keeping lines of communication open. This approach is especially important as startups play an ever-larger role in commercialising high-impact innovations.

Uniting diverse talents

Navigating partnerships between science organisations and startups isn’t just about having the latest tech at your fingertips—it’s about bringing together the right people and perspectives. Technology can certainly make collaboration easier, but it’s not a cure-all. The real magic happens when the deep technical know-how of researchers meets the entrepreneurial drive of startup founders, creating space for meaningful innovation.

Still, even with all the collaboration tools available today, many partnerships fall short of their potential. Two issues tend to crop up again and again. First, organisations often jump into new systems without rethinking how they actually work together—like installing state-of-the-art software but sticking to old, inefficient habits. Second, when project goals aren’t clear and data isn’t aligned, teams can end up working at cross purposes, slowing down the move from idea to market.

Good management can make all the difference here. The most effective collaborations bring together cross-functional teams—researchers, entrepreneurs, and other key players—who regularly check in on progress and keep everyone focused on shared milestones. Setting clear, measurable targets keeps things on track and helps spot issues early.

Compelling examples of collaboration between research labs and startups can be seen at the University of Eastern Finland, where joint efforts have led to innovative photonics applications for consumer electronics.

Similarly, the National University of Singapore has partnered with startups through a dedicated program focused on flexible electronics and hybrid systems, driving the development of advanced consumer electronics technologies. These partnerships highlight how academic institutions and startups are working together to push the boundaries of innovation in the consumer electronics sector.

Also Read: Bridging the digital divide: Addressing Malaysia’s skills gap

By combining academic expertise with startup agility, these collaborations have rapidly advanced from lab prototypes to market-ready products.

Starting point

One effective way to kick off collaborations between startups and research institutions is by gaining a thorough, project-level understanding of the partnership landscape. Once that foundation is in place, partners can use a collaboration health map to spot inefficiencies and opportunities at various stages—whether it’s during prototype testing or preparing for market launch.

This kind of tool helps leaders identify the root causes behind common challenges such as misaligned goals or wasted resources. With those insights, they can roll out targeted actions that address the real problems, rather than treating surface symptoms.  Moreover, this approach helps ensure that improvements are sustainable and don’t fade over time.

By adopting these strategies, startups and science organisations can work more smoothly together and unlock greater value for everyone involved. Of course, the exact approach will vary depending on each partnership’s goals and setup. But no matter the details, taking a proactive stance on managing collaboration can lead to smarter decisions and stronger, more rewarding partnerships.

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Consistency has long been framed as a discipline problem. If you want to stay visible, the advice goes, you simply need to post more, work harder, and show up daily — even when you don’t feel like it.

That framing no longer holds in the AI era.

What we are seeing instead is a shift: Consistency is becoming a systems and design problem, not a willpower one. And the founders who understand this early are the ones building leverage without burning out.

From “working harder” to “designing fewer steps”

I wrote the book The Lazy Person’s Guide to Success, built around a simple idea: If a task takes 100 steps, the real work is figuring out how to reduce it to 10.

That logic still applies today — only now, AI accelerates it dramatically.

Before Seraphina AI, efficiency came from project management tools, SOPs, and documentation. I still use Asana extensively for this reason: It structures information, preserves institutional memory, and makes work retrievable.

What AI changes is not organisation, but execution velocity.

Instead of asking people to remember complex workflows, AI can now guide them through processes in real time. The system doesn’t just store instructions; it actively assists. That distinction matters.

Also Read: Singapore’s AI ambitions face crucial test amid economic and talent pressures

AI as teammate, not replacement

The most effective founders are not using AI as a shortcut for thinking. They are using it as a thinking partner.

I see Seraphina AI as a digital twin or personal assistant — a teammate that is always available, never fatigued, and able to respond on demand. It augments human judgment rather than replaces it.

This is especially evident in content creation.

The bottleneck is no longer production capacity. It is attention and articulation.

Why most people believe they “don’t have content”

When people say they don’t know what to write, they are usually confusing content with output.

In reality:

• conversations,
• reactions,
• opinions formed while reading,
• reflections shared with peers,

are already content — just undocumented.

AI closes this gap by lowering the cost of capture.

Voice notes, short reflections, or informal messages can be transcribed, structured, and adapted into written formats without losing the original voice. The authenticity remains because the source material is human. AI simply handles transformation and distribution.

Micro habits outperform motivation

The most sustainable form of consistency comes from micro habits, not grand commitments.

A simple example:
When an idea arises, record it immediately — without editing, formatting, or judging its value.

That single habit:

• reduces friction,
• bypasses perfectionism,
• and creates a reliable input stream for AI-assisted processing.

Over time, journaling becomes blogging. Blogging becomes dialogue. Dialogue becomes visibility.

The system compounds quietly.

Also Read: Is AI making it harder for tech startups to survive?

Visibility as choice — and requirement

Visibility today is optional only in theory.

You can choose to remain invisible and still be competent. But if leverage, reach, or influence matter, visibility becomes a functional requirement.

Importantly, visibility does not demand virality. It demands continuity.

Not every idea will resonate with everyone. But resonance does not scale linearly — it clusters. And clusters form communities.

The real blocker is perfection, not fear

In practice, the primary inhibitors of consistency are:

• overthinking value,
• waiting for “better” ideas,
• and mistaking polish for usefulness.

In reality, something does not need to be universally valuable to matter. It only needs to be relevant to someone.

Consistency builds familiarity. Familiarity builds trust.

The skill that matters most as AI does more

As AI expands its capabilities, the differentiator is no longer speed or output.

It is communication.

The ability to articulate thinking, share perspective, and remain present in public discourse is becoming the defining human advantage.

