Thailand’s bid to become a regional centre for artificial intelligence is moving from policy ambition to ecosystem-building.

At the Huawei Thailand Digital & AI Summit 2026 in Bangkok, Huawei joined Thai customers, local large language model developers, AI associations and universities to launch the Thailand AI Ecosystem Initiative, a programme aimed at strengthening the country’s AI infrastructure, governance, talent base and industry adoption.

Also Read: Huawei Cloud bets on Thailand as enterprises move from AI pilots to production

The summit, co-hosted by Huawei and Thailand’s Ministry of Digital Economy and Society, was held on July 23 and 24 under the theme “Advancing All Intelligence Thailand”. It brought together more than 3,000 participants, including government representatives, telecom operators, enterprise customers and technology partners.

The initiative arrives at a time when Southeast Asian governments are trying to capture more value from AI, not only by encouraging companies to use the technology, but also by building the underlying foundations needed to develop, deploy and govern it locally. For Thailand, that means cloud and computing infrastructure, trained professionals, trusted data sets, industry-specific models and clearer rules on responsible use.

H.E. Chaichanok Chidchib, Thailand’s Minister of Digital Economy and Society, said the country’s AI development agenda rests on three pillars: infrastructure, trust and people.

“The future of AI cannot be created by any single organisation alone,” he said, adding that Thailand is positioning itself as a “real-world AI Governance Sandbox”, where companies, researchers and regulators can work together to turn high-level AI principles into practical rules and deployment models.

That framing is important. In Southeast Asia, AI policy often runs ahead of implementation. Governments are keen to promote innovation, but they also have to manage concerns around privacy, security, bias, labour disruption and foreign technology dependence. A sandbox approach gives Thailand a way to test governance models while still encouraging private-sector experimentation.

From AI pilots to industrial deployment

Huawei’s message at the summit was that Thailand is entering what the company calls the “Agentic AI era”. In simple terms, agentic AI refers to systems that do more than answer prompts. These AI agents can plan tasks, use software tools, remember context and take steps towards a goal with some degree of autonomy.

For businesses, the appeal is clear. A bank may use AI agents to assist with software development, document review or customer operations. A hospital could use them to support administrative workflows. A manufacturer might deploy them to analyse equipment data and recommend maintenance action. But moving from isolated pilots to real-world deployment is difficult, especially in markets where companies still face gaps in computing capacity, data readiness and specialised AI talent.

Austin Zheng, Deputy Managing Director of Huawei Thailand, said local industries face five main bottlenecks in AI implementation: computing resources, data, models, security and talent. To address these, Huawei introduced its ACT framework: assess high-value scenarios that can produce commercial outcomes; calibrate AI models with high-quality vertical data; and transform business operations by developing AI talent and speeding up application development.

The framework reflects a broader shift in enterprise AI. After the first wave of experimentation with generative AI tools, companies are asking harder questions: Which use cases are worth funding? Do we have the data to support them? Can the models understand our industry? How do we protect sensitive information? And who inside the organisation can maintain these systems once the vendor leaves?

For Thailand, these questions are especially relevant in sectors such as finance, telecoms, healthcare, logistics and public services, where AI could improve efficiency but where mistakes can carry real consequences.

A local ecosystem, not just imported technology

The Thailand AI Ecosystem Initiative is being positioned as a collaborative effort rather than a single-vendor programme. It was jointly proposed by Huawei, the AI Association of Thailand, telecom operators, local large model developers and universities.

Also Read: Agentic AI ambitions in Singapore run into legacy systems and data quality gaps

Its stated goal is to build an inclusive local AI ecosystem that balances innovation with trustworthy governance, while developing digital talent and helping Thailand become a core engine for AI innovation and industrial deployment in Southeast Asia.

The emphasis on local models and local talent is significant. Much of the global AI conversation is still dominated by US and Chinese technology giants, but Southeast Asian markets have their own languages, regulatory conditions and industry needs. AI systems trained mainly on English-language or foreign data may struggle with Thai-language context, local public-sector workflows, or industry-specific terminology used in domestic companies.

This is where universities, AI associations and local model developers become important. They can help create trusted local corpora — curated data sets that reflect the local language and operating environment — while training the next generation of engineers and AI practitioners.

Huawei said it will support talent development through its ASEAN Academy and continue building its local presence in Thailand. The company has operated in the country for 27 years and uses the slogan “In Thailand, For Thailand” to describe its local strategy.

Infrastructure as the new AI battleground

Behind the ecosystem language sits a more concrete issue: computing power.

Hong-Eng Koh, Global Chief Public Services Industry Scientist of Huawei’s Global Public Sector Business Unit, said AI is evolving from assistive tools into industrial agentic applications, creating heavy demand for frontier models and unified computing architecture. Huawei said its AI portfolio covers model iteration, trusted local corpora, AI agent development and talent training, supported by infrastructure such as the Atlas 950 SuperPoD for large-scale computing coordination.

The company said its AI solutions have supported more than 2,600 enterprises across over 30 industries worldwide, spanning more than 500 business scenarios in sectors including finance, transportation, manufacturing and healthcare.

These figures speak to Huawei’s global ambitions, but Thailand’s market will be shaped by local execution. Enterprises need reliable infrastructure, but they also need integration partners, compliance support and measurable returns. AI adoption in Southeast Asia is rarely a simple matter of buying the most advanced model; it is often about fitting technology into messy, existing systems.

Huawei’s rivals in Thailand’s AI race

Huawei is competing in a crowded field. Global cloud and AI players such as Amazon Web Services, Microsoft, Google Cloud and Oracle are investing heavily across Southeast Asia, while Alibaba Cloud and Tencent Cloud remain active among regional digital businesses and Chinese-linked enterprises. In AI infrastructure, Nvidia’s chips and software ecosystem remain central to many enterprise deployments, while local telecom operators and data centre providers are also positioning themselves as partners for sovereign cloud, edge computing and AI workloads.

This competition is likely to benefit Thai enterprises by widening their options, but it also raises strategic questions for policymakers and companies. As AI becomes critical infrastructure, decisions about vendors, data location, compute supply and model governance will carry long-term implications.

For Huawei, the Thailand AI Ecosystem Initiative is both a policy-aligned partnership and a market-building move. It ties the company more closely to Thailand’s national AI agenda while giving it a role in infrastructure, applications, governance and training.

For Thailand, the test will be whether such initiatives can produce more than summit-stage announcements. Becoming ASEAN’s leading AI hub will require not only partnerships with large technology companies, but also stronger local research, industry adoption, startup participation, interoperable standards and rules that earn public trust.

Also Read: The coming identity crisis of agentic AI

The country has many of the ingredients: a sizeable domestic market, a strategic location in mainland Southeast Asia, active telecom and banking sectors, and a government eager to push digital transformation. The next step is turning those ingredients into deployable AI systems that solve real problems for citizens and businesses.

Huawei’s initiative adds momentum to that effort. Whether Thailand can convert it into regional leadership will depend on how quickly its ecosystem can move from ambition to execution.

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For many startups in Southeast Asia, Europe appears attractive for obvious reasons. It offers affluent consumers, mature digital infrastructure, access to capital, strong demand for innovation and a large number of business customers looking for new technologies. From a distance, it can also look relatively simple: one continent, a shared regulatory framework in many areas, common payment standards and a single narrative built around expansion into “the European market”.

