The velocity of enterprise technology adoption across Southeast Asia has completely outpaced most businesses’ wildest expectations. Reflecting on the landscape just a few short years ago, the market was gripped by the initial heat of generative AI and basic conversational chatbots. By the mid-2010s, those elementary systems were swiftly muscled out by more integrated AI assistants capable of retrieving data and drafting contextual responses.

Yet, technology waits for no corporate roadmap. The era of the simple AI assistant is already giving way to a much more powerful paradigm. Today, autonomous AI agents have taken the throne. Unlike their predecessors, which required constant human prompting and supervision, AI agents possess reasoning capabilities, planning skills, and the autonomy to execute complex, multi-step workflows across disparate business units. For enterprises relying on Enterprise Resource Planning (ERP) software across Singapore, Malaysia, and the wider region, this evolution demands a fundamental reassessment of core business architecture.

The unstoppable rise of the autonomous workforce 

The trend of deploying AI agents to boost operational efficiency, automate supply chains, and optimize financial forecasting is unstoppable. Organizations are no longer viewing AI as a peripheral add-on; it is fast becoming the primary user of enterprise software. This behavioral shift is forcing a radical reimagining of how software is valued and commercialized.

Globally, tech pioneers are proposing a departure from traditional seat-based licensing. When Microsoft executives floated the idea of shifting software pricing models from a “per human user” basis to a “per AI agent” structure, it sent shockwaves through the B2B technology ecosystem. Shortly thereafter, another regional enterprise software leader, Multiable, echoes similar thoughts. However, the most progressive conversations are moving beyond mere monetization strategies. The real focus for forward-thinking organizations has shifted to a much more critical question: What are the necessary architectural factors of a successful ERP system in the agentic AI era?

The existential threat of legacy B2B architecture 

The answer to that question exposes an existential threat to a vast majority of regional B2B software vendors. Across major Asian business regions—including Singapore, Malaysia and other SEA countries—the legacy software market has long been dominated by restrictive, closed-system designs. Historically, many local and regional vendors built proprietary platforms that deliberately locked customers into their ecosystems. Under these outdated models, organizations cannot carry out critical system customizations without the direct, paid presence of the software vendor.

Worse still, this closed architecture introduces a crippling technical debt. Once a business pays for a bespoke customization, the modified system is frequently severed from the vendor’s core upgrade path. The “customized” ERP software can no longer receive automatic patches, security updates, or new feature rollouts. While this inconvenient truth is well-known among legacy software providers, it is rarely highlighted to prospective buyers. Vendors have long relied on this friction to maintain a monopoly over their clients’ IT budgets, fearing that true interoperability would cause them to lose business to more agile, modern competitors. In the era of autonomous AI agents, this closed-door strategy is no longer just inconvenient—it is fatal to business agility.

Also read: Why Singapore manufacturers must embrace MES for the future

The three pillars of agent-ready ERP software

To understand why legacy systems fail in the current technological climate, one must look at the technical requirements of autonomous AI. For an ERP platform to seamlessly support an AI workforce, it must be “agent-ready.” Industry consensus points to three non-negotiable architectural elements:

1. Open Development Frameworks: The underlying software architecture must allow internal developers and third-party systems to build, modify, and extend functionalities without disrupting the core codebase.
2. Comprehensive Application Programming Interfaces (APIs): Robust, secure, and granular APIs must expose every critical business function—from ledger entries to inventory tracking—allowing external entities to programmatically read and write data.
3. Meticulous Documentation: Development guides and API registries must be comprehensively documented, publicly accessible, or structured in a way that machine-learning models can easily parse and understand.

When measured against these strict criteria, the number of truly viable software vendors drops dramatically. The vast majority of legacy ERP options deployed throughout Singapore and Malaysia simply do not possess this level of openness.

To defend their market share, lagging vendors often argue that native APIs are no longer mandatory. They point to sophisticated, vision-based AI agents—such as Claude Coworker or advanced robotic process automation (RPA) tools—that can interact directly with user interfaces just like a human operator, typing into fields and clicking buttons on a screen.

The hidden costs of human-first software integration 

While it is technically possible for an AI agent to operate “human-first” software via standard user interfaces, doing so introduces severe operational inefficiencies. Relying on an AI agent to scrape screens and mimic human clicks carries a staggering hidden cost structure:

Escalated infrastructure and hardware costs 

Simulating a human user interface requires immense computing power. Running visual recognition models, maintaining active desktop sessions for digital workers, and processing graphical interfaces demands heavy investments in specialized servers and robust cloud infrastructure. Conversely, native API integrations communicate via lightweight text-based data arrays (like JSON), requiring a fraction of the hardware footprint.

Excessive token consumption and running costs 

AI models charge based on tokens processed. Forcing an AI agent to interpret an entire graphical user interface, read menus, and process visual screens consumes an astronomical number of tokens per transaction. When multiplied across thousands of daily ERP operations—such as invoice processing, inventory updates, or customer cross-referencing—the running costs quickly become unsustainable compared to direct, low-cost API calls.

