Southeast Asia’s innovation ecosystem is increasingly defined by collaboration across borders, industries, and organisational types. Startups are scaling faster, corporates are seeking external innovation more actively, and investors are looking beyond traditional hubs for the next wave of high growth companies. As a result, platforms that connect founders with enterprise partners, capital, and global networks are becoming central to how innovation happens in the region.

Plug and Play, headquartered in Silicon Valley with a strong presence across the Asia Pacific, operates at this intersection. With more than 60 locations in over 30 countries and partnerships with more than 550 global organisations, including EDB, GIFT City, Amazon, MPA, and HTX, the platform connects startups with the corporates, governments, and investors that can help them scale.

A Record Year for Innovation

In 2025, Plug and Play accelerated more than 2,800 startups across its global network, with close to 30% of those coming from Asia. The firm also invested in more than 250 startups during the year, with nearly half of those investments focused on artificial intelligence, reflecting the firm’s conviction in AI as a transformative force across industries.

Globally, Plug and Play has continued to expand its venture activities, launching industry- and geography-focused funds and growing its assets under management to more than US$1 billion. Recent expansions into new markets, including Phnom Penh and Taiwan, underscore the firm’s commitment to supporting innovation where it’s happening, not just where it’s traditionally been.

Also read: Join 150+ builders creating AI workflows that solve real SME problems

Connecting Southeast Asia to Global Opportunities

In Southeast Asia, Plug and Play APAC offers founders more than just their network. Through its market access programmes, including some in partnership with Enterprise Singapore under the Global Innovation Alliance, startups gain structured pathways into key markets such as San Francisco, the UAE, Manila, and Jakarta. More than 800 startups every year have benefited not only from corporate and government-focused programmes but also from access to Plug and Play’s global network of investors and corporates, enabling them to soft-launch in new markets with the right introductions and support.

Meet Plug and Play APAC at Echelon Singapore 2026

Plug and Play APAC joins Echelon Singapore 2026 as a bronze sponsor. The two-day event, held at Suntec Singapore Convention and Exhibition Centre on 3–4 June 2026, brings together Southeast Asia’s startup community through content stages, exhibitions, networking sessions, and knowledge-sharing activities designed to support regional innovation.

Also read: Meet the companies taking the floor at Echelon Singapore 2026

Attendees can connect with the Plug and Play APAC team to explore accelerator participation, corporate innovation partnerships, investment collaboration, and cross-border expansion opportunities. Selected startups may receive fast-track introductions to relevant Plug and Play accelerator programmes and corporate partners, an exclusive opportunity designed to help founders accelerate partnerships and international market access.

Whether you’re a founder looking to scale, a corporate seeking innovation partners, or an investor exploring the region’s next wave of high-growth companies, Echelon 2026 is the right place to start the conversation.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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Plug and Play APAC: Leading the way in open innovation platforms

Plug and Play is the leading innovation platform, connecting startups, corporations, venture capital firms, universities, and government agencies.

Headquartered in Silicon Valley, we’re present in 60+ locations across five continents. We offer corporate innovation programs and help our corporate partners in every stage of their innovation journey, from education to execution. We also organize startup acceleration programs and have built an in-house VC to drive innovation across multiple industries where we’ve invested in hundreds of successful companies including Dropbox, Guardant Health, Honey, Lending Club, N26, PayPal, and Rappi.

Our Asia Pacific headquarters was launched in Singapore in 2010 and we are now present in five cities in Southeast Asia with additional locations in China, Japan, Korea and India. We work closely with both the public and private sector with programs, innovation initiatives and startup investments across the region.

Click here for more information.

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There’s a conversation happening in boardrooms, co-working spaces, and coffee shop offices across Southeast Asia. It’s not about whether AI will change how businesses operate. That question has already been answered.

The conversation now is about timing. And for most SMEs, the answer they’re giving themselves is the most expensive one possible: not yet.

It’s part of the reason the AI Workflow Competition at Echelon Singapore 2026 exists. Not as a theoretical exercise in what AI could do for small businesses, but as a direct response to the execution gap that’s keeping real operational solutions from reaching the SMEs that need them most. The competition connects businesses carrying genuine workflow challenges with builders who can solve them, and it does so in a structured environment designed to produce deployable results, not demo-ready concepts.

But the competition is a symptom of something larger. The window for SMEs to move on AI automation is not staying open indefinitely.

The window is narrowing

For the past few years, AI automation has existed in a comfortable grey area for small and medium enterprises. Interesting, but experimental. Promising, but unproven. Worth watching, but not yet worth acting on.

That grey area is disappearing.

The tools have matured. The costs have dropped. The use cases are no longer theoretical; they’re documented, repeatable, and increasingly accessible to businesses without dedicated technology teams or enterprise budgets. What was a competitive advantage for early adopters twelve months ago is becoming the baseline expectation for operationally efficient businesses today.

The SMEs that moved early are not struggling to integrate AI into their workflows. They’re struggling to remember what operations looked like before it.

What’s actually changed

The shift isn’t just about technology becoming more capable, though it has. It’s about the nature of what automation can now handle.

Early automation was brittle. Rule-based systems that worked perfectly until they didn’t, breaking the moment they encountered something outside their narrow parameters. The operational reality of most SMEs, with varied inputs, inconsistent formats, and context-dependent decisions, was fundamentally incompatible with automation that demanded consistency it could never guarantee.

AI-powered workflows are different in kind, not just degree. They handle variation. They extract meaning from unstructured inputs. They make contextual decisions rather than following rigid logic trees. They don’t just execute instructions; they interpret intent.

Also Read: SMEs invited to turn real workflow challenges into AI solutions

This distinction matters enormously for SMEs, whose operations rarely conform to the clean, predictable patterns that traditional automation required. An AI workflow that can process a supplier invoice regardless of format, extract the relevant information, cross-reference it against existing records, flag discrepancies, and route for approval: that’s not incremental improvement. That’s a fundamental change in what a small finance team can accomplish.