That perception is convenient, but commercially dangerous. Europe is not one market. It is a collection of countries with different languages, purchasing behaviours, levels of trust, expectations around service, approaches to risk, sales cycles and relationships with brands. Even neighbouring countries can respond very differently to the same offer, the same pricing structure or the same communication strategy. A startup that treats Europe as a single destination may therefore spend heavily on translation, acquisition and partnerships without ever understanding why its results remain inconsistent.

The first mistake is often strategic rather than operational. Companies decide to “launch in Europe” before choosing which specific European market they are actually prepared to understand. They build one website, translate it into several languages, run regional campaigns and assume that product-market fit will travel automatically. In reality, international expansion is not the reproduction of a domestic model across a larger territory. It is a sequence of local commercial decisions, each with its own constraints.

Europe shares rules, not customer behaviour

The European Union has created significant regulatory and economic integration, but regulation does not erase national market cultures. A company can comply with the same legal framework in France, Belgium and Luxembourg while facing completely different buying behaviours in each country. It can offer the same product in Germany and the United Kingdom, yet encounter different expectations regarding proof, pricing, onboarding and customer support.

France, for example, often requires a high degree of reassurance before a new provider is considered credible. Buyers may want detailed explanations, references, local language support and a clear demonstration that the company understands their environment. In the United Kingdom, the same audience may respond more quickly to a sharper commercial proposition, clearer differentiation and a direct explanation of return on investment. Switzerland can demand premium execution, precision and trust, while Belgium may require a more fragmented approach because linguistic and regional realities affect how companies communicate and decide.

Also Read: AI is making Southeast Asia’s startups faster, not richer, yet

These differences influence much more than marketing. They affect the sales process itself. The number of people involved in a decision, the acceptable level of risk, the importance of local partners, the preferred communication style and the pace of negotiation can all vary. A startup may interpret a slow response as lack of interest when the real issue is insufficient credibility. It may lower its price when the market was actually waiting for stronger proof. It may increase advertising when the problem lies in the structure of the offer.

Europe is unified enough to create the illusion of simplicity, but diverse enough to punish that illusion.

Translation cannot repair a weak market entry strategy

One of the most common shortcuts is to equate localisation with translation. A startup translates its website, advertisements and product interface, then assumes it has adapted its offer. This can make the company technically accessible while leaving it commercially irrelevant.

Translation changes the language of a message, but not necessarily its meaning in context. A promise centred on speed may work in one country and appear superficial in another. A highly informal brand voice may create proximity in one market and reduce credibility in another. A pricing page that feels transparent to one audience may appear incomplete elsewhere if buyers expect stronger guarantees, human support or more detailed contractual information.

The same problem applies to product packaging. European customers may differ in the way they evaluate subscriptions, free trials, annual commitments, implementation support or data protection. A model that performs well in Singapore may need a different level of explanation, onboarding or after-sales support in France. A product can remain technically identical while the commercial architecture around it must change.

Startups should therefore separate three questions that are too often mixed together: Is the product relevant? Is the offer understandable? Is the company credible? A market can show strong need for the product and still reject the company because the offer is poorly framed or because the startup has not built enough local trust. That distinction is essential, because otherwise teams may modify the product when the real weakness lies in positioning, distribution or communication.

The right entry point matters more than continental ambition

The most effective European expansion strategies usually begin with one market, not five. Choosing an entry country forces the company to make specific decisions. Which customers will be targeted first? Which problem will be emphasised? Which local proof is missing? Which channels are realistic? Which partnerships could reduce the cost of credibility?

The best entry market is not always the largest. It may be the one where the company already has a partner, where the founder’s network is strongest, where English can be used during the first phase, or where the competitive environment leaves a clearer position available. A smaller market can provide faster learning and more useful references than an ambitious launch across several countries at once.

Also Read: Why investors often back Vietnamese startups more aggressively than Thai peers

This does not mean that startups should abandon regional thinking. It means they should build it progressively. A successful first market creates evidence: customer feedback, local references, sales objections, onboarding data and a clearer understanding of what must change. These lessons can then influence the next market rather than forcing the company to repeat the same assumptions at greater cost.

The sequence also matters for brand development. If a startup enters several countries simultaneously, each local team may adapt the message independently, creating different versions of the company before its European identity has stabilised. Starting with one market allows the company to determine which parts of its positioning are fundamental and which can be adapted without creating inconsistency.

AI can accelerate adaptation, but not replace judgement

Artificial intelligence can significantly reduce the cost of preparing for European expansion. It can support market research, analyse customer reviews, compare competitors, identify recurring objections, generate alternative messages and accelerate multilingual content production. For a startup with limited resources, this creates genuine leverage.

The danger begins when AI is used as a substitute for local understanding. Models can summarise patterns, but they cannot automatically determine which differences are commercially meaningful. They may reproduce outdated assumptions, flatten cultural nuance or generate recommendations that sound plausible without reflecting how buyers actually behave. A startup that relies only on AI can produce sophisticated localisation at high speed while remaining disconnected from the market.

The strongest use of AI is therefore iterative. Teams can use it to create hypotheses, prepare interviews, compare market narratives and structure large volumes of information. Those hypotheses must then be tested with customers, local advisors, partners and sales conversations. The purpose of the technology is not to eliminate human judgement, but to make learning faster and more systematic.

Startups should also avoid using AI to multiply content before clarifying their European positioning. Producing ten localised campaigns is not progress if the underlying value proposition remains vague. Technology should amplify a strategy that is already coherent, not conceal the absence of one.

For Southeast Asian startups, Europe can still be an exceptional growth opportunity. But the continent rewards precision more than scale at the beginning. The companies most likely to succeed will be those that stop asking how to enter Europe and start asking which European market they are ready to understand first.

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For small businesses, customer rewards are often a frustrating trade-off. A discount can bring in a buyer, but it eats into margin. Cashback can nudge a transaction, but it is now so common that many consumers barely notice it. Larger companies can absorb the cost of eye-catching giveaways or loyalty campaigns; most SMEs cannot.

Bundle, a rewards infrastructure startup, is trying to change that model by letting brands pool their incentive budgets into shared reward pools. The company has raised US$5.5 million in pre-seed funding led by Ethereal Ventures and Further Ventures, with the capital earmarked for product development, regulatory coverage, strategic partnerships, and market launches across Asia.

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

The platform plans to roll out first in Singapore, the Philippines, and Vietnam, where more than 50 founding brands have joined ahead of launch. Bundle said it intends to expand across Asia before moving into other global markets, using what it describes as a compliance-first operating model.

At its simplest, Bundle is a networked rewards platform. Instead of each company running a separate campaign with its own modest prizes, participating brands contribute to a larger common pool. Customers earn Bundle tickets by taking eligible actions, such as making a purchase, referring a friend, or completing another campaign objective. Those tickets give them access to shared rewards that would typically be too expensive for a single small business to fund alone.

Why rewards need a rethink

The pitch is landing at a time when SMEs across Southeast Asia face a difficult marketing environment. Digital advertising costs have risen over the past decade, while consumers are harder to retain across crowded e-commerce, food, travel, fintech, and lifestyle apps. Promotions remain a common lever, but repeated discounting can train customers to wait for deals rather than build loyalty.