In that sense, consistency is not about effort. It is about design.

And “lazy” consistency — done correctly — is not a lack of ambition, but a strategic choice to let systems do what systems do best, so humans can focus on what only humans can do.

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Artificial intelligence (AI) is expanding at breakneck speed, presenting a paradox for global energy systems. On one hand, AI-driven innovations promise efficiency gains in renewable energy management and smarter grids. On the other, the surging power demands of AI threaten to strain electricity infrastructure and increase reliance on fossil fuels.

Current projections indicate data centres — the digital fortresses powering AI — could consume over 1,000 TWh of electricity by 2026, roughly double their 2022 usage. (For perspective, that’s comparable to Japan’s annual power consumption, or about 90 million US homes.)

In the European Union alone, data centre energy use is forecast to reach 150 TWh by 2026, ~ four per cent of EU demand. Gartner even predicts that 40 per cent of existing AI data centres will hit power capacity limits by 2027, underscoring the urgent infrastructure challenge.

This surge places immense pressure on power grids. Cutting-edge AI models require enormous energy: Training a single large language model (LLM) like OpenAI’s GPT series can devour tens of gigawatt-hours of electricity . Some hyper-scale AI data centres already draw 30-100 megawatts each, and future facilities may exceed 1,000 MW (1 gigawatt) — about the output of a large power plant .

One industry analysis notes tech giants are pursuing “gigawatt-scale” data centre campuses to support AI workloads . By 2030, Microsoft and OpenAI’s planned “Stargate” supercomputer could require an astonishing five GW of power.

In response, tech companies are exploring diverse energy strategies. Google, for instance, is investing in advanced nuclear power: it signed a deal to purchase energy from small modular reactors (SMRs), aiming to add 500 MW of carbon-free power by 2030.

Microsoft is turning to nuclear with the Three Mile Island nuclear power plant deal, Amazon, and Meta are turning to conventional power plants — in some regions, new natural gas-fired generators — to guarantee reliable juice for AI data centres, a strategy supported by utilities. In Wisconsin, regulators approved a US$2 billion gas plant deemed “critical” for Microsoft’s new AI hub.

These moves underline a hard truth: renewables alone can’t yet meet AI’s ravenous base-load demand, prompting a dual-track energy race between carbon-free solutions and fossil fuels.

This brings up pressing questions for business leaders:

• Will AI ultimately drive sustainability gains or an energy crisis?
• How are regional disparities and geopolitics shaping AI’s energy footprint?
• What technological breakthroughs could enable sustainable AI growth?
• And how should corporate strategy adjust to balance AI’s benefits against its energy and carbon costs?

This three-part guide examines the forces at play — from data centre trends and energy innovations to policy and geopolitical factors — to help corporate decision-makers navigate AI’s energy revolution.

The goal: understand the macro and geopolitical impacts of AI’s energy consumption, and chart a course that leverages AI’s power responsibly and sustainably.

The energy cost of AI: Hard truths and hidden opportunities

Global data centre electricity consumption reached an estimated 460 TWh in 2022, with AI and cryptocurrency operations accounting for roughly 14 per cent of that load, according to the International Energy Agency (IEA).

Now AI is pushing those numbers dramatically higher. Projections show data centres worldwide could consume over 1,000 TWh by 2026 — roughly doubling in just four years. By 2030, some forecasts see a further 160 per cent increase in data centre power demand driven by AI.

Also Read: Eco-investing: Driving change through climate technology and strategic finance

This growth is concentrated in key AI hubs and “cloud clusters” with serious consequences for local grids:

• In Northern Virginia’s famed “Data Centre Alley,” the massive concentration of servers has led to power quality issues. The region now experiences voltage distortions four times higher than the US average, raising the risk of appliance damage and even fires for surrounding communities. Utilities warn that traditional grid infrastructure is straining to keep up with the load.
• In central Ohio, data centre capacity has quadrupled since 2023, consuming so much electricity that utility AEP had to halt new data centre connections, despite a 30 GW queue of projects waiting to plug in. Simply put, the grid can’t be expanded fast enough to accommodate the sudden surge in demand.
• Ireland faces a similar crunch — by 2026, data centres are projected to gobble up 32 per cent of Ireland’s electricity. Dublin’s metro grid is so stressed that the government imposed a moratorium on new data centres in the area, shifting over US$4 billion in planned investments to other countries.

The energy intensity of AI is a key reason demand is outpacing capacity. A few eye-opening facts illustrate the scale:

• Training a single large AI model can consume enormous amounts of electricity. For example, training ChatGPT/GPT-3 (with 175 billion parameters) is estimated to use on the order of 1-1.3 GWh (gigawatt-hours) of energy — roughly the yearly electricity usage of over 1,000 US homes. And that’s for one training run. Newer models like GPT-4 are even more power-hungry — estimates suggest on the order of 50-60 GWh for a full training cycle, which would be enough to power ~4,500 homes for a year (and emits tens of thousands of tons of CO₂). In other words, one large AI model’s training = years of household electricity.
• Running AI models (inference) is also energy intensive. AI queries consume about 10× more electricity than a typical Google search. Every time you ask ChatGPT a question, a network of GPUs fires up, drawing far more power than a standard web search. Multiply this by millions of queries, and the energy adds up fast. Microsoft and Amazon have responded by securing huge dedicated power supplies for their cloud AI operations — on the order of 500 MW to 1,000 MW per data centre campus — to ensure they can handle the surging demand. For perspective, a single 1,000 MW data centre campus could consume as much power as 750,000 homes.
• The sheer consumption of top tech companies is staggering. In 2023, Microsoft and Google each used ~24 TWh of electricity — more power than entire countries like Iceland, Jordan, or Ghana consume in a year. This puts their usage above that of over 100 nations. While these firms have aggressive renewable energy programs, the scale of their energy draw highlights how big the AI computation boom has become.
• The cloud giants are investing heavily to keep this sustainable. Microsoft recently announced a US$10+ billion deal with Brookfield to develop 10.5 GW of new solar and wind farms by 2030 — an unprecedented corporate clean power purchase aimed squarely at running its AI and cloud data centres on carbon-free energy. Amazon and Google are similarly pouring funds into renewables and even experimental technologies (like advanced geothermal and batteries) to offset their growing AI footprint.