The same logic applies across functions. Customer service workflows that understand query intent, not just keywords. Inventory systems that identify patterns in demand data rather than simply tracking counts. Onboarding processes that adapt to context rather than forcing every new hire through identical steps regardless of role or background.

The technology to build these workflows exists today. It is not expensive. It is not inaccessible. What it requires is translation: the capacity to take a real business problem and engineer a solution that works in production, not just in a demo.

The real barrier isn’t technology

Ask most SME owners why they haven’t implemented AI workflow automation, and the answers cluster around a few familiar themes: concerns about cost, uncertainty about where to start, lack of internal technical expertise, and a general wariness born from years of overpromised and underdelivered enterprise software.

These concerns are legitimate. But they’re also increasingly outdated as explanations for inaction.

Cost is no longer the barrier it once was. Cloud infrastructure, accessible LLM APIs, and no-code automation platforms have dramatically reduced the investment required to build functional AI workflows. Solutions that would have required a dedicated engineering team three years ago can now be built and deployed by a skilled individual working within weeks.

The starting point question has also become easier to answer. The highest-value AI automation opportunities in most SMEs follow recognisable patterns: document processing, customer inquiry management, data reconciliation, approval workflows, reporting automation. These aren’t exotic edge cases. They’re operational table stakes that appear, in different forms, across virtually every industry and business model.

What remains genuinely scarce is execution capability: the ability to take a real business problem, understand its operational context, and build automation that works reliably in the hands of non-technical teams. Not impressive demos. Not sophisticated architectures. Working solutions that deliver measurable outcomes and don’t require a developer on call to function.

This is the gap that actually matters. And it’s the gap that the broader AI ecosystem is now beginning to close.

Also Read: Builders wanted: Close the AI execution gap for SMEs

Why collaboration is the model that works

The traditional paths to SME automation, whether hiring a consultant, adopting a SaaS tool, or building in-house, all share a common flaw. They treat automation as a product or service transaction rather than a problem-solving collaboration.

Consultants interpret problems through their existing frameworks. SaaS tools ask businesses to conform to their logic. In-house builds rarely happen because the talent and bandwidth don’t exist simultaneously. The result, across all three approaches, is a persistent gap between the automation that SMEs need and the automation they actually get.

The model that consistently produces better outcomes starts differently. It starts with the actual problem, described by the people who experience it, in the specific operational context where it exists, and works backwards to a solution. It treats the business owner as the authority on the problem and the builder as the authority on implementation, and it structures collaboration so both can contribute what they actually know.

This sounds obvious. It rarely happens in practice, because most procurement and development processes create distance between problem and solution rather than closing it.

The businesses and builders who figure out how to close that distance, who build genuine collaboration structures rather than transactional relationships, will define what SME automation looks like in Southeast Asia over the next decade.

The builders who will matter

There’s a generation of technical talent in Southeast Asia that understands AI tooling better than most enterprise technology teams. Developers who have spent time with LLMs, automation platforms, and API integrations. Engineers who can architect solutions that are both technically sophisticated and operationally pragmatic.

What many of them haven’t had is access to real business problems. Genuine operational challenges, with real constraints, real edge cases, and real accountability for outcomes. The gap between capability and credibility, between knowing how to build and being able to prove you’ve built things that work, is significant for builders early in their careers or transitioning into AI automation.

The builders who close this gap won’t do it by building more impressive demos. They’ll do it by solving real problems in real environments and documenting the outcomes. By proving that they understand the difference between a system that works in controlled conditions and one that works in production. By treating business impact as the measure of success, not technical sophistication.

These are the builders the market needs. They’re also the builders who will find the most commercial opportunity as SME automation moves from experimental to essential.

Also Read: Why the future of AI automation belongs to builders who ship

What comes next

Southeast Asia’s SME sector is at an inflection point. The operational efficiency gains available through AI workflow automation are no longer marginal; they’re substantial enough to materially change what small teams can accomplish, what growth is achievable without proportional headcount increases, and what competitive positioning looks like in markets where margins are tight and agility matters.

The businesses that act now, that identify their highest-friction workflows, find builders who can translate those problems into working solutions, and implement automation that actually runs in their environments, will not look back.

The businesses that wait will find themselves explaining the delay to teams who are increasingly aware that the tools exist and the cost is justified.

The automation era isn’t a future state to prepare for. It’s the present reality to engage with.

The question isn’t whether to automate. It’s whether to act before or after your competitors do.

The AI Workflow Competition at Echelon Singapore 2026 is open now. Submit your challenge or register as a builder.

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After a robust performance in 2024—when Singapore’s economy expanded by 4.4 per cent, outpacing the previous year’s growth—business leaders entered 2025 with renewed optimism.

However, as the year unfolded, the outlook became clouded by intensifying geopolitical rivalries in the Middle East and Europe, and most notably, by the escalation of global trade wars. The Trump administration’s imposition of high tariffs sent shockwaves through international markets, capturing the attention of business communities worldwide and reminding us of Singapore’s vulnerability to external shocks as one of the world’s most open economies with a trade-to-GDP ratio exceeding 300 per cent.

Business leaders are increasingly concerned about the risks and uncertainties facing Singaporean companies, particularly how these will affect growth and strategy. Slower economic growth, weaker sentiment, and reduced international trade are dampening investment and demand across sectors. Supply chain disruptions and higher import costs from tariffs and global volatility are impacting both export-oriented and domestic businesses. Even those focused on the local market may face delays, rising prices, and softer consumer demand as households tighten spending.