That problem is particularly sharp in Southeast Asia, where SMEs make up the overwhelming majority of businesses and remain central to employment and economic output. Across Asia Pacific, SMEs account for nearly all businesses and contribute around 40 per cent of national economic output, according to figures cited by Bundle from the Asian Development Bank’s Asia Small and Medium-Sized Enterprise Monitor 2025.

In that context, a shared rewards network could give smaller companies access to a type of marketing mechanic usually reserved for large consumer brands. The challenge is whether customers find pooled rewards compelling enough to change behaviour, and whether brands can measure the uplift clearly against other acquisition tools.

Bundle said early tests suggest there is demand. During a pilot across five markets with five companies, the platform rewarded more than 1,100 winners and distributed over US$1000,000 in rewards, including a top reward of US$50,000 The company said participating brands saw conversion uplifts of up to four times compared with traditional promotional campaigns.

Those numbers are still early, and pilots can be easier to optimise than full commercial rollouts. But they point to the core behavioural bet behind Bundle: that consumers may respond more strongly to the chance of earning a meaningful reward than to another small discount.

Also Read: From transparency to impact: The role of blockchain in socially responsible marketing

“For decades, brands have relied on discounts and cashback to attract customers. Yet, customer acquisition costs continue to rise, conversion rates continue to decline, and businesses sacrifice billions of dollars in margins every year on incentives that customers increasingly ignore,” said Bundle co-founder and CEO Bader Al Kalooti. “People do not get excited by another small discount; they are motivated by the opportunity to earn something meaningful.”

Blockchain in the background

Bundle’s investors frame the company as part of a newer wave of blockchain-enabled consumer businesses, though the product appears designed to keep the technical layer largely invisible to end users. In practice, “blockchain rails” could help support transparent reward tracking, settlement, or interoperability across partners, but the mainstream customer experience will likely depend less on the underlying technology and more on whether campaigns are easy to understand and redeem.

Min Teo, Managing Partner at Ethereal Ventures, said Bundle uses “shared rewards and blockchain rails to help brands drive loyalty and customer growth at scale”. Further Ventures Partner Robbie Nakarmi added that the model could give brands “access to larger rewards to drive higher conversions from their marketing campaigns at a fraction of the cost”.

For Southeast Asia, that distinction matters. The region has seen strong crypto adoption in markets such as Vietnam and the Philippines, but consumer-facing blockchain products have also faced scepticism, especially where the value proposition feels speculative or too technical. Bundle’s ability to position blockchain as infrastructure rather than a headline feature may be important as it courts SMEs outside Web3-native circles.

Regulation will be another factor. Rewards, prize draws, incentives, and promotional campaigns can fall under different rules depending on the market. Singapore, the Philippines, and Vietnam each have their own requirements around consumer promotions, marketing, payments, and data handling. Bundle said part of the new funding will go into expanding regulatory coverage, a necessary step if it wants brands to run campaigns across multiple jurisdictions through one platform.

The competitive landscape

Bundle sits at the intersection of loyalty software, promotional technology, and cashback alternatives. Globally, companies such as Antavo, Talon.One, Voucherify, Yotpo Loyalty, and Smile.io help brands build loyalty, referral, and incentive programmes. In Southeast Asia, adjacent players include cashback and rewards platforms such as ShopBack and Fave, which have trained consumers to expect money-back offers and merchant-funded incentives.

Bundle’s difference is that it is not simply giving each merchant a loyalty tool or cashback channel. Its bet is on aggregation: many brands contributing to shared prize pools to create rewards that feel larger than what each could offer alone. That also means its real competition may include the status quo — discount codes, marketplace vouchers, bank card promotions, and platform-led campaigns from major e-commerce and super-app players.

Founded by Al Kalooti and Mostafa Wanas, Bundle brings together experience from consumer technology, e-commerce, and crypto. Al Kalooti previously held leadership roles at Amazon and Binance, where he served as Head of MENASA and Turkey, after multiple venture-backed startup exits. Wanas has advised and scaled digital products and consumer technology companies.

The company’s next test will be execution. Convincing 50 founding brands to join ahead of rollout gives Bundle a starting network, but networked products only become valuable when both sides — brands and consumers — return repeatedly. For SMEs, the appeal will be measurable customer acquisition at predictable cost. For consumers, it will be whether Bundle tickets feel like a genuine chance at something worthwhile, not another layer of marketing noise.

Also Read: Can blockchain function as a medium for social good and digital philanthropy?

If Bundle can solve both sides, it may offer a new route for smaller brands in Southeast Asia to compete for attention without burning margin on endless discounts. If it cannot, it risks becoming one more promotional tool in a region already full of them.

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Across Southeast Asia, organisations are moving rapidly from AI exploration into practical business applications.

Banks are experimenting with AI assistants for customer service and internal productivity. Insurance companies are exploring AI-supported claims processing. Logistics and manufacturing companies are adopting AI for operations optimisation.

The first phase of enterprise AI was about access: How can employees use generative AI tools?

The next phase is becoming a much harder question: How can organisations scale AI adoption while maintaining security, compliance and control?

This shift marks the emergence of a new enterprise requirement: AI governance.

The challenge is no longer access to AI models

Today, accessing powerful AI models is easier than ever.

Employees can use commercial AI assistants. Developers can integrate APIs from multiple AI providers. Business teams can create AI workflows without waiting for traditional software development cycles.

However, enterprise adoption introduces new risks.

• An employee may accidentally share confidential information with an external AI service.
• A developer may connect an application to an unapproved model.
• A department may use AI tools without security teams knowing.

A company may have no clear record of:

• Who used AI
• Which model processed the request
• What data was involved
• Whether sensitive information was protected
• Why a specific model was selected

For consumer AI usage, these questions may not be critical. For regulated industries, they are fundamental governance requirements.

Also Read: Say it out loud: AI is forcing companies to explain themselves

Enterprise AI needs an accountability layer

Traditional IT environments already have governance mechanisms. Companies manage:

• Identity access
• Application permissions
• Network security
• Data protection
• Audit logging

However, AI introduces a new operational boundary.

The interaction is no longer only between: User → Application → Database.

It becomes: User → AI Application → AI Model → External/Internal Data Sources.

This creates a new governance challenge. Organisations need visibility and control over AI interactions before sensitive information reaches AI models.

An enterprise AI governance layer should help answer: Who is using AI? Identity, department and application context are important.

What data is being processed? Sensitive information such as customer records, financial data or confidential documents requires protection.

Which models are approved? Enterprises may use multiple AI providers depending on:

• Security requirements
• Geographic restrictions
• Performance
• Cost

Why was this model selected? AI routing should become explainable. A governance system should provide evidence of:

• Selected model
• Rejected alternatives
• Policy decisions
• Masking actions
• Fallback decisions

Southeast Asia has unique AI governance challenges

Southeast Asia presents a particularly interesting environment for enterprise AI adoption. The region includes:

• Highly regulated financial markets
• Rapidly growing digital economies
• Cross-border business operations
• Diverse regulatory environments

Also Read: Why AI literacy may become the new financial literacy

Financial institutions in Singapore and Hong Kong, for example, must balance innovation with strict requirements around customer data protection. Growing enterprises across ASEAN need AI capabilities but often lack large AI governance teams. This creates demand for practical solutions that allow companies to innovate while maintaining responsible AI operations.