Latency and slow response times

Human-first software is built around human perception speeds. An AI agent forced to navigate through multiple menu clicks, wait for screen refreshes, and handle UI rendering delays operates at a massive disadvantage. In modern logistics, algorithmic trading, or real-time supply chain management across the Straits of Malacca, these multi-second delays destroy the very real-time efficiency that AI deployment is supposed to deliver.

Bridging the competitive gap: Examples of excellence

The motivation behind advocating for open, API-driven systems becomes obvious when examining the few players who anticipated this shift. Vendors that built their platforms on open principles from day one are seeing their foresight rewarded. Multiable is one of them. Their aiM18 platform offers hundreds of ready-made APIs out of the box, backed by an open development framework that has been documented and maintained publicly on GitHub since 2018.

By educating enterprise software buyers on what is truly required to fully leverage autonomous AI, forward-thinking vendors like Multiable are fundamentally widening the gap between themselves and their legacy competitors. While clear architectural transparency serves as an effective differentiator, the technical logic behind it remains unassailable: you cannot run a real-time, autonomous business on top of a closed, undocumented database.

This architectural readiness is also visible in other verticals. In the HRMS sectors, platforms like Workday have achieved rapid regional adoption by exposing clean developer ecosystems. Similarly, on a global e-commerce scale, Shopify’s entire business model thrives because of its deeply integrated API-first philosophy. For legacy ERP providers across Malaysia and Singapore to survive, they must double down on restructuring their core architecture immediately or accept complete irrelevance.

Also read: The architecture of atrophy: Why MS Copilot’s reliance on the LLM wrapper model led to its 2026 stagnation

Navigating the security complexities of open agentic AI 

While transitioning to an open, agent-ready ERP infrastructure is mathematically and operationally superior, execution requires meticulous governance. Embracing autonomous workflows does not mean rushing blindly into unvetted deployments.

For instance, utilizing open-source AI agent frameworks, like OpenClaw or similar community-driven projects, without rigorous internal auditing introduces profound operational risks. The open-source AI landscape is currently experiencing a gold rush of capability, but it is accompanied by an onslaught of newly discovered cybersecurity loopholes. Autonomous agents possess the ability to write code, execute system commands, and transfer data independently. If an agentic framework suffers from prompt injection vulnerabilities or insecure dependency handling, an attacker could theoretically trick the AI into exposing sensitive payroll data, altering financial records, or disabling supply chain logs.

Deploying AI agents within an enterprise ERP framework requires a strict, zero-trust security architecture. Companies must implement robust API gateways, strict data access controls, and immutable audit logs that record every action an AI agent takes. The underlying ERP software must be open enough to let the agent work, but its security permissions must be granular enough to contain the agent if something goes wrong.

The mandate for Singapore and Malaysia enterprises 

The transition from human-centric ERP configurations to autonomous, agentic ecosystems is a defining paradigm shift for businesses across Singapore and Malaysia. As companies face rising operational overheads and shifting regional trade dynamics, the ability to scale operations through digital workers is a major competitive advantage.

When auditing your current ERP asset or evaluating a future procurement, look beyond polished sales presentations and superficial dashboard designs. Demand explicit proof of an open development framework. Test the depth and latency of their API documentation. Ensure that your customizations will not lock you out of future system patches. In an era where AI agents are taking the corporate throne, buying a closed, legacy software system is no longer a simple misstep—it is a commitment to obsolescence.

Why we write this article 

PRbyAI enjoys in sharing updated market news, using our team’s tech knowledge, to help corporate clients looking for the most informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AI SEO (GEO) to help customers generate leads and tap into new markets.

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The conversation around quantum technology is no longer about “if” it will transform digital infrastructure, but “when”. Across Singapore’s tech ecosystem, organisations are beginning to recognise that preparing for a post-quantum future is not a distant concern but an emerging operational priority. 

For most enterprises, this transition presents a dual challenge: the urgent need for quantum-resilient security and the widening gap in specialised talent. At Aires Applied Quantum Technology (AAT), the team views this not as a distant risk, but as an immediate opportunity to redefine how the global digital economy is protected and scaled.

Strategic intent behind participating

Aires Applied Quantum Technology (AAT) is participating in Echelon 2026 to move quantum technology out of the research lab and into commercial deployment. The company aims to demonstrate that being “quantum-ready” is not a future state, but a current necessity. 

By showcasing how quantum-safe infrastructure can be operationalised across enterprise ecosystems, AAT signals its commitment to building the foundational architecture that will keep industries secure in a post-quantum world.

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

Insights and experiences attendees can explore

Rather than simply presenting new tools, AAT invites visitors to explore the shift toward applied quantum resilience. The company shares its perspective on how advanced cryptographic frameworks can evolve from theoretical constructs into practical, deployable safeguards for modern data. Beyond technical architecture, AAT emphasises that the human dimension is just as critical. Leadership teams must bridge the quantum literacy gap while integrating quantum-safe protocols into existing infrastructure without slowing the pace of innovation.