Despite these headwinds, Singaporean businesses are not without recourse. Drawing on insights from the recent IndSights Research Business Leaders Forum, this article explores the key risks and uncertainties for 2025—and, crucially, the strategies that can help companies navigate this challenging landscape and continue to thrive.

Turning uncertainty into opportunity

While headlines may paint a sobering picture, Singaporean businesses should not be discouraged by prevailing uncertainty.

At the forum, Ho Kwon Ping, Founder of Banyan Group, reminded leaders that resilience is forged in adversity. He cautioned against letting constant news and media commentary dictate business sentiment or strategy. Instead, Ping urged leaders to critically assess their unique position and seek ways to pivot and adapt for success in this new environment.

Mitigating protectionism through diversification of markets

Amid heightened global uncertainty and volatility, Ping stressed the importance of market diversification for Singaporean businesses. Given Singapore’s limited domestic market, he cautioned against a local-only focus and advocated making overseas expansion a key part of long-term strategy.

Drawing from his own entrepreneurial journey, Ping noted he launched his first resort in Thailand, only opening one in Singapore three decades later, underscoring his commitment to building an international brand. He also cited fellow panellist Elizabeth Liau, Founder of Maison de L’asie, who prioritised overseas growth by marketing her luxury perfumes in non-traditional destinations such as Lithuania, Paraguay, and Romania.

Their experiences demonstrate that, especially during periods of global volatility, businesses should look beyond conventional markets to remain agile, manage risks, and achieve sustainable growth.

Also Read: Navigating trade turbulence: Digital transformation enhances global logistics amid rising tariffs

Choosing the right partner is key to local market success

Evelyn Tay, CEO of EtonHouse International Education Group, which operates 100 schools in nine countries, emphasised the importance of partnering with the right local stakeholders. She noted that having local partners aligned with the company’s values is vital, as it provides insights into localisation and access to key networks.

Tay shared that while EtonHouse’s standard operating model worked well in Suzhou, it needed further fine-tuning in Chengdu due to differences in affluence and parental work patterns. This underscores the need for a local partner, as even within the same country, market dynamics can vary significantly.

Striking a balance between brand identity and cultural nuances

Liau, with her extensive experience marketing luxury fragrances globally, emphasised that definitions of luxury differ and shift across markets, demanding brands to balance authenticity and adaptation. For businesses to expand into new markets, it’s essential to focus on the local consumers and appeal to their tastes, preferences and needs.

Additionally, Liau noted the growing role of digital marketing and the need for an omni-channel strategy, where regional influencers and digital ambassadors can significantly enhance brand equity—even without a physical presence. Digital marketing also offers a cost-effective approach for businesses looking to diversify into global markets, enabling them to reach and engage new audiences efficiently while managing resources strategically.

For Singaporean businesses, investing in aligned local partnerships and developing digital strategies is vital for building brand presence and achieving sustainable growth in diverse global markets.

Diversify supply chains for stability

To remain sustainable and tackle the impact of the trade war, businesses can look to diversifying business supply chains, particularly for companies deeply involved in cross-border trade. Ping highlighted the importance of understanding Singapore’s unique and evolving position in the regional supply chain.

With Thailand and Indonesia joining BRICS and expanding their supply chain networks into markets like Brazil, Egypt, and Ethiopia, Singaporean businesses now have indirect access to new and previously untapped markets, opening fresh opportunities for trade and collaboration.

Also Read: The perfect storm: Jobs plunge, tariffs hit, and crypto volatility soars

“1 + 1 + 1 greater than 3” means more cooperation between ASEAN nations

The ASEAN Trade in Goods Agreement (ATIGA) has been particularly beneficial in this regard, with six out of ten ASEAN members eliminating import duties and the remaining four reducing them to minimal levels. This has fostered a robust internal market within Southeast Asia that remains resilient despite external pressures.

Reflecting this momentum, Prime Minister Lawrence Wong attended a two-day ASEAN summit in Kuala Lumpur, where leaders reaffirmed their commitment to ASEAN’s integration and community building, and discussed ways to promote a free, open, and inclusive regional architecture both within the bloc and with external partners. The talks also focused on strengthening ASEAN’s resilience by making intra-regional trade more seamless and tapping into new growth areas such as the digital and green economies.

Beyond Southeast Asia, Singaporean businesses can also explore new opportunities in regions such as the Gulf. Recent ASEAN summits have strengthened economic ties between ASEAN, the Gulf Cooperation Council (GCC), and China, with leaders affirming their commitment to deeper cooperation in trade, investment, and supply chain diversification.

Local and on-the-ground partnerships

As highlighted by our panellist, Jasper Lee, Founder of Kingsmen Global, a leading freight forwarding company, it is crucial for businesses that are looking to diversify into new markets to work closely with their logistics providers.

Understanding lead times, customs requirements, and regulations is essential to ensure timely deliveries and avoid costly disruptions at borders. This reinforces the earlier point on the importance of strong local partnerships—not only for market entry but also for maintaining resilient and agile supply chains, regardless of industry or destination.

Charting a path forward

Despite the challenges posed by global complexities and trade tensions, Singaporean businesses are encouraged to stay optimistic and seize the opportunities that arise during uncertain times. The strategies shared by our panellists highlight the importance of adaptability and resilience.

Coupled with Singapore’s pro-business stance and commitment to maintaining a conducive environment, businesses are well-positioned to navigate the evolving landscape.

This article covers key moments from the Business Leaders Forum 2025 by IndSights Research. Additional highlights from the discussion are available here.

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Over the past decade, organisations have poured billions of dollars into storing and analysing data to make informed decisions and enhance operational efficiency. Despite these efforts, many still struggle to create meaningful business value from these insights. The challenge is not a lack of technology, but instead the lack of a scalable framework that enables organisations to deploy AI at scale efficiently and repeatably.