Moving from AI pilots to production requires new thinking

Many organisations successfully complete AI pilots. The challenge is scaling.

A pilot may involve:

• A small team
• Limited data
• Manual review

Production deployment involves:

• Thousands of users
• Multiple departments
• Multiple AI providers
• Continuous monitoring

At this stage, AI governance cannot remain a policy document. It needs to become part of the technical architecture.

The future enterprise AI stack will likely include:

• AI access governance
• Prompt inspection
• Sensitive data detection
• Policy enforcement
• Model routing
• Audit evidence
• Usage and cost visibility

The next enterprise AI infrastructure layer

As cloud computing matured, organisations built cloud governance platforms. As APIs expanded, organisations built API management platforms. As AI adoption accelerates, enterprises will need similar governance capabilities for AI usage.

The next generation of AI infrastructure will not only focus on making models faster or cheaper. It will focus on making AI adoption:

• Secure
• Explainable
• Compliant
• Accountable

The organisations that successfully scale AI will not necessarily be those with access to the largest models. They will be those that build the right governance foundation around AI.

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

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

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For a small-cap company on the Singapore Exchange’s Catalist board, iWOW Technology’s latest fundraising could have passed as a fairly standard capital markets move. The company placed 66.7 million new shares at US$0.17 each, raising gross proceeds of about US$11.1 million.

But the investor list, timing and stated use of funds point to something more interesting than balance-sheet housekeeping. iWOW is trying to reposition itself from a wireless technology and Internet of Things company into a broader “longevity” platform , one that sells to families, healthcare providers and institutions grappling with the realities of an ageing population.

Also Read: Asia’s longevity shift: How healthspan innovation is transforming technology and everyday life

That shift matters because Southeast Asia’s ageing story is no longer a distant policy concern. Singapore is already one of Asia’s fastest-ageing societies, while Thailand and Vietnam are moving in the same direction at speed. Families are living longer, chronic illness is becoming more common, and care systems designed for younger populations are under pressure. The commercial opportunity is obvious. The harder question is whether companies like iWOW can build durable businesses in a sector where the need is real, but execution is difficult.

A healthcare bet, not just a tech placement

The placement drew participation from a notable group of institutional investors, including Amova Asset Management, Areca Capital, Asdew Acquisitions, Avanda Investment Management, Azure Capital, Ginko-AGT Global Growth Fund, ICH Synergrowth Fund, Lion Global Investors, Tokio Marine Life Insurance Singapore, UOB Asset Management and Value Partners Hong Kong.

For a company of iWOW’s size, that is a meaningful roster. Several of these investors manage funds under Singapore’s Enhanced Fund Management Incentive scheme, which provides tax advantages for qualifying investment activities. That context is important: part of the demand for small and mid-cap local equities may be structural, not simply a pure vote of conviction on iWOW’s strategy.

Still, the participation of Dr Lim Cheok Peng is harder to dismiss as routine. Lim, a cardiologist, served as Managing Director of IHH Healthcare from 2011 to 2013 and helped steer the group through its 2012 dual listing on Bursa Malaysia and the Singapore Exchange. That transaction was, at the time, one of the world’s largest healthcare initial public offerings.

His involvement gives iWOW something it does not naturally possess from its earlier identity: healthcare credibility. The company’s roots are in wireless communications research and development. Its future pitch, however, is increasingly being framed around senior care, wellness and chronic disease management. In that light, the placement is less a tech story than an attempt to win investor confidence in a healthcare-adjacent transformation.

The three-part longevity thesis

iWOW’s strategy rests on combining three areas: safety, nutrition and social connection.

The safety piece comes from its Buddy of Parents, or BOP, business. This includes AI-powered sensors, wearable emergency buttons and fall-detection devices aimed at helping older adults live more independently while giving families and caregivers some visibility into their wellbeing. The company has also showcased tools that use Wi-Fi sensing to detect movement through walls without cameras, a potentially useful feature in markets where privacy concerns can slow adoption of camera-based monitoring.

The nutrition pillar comes from iWOW’s acquisition of The Gentle Group, completed earlier this year for about US$8.3 million. The Gentle Group provides therapeutic nutrition products for seniors and people managing conditions such as dysphagia, diabetes and kidney disease. According to the company, the business grew at an annual rate of about 51 per cent between FY2022 and FY2025.

Also Read: The ageing economy: Why investors should bet on longevity over AI

The third component is social connection. Through a tie-up with US-based GetSetUp, iWOW is adding digital literacy classes and community programming for older adults. This speaks to a less discussed but increasingly important part of ageing: isolation. For many seniors, especially in urban Asian societies where families are smaller and adult children are working longer hours, ageing is not only a medical issue. It is also a social one.

On paper, the logic is sensible. Families caring for an elderly parent often deal with safety risks, dietary needs and loneliness at the same time. A single company that can address those concerns could capture more of the household care budget than a narrow point solution.

The challenge is that these are very different businesses. Hardware requires manufacturing, distribution and after-sales support. Therapeutic nutrition depends on product formulation, regulatory compliance, clinical trust and supply chains. Community programming needs engagement, content and retention. Calling them a platform does not make integration automatic.

Rivals are already circling the same problem

iWOW is entering a market that is fragmented but far from empty. In Southeast Asia, Homage has built one of the region’s better-known eldercare platforms, connecting families with caregivers and offering telehealth services across Singapore, Malaysia and Australia. Its backers include Sheares Healthcare, linked to Temasek, and Golden Gate Ventures.

SmartPeep, another Singapore-linked player, has focused on vision-based fall detection for hospitals and care facilities. Globally, the AgeTech space has expanded quickly, with initiatives such as AARP’s AgeTech Collaborative tracking companies across caregiving, mobility, remote monitoring, financial planning and home safety.

Against that field, iWOW’s possible advantage is not that it has found a problem others missed. It has not. Its edge, if one emerges, may come from being publicly listed, having access to capital markets and carrying an order book of about US$69.3 million as of April 2024. That gives it a kind of balance-sheet visibility that many venture-backed startups, still trying to prove profitability, do not yet have.

But being listed also cuts both ways. Public investors tend to be less patient with vague platform narratives than venture capitalists. If the company is serious about becoming a longevity player, it will need to show that acquisitions can be integrated, revenue can compound, and margins will not be diluted by stitching together unrelated operations.

Why the next few quarters matter

The broader opportunity is not in doubt. Singapore’s ageing population will increase demand for home monitoring, assisted living, preventive healthcare and specialised nutrition. Across Southeast Asia, governments are also trying to shift care away from hospitals and into homes and communities, partly because institutional care is expensive and labour-intensive.

That creates room for private companies, but it does not guarantee winners. AgeTech businesses often face long sales cycles, fragmented buyers and emotionally complex purchase decisions. Adult children may pay for products, seniors may be the users, and healthcare professionals may influence adoption. That makes distribution harder than in typical consumer technology.

For iWOW, the key test is whether The Gentle Group can be folded into its existing channels in a way that strengthens both businesses. If nutrition products can be sold alongside monitoring tools, and if community services create repeat engagement, the company may have the beginnings of a defensible eldercare ecosystem.