Despite these efforts, power constraints are emerging as a growth limiter for AI. Industry analysts warn that in the next few years, many data centre operators (especially those not backed by big tech) may find it difficult or prohibitively expensive to get the electricity they need.

Gartner projects that by 2027, 4 in 10 AI data centres worldwide could hit their power capacity ceiling, meaning their expansion will be stalled by energy shortages. For enterprises, this could translate to slower cloud rollouts or higher costs as energy prices rise.

However, within this hard truth lies a hidden opportunity — AI itself can help solve the energy challenge. As we’ll explore, the same technology driving up consumption can also drive greater efficiency and new solutions, if wielded wisely.

Also Read: The key to tackling climate change: Electrify shipping

Comparing AI models: Power hunger from GPT to KNN

Not all AI is equally power-hungry. There is a vast gap in energy consumption between large, state-of-the-art AI models and more traditional algorithms. Understanding this spread can help leaders choose the right AI tools for the job — balancing capability and cost. The table below compares examples of AI models:

As the table shows, today’s largest AI models (like GPT-3/4) dwarf earlier AI in power needs. Training GPT-4 can use about 50,000× more energy than training a typical convolutional neural network (CNN) like ResNet-50 used for image recognition.

And an old-school algorithm like k-nearest neighbors (KNN) or an ARIMA forecast model might use a million-times less energy — essentially negligible in comparison.

This doesn’t mean companies should avoid large AI models altogether; rather, it underscores the importance of right-sizing AI to the task. You don’t always need a billion-parameter model if a simpler one works — and the energy (and cost) savings from a leaner approach can be huge.

Key takeaway: AI’s energy footprint isn’t uniform. Generative AI and other complex models can be incredible but come with extreme energy costs.

Business leaders should evaluate whether a smaller, more efficient model could meet their needs. In many cases, optimized or “distilled” models, or running AI at the network edge, can deliver acceptable performance while using a fraction of the power. This efficiency-centric approach to AI adoption will become increasingly vital as energy pressures mount.

Fossil fuel lock-in vs a nuclear renaissance

The tug-of-war between AI’s energy demand and clean energy supply is pushing companies down two very different paths. On one side, some firms and regions are doubling down on fossil fuels to keep the lights on for AI. On the other, there’s a growing movement toward a nuclear revival (along with renewables) to power AI sustainably.

Also Read: What does Trump mean for SEA climate scene?

On the fossil fuel front, oil and gas producers see AI’s rise as a new source of demand for hydrocarbons. BP’s CEO Murray Auchincloss, for example, predicts AI’s infrastructure build-out could drive an extra 3-5 million barrels per day of oil demand growth through the 2030s, as data centres and associated supply chains consume more energy (fuel for generators, diesel for construction, etc.). Likewise, Shell’s latest Energy Security Scenarios project natural gas demand reaching 4,640 billion cubic meters annually by 2040, partly to fuel backup generators for data centres and provide grid stability in an AI-enabled economy.

These trends raise concerns that AI could inadvertently lock in a new wave of fossil fuel dependence right when the world is trying to decarbonise. For instance, in the US, some utilities are proposing 20+ GW of new gas-fired power plants by 2040largely to meet data centre growth.

This runs directly against climate goals — building gas infrastructure that could last 40-50 years to serve what might be a short-term spike in AI-related demand.

Conversely, a potential “nuclear renaissance” is being driven by AI’s 24/7 power needs and corporate clean energy pledges. Nuclear power offers steady, carbon-free electricity that is highly appealing for always-on AI workloads. We’re seeing concrete steps in this direction:

• Microsoft is investing US$1.6 billion to help reopen the dormant Three Mile Island nuclear plant in Pennsylvania, aiming to secure 24/7 carbon-free power for its AI data centres by 2028. This would repurpose an existing nuclear reactor to directly feed Microsoft’s cloud operations — a bold bet on nuclear as a reliable green energy source for AI.
• Amazon and Google have each committed at least US$500 million in financing to startup companies developing small modular reactors (SMRs). Their goal is to have about 5 GW of new nuclear capacity from SMRs online by the mid-2030s. Google’s agreement with Kairos Power, for instance, targets the first SMR operational by 2030. If successful, these would be game-changers: modular reactors could be built near data centres to provide dedicated clean power.
• In Europe, policymakers are increasingly viewing nuclear as essential for meeting AI’s power demands. The EU projects that nuclear-powered data centres (where data centres are co-located with nuclear plants or dedicated reactors) could supply 15-25 per cent of the new electricity needed for AI and digital growth through 2030. France and the UK have floated incentives for data centre operators to hook into existing nuclear plants, while countries like Romania and Estonia are partnering on SMR deployment with an eye toward tech sector needs.

The contrast is striking: Will the AI era deepen our fossil fuel dependence or accelerate the shift to alternative energy?

In practice, both are happening — but the balance could tip one way or the other based on economics and policy. Natural gas plants currently often win on cost and speed (a gas turbine can be built faster than a nuclear plant and is a proven solution to instantly boost capacity).