This is where the idea of an AI Factory comes into play — a structured approach that standardises procedures, coordinates specialised AI components, and transforms raw computing into quantifiable commercial results.

What is an “AI Factory”?

Imagine a traditional car manufacturing plant. In a factory, there is a production line where raw materials are put through a systematic process to produce a final product. In this case, it is a car. The assembly line in a factory is built on the principle of division of labour – each station in the assembly process handles a specific task, for example, engine installation and door mounting. And the goal? Producing high volumes of reliable, high-quality vehicles efficiently.

An AI Factory works on the same principle. Rather than building cars, an AI Factory produces intelligence, which could be AI models, real-time predictions, or other applications. Like a car factory, the AI Factory ensures its products meet quality standards. The intelligence must be reliable, quantifiable, and constantly improved.

Data is the raw material in this “AI Factory”, and the production line is the automated workflow that manages the entire AI lifecycle — from data ingestion to model training, validation, deployment, monitoring, and feedback. Similar to a manufacturing line that transforms raw materials into products, the production line in an AI Factory operates as an internal operating model that incorporates computation, storage, software, processes, and teams.

In an AI Factory, tokens become the universal unit of “work” for large language models (LLMs) and many generative systems. It is the equivalent of widgets on a manufacturing line. A token is the atomic chunk of input or output that the model processes, which can be subword segments for text or comparable units for other modalities. Each prompt consists of a series of input tokens, and each response comprises a series of output tokens.

We are seeing this systematic approach advance rapidly in sectors such as manufacturing, biomedical research, and smart cities. It helps businesses harness data to generate insights, accelerate innovation, and unlock new growth opportunities.

Performance indicators

Why measure tokens instead of megawatts (MW) or petabytes (PB)? This is because MW and PB only describe the power consumed and data stored, without indicating the amount of actual AI work performed. Depending on the model selection, prompt length, and job complexity, two identical GPU clusters may use comparable amounts of power but handle quite different workloads. Similarly, PB indicates the amount of data that is available, not the amount that was used or altered.

Also Read: Creating sustainable futures: The vision of steady-state societies and still cities

In contrast, tokens are directly tied to the compute workload, cost (since most providers charge per token), and user experience through speed and responsiveness. Tracking tokens enables AI Factory operators to plan and optimise, such as choosing smaller or more efficient models for lightweight tasks, trimming unnecessary prompts, and restructuring workflows, so heavy lifting only happens where it adds real value.

Beyond data centres

A data centre provides the computational infrastructure, while an AI Factory is a complete intelligence manufacturing system built on top of it. Data centres measure performance in storage capacity and energy efficiency; AI Factories measure success in the intelligence produced.

Every AI Factory operates on a repeatable cycles that include data for model training, validation, deployment, monitoring, feedback, and more. When this cycle is standardised, automated, and observable, organisations can take on multiple AI projects concurrently, share components across teams, and consistently deploy dependable models into production. This is what turns unprocessed computation into reliable, scalable, and measurable outcomes.

Putting it into practice

Based on our experience in helping enterprises build AI factories, we have identified a few key points that businesses should take note of. First, reducing deployment complexity is non-negotiable. We have seen deployment times drop from weeks to under 30 minutes when infrastructure is designed for rapid standup, allowing teams to focus on intelligence production.

Second, hardware and software must be purposefully aligned. If businesses treat them as separate layers, it could create friction at every stage. Third, an energy efficiency strategy cannot be an afterthought, as it directly impacts both operational costs and the ability to scale intelligence production.

Continuous lifecycle management is an important component of an AI Factory. Successful deployments share this discipline. Our team collaborates closely with clients on everything from performance optimisation and reliability hardening to ongoing validation and integration. Enterprises should also ensure their AI Factory operates smoothly and effectively by empowering a dedicated team of specialists, project managers, and field experts — whether it is streamlining storage pipelines, creating liquid cooling systems, or fine-tuning network topology.

Also Read: How to use blockchain to fund and create a greener future

Road ahead

Over the next decade, we believe AI Factories will evolve from isolated high-performance computing clusters into self-optimising production systems. Manual intervention at each stage will no longer be required, and models will automatically refine themselves in real-time, continuously based on feedback.

Adoption is expected to expand beyond hyperscalers and research institutions to include enterprises, governments, and manufacturing sectors – each operating domain-specific AI Factories optimised for their own needs, from smart cities to autonomous production to biotechnology.

Three major shifts are likely to accelerate this transformation:

• Standardisation and interoperability: Open frameworks that allow seamless integration of compute, storage, and orchestration tools across vendors.
• Energy efficiency and sustainability: Innovation in cooling, power delivery, and green data centre design must keep pace with AI’s exponential compute demand.
• Talent and ecosystem development: Building a pipeline of AI engineers, system architects, and domain experts capable of operationalising these AI Factories across industries.

AI Factories resolve the data paradox that has persisted for over a decade by creating a production system that continuously transforms data into deployed intelligence. When businesses standardise the loop, orchestrate specialised agents, and measure work performed in tokens, they gain a unified view and control over performance, cost, and delivery speed. The data centre remains a powerhouse, while the AI Factory acts as the operating production system for intelligence at scale.

Enterprises that outperform their peers won’t be those with the most data, nor will they rely solely on better algorithms and applications. Success belongs to those who industrialise intelligence production. We aim to support customers in building, running and continuously improving these systems, turning conceptual ideas into reality through integrated hardware, software, and services. This is how businesses, cities, and academic institutions can finally turn decades of data into sustained competitive advantage.

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AI teams across Southeast Asia are shipping faster than ever. Weekly updates. New models. Bigger context windows. Inside the company, it feels like momentum.

But outside? Users feel something very different. They feel confused.

Across Indonesia’s SME tools, Thai e-commerce automation, and Singapore’s fintech apps, the same quiet pattern keeps showing up: The product gets better. The user experience gets worse.