Also Read: Asia’s silent health crisis, and why startups should be paying attention

If not, the risk is that iWOW becomes a collection of adjacent assets tied together by a fashionable word: longevity.

The institutional names in this placement suggest there is appetite for a public-market ageing play in Singapore. The next question is whether iWOW can turn that appetite into operating proof.

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For much of the past two years, Southeast Asian boardrooms have been flooded with AI experiments. Banks have tested copilots, retailers have trialled demand forecasting tools, government agencies have explored automation, and conglomerates have run internal hackathons to show what generative AI can do.

Also Read: Southeast Asia doesn’t have an AI adoption problem, it has a scaling problem

The harder question is no longer whether AI works in a demo. It is whether it can survive real enterprise conditions: messy data, fragmented systems, regulatory constraints, security requirements, and teams that need to change how they work.

That is the gap Temus is trying to address with its strategic investment in Thinking Machines Data Science, a Philippines-headquartered AI and data company and OpenAI’s first official Services Partner in Asia Pacific.

The companies did not disclose the size of the investment. Thinking Machines will continue to operate under its own brand and leadership as “Thinking Machines Data Science, a Temus entity”. Its founder, Stephanie Sy, will join the Temus leadership team and co-lead the combined Applied AI and Data team as Managing Director, alongside Sutowo Wong.

Founded in Manila in 2015, Thinking Machines has spent the past decade building data platforms and AI systems for organisations across financial services, retail, conglomerates, and the civic sector. It says it has served more than 110 clients and trained over 10,000 professionals in applying AI. The company operates offices in Manila, Singapore, and Bangkok.

Temus, established by Temasek in 2021, is a Singapore-based AI and digital transformation company with around 500 employees. It works with public sector agencies and private enterprises across healthcare, defence, financial services, education, and government, aligning itself with Singapore’s Smart Nation and National AI Strategy ambitions.

The deal brings together two capabilities that are increasingly difficult to separate: AI engineering and organisational transformation.

From proof-of-concept to operating reality

Southeast Asia has no shortage of AI interest. The region’s large banks, telcos, retailers, logistics groups, and government agencies have all started testing AI systems, particularly after the rise of large language models such as OpenAI’s GPT family. But the region’s enterprise landscape is uneven. Many organisations still operate on legacy technology stacks. Data is often trapped across business units. Compliance rules differ across markets. Talent is scarce, especially for teams that can translate AI models into production systems.

That makes implementation more complex than buying software or plugging into an API.

“Many enterprises are navigating the challenge of running AI systems that hold up under real operating conditions: constrained data, regulatory requirements, complex workflows,” said Sng Ren Yeong, CEO of Temus. “That requires a different level of engineering, governance, and integration.”

This is where Thinking Machines’s track record matters. The company is not positioned as a research lab or model developer. Its work sits closer to the ground: helping organisations prepare their data, build usable AI applications, and integrate them into workflows. Its recognition as OpenAI’s first official Services Partner in Asia Pacific, followed by advanced partner status, gives it added visibility at a time when enterprises are looking for help to deploy generative AI safely and effectively.

Also Read: How to use AI to become a better investor

Temus, meanwhile, brings delivery infrastructure, public sector relationships, and Singapore-based scale. In May 2026, it launched its AI Foundry at Asia Tech x Summit with support from Digital Industry Singapore, aiming to help organisations move faster from experimentation to deployment.

The Thinking Machines investment gives that platform deeper regional execution capability, particularly in the Philippines, Thailand, and other Southeast Asian markets where demand for AI adoption is growing but implementation support remains limited.

A regional company built for regional constraints

Sy framed the deal as a way to expand without losing the company’s identity.

“We built Thinking Machines because we believed that the region deserved world-class AI capability — built here, for here,” she said. “Joining the Temus group means we can pursue that ambition at a scale we could not have reached alone. Our team, our brand, and our commitment to our clients remain unchanged.”

That “built here, for here” point is more than a slogan. Southeast Asia’s AI needs differ from those of the US, Europe, or China. Enterprises often operate across multiple languages, regulations, customer behaviours, and infrastructure maturity levels. A model or workflow that performs well in Singapore may require substantial reworking in the Philippines, Indonesia, Vietnam, or Thailand.

For AI service providers, this creates both a challenge and an opening. Global technology vendors offer powerful tools, but companies still need local partners that understand procurement realities, data limitations, industry practices, and cultural context. That is especially true in regulated sectors such as banking, healthcare, and government, where AI adoption depends as much on governance and trust as on model performance.

The deal also reflects a broader shift in Southeast Asia’s AI market. Early excitement around generative AI was often centred on productivity tools and chatbots. Enterprises are now asking tougher questions: which use cases produce measurable savings or revenue, how AI decisions can be audited, and how systems can be maintained over time.

In this environment, the winners may not be the loudest AI evangelists, but the companies that can do the unglamorous work of integration, training, monitoring, and change management.

Competing in a crowded transformation market

Temus and Thinking Machines will face a competitive field. Global consulting and technology services firms such as Accenture, Deloitte, IBM Consulting, and Capgemini are investing heavily in AI transformation offerings across Asia. Regional players, including NCS in Singapore and FPT Software in Vietnam, also have strong enterprise relationships and large engineering teams. Cloud providers such as Microsoft, Google Cloud, and AWS are not direct consulting rivals in every deal, but their partner ecosystems shape how enterprise AI projects are designed and delivered.

Where the Temus-Thinking Machines combination may seek an edge is in pairing Singapore institutional backing with a Southeast Asian delivery culture. Temus offers scale and access to complex enterprise and government transformation projects, while Thinking Machines brings a decade of AI-specific implementation experience from markets outside Singapore’s relatively mature digital economy.

That combination could prove useful as companies move beyond narrow pilots into more ambitious deployments that cut across business units, customer channels, and compliance functions.

Why the deal matters

For Singapore, the investment supports its push to be a regional hub for applied AI, not just AI policy and research. The city-state has spent years building its digital economy infrastructure, and its National AI Strategy has placed emphasis on adoption in sectors such as finance, health, logistics, and government services.

For the Philippines, the deal is a notable signal that homegrown AI capability can scale regionally. Thinking Machines is among the more visible examples of a Manila-built technology services firm expanding into higher-value AI work, rather than competing mainly on outsourced labour or back-office services.

For Southeast Asian enterprises, the practical impact will depend on execution. The promise is a broader bench of specialists that can support AI projects from data readiness and engineering to deployment, governance, and adoption. The risk, as with any integration, is maintaining the speed and culture of a specialist firm inside a larger organisation.

Also Read: Artificial Intelligence as a question of national security and independence

Temus and Thinking Machines say existing client engagements and teams will continue without disruption.

The timing is clear. Across the region, companies have spent the first phase of the AI boom learning what is possible. The next phase will be about what can be trusted, scaled, and embedded into daily operations. Temus is betting that Thinking Machines can help it win that harder phase.

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The crypto space climbed 1.1 per cent to US$2.21 trillion in the last 24 hours. What caught my eye first was where the demand concentrated. Layer 1 tokens led the charge, with the category gaining 1.45 per cent and outperforming the broader landscape. Ethereum and Solana dominated social media conversations, and the timing was no accident. Ethereum celebrated its 11th anniversary on the same day, and posts commemorating the mainnet launch generated a wave of bullish energy across trading communities.