Indeed, “the only concrete plans I’m seeing are natural gas plants,” notes one energy consultant about data centre expansions. Yet, as carbon costs rise and modular nuclear tech matures, nuclear and renewables could prove the more attractive long-term play.

For corporate leaders, this means energy strategy is becoming inseparable from AI strategy. Companies may need to directly invest in energy projects (like Microsoft’s and Google’s deals) to ensure their AI ambitions have a viable power supply. Those that succeed in securing reliable, clean energy will not only meet sustainability goals but also gain an operational advantage (avoiding the risk of power constraints slowing their AI deployments).

This is part one of a three-part series exploring AI’s energy impact.

Part two of this series examines how AI can enhance energy efficiency and optimise grid management to address this challenge.

This article was originally published here and co-authored by Xavier Greco, Founder and CEO of ENSSO.

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The digital era is underpinned by a technology so small it is nearly invisible: the semiconductor. These tiny chips power the devices and systems that define modern life, from smartphones and electric vehicles to AI servers and medical imaging equipment.

As the global demand for semiconductors grows, the race to build resilient, agile, and forward-looking supply chains has never been more critical. For tech founders, especially in Southeast Asia (SEA), understanding this ecosystem is not just strategic—it is existential.

According to Source of Asia, SEA has carved a significant niche in the global semiconductor value chain. While front-end fabrication remains dominated by Taiwan and South Korea, the region has emerged as a vital centre for back-end processes: Assembly, Testing, and Packaging (ATP). These steps are essential to the chip lifecycle and offer enormous value for tech companies seeking reliable, cost-efficient solutions.

Countries such as Malaysia and Vietnam are rapidly becoming semiconductor hotspots. This is driven by low operational costs, supportive government policies, and modern infrastructure. These advantages, coupled with a skilled workforce, have made the region attractive to multinationals and startups alike.

As industries from automotive to telecommunications deepen their reliance on semiconductors, SEA’s role in maintaining global supply chain stability continues to grow. This makes it an ideal launchpad for startups aiming to scale amid geopolitical flux and accelerating digital transformation.

Also Read: GridCARE raises US$13.5M led by Xora to fuel AI’s energy needs

Navigating the semiconductor age demands more than just sourcing components. It requires forming the right strategic partnerships—those that bring not only capital but also technical expertise, global reach, and shared vision.

Infineon Technologies exemplifies such a partner. As a global semiconductor leader, Infineon is committed to driving decarbonisation and digitalisation through power systems and IoT solutions. Their products support everything from clean mobility to smart energy systems. With over 58,000 employees across more than 100 countries, Infineon is not just delivering chips; they are engineering a better tomorrow.

Partnerships like these are crucial for tech founders building hardware or AI-enabled platforms. Having access to high-quality semiconductor technologies, paired with expertise in sustainability and systems integration, can provide a competitive edge in both product performance and market perception.

The capital conduit: Investing in innovation

While tech is the engine, capital is the fuel. Vertex Ventures Southeast Asia and India (VVSEAI) has long recognised this dynamic. The fund has helped build companies such as Grab and PatSnap by not just writing cheques, but also providing strategic counsel, talent access, and introductions to customers and partners across the globe.

For tech founders in the semiconductor-adjacent space—whether in manufacturing, logistics, or AI—VVSEAI offers a unique combination of regional insight and global connectivity. With a presence in every major innovation hub through the Vertex Global Network, their teams share learnings across borders to help startups scale faster and smarter.

As chips grow more complex and demand for efficiency spikes, AI becomes indispensable in semiconductor operations. Innowave Tech is pioneering this shift. The company’s industrial AI solutions address challenges across predictive maintenance, quality assurance, and process automation. By replicating human judgment through edge AI and deep learning, Innowave helps manufacturers streamline operations and reduce downtime.

Also Read: Singapore’s AI ambitions face crucial test amid economic and talent pressures

One of Innowave’s most powerful contributions is in supply chain optimisation. By digitising material flows and applying analytics to forecasting and logistics, they create agile networks that respond swiftly to market changes—a capability that has become mission-critical in today’s unpredictable geopolitical climate.

Future-proofing through knowledge

Understanding the intricacies of semiconductor supply chains is no longer the domain of engineers and operations managers alone. Founders must grasp the broader implications—from sustainability and digital twin adoption to geopolitical risk and capital flow.

This is why the panel discussion “Building in the Semiconductor Age: What Tech Founders Need to Know About Supply Chains, Partnerships, and Strategic Positioning” is unmissable.

Join industry leaders Teong Wei Tan (Infineon), Chan Yip Pang (Vertex Ventures), and Jinsong Xu (Innowave Tech) as they decode the future of semiconductors and what it means for entrepreneurs.

Echelon Singapore 2025
Suntec Singapore
June 10–11
Panel: June 11, 10:30 AM – 11:20 AM at Forge Stage

Secure your seat now and future-proof your startup for the semiconductor-powered decade ahead.

The post Building smart: A tech founder’s guide to the semiconductor supply chain revolution appeared first on e27.

Imagine a Singaporean biotechnology startup leveraging artificial intelligence (AI) in diagnostic solutions to determine the most effective cancer treatments for more rapid recovery. Meanwhile, a small agritech company might deploy AI-powered drones to enhance irrigation, pest management and crop health monitoring in rural India. 

AI innovations have rapidly become a non-negotiable driver of success in technology startups, particularly across Asia. Yet, despite these innovations’ ability to streamline functions, boost invention and personalise customer experiences, technology startups face several challenges that can hinder achievement.