This isn’t a technical failure. It’s a comprehension failure.

And it’s driven by a simple truth founders overlook: AI products evolve exponentially. Users don’t update their mental models at the same speed.

That mismatch opens a gap. The Velocity-Comprehension Gap, and churn starts there.

The hidden gap that shrinks retention across SEA

Founders optimise for velocity. Users optimise for predictability.

Every time your product changes faster than users can adapt, a trust deficit forms. That’s the Velocity-Comprehension Gap:

It’s the distance between:

• How fast your AI system changes
  
• How fast users can update how they think it works
  

When the gap is small, adoption compounds. When it’s large, confusion compounds. And confusion erodes trust faster than bugs ever could.

One founder in Manila told me he didn’t fully grasp this until the morning he woke up to dozens of user messages asking if the app was “broken,”  right after a major performance upgrade.

The product had improved dramatically. But his users were still anchored to last month’s version. “We weren’t losing users,” he admitted. “We were losing their understanding.”

This is what the gap feels like from the inside.

Also Read: How startups and VCs can propel Indonesia’s energy transition

How AI velocity breaks in the wild

Southeast Asia’s digital landscape makes the gap wider because markets adapt at different speeds. Here are the three patterns I see show up again and again.

• Behavioural drift

The team improves reasoning. The model tightens its logic. Outputs get smarter.

Users experience this as the product “acting differently today.”
Even beneficial changes feel like instability.

Vietnamese merchants using chat-based automation tools regularly report that their AI helpers seem “less consistent,” even as accuracy improves.

• UX Desync

The intelligence evolves. The interface doesn’t.

Users interact with workflows written for last quarter’s model. The system responds with logic from today.

Regional HR platforms upgrading their LLMs see this instantly. Users assume the system is failing because the UI no longer matches the behaviour underneath.

• Meaning debt

The product updates. The narrative doesn’t.

Over time, users can’t clearly explain:

• What the product does now
  
• How it behaves today
  
• What changed
• Where the value is

Meaning collapses. Then comprehension collapses. Then churn accelerates.

Users don’t judge AI by accuracy — They judge it by predictability

Founders love metrics like accuracy, latency and model size. Users don’t think that way.

Their trust hinges on a single, human question: “Do I understand how this thing works well enough to rely on it?”

Predictability creates trust. Unsignalled change destroys it.

This is the blind spot slowing down many AI startups across SEA. They’re not shipping too fast; they’re shipping faster than the story can support.

The three-step framework that closes the gap

Here’s a practical system AI teams in Southeast Asia can use.

• Slow the surface, not the system

Let the backend evolve rapidly. But make user-facing changes intentional, guided and paced. Surprise is the enemy of trust.

• Normalise the change

Tie the new behaviours to something users already understand. Bridge the unfamiliar with the familiar. Make evolution feel expected.

• Communicate in mental models, not patch notes

Users don’t need technical details. They need orientation.

Also Read: Is AI making us lonely? Or is it helping us feel less alone?

Teach them:

• What the system now understands
• How it now reasons
• What they should expect
• Why this change helps them

When you update the model, update the meaning.

Outcome:

• More predictability
• Lower cognitive load
• Higher trust
• Compounding adoption

Real-world patterns from the region

Case one: The agent that became “too smart”

A Singapore AI ops assistant improved significantly. Users thought it “changed personality.” Trust dropped even as performance rose.

Case two: The dashboard that outgrew its UI

A KL analytics startup upgraded its intelligence. The interface didn’t keep up. Users assumed the product was inaccurate.

The product wasn’t weak. The story was.

Case three: The startup ships weekly, losing users monthly

An Indonesian productivity tool pushed weekly updates. Users couldn’t keep up. Support tickets exploded. Retention cratered.

Velocity became noise. Noise became confusion. Confusion became churn.

Why Southeast Asia feels this more intensely

SEA markets move at different speeds:

• Jakarta SMEs adopting AI for the first time
  
• Singapore enterprises expecting zero-friction UX
  
• Thailand’s creators depend on stable AI tools for income
  
• The Philippines is balancing low-cost accessibility with rapid innovation
  

These maturity gaps widen comprehension gaps. Meaning becomes a competitive moat. Trust becomes a regional advantage.

Founders who recognise this early will own retention.

The takeaway: Speed isn’t the threat — unstructured speed is

AI startups across Southeast Asia aren’t failing because they move too fast.
They’re failing because users can’t keep up with the story.

Fix the Velocity-Comprehension Gap, and you gain:

• Higher retention
• Smoother onboarding
• Fewer support tickets
• Deeper trust
• Stronger brand differentiation

The future belongs to founders who can ship fast without leaving their users behind.

Velocity isn’t the enemy. Confusion is. And in Asia’s AI race, clarity has become the strongest competitive advantage.

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Datakrew has raised US$2.6 million in a pre-series A round led by Greenwillow Capital Management, with participation from Beenext, 500 Global, SEEDS (now SG Growth Capital), XA Network, AngelList, and other investors.

It is not a monster round, and that is precisely the point: the Singapore-based deeptech startup is going after a market where trust is earned slowly, pilots are painful, and a single bad call can turn “AI insights” into a liability.

Also Read: 5 ways Indian EV makers can achieve world-class manufacturing efficiency

Founded in 2019, Datakrew sells what the EV industry increasingly needs but rarely standardises: battery intelligence for fleets and operators — tools that turn raw telemetry into signals about battery health, degradation, safety risk, and performance. Its core product, OXRED MyFleet, sits in the messy middle between vehicle hardware and business outcomes: fewer breakdowns, fewer roadside incidents, tighter maintenance scheduling, and better decisions on when to rotate, refurbish, or retire packs.