This was not some manufactured hype cycle. People were genuinely reflecting on what Ethereum has built over more than a decade, and that reflective mood translated into real purchasing activity. Capital rotated into these core protocol assets in a way that signals a risk-on shift within crypto itself, not just a mindless beta play riding external momentum. I find that encouraging because it suggests participants are making deliberate allocation choices rather than simply chasing whatever ticks up first.

The supporting conditions around this advance also tell a compelling story. Bitcoin liquidations plunged 62 per cent over 24 hours to just US$22.55 million. Read that number again. When forced selling dries up to that degree, it removes a persistent ceiling that had previously capped attempts at prolonged upward movement.

Traders who might have been squeezed out of positions simply were not there to create that downward weight. The market had room to breathe, and it used that room effectively. A cleaner base with less leverage hanging over it gives any climb more legitimacy, and I believe we are watching exactly that unfold.

Also Read: The Fed held rates, but the real story is what that means for crypto and risk assets

No honest assessment of this session can ignore the macro backdrop, because crypto did not advance in isolation. The correlation between digital assets and the S&P 500 hit 76 per cent over the past 24 hours, while the correlation with Gold reached 79 per cent. Those are high numbers, and they confirm this was a broad, macro-driven rotation rather than something unique to the blockchain world. Wall Street rebounded with force after a bruising stretch.

The Nasdaq jumped 2.8 per cent to snap a six-day losing streak. The S&P 500 climbed 1.7 per cent to 7,437.63. The Dow Jones Industrial Average surged 613.92 points, or 1.2 per cent, to close at 52,208.06. Microsoft alone skyrocketed 16 per cent after robust cloud and Azure results eased investor anxiety over artificial intelligence spending, adding a record US$450 billion in market value in one session.

Chip stocks followed suit, with Micron Technology soaring 18 per cent and Advanced Micro Devices climbing over 13 per cent. Across the Pacific, South Korea’s Kospi Index rocketed by up to 15 per cent in a historic intraday rebound powered by SK Hynix and Samsung Electronics, while Japan’s Nikkei 225 jumped over 5 per cent.

When traditional markets rally with that kind of determination, crypto benefits from the improved liquidity environment, and pretending otherwise would be intellectually dishonest. I view this correlation as a positive for now because it means digital assets are participating in a genuine global risk-on rotation rather than floating untethered from reality.

Also Read: The market is pricing in regulatory clarity that does not exist yet. Why crypto is fragile?

Looking ahead, the technical picture presents a clear test. The total market cap sits right at the US$2.21 trillion pivot point, and the immediate hurdle is the 23.6 per cent Fibonacci level at US$2.23 trillion, with a stronger barrier at the recent swing high of US$2.26 trillion.

If buyers can push through that zone, the advance gains real credibility. If they cannot, we likely return to the range-bound trading that has defined recent weeks. For Ethereum specifically, analysts point to US$1,975 as the key breakout level that could open a path toward US$2,300. I will be watching that threshold closely because a decisive reclaim there would confirm the anniversary-driven enthusiasm has legs beyond a single news cycle.

One event looms large over the next 24 hours and could inject significant volatility into the picture. Over US$10.5 billion in Bitcoin and Ethereum options expire on July 31. That is an enormous notional amount, and an expiry of this magnitude has historically created sharp price swings as market makers adjust their hedges and positions roll over.

The climb we witnessed could either accelerate through expiry as bullish positioning reinforces itself, or it could stall and reverse as profit-taking meets the mechanical selling that large expiries often generate. I lean toward the former given the reduced liquidation environment, but I would not bet the house on it.

My overall read is cautiously bullish, and I use the word cautiously deliberately. The ingredients for a lasting push higher are present. Narrative-driven demand in Layer 1 tokens gives the run a story and a reason to exist beyond pure speculation. The macro backdrop broadly supports risk assets.

Leverage has flushed out, leaving a healthier structure underneath. But translating one good day into a trend requires follow-through, and the US$2.23 trillion to US$2.26 trillion barrier will demand exactly that. Social mood can ignite a move, but only continued capital inflow can carry it through meaningful overhead supply.

All things considered, this session felt like the market exhaling after holding its breath for too long. The combination of Ethereum’s milestone, Solana’s continued relevance, a dramatic drop in forced selling, and a powerful global equity rebound created conditions where buyers finally had permission to step in. Whether they maintain that confidence through a massive options expiry and into next week remains the open question. But for now, the tape looks constructive, the narrative feels organic, and the macro winds are at our backs.

—

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

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

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Ask most people what Dropbox does, and they’ll tell you it’s where they store files. Ask Kenny Takeuchi, the company’s newly appointed VP of APJ Sales, and he’ll tell you that’s precisely the problem the tech firm is trying to move past.

As ChatGPT, Claude and Gemini become the starting point for how knowledge workers actually get things done, Dropbox has made a calculated decision: rather than building its own AI model to compete with the giants, it’s positioning itself as the connective tissue between them.

Also Read: Do you know what ChatGPT is saying behind your back?

On July 14, the company announced expanded integrations across all three major AI platforms –Dropbox x ChatGPT, Dropbox x Claude, and Dropbox x Gemini Spark. The move reflects a broader strategic bet on becoming what Takeuchi calls the “trusted context layer” for AI-enabled work.

It’s a phrase that sounds like corporate jargon until you unpack what it actually solves.

The problem nobody’s talking about

Here’s the uncomfortable truth about generative AI in the enterprise: the output is often brilliant, but it’s also disposable. A sales team drafts a renewal proposal in ChatGPT, a marketer brainstorms campaign copy in Claude, a developer scaffolds code in Gemini, and then what? The file lives in a chat thread. Nobody else on the team can find it. There’s no version control, no permissions structure, no link back to the original contract or pricing sheet that informed it in the first place.

“AI is only as useful as the information it can work from,” Takeuchi says. “If the underlying content is fragmented, outdated or disconnected from everyday workflows, even the most capable AI models will struggle to produce reliable results.”

This is where Dropbox’s pitch gets interesting. Instead of asking organisations to abandon the tools they’ve already invested in, such as the AI platforms, shared drives, and legacy systems, Dropbox is threading itself through the gaps.

For instance, a sales rep in Singapore working on a renewal proposal in ChatGPT doesn’t need to hunt down the latest contract manually. They can pull it directly into the conversation from Dropbox, and once the AI-assisted work is done, push it back into Dropbox so it becomes part of the team’s shared record, not something trapped forever in a chat window.

“That’s an important distinction,” Takeuchi explains. “Our goal isn’t simply to help AI generate content. It’s to help teams turn AI activity into work that can be saved, shared, reviewed and reused.”

Betting on an open ecosystem, not a single bet

What’s notable about Dropbox’s approach is what it isn’t doing. It isn’t racing to build a proprietary foundation model. It isn’t asking customers to pick a side in the ChatGPT-versus-Claude-versus-Gemini contest playing out across the industry. Instead, it’s wagering that enterprises will never actually consolidate around one AI tool at all.