Challenges faced by tech startups in the AI age

Despite AI solutions having the power to transform technology startups, integrating them isn’t always straightforward. These are some of the greatest integration difficulties in startup culture.  

Talent acquisition and up-skilling

The skills required for AI-influenced jobs change 25 per cent faster than jobs less impacted by AI, meaning workers must continuously up-skill to stay relevant. Compensation for positions relying on AI expertise also tends to be 25 per cent higher, incentivising professional development and highlighting the importance of AI to companies.

As it stands, talent availability is lacking. Saikat Banerjee — a leader at Bain & Company’s AI, Solutions, and Insights firm — says there will be 1.5 to 2 times more AI-related job openings than there are professionals to fill them by 2027. 

According to an MIT Sloan study, 85 per cent of entrepreneurs agree they critically need an AI strategy, whether to seek new opportunities, encourage groundbreaking product development or gain deeper insight into the customer journey. 

Data collection and governance 

Because startup companies are in their infancy, they do not always have relevant data points to train AI models. Data quality and diversity are also crucial. Otherwise, inputs may result in inaccuracies, biases and inadequate predictions, with serious consequences in health care or financial settings. 

Data privacy regulations are on the rise throughout Asia. For instance, Korea’s Personal Information Privacy Commission (PIPC) has issued rules allowing consumers to ask about AI decision-making, such as how it makes certain hiring decisions. Hong Kong also encourages responsible AI use in businesses by promoting fairness and transparency.

Also Read: How Hasan Venture Capital uses AI to build an ethically grounded investment future

Infrastructure and computing power

Technology startups must contend with the high costs of cloud computing and specialised equipment for training AI models. As these solutions grow more sophisticated, the need for expansion and additional resources may further strain a startup’s budget. 

Areas with inconsistent internet connectivity could also affect AI performance. According to one report, internet use is 22.5 per cent lower in rural Southeast Asia than in urban areas, except Singapore and Brunei. Climate change impacts in Indonesia, the Philippines and Vietnam, especially, may also hinder broadband infrastructural investments. 

Biases and fairness

Startups must address biases within AI systems. This includes unfair decision-making based on gender, age or race. Failing to mitigate biases could hurt a startup’s reputation and lead to noncompliance. 

Biases may occur during data collection due to insufficient information capture. It might also happen when data gets fed to the models during training. Some regions have introduced new rules requiring companies to recheck information for fairness before continuing conditioning models. 

Funding and investment

Because AI is still developing, technology startups must secure funding to demonstrate the tools’ potential to stakeholders. The most effective approach is establishing clear AI initiatives with each project’s likely return on investment. Asian markets can seek government grants and venture capital for AI specialisations.

China is a prime example of this, having previously invested 23 per cent of US$912 billion in government venture capital funds to 1.4 million early-stage AI startups. The Chinese government issues much of this venture capital to firms with lower software development costs and those with signs of higher growth from the investment.

Integration and implementation

AI implementation may be difficult in existing systems and workflows, especially if teams are resistant or lack proper training. These factors can also put a startup at risk of scams. 

For example, AI models need access to sensitive data. If personal information gets into the wrong hands, businesses and their customers may be susceptible to scammers. Bad players may use AI tools to create convincing deepfakes of people or communications to collect money. Others may use fraudulent chatbots impersonating customer service representatives to steal credit card information.

Also Read: Navigating the trust labyrinth: My perspective on ethical AI marketing

According to a Deloitte report, only 33 per cent of employees have received generative AI training, and 35 per cent say they weren’t satisfied with their learning. A company must ensure a clear strategy for AI integrations and prepare its employees for the change. 

Tips for startups to overcome these challenges

Technology startups must keep up with evolving AI advancements even as they find their footing. Companies should concentrate their investments in talent acquisition, data management and computing infrastructure for maximum returns. 

Integrating AI into a company’s business plan should focus on concrete outcomes and revenue. Seeking investors with AI knowledge and pursuing federal grants and funding programs — including crowdfunding — is another way to garner capital, test the market and reduce risk. 

A successful technology startup is only as good as those working there. Therefore, finding the best talent with AI expertise and providing comprehensive training and professional development is essential.

Additional suggestions for overcoming the challenges of AI in a technology startup include:

• Explore public data platforms and exchanges.
• Enhance training data by modifying existing points and creating new, quality data from scratch.
• Implement stringent data management and security measures.
• Utilise cloud computing for adaptability and scalability.
• Improve AI model efficiency for the most productive resource utilisation.
• Integrate AI with smaller, more concentrated projects, such as resolving specific business-related issues.
• Make improvements to AI tools according to feedback and results.
• Encourage employee and stakeholder engagement during AI implementation.
• Support employees with AI training.

It is equally important to address potential biases in AI technology. Startup owners might consider launching an ethics committee or advisory board to establish responsible AI development and utilisation. The committee will review AI projects, detect possible biases, and prioritise transparency to build trust and manage risks.

Embracing AI in the startup landscape

As AI advances, startups should find ways to adopt it in practice. Although the challenges are valid, AI can transform businesses for the better. Considering startups must build themselves from the ground up, embracing AI responsibly and gradually is a sure path to success.

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Singapore’s push to lead in artificial intelligence faces mounting headwinds as global economic pressures and persistent talent shortages undercut momentum.

According to a new survey by global HR and payroll platform Deel, conducted with Milieu Insight, 81 per cent of Singaporean companies report negative impacts from global tariffs, with many forced into difficult workforce decisions such as wage freezes, reduced hiring, and retrenchments.

Also Read: Southeast Asia’s AI divide: SleekFlow report warns of widening gap

The findings—based on responses from 350 business leaders across SMEs and large enterprises—reveal a critical inflection point for the island nation’s digital transformation agenda. Despite AI’s promise to boost productivity and efficiency, adoption remains uneven and cautious across the market.