The company claims it has “recorded and analysed more than 10,000 battery assets across seven countries” and holds “over 105 million kilometres” of proprietary EV telemetry.

Datakrew also says its US-patented OBD-II device, ITUS Max, captures over 120 parameters, while OXRED MyFleet produces over 70 secondary metrics to estimate a battery’s future state of health.

The new money will fund products including OXRED GuardianAI and OXRED InsurShield, plus hires across sales and battery machine learning as it pushes into Europe and the Americas.

What this funding means for Southeast Asia

Southeast Asia’s EV story is often told through passenger cars and consumer adoption. Datakrew’s pitch is more industrial: the region’s near-term value is in commercial fleets — delivery vans, ride-hailing vehicles, buses, and two- and three-wheelers — where utilisation is high, and downtime is expensive.

That creates a natural opening for predictive battery maintenance, even if the market is still early. The region is fragmented across vehicle types, standards, climates, and charging behaviours. Heat, humidity, stop-start driving, inconsistent charging infrastructure, and uneven maintenance practices all accelerate degradation or, at a minimum, make it harder to predict. In other words: Southeast Asia is a harsh classroom for battery models—and a lucrative one if you can make them work.

How big is the market?

Hard numbers for “predictive battery maintenance” in Southeast Asia are scarce because spending is split across software subscriptions, telematics contracts, OEM warranties, workshop services, and insurance. A practical way to size it is as a serviceable market tied to commercial EV fleets: even modest per-vehicle annual spending on monitoring and diagnostics becomes meaningful once fleets scale, because batteries are the dominant cost centre and failures ripple through operations.

Also Read: Electric vehicles at the crossroads: Trust vs innovation

As fleets expand across Indonesia, Thailand, Vietnam, Malaysia, the Philippines, and Singapore, the addressable spend on battery analytics and risk tools plausibly moves into the hundreds of millions of US dollars annually over the next few years, with upside as electrification shifts from pilots to full fleet refresh cycles.

What drives growth in Southeast Asia

• Fleet economics: Operators can tolerate many things; they cannot tolerate unpredictable downtime.
• Financing and leasing: Lenders and lessors want better visibility into residual value and pack health.
• Insurance pressure: Higher repair costs and battery-related incidents push insurers towards telemetry-backed pricing.
• Regulatory direction: Safety expectations are rising, even if rules differ widely across countries.
• Second-life and resale: Better health data makes batteries easier to re-trade, repurpose, or warrant.

The catch: Southeast Asia is also where analytics vendors can die by a thousand integrations. Data access is inconsistent, OEMs guard diagnostic channels, and fleets run mixed vehicle brands. Any platform promising cross-fleet battery truth needs to survive the real world of missing signals, messy retrofits, and workshops that do not want more dashboards.

Where predictive battery maintenance in SEA is headed

The market is likely to move through three phases:

• Visibility (now): Basic health scoring, alerts, and anomaly detection—useful, but often descriptive rather than predictive.
• Decision support (next): Maintenance scheduling, charging policy optimisation, and pack rotation recommendations that are directly tied to cost and uptime.
• Risk and finance plumbing (later): Battery passports, warranty arbitration support, and insurance-linked products where analytics becomes part of contracts, not just operations.

Datakrew’s roadmap hints at that direction, especially with products framed around guardrails and insurance rather than only fleet dashboards. If battery analytics becomes embedded into underwriting, leasing, and warranty workflows, vendors gain stickier revenue and clearer ROI. They also inherit sharper accountability: if the model misses a failure, someone pays.

The US and Europe: bigger markets, tougher rules, stronger incumbents

Datakrew says it will expand into Europe and the Americas. The opportunity is straightforward: more EVs, bigger fleets, higher labour costs, and stricter compliance expectations—conditions that make predictive maintenance economically attractive.

Also Read: Electrifying Southeast Asia: Unleashing the radical potential of electric vehicles

Europe is heading towards deeper battery traceability and standardisation, including initiatives often described as a “battery passport” direction of travel. That increases demand for structured data, consistent diagnostics, and auditable health metrics across a battery’s life.

The US is a fleet-first electrification story in many segments—delivery, municipal vehicles, logistics—where operational uptime and total cost of ownership dominate purchasing decisions.

But the competitive reality is harsher. In mature markets, Datakrew competes not only with startups but also with:

• OEM platforms that already sit on privileged data.
• Tier-1 suppliers and diagnostics giants that can bundle analytics with hardware.
• Fleet telematics incumbents expanding into EV-specific insights.

In the US and Europe, the prize is large—arguably multi-billion-dollar over time when you include adjacent spend across telematics, diagnostics, warranty analytics, and insurance—but the bar is higher: security reviews, procurement bureaucracy, and legal exposure around safety claims.

Who else is fighting for this territory

Globally, the competitive set spans EV battery analytics specialists, broader telematics players, and OEM-adjacent platforms. Notable names include:

• Battery analytics specialists (global): TWAICE, ACCURE Battery Intelligence, Volytica Diagnostics, Qnovo, Eatron
• Fleet telematics expanding into EV insights (global): Geotab, Samsara
• OEMs and cell makers (global, indirectly competing): OEM-native diagnostics stacks and battery makers offering embedded monitoring and lifecycle services

In Southeast Asia, the picture is thinner: many fleets still rely on OEM dashboards and general-purpose telematics, while local integrators stitch together monitoring on a per-fleet basis. That gap is the opening Datakrew is trying to exploit—but it is also why credibility matters more than branding. Battery health is not a “move fast” domain. It is a “be right, then scale” domain.

Also Read: Inside Thailand’s EV and battery push: Balancing growth with sustainability

US$2.6 million will not buy domination. What it can buy is time: to prove models under Southeast Asia’s messy operating conditions, to land reference fleets, and to walk into Europe and the US with evidence rather than ambition. In battery intelligence, the difference is everything.