“The reality is that enterprises aren’t standardising on a single AI tool,” Takeuchi says. “Different teams use different tools for different kinds of work, and that’s likely to remain true for the foreseeable future.”

Also Read: Beyond the cloud: Entering the Web3 horizon for greater security

That thinking shapes how the three integrations are designed, and they’re deliberately not identical. The ChatGPT integration leans into organising files, generating shareable links and executing multi-step workflows. The Claude integration, spanning Claude, Claude Cowork and Claude Code, is built for more technical, developer-adjacent tasks. Gemini Spark’s integration focuses on accessing and sharing files within Google’s newer agentic workflows.

“Some help people find and preview content, others support technical workflows, and others help turn AI-generated output into something that can be saved, shared and built on by a team,” Takeuchi notes. “The value isn’t that every integration does the same thing; it’s that together they support the different ways people work with AI across an organisation.”

Governance without reinventing the wheel

For enterprises, particularly in security-conscious markets across Asia Pacific, the obvious anxiety is data governance. Who sees what, and who decides?

Takeuchi is careful to draw a clean line here: Dropbox governs the content itself, while the AI platforms manage how their own connectors are deployed inside an organisation. Crucially, users only ever see what they already had permission to access in the first place.

“That means organisations can introduce AI using the governance models they already trust, rather than creating entirely new ones,” he says.

This layered structure also gives Dropbox room to accommodate wildly different risk appetites across the region, a genuine consideration given how differently AI adoption is unfolding across APJ.

Japan’s caution, Southeast Asia’s speed

Having spent two decades building his career in Japan with stints at Adobe, Salesforce Japan, Databricks Japan and DocuSign Japan, Takeuchi is unusually well-placed to speak to the region’s contradictions.

“It’s inaccurate to think about APJ as a single market,” he says bluntly. “While the appetite and curiosity for AI is remarkably consistent across the region, the pace of adoption and the reasons behind it can be very different.”

Japan, he explains, has the technical capability to move fast but often chooses deliberate, governance-first rollouts. Markets like Singapore and India, by contrast, are frequently more focused on scaling initiatives and proving business value quickly. “Neither approach is better than the other,” he adds. “They’re simply different starting points.”

That nuance extends to how Dropbox packages its pitch: some customers deploy AI broadly across the workforce from day one, others start with a narrow, tightly controlled set of connectors. “Our approach supports both,” Takeuchi says. “Ultimately, our goal is to give organisations flexibility. They should be able to adopt AI at a pace that reflects their own business priorities and risk tolerance, not ours.”

A familiar playbook, applied differently

Takeuchi’s time at DocuSign offers a useful parallel for where he sees Dropbox heading. E-signature was the entry point, but customers soon started asking about the entire agreement lifecycle: what happens before and after a document gets signed.

Also Read: Gemini’s SEA growth puts local-language AI at the centre of the assistant race

He sees the same pattern repeating. “For years, storing files was the primary job. Today that’s almost expected,” he says. “The harder challenge is helping organisations keep knowledge connected as work spreads across documents, conversations and an increasing number of AI tools.”

Dropbox says it’s seeing the strongest early traction in construction, technology and professional services, the industries that generate and shuffle enormous volumes of documentation daily and where the cost of fragmented knowledge is acutely felt.

What success actually looks like

For a newly appointed regional sales leader, the obvious yardstick is revenue growth, and Takeuchi doesn’t pretend otherwise. But he’s framing the next 12 to 18 months around something less easily quantified.

“The measure I’ll be paying closest attention to is whether customers feel work has become less fragmented,” he says. “If people can work seamlessly without worrying about where information lives, whether they have the latest version or how to share it with colleagues, then we’ve created meaningful value.”

Whether that ambition translates into a durable market position, or simply a well-argued footnote in the broader AI platform wars, will depend on whether enterprises actually want a “context layer” at all, or whether they’ll eventually demand the AI giants solve this problem themselves. For now, Dropbox is betting that the fragmentation is the real opportunity, not a temporary inconvenience.

The post Why Dropbox refuses to pick a side in the ChatGPT-Claude-Gemini fight appeared first on e27.

Tell someone, “I’m going to make pancakes,” and see how they interpret it in their head.

In New York, they’ll picture a fluffy stack with maple syrup. In Amsterdam, a thin, buttery pannenkoek the size of the plate. In Singapore, perhaps min jiang kueh, dense with crushed peanuts. In Sydney, ricotta hotcakes at weekend brunch.

Same sentence. Four completely different plates of food. The words didn’t carry the meaning — the listener’s context did. 

Business communication has quietly relied on this trick for decades. We say something general, and the audience fills in the rest. It worked because the audience was human.

It is about to stop working because, as Cloudflare’s CEO reported in June, bot traffic surpassed human traffic on the internet for the first time.

Humans fill gaps, AI doesn’t

Here is what happens when a human visits a company website. They see a cybersecurity client here, a crisis project there, a media training page somewhere else. And they conclude, without being told: these people could handle our data breach.

Nobody wrote that sentence anywhere on the site. The visitor inferred it.

Humans connect dots, fill gaps and give the benefit of the doubt. Most corporate websites are built on the assumption that we will.

AI does not do this.

When someone asks ChatGPT, Gemini or DeepSeek what a company does — and, increasingly, that is the first thing a prospective customer, investor or journalist does — the model can only work with what was actually said. If you never wrote, “We handle data-breach communications,” then, as far as the machine is concerned, you don’t.

AI cannot smell competence. It cannot read between the lines. There is no benefit of the doubt. Unsaid means invisible.

Also Read: Why AI literacy may become the new financial literacy

We tested this on ourselves

I run a PR consultancy in Singapore. For years, we described ourselves the way most agencies do: “B2B technology PR.” Accurate and, today, almost meaningless. It relies entirely on the reader to work out what that means for them.

When we rebuilt our website to make the business more legible to AI systems this year, we had to undertake an intense exercise: saying exactly what we do, out loud, in words a machine cannot misread.

In doing so, we discovered we had been describing ourselves incorrectly. We don’t just do B2B technology PR; what we actually do, over and over, is help international technology companies enter Southeast Asian markets. Market-entry PR had been the agency’s pattern for the past decade — and we had never once said it plainly.

Our work hasn’t changed. How we describe it has. Once we clearly articulated our proposition on the website, within weeks, AI tools began describing us accurately and recommending us for the work we actually do. Machine readers need us to be as clear as possible.

Conducting that exercise is harder than it sounds. Try writing down what your company does without using the words “solutions,” “holistic,” “end-to-end” or “innovative.” Most executive teams cannot do it on the first attempt.

Ambiguous communication is rarely intentional. It is a byproduct of how humans communicate: both sides meet halfway, each filling in what the other has left out. Machines miss what’s implied.

Analysts have noticed

This year, Gartner made a prediction that startled the communications industry: that by 2027, mass adoption of AI tools as a replacement for traditional search will double PR and earned media budgets.

The rationale is this: as ChatGPT traffic grew 608 per cent year on year, evidence accumulated that AI answer engines overwhelmingly favour credible, non-paid sources, and Gartner argues that making a company legible to these systems is a communications skill, not a technical one.

Yet the industry’s own data confirms the scramble: Muck Rack’s State of PR 2026 survey found 73 per cent of PR professionals now call generative engine optimisation important to their strategy — while 29 per cent admit nobody at their organisation owns it.