Global shocks temper AI optimism

Singaporean businesses find themselves squeezed between escalating operational costs due to tariffs and the imperative to invest in innovation. More than half (56 per cent) of respondents cite increased costs, with AI-forward companies feeling this pinch even more acutely (86 per cent).

Yet, the potential benefits of artificial intelligence remain compelling. Companies leveraging AI report tangible gains: 71 per cent cite improved productivity, 61 per cent report operational optimisation, and 50 per cent realise cost savings. Nearly a third (31 per cent) have accelerated AI and automation in response to global instability—an indication that AI is seen as a resilience tool in volatile times.

Talent bottlenecks slow AI deployment

Even as the benefits of AI become clearer, Singapore’s talent pipeline lags behind. A staggering 68 per cent of businesses are still in the early stages of AI adoption, with only 12 per cent of SMEs reaching intermediate levels, compared to 43 per cent of larger enterprises.

Talent shortages are the main culprit. Nearly half of the respondents say local AI expertise is insufficient, and high salary expectations, limited career growth, and skill mismatches further hinder recruitment.

As a stopgap, 62 per cent of firms are open to hiring from overseas, but only 20 per cent have budgets set aside to reskill their current workforce—a disconnect that could stall sustainable progress.

“Talent remains the single biggest barrier to scaling AI,” said Nick Catino, Global Head of Policy at Deel. “Cross-border hiring can fill gaps, but must be paired with effective knowledge transfer to uplift local teams”.

Government support recognised but underutilised

Singapore has laid out comprehensive strategies to foster AI, including the National AI Strategy (NAIS 2.0). However, awareness and engagement remain low.

While 92 per centof businesses see government support as vital—particularly in funding and upskilling—only 5 per cent are actively engaging with existing AI frameworks. A striking 95 per cent say they are unfamiliar or only mildly familiar with the governance framework.

Also Read: AI and automation in Southeast Asia: Which jobs are at risk and which will thrive?

This lack of engagement comes despite calls for stronger regulatory guardrails from 57 per cent of the respondents. The gap suggests that while the government’s intent is clear, execution and awareness-building efforts need urgent reinforcement.

Aligning talent, policy, and tech for a future-ready Singapore

As the AI race intensifies, Singapore must bridge its knowledge and talent gaps to sustain its leadership. Proactive engagement with policy frameworks, robust upskilling strategies, and targeted AI investments will be essential. Only through this alignment can the city-state realise the transformative potential of AI—turning today’s headwinds into tomorrow’s competitive edge.

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One of the most overlooked reasons why businesses lose money — whether in outsourcing, sales, partnerships, or overseas investments — is not incompetence or bad luck, but a deeper structural issue: the principal–agent problem.

The principal–agent problem occurs whenever one party (the principal) depends on another party (the agent) to act on their behalf. In theory, both should want the same outcome. In reality, their incentives rarely match.

The agent often gets paid immediately. The principal only wins or loses over time.

So the agent pushes risky, unsuitable, or outright worthless products — with zero accountability.

This gap creates the perfect environment for moral hazard — a situation where the agent takes risks, exaggerates promises, or cuts corners because they don’t suffer the consequences. The reward is theirs. The downside is yours.

We see this everywhere:

• A real estate broker earns a commission upfront, even if the project collapses later.
• A sales consultant overpromises because they’re paid for closing, not delivering.
• An overseas agent recommends a vendor they secretly have a side deal with.
• A “financial advisor” pushes long-term products they barely understand but that give them the highest commission.
• Recruiters sell you a candidate because their incentive is placement, not performance.

In each case, the agent gets their reward long before you experience the true outcome. And by the time you discover the risks, it’s too late.

When the agent’s payoff is front-loaded while the principal’s risk is long-term, misalignment becomes extreme.

Moral hazard doesn’t require malicious intent

Sometimes the agent simply doesn’t know what they’re selling, doesn’t understand the risks, or never has to live with the consequences.

The structure itself encourages overconfidence and under-disclosure.

The incentives make it rational for agents to behave this way — even if it harms the principal.

Also Read: AI’s biggest bottleneck isn’t intelligence but fragmentation: i10X co-founder

Why moral hazard produces predatory behaviour: Because the seller wins even if you lose, this is why so many business deals are filled with:

• Inflated projections that exaggerate the upside
• Minimise or hide risk
• Aggressive persuasion
• Zero accountability
• Fake credibility (watches, cars, “success lifestyle”)
• Attack or gaslight anyone who questions them

Because once they collect the fee, they disappear.

And when you combine:

Information asymmetry (they know more than you about this market)

Principal–agent problem (their goals differ from yours)

Moral hazard (they don’t suffer if they’re wrong)

You get a perfect recipe for:

• Overconfidence
• Deception
• Exploitation
• Bold promises without accountability

This explains why entire industries become magnetised toward unethical behaviour — simply because the system rewards the wrong things.

So how do you fix it?

You don’t fix it with trust. You fix it with structure.

Structuring business partnerships for greater accountability

You don’t fix the principal–agent problem by hoping for good behaviour. You fix it by engineering the incentives so that bad behaviour is punished, and good behaviour is rewarded.

The only proven way to reduce this moral hazard is to align incentives, share risk, and impose accountability.

Create shared “skin in the game”

If the agent benefits only when you benefit, incentives realign instantly.

Examples:

• Profit-sharing instead of upfront fees
• Milestone-based payments instead of full deposits
• Escrow release tied to verified outcomes
• Advisors who invest in the same assets they recommend
• Consultants paid based on measurable deliverables

This transforms the relationship from: “Your risk, my reward” → “Our performance, shared reward.”