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At Echelon Philippines 2025, a panel featuring Teddy Peralta of Altara Ventures, Jose Dalino of PayMongo, and Gian Paulo dela Rama of Sprout Solutions — moderated by Maansi Vohra of Monk’s Hill Ventures — explored the growing relevance of embedded finance for startups.

The discussion centred on the idea that financial services are increasingly becoming a core component of non-financial businesses, with panellists suggesting that every startup will, in some form, become a fintech company. The group noted that embedded finance is best suited to more mature startups with a stable customer base, where integrating financial tools can meaningfully deepen customer value and drive sustainable growth.

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In a connected world, we assume information flows freely. In reality, most ecosystems are fragmented: companies, institutions, and even countries operate in silos.

And where there are silos, there are opportunities.

The power of the bridge

Sociologist Ronald Burt once coined the concept of “structural holes”— the invisible gaps that exist between disconnected groups, organisations, or communities.

In every network — whether social, industrial, or geopolitical — value doesn’t just come from what you know, but who doesn’t yet know each other. Those who bridge gaps between clusters become information brokers: people or platforms who can translate, connect, and facilitate exchanges that others can’t.

Think of LinkedIn for professionals, Stripe for online payments, or Airbnb for spare rooms.

Each of them identified a “structural hole” — a broken or missing connection between two sides of a market — and built a bridge that transformed inefficiency into opportunity.

This dynamic is not theory. It is the basis of some of the most valuable business models in the world. Behind every marketplace, every platform, and every cross border service sits the same insight: when two groups need each other but have no efficient way to meet, whoever creates that pathway becomes indispensable. This is why categories like B2B marketplaces, global talent platforms, and intergovernmental digital infrastructure are expanding rapidly. The world isn’t short of capability; it is short of connection.

In Southeast Asia, these gaps are everywhere:

• Between local SMEs and global partners
• Between talented workers and companies abroad
• Between foreign investors and on-the-ground operators

Whoever builds the bridge owns the flow of trust, information, and eventually — value.

Why it matters now

As globalisation slows and regionalisation accelerates, the new competition isn’t between countries, it’s between networks: Whoever can connect supply chains, talent pools, and markets fastest will dominate the next decade of trade.

But while technology connects us, trust still lags behind.

Digital rails can be built quickly, but human confidence moves slowly. That is why many high-potential collaborations still die in the early stage — not for lack of opportunity, but for lack of a trusted interpreter who can manage expectations, translate cultural nuance, or reduce perceived risk. In emerging markets, this trust gap is often wider than the technology gap.

Also Read: From uncertainty to action: Power of AI and digital shaping deal strategies in turbulent times

Many founders and policymakers still operate within their local comfort zones, unaware that just one connection across borders could unlock exponential value.

This is where network aggregators — companies, platforms, or consultants that specialise in connecting these isolated clusters — play a vital role. They aren’t middlemen; they are multipliers.

They compress time. They reduce friction. They turn what would otherwise be a six-month relationship-building exercise into a six-day warm introduction. In capital markets, they become credibility amplifiers; in talent markets, they become mobility engines; in supply chains, they become resilience builders.

The opportunity for builders

If you’re a founder or strategist, look for gaps, not crowds. Ask:

• Who in my industry doesn’t talk to each other — and why?
• What friction prevents partnerships from forming?
• How can I make the first connection easier, faster, or safer?

In fragmented markets, the one who connects others doesn’t just create value: they control it.

And as ecosystems become more interdependent, the advantage of the connector will only grow. The next iconic companies will not only build products — they will build bridges.

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Malaysian SMEs are embracing AI at an impressive speed, but a new report by Xero suggests this enthusiasm masks a deeper uncertainty that could hinder long-term progress. The study, Building a Future-Ready Economy: Examining AI Readiness and Adoption Among Malaysia’s MSMEs, describes this divide between optimism and confidence as a widening “confidence gap.”

According to the report, an overwhelming 81 per cent of Malaysian SMEs surveyed have already adopted some form of AI. Many see the tech as an essential part of future business operations, with 77 per cent believing AI will be standard practice by 2030. Another 75 per cent say AI will be beneficial to their business.

For now, most Malaysian SMEs are prioritising practical, short-term gains. The top expected benefits of AI include increased efficiency (63 per cent), cost savings (52 per cent) and improved employee productivity (48 per cent). As one business owner quoted in the report put it, companies are drawn to AI tools that “solve today’s problems before tomorrow’s ambitions.” Only 47 per cent associate AI with driving innovation, while a mere 33 per cent see it as a means for competitive differentiation.

Many firms are starting small, experimenting with accessible tools such as general-purpose conversational AI (55 per cent) and creative generative AI tools (38 per cent). These early steps suggest that Malaysian SMEs are primarily utilising AI for routine tasks rather than integrating it deeply into their core operations.

Yet the report’s central finding is that this enthusiasm is not matched by strategic confidence.

Also Read: With US$6M in support, GenAI Fund aims to close the gap between AI innovation and corporate adoption

Although adoption is high, 82 per cent of respondents say they need more education before they can implement AI with certainty. Only 56 per cent say they are familiar with business-relevant use cases, and 61 per cent admit they are overwhelmed by the sheer number of AI solutions and tools on the market.

This hesitancy results in what the study refers to as low intentionality—SMEs recognise the need to use AI, but many are uncertain about how to utilise it effectively. One respondent admitted that while AI tools are helpful for daily tasks, “trusting the technology with bigger decisions still feels risky.”

That lack of trust is one of the most significant barriers highlighted in the report. Data privacy and security top the list of concerns at 59 per cent, followed by fears of over-dependency on AI (51 per cent). Nearly four in 10 worry about the accuracy or quality of AI outputs, while 38 per cent point to ethical or plagiarism-related issues.