In all honesty, that headline figure has been challenged as more marketing than research, and the sceptics, like me, have a point. Whether budgets double is anyone’s guess.

But the underlying shift is not in dispute: ambiguity has become a tax. AI systems cannot recommend what they cannot parse.

Also Read: Southeast Asia in the 2026-2030 world order: Trade, chips, AI, and capital

In Southeast Asia, this problem multiplies

Here is where it gets interesting for this region, because the pancake problem does not only apply to breakfast.

In my experience, “fintech” often signals something different in Jakarta — consumer, mass-market, inclusion-driven and reputationally loaded — than it does in Singapore, where it is more likely to mean infrastructure and institutions.

“Compliance” carries a different weight in Manila than in Sydney. “Enterprise” describes a different kind of buyer in Bangkok than in Kuala Lumpur. Southeast Asia is not homogeneous: there are widely varied vocabularies and sets of assumptions.

It is not one market for machines either. Different countries are now building their own AI tools, trained on different information and operating under different rules. The AI a buyer consults in Indonesia will not describe your business in the same way as the one a buyer consults in Australia.

Regional companies have always known that trust must be earned market by market. Now clarity must be, too.

Machines don’t take hints

For decades, vague language was permissible because humans are generous readers. Now, the first impression of your company is increasingly formed by a machine — and the machine only knows what you say about your company out loud.

So say it.

Plainly, specifically and in the words each market actually uses.

Ask the AI tools what they think you do. If the answer is wrong, the fault may not lie entirely with the machine. You may simply not have articulated the business clearly enough.

—

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

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

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One of the most persistent mistakes in product and growth strategy is the assumption that the person using the product is the person who matters most in the buying decision. That belief is comforting because it gives teams a clean story. Build something people love, remove friction, improve the experience, and growth will follow.

In many markets, that story is incomplete.

The user may be the visible actor, but the real decision can sit elsewhere. In enterprise software, financial services, security, regulated operations, healthcare, infrastructure, and increasingly in any category touching data or operational risk, the product is often judged by functions that never use it in the way the end user does. Procurement will test commercial discipline. Legal will test exposure and enforceability. Security will test control. Finance will test cost logic, payback, and budget legitimacy. Risk will test survivability under stress. The user may still matter, but the user is no longer the whole market.

This is where many otherwise strong product teams lose strategic altitude. They continue optimising for adoption while the actual buying system is optimising for assurance, control, and budget protection. They interpret slow progress as a sales problem or a messaging problem, when in reality the product has not yet been made legible to the real customer.

The myth of the user led buying model

Consumer shaped thinking has had an enormous influence on modern product practice. It has improved usability, sharpened empathy, and corrected years of enterprise indifference to the people expected to live with bad systems. That was necessary. But it also created a distortion. Too many teams now behave as though user love is sufficient to unlock commercial success in markets where institutional buying logic still dominates.

It rarely is.

A product can be intuitive, elegant, and strongly demanded by an operating team and still fail to move forward. Not because the value is weak, but because the organisation buying it is asking a different set of questions. Can this vendor be governed properly? Are the contractual terms survivable? Does the pricing model create long-term exposure? Will this product introduce regulatory ambiguity? Are the data rights acceptable? Is the implementation risk worth the return? Does this purchase create new headcount, hidden cost, or architectural dependency? These are not marginal questions asked on the side. They are often the core questions.

Also Read: Your customers are not buying your product, they are buying a better version of themselves

The product is not being evaluated only for usefulness

Most teams understand the need to show product value. Far fewer understand that value is being assessed through different lenses by different internal audiences. The user is asking whether the product helps them do something better, faster, or more effectively. Procurement is asking whether the commercial structure can be managed without regret. Legal is asking whether the downside is bounded. The CFO is asking whether the economics are credible and whether this deserves capital ahead of other demands on the budget.

None of these perspectives is irrational. They are performing their roles exactly as they should. The problem arises when product leaders treat them as obstacles rather than as customers in their own right.

Procurement is often about buying risk shape, not just price

Procurement is routinely misunderstood by product teams. It is seen as the function that arrives late, pushes on price, and creates delay. That is a shallow reading of what is actually happening.

In serious buying environments, procurement is not only about negotiating cost. It is testing whether the vendor behaves with discipline, whether the deal structure is coherent, whether commitments are clear, and whether the organisation is about to enter an arrangement it will later struggle to unwind. Procurement is often less interested in your product narrative than in whether your commercial model creates hidden expansion, ambiguous service scope, unbounded support expectations, or contractual lock-in without reciprocal protection.

A team that has only learned to sell value often struggles here because procurement is examining maturity. Loose packaging, vague service descriptions, inconsistent pricing logic, missing governance terms, and fuzzy implementation commitments all signal future pain. Even a strong product can start to look risky if the commercial architecture around it feels improvised.

Legal is evaluating future failure, not present excitement

Legal does not buy possibilities. Legal models fail. That distinction matters.

When product teams present a new capability, they often describe what the product can do at its best. Legal is usually concerned with what happens when it does not. What if the data flows are disputed? What if a regulatory complaint is raised? What if the service fails during a critical period? What if an automated output creates harm? What if an external dependency breaks? What if customer information is retained too long or used in a way that exceeds consent? What if an internal team relies on a claim that later proves indefensible?

This is not cynicism. It is the institutional function responsible for asking what others are tempted to postpone.

Also Read: The agent as customer: Jensen Huang’s trillion-dollar bet on AI’s next era

The CFO is not buying features; the CFO is buying economic confidence

Perhaps the biggest mismatch in modern product storytelling is with finance. Product teams often believe that if user demand is visible enough, budget logic will follow. In practice, the CFO is often evaluating a completely different object.

The CFO is not buying your roadmap. The CFO is buying confidence in the economic shape of the decision. That includes the direct cost, the total cost, the speed of value, the certainty of value, the downside if adoption underperforms, and the extent to which this spend displaces something else with a clearer return. Even where the numbers appear favourable, finance will still ask whether the value is measurable, durable, and attributable enough to deserve investment.

This is where many good products become strategically weak. They talk in terms of empowerment, efficiency, collaboration, and innovation, while finance needs to understand cost avoidance, revenue protection, compliance reduction, productivity recovery, margin impact, capital discipline, or risk containment. The product story may be true, but it is not yet in a language that capital allocation can trust.

The internal sponsor is often carrying too much of the load

One of the most overlooked signs of strategic weakness is when a product depends too heavily on an internal champion to do all the translation work. The user or business sponsor loves the product, sees the value, and wants the deal to happen. But they are left carrying the burden of explaining security posture, financial rationale, implementation risk, legal safeguards, and commercial structure to functions that were never part of the original product conversation.

That is not a sales inconvenience. It is a design failure in the route to market.

A strong product organisation does not simply create demand in the user base. It equips the buying system. It gives the sponsor material that travels across functions. It anticipates objections that are not really objections but legitimate decision criteria. It understands that internal advocacy has limits, especially in large institutions where each function is being judged on whether it prevented the wrong kind of decision, not on whether it accelerated the exciting one.

If your deal advances only when a heroic sponsor spends political capital carrying you through the organisation, your model is less scalable than it appears.

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

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

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