If the agent refuses performance-linked compensation, that’s a red flag.

Break the information asymmetry

The principal–agent problem amplifies when the agent knows 10x more than the principal.

You fix this by:

• Third-party verification
• Independent due diligence
• Local experts auditing claims
• Transparent documentation
• Competitor comparison
• Data access (not only brochures)

Use escrow and controlled payment structures

Most predatory deals survive because payment is front-loaded.

We solve this with:

• Escrow accounts
• Release-by-milestone payments
• No commission until a quality check is passed
• Split payments tied to measurable deliverables

When agents know they won’t get paid unless the job is real, inflated promises disappear.

Align incentives with long-term outcomes

The principal–agent problem is a timing problem:

• The agent gets paid now.
• The buyer suffers consequences later.

Solutions:

• Tie fees to long-term performance
• Lock advisors into accountability periods
• Require warranties or after-sales responsibilities
• Stagger commissions so payout matches the risk window

This discourages short-term “pump and dump” behaviour.

Increase transparency

Misalignment thrives in the dark.

Reduce it by:

• Open-book reporting
• Shared dashboard of project status
• Mandatory disclosure of incentives
• Conflict of interest declarations
• Recording sales calls/documentation

When incentives are visible, bad actors can’t hide them.

Also Read: How to spot the signals that move the needle: A Founder’s guide to cutting through the clutter

Certifications, vetting, and competence checks

Unqualified agents cause as much harm as malicious ones.

Solutions:

• Minimum knowledge requirements
• Mandatory training on product risks
• Verified local licensing
• Background checks
• Complaint history reviews

Many “sales agents” don’t even understand what they’re selling — eliminating incompetent agents protects the principal.

Separate advice from sales

This is the reform that transformed modern financial regulation.

To reduce misaligned incentives:

• The advisor should not be the seller
• The seller should not be the evaluator
• The promoter should not structure the fee
• Advice should be fee-based, not commission-based

When the same person advises you and sells to you, chances are that you will end up on the losing side of the trade.

Build feedback loops + consequences

Bad agents thrive because there is no downside for deception.

Fix this with:

• Blacklisting bad vendors
• Public reviews
• Performance scoring
• Contractual penalties
• Mandatory refunds for negligence

Give the agent something to lose for bad behaviour and create a structure to encourage positive change.

Also Read: A Founder’s field guide on 10x talent

The danger isn’t just “bad people.” It’s bad incentives that reward bad behaviour.

When incentives change, behaviour changes.

And in global markets — especially cross-border investments, outsourcing, and vendor sourcing — solving the principal–agent problem is the difference between sustainable growth and expensive mistakes.

Because when the ecosystem is structured to protect the principal, corruption declines, quality improves, and everyone performs better.

In the end, moral hazard is not about morality — it’s about incentives.

In summary: Fix incentives → reduce moral hazard → improve markets → protect investors.

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For years, F&B owners viewed delivery platforms as a necessary evil, a simple transaction channel to reach customers who didn’t want to leave their couches. But in 2025, that relationship has undergone a radical transformation. Platforms are no longer just delivering bags of food; they have become “demand orchestrators” that dictate how, when, and at what price restaurants operate.

The annual Food Delivery Platforms in Southeast Asia report by Momentum Works highlights a significant shift in platform power. Through aggressive expansion of dine-out offerings, product advertising, and sophisticated data sets, platforms like Grab and ShopeeFood are extending their influence deep into the offline F&B ecosystem — a market far larger than the US$22.7 billion food delivery sector itself.

Also Read: The China playbook comes to Southeast Asia’s food apps

Dine-out: The high-margin Trojan Horse

The most visible move in this strategy is the rise of “Dine Out” deals. By offering vouchers and discounts for in-store dining, platforms can capture a slice of a restaurant’s total revenue without the high operational costs associated with delivery riders. For the platforms, this is a high-margin touchpoint that deepens user engagement. For the merchant, it creates a dangerous level of dependency.

When a customer uses a Grab Dine Out voucher, the platform isn’t just a courier; it is the entity that brought the customer through the door. This allows the platform to collect data on offline visit patterns, category benchmarks, and pricing dynamics that the merchant themselves cannot see. This creates what the report calls “structural data asymmetry”: the merchant sees only their own performance, while the platform sees the entire market.

The algorithm as the new head chef

This data asymmetry is being weaponised through advertising products. Grab, ShopeeFood, and Line Man are all aggressively pushing “ads products” for F&B chains and small-to-medium enterprises (SMEs). These range from simple boosted placements for a “Warung” or street vendor to sophisticated, AI-driven keyword targeting for multi-national QSR chains.

The report notes that pricing, promotions, and visibility are increasingly becoming platform-led rather than merchant-led. Through curated discovery feeds and targeted vouchers, platforms effectively choose which price points convert and which restaurants get exposure.

Merchants are forced to “treat platforms as operating environments rather than simple sales channels,” designing their menus and price tiers around platform logic to avoid being buried by the algorithm.

Dark kitchens and the limits of efficiency

Interestingly, the platforms’ attempt to control the supply side through “dark kitchens” has largely stalled in Southeast Asia. Unlike China or the Middle East, where dark kitchens contribute up to 30 per cent of order volume, the model was deployed in Southeast Asia before the ecosystem was ready.

Also Read: The subsidy wars are ending, and only two will survive

Without the extreme demand density found in cities like Dubai or Shanghai, centralised production facilities proved economically unviable at scale. Most dark kitchen startups in the region have either shut down or pivoted toward building their own offline brands. This failure suggests that while platforms can orchestrate demand, they cannot yet easily manufacture supply.