This uncertainty is reflected in the sharply divided attitudes toward AI-led decision-making. SMEs are split three ways: 33 per cent trust AI to make critical business decisions, 33 per cent do not trust it, and the remaining third remain neutral. According to the report, such indecision limits the value Malaysian SMEs can ultimately extract from AI.

Governance is another stumbling block. Among SMEs that have already adopted AI, 30 per cent have no policies or guidelines in place to govern its use. Without structured rules, many businesses are reluctant to expand their reliance on the technology. As the report notes, trust and responsible use are still “underdeveloped pillars” in Malaysia’s AI landscape.

Also Read: Exit or be left behind: The harsh new reality for SEA startups

Interestingly, cost is no longer the main obstacle to adoption. Instead, SMEs emphasise knowledge and guidance as their top priorities. When asked what would help them adopt AI more confidently, 61 per cent cited training and education, followed by access to technology (52 per cent) and advisory or consulting support (50 per cent). Financial assistance ranked far lower, with just 37 per cent saying grants or subsidies would make a significant difference.

This shift highlights a broader concern that businesses do not simply need more tools; they require the expertise to deploy them effectively.

The majority of SMEs also want stronger oversight. Nearly 68 per cent believe authorities should play a more active role in regulating AI in business, signalling a desire for structured safeguards and clearer national standards.

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Image Credit: Nicholas Chester-Adams on Unsplash

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I was sitting in the studio at Channel News Asia, recording a podcast on agentic AI. We were talking about tools. Workflows. The future of jobs. At one point, the hosts shared something simple: They had been prompting AI, and the output just wasn’t right.

So they adjusted the prompt. Then adjusted it again. And suddenly, the output improved.

That’s when it clicked for me. This wasn’t an AI problem. It was a communication problem.

AI is not the problem — your instructions are

Most founders think AI is about tools. Which tool to use? Which model is better? Which platform is more “agentic”?

But after building and training my own systems, I’ve realised something much simpler: AI doesn’t fail because it’s not smart enough. It fails because we’re not clear enough.

When AI gives you a generic output, it’s not a capability issue. It’s a clarity issue.

The uncomfortable truth: AI is exposing your thinking

AI responds in seconds. Which means your thinking gets reflected back to you… immediately.

If the output is off, vague, or misaligned? That’s not a delay. That’s the diagnosis.

AI doesn’t wait for you to realise you were unclear. It shows you instantly.

With humans, it’s different. They still execute. They:

• Fill in gaps.
• Make assumptions.
• Try to “figure it out”.

And sometimes, they even deliver something that looks correct.

But let’s be honest: Output ≠ understanding.

Also Read: Cruising the startup ocean: Sailing toward an unfixed horizon

The illusion most founders are operating in

Here’s the slightly uncomfortable part. A lot of teams are not aligned. They’re just… coping. Work gets done. Slides get delivered. Campaigns get launched.

But underneath:

• Expectations are misaligned.
• Thinking is inconsistent.
• Time is wasted fixing avoidable mistakes.

Why don’t people ask?

Because:

• They don’t want to sound like they don’t understand.
• They assume they’ll figure it out.
• Or they interpret based on their own logic.

AI doesn’t do that.

It either:

• understands
  or
• exposes that it doesn’t.

There’s no ego. No masking.

The framework: CLEAR briefing system

If prompting AI feels hard, it’s because briefing is hard. So here’s a simple model I use across both AI and teams: C.L.E.A.R.

• C – Context: What is happening? Why does this task exist? → “We’re launching a webinar to convert leads into a paid programme.”
• L – Logic: How should this be approached? What thinking model is used? → “Use a Hook → Story → Offer → CTA structure.”
• E – Expectation: What does success look like? → “Conversion-focused, not just informational.”
• A – Aesthetic / Angle: What is the tone, style, or positioning? → “Direct, structured, slightly provocative.”
• R – Result Format: What exactly should be delivered? → “Write a 60-second talking head script + captions for three platforms.”

Why does this work? Because most people skip at least two to three of these. They say, “Help me write a post.” And expect:

• Clarity
• Alignment
• Quality

That’s not prompting. That’s hoping.

Also Read: Turning sustainability into a growth strategy for Singapore SMEs

Mini “How-to” for founders (you can apply this today)

If you’re using AI – or managing a team – try this:

• Step 1: Take your last instruction. Something like: “Create content for my event.” Now rewrite it using CLEAR.
• Step 2: Compare the output. You’ll notice:

• Less back-and-forth.
• Higher quality output.
• Better alignment.

• Step 3: Watch your own thinking. This is the real game. If you struggle to:

• Define the outcome.
• Explain your logic.
• Articulate expectations.

That’s not an AI problem. That’s a thinking problem.

One thing AI taught me about myself

There are days when I get lazy. I give shorter instructions. Less context. I assume continuity.

And when the output comes back wrong, I catch myself thinking: “Why is this off?”

Then I realise:

• I didn’t reset the context.
• I didn’t clarify that it was a new task.
• I assumed understanding.

The AI didn’t misunderstand me. It followed exactly what I said. Just not what I meant.

Let’s make this a little uncomfortable

We like to say: “AI isn’t good enough yet.” But here’s the real question: Are you clear enough yet?

Because right now, the gap isn’t just:

• Human vs. AI.

It’s:

• Clear thinkers vs. unclear thinkers.

And the scary part? AI is amplifying both.

The future of work isn’t AI vs. humans

You’re not competing with AI. You’re competing with people who:

• Can think clearly.
• Communicate precisely.
• And leverage AI effectively.

And those people? They move faster. They execute better. They scale without friction.

AI is not replacing leadership. It is exposing it.

And in the AI era, clarity is no longer optional. It’s your competitive advantage.

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