The merchant squeeze

As platforms evolve into demand orchestrators, the power dynamic has shifted decisively. Medium-sized merchants are the most vulnerable, finding it increasingly difficult to build brand loyalty that exists independently of the platform’s discovery feed. To survive in 2026 and beyond, the report suggests that restaurants must build “complementary brand touchpoints” outside of the apps to avoid becoming interchangeable commodities in a world ruled by platform mechanics.

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Across the Asia Pacific, eSIM is still widely seen as a consumer travel feature. Something useful for tourists who want data on arrival without buying a local SIM. That perception has quietly limited how enterprises, OTAs, and corporate travel teams think about the technology.

In reality, eSIM has become a business infrastructure layer. For organisations operating across APAC, it directly impacts cost control, operational efficiency, and customer experience. The companies that still see eSIM through a travel-only lens are leaving measurable value on the table.

This matters more in APAC than in most regions.

Why APAC enterprises feel the pain more acutely

APAC businesses operate across fragmented markets. Network quality varies country by country. Cross-border travel is frequent. Mobile connectivity is central to daily operations, not a nice-to-have.

GSMA Intelligence shows that many APAC markets have fast smartphone upgrade cycles, which means a high share of employees already carry eSIM-compatible devices. The capability is already in their pockets, but enterprises have not fully operationalised it.

At the same time, enterprise mobility and device usage are increasing. From field teams to corporate travellers to POS terminals and scanners, mobile data has become part of core operations. IoT Analytics has observed that enterprises adopting eSIM do so because remote provisioning and network switching reduce the need for physical intervention, which is especially valuable in geographically spread regions like Southeast Asia.

The environment is ready. The mindset often is not.

Regional workforce travel and cost control

Consider a regional sales or consulting team operating across Singapore, Indonesia, India, Thailand, and Malaysia. Travel is routine. Connectivity is assumed. Roaming costs, however, are anything but predictable.

Traditional roaming creates two problems. First, costs spike unevenly and appear late in the billing cycle. Second, employees often lose productivity on arrival while trying to connect or purchase local SIMs.

This is where eSIM changes the operating model. Instead of roaming, enterprises can issue regional data plans that activate before travel. Employees are connected. Finance teams gain cost visibility.

Also Read: Singapore’s Airalo becomes first eSIM unicorn after US$220M round

When AlixPartners analysed enterprise roaming behaviour, they found that organisations switching to eSIM-based connectivity could reduce roaming spend by up to 35 per cent. For corporate travel teams managing dozens or hundreds of trips a quarter, that reduction is meaningful. More importantly, it brings predictability to a line item that has traditionally been volatile.

The value is not only in savings. It is in removing friction from the first hour of every business trip.

OTAs and travel platforms are improving end-to-end CX

OTAs and travel platforms compete aggressively on experience. Flights and hotels are increasingly commoditised. What differentiates brands is how smooth the journey feels.

Connectivity is one of the most common failure points in that journey. When travellers land without data, they struggle with transport, check-ins, and navigation. Support tickets follow.

For OTAs, eSIM becomes a CX layer rather than a telecom add-on. Connectivity can be bundled into bookings or offered contextually before departure. The traveller arrives connected, and the platform reduces downstream support load.

For corporate travel managers and platforms, connectivity is increasingly treated as part of trip readiness. BCEN Global highlights how organisations using eSIM improve onboarding and reduce friction for mobile users by ensuring connectivity at the moment it is needed.

In a competitive OTA landscape, that reliability translates directly into brand trust.

Large device fleets and IoT rollouts

Many organisations deploy device fleets across APAC. POS terminals, kiosks, scanners, trackers, and sensors are common across retail, logistics, and mobility sectors.

With physical SIMs, every device requires manual handling. Activation, replacement, and troubleshooting all depend on physical access. As fleets grow, this becomes a bottleneck.

IoT Analytics points out that enterprises adopt eSIM because it enables central provisioning, remote updates, and easier cross-border expansion. In practical terms, this means faster rollouts and lower operational overhead.

Consider a fleet of 1,000 devices deployed across multiple countries. If each physical SIM activation takes 20 minutes, that is more than 300 hours of manual work. eSIM reduces that time dramatically by allowing centralised, automated provisioning.

Also Read: The impact of eSIM on international roaming and travel

Making the ROI clear

For enterprise decision-makers, the ROI from eSIM typically shows up in four areas:

• Cost reduction through lower roaming spend and fewer SIM logistics.
• Efficiency through faster activation and reduced manual handling.
• CX improvement through reliable connectivity for employees and customers.
• Scalability through easier expansion into new markets.

These outcomes are why eSIM adoption is accelerating at the enterprise level, even if public perception still frames it as a consumer feature.

When enterprises and OTAs should pilot eSIM

eSIM is best introduced as a pilot, not a full transformation. Organisations should consider starting when any of the following apply:

• Regional workforce travel is frequent.
• Field teams rely on mobile data.
• Devices or terminals are deployed across markets.
• Customer experience suffers when connectivity fails.
• Telecom costs lack predictability.

If two or more are true, a pilot often delivers value within a single quarter.

Reframing eSIM as infrastructure

eSIM is no longer just about avoiding airport SIM queues. For APAC enterprises and travel platforms, it is a way to regain control over cost, reliability, and scale.

The reason many organisations underuse eSIM is simple. They still see the tourist. They miss the infrastructure.

In a region as mobile and fragmented as APAC, that blind spot is expensive. The organisations that correct it early operate with less friction and greater confidence as they scale across borders.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Image courtesy of the author.

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