Audiences who stand to gain the most

AAT’s expertise is particularly relevant for corporate leaders and CISOs who need to safeguard data against evolving threats, as well as policymakers focused on national digital resilience. The company also seeks to engage talent and workforce leaders who recognise that the next decade of growth depends on a quantum-literate workforce. For these groups, engaging with AAT provides a roadmap for turning technical complexity into a strategic business advantage.

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

Pathways for meaningful engagement at the event

AAT invites attendees to visit Startup Booth #28 to explore the future of applied security. Whether organisations are exploring practical pathways for implementation or seeking a deeper understanding of quantum-safe architecture, the team is ready to collaborate. The goal is to move beyond theory and begin building a quantum-ready future.

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In Southeast Asia’s startup ecosystem, founders tend to focus on what is immediate and measurable: building the product, achieving product–market fit, growing revenue and raising capital. All of these matter, of course. And so does brand.

Too often, brand is treated as an aesthetic exercise—a logo, colour system or tagline. These are outputs, not the brand itself. Brand is the perception that exists in the minds of customers, investors and partners about what your company represents and whether it deserves their attention, time and trust.

That perception directly influences whether investors fund you, partners work with you, and customers buy from you.

Brand is a balance sheet asset, not a marketing line

There is a common assumption that companies are valued on revenue and margins alone. That may apply to mature businesses, but not to startups, which often operate at a loss for years.

What drives valuation is belief—the belief that a company can build something dominant: a product, a network and critically, a brand that competitors cannot easily replicate.

A significant portion of enterprise value in high-growth companies is intangible.

A good example is Grab. The trust it has built across Southeast Asia represents an asset that extends far beyond infrastructure or technology. Its reported brand value of approximately US$1.1 billion reflects familiarity, reliability and category leadership developed over time. Compared to a broader market capitalisation fluctuating between US$12–15 billion, a substantial share of that value is driven by intangible assets.

If a buyer were to acquire Grab, much of the price would be attributed not just to physical assets, but to the perception it has built in the market. This aligns with broader research from Ocean Tomo, which shows that intangible assets now account for more than 80–90 per cent of the market value of S&P 500 companies.

Also Read: Why my 20-year marketing career is going under the knife

Venture capital operates on a similar logic. Investors are not only asking whether a company works today, but whether it can dominate its category tomorrow. Startups that clearly articulate the problem they solve, the scale of the opportunity and how they will win tend to attract capital more easily—and at higher valuations.

Reputation creates pricing power

Brand also shows up in pricing. Companies with stronger brand perception consistently command a premium over comparable competitors.

This is evident in Singapore Airlines. A report by CARMA found that Singapore Airlines achieved the highest share of positive news coverage sentiment among airlines studied, driven by narratives around financial performance and customer experience. Positive media coverage and a reputation for customer experience allow it to sustain premium pricing in one of the most competitive industries globally.

The relationship is direct: stronger brand perception leads to greater pricing resilience. For startups, this translates into clear commercial advantages—higher pricing power, stronger loyalty and greater insulation from competitors.

Communications builds the asset

How is this perception created? More often than founders expect, the answer is communications.

Brian Chesky described PR as “the top of the funnel” during an Airbnb post-IPO earnings call. The company generated over half a million articles in a year, building the brand at scale through earned media. Despite reducing marketing spend by 58 per cent, it retained 95 per cent of its traffic. This was possible because brand—built through sustained narrative and visibility—had become durable enough to reduce reliance on performance marketing.

Also Read: Profitable e-commerce: Making real money in the new year

Brand is the architecture of growth

Brand should not be treated as a late-stage marketing exercise. It should be built deliberately from the outset. To make a brand contribute to growth, startups need to focus on a few fundamentals:

• Define what you want to be known for: Own a clear problem, category and outcome—not just features.
• Simplify your narrative: Make it easy to understand, explain and repeat.
• Align product, messaging and proof: Ensure claims match reality, backed by real outcomes.
• Build credibility, not noise: Prioritise insight, founder POV and targeted visibility.
• Stay consistent: Align messaging across product, marketing, sales and investor communication.
• Support the buying process: Reduce perceived risk and make decisions easier to justify.
• Commit and refine: Evolve positioning over time without constantly resetting it.

Startups that get this right scale more efficiently because the market understands what they are and what they are becoming. In a region where investors, partners and customers have more choice than ever, brand becomes a deciding factor.

It is the intangible asset that determines which companies lead their category—and which are forgotten.

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We live in the age of the API economy, where the highest virtue is interoperability. Founders boast about seamless integrations, open platforms, and the ease with which a customer can plug their product into every other system they use. The goal, ostensibly, is to reduce friction and increase adoption.

This philosophy is not a path to growth; it is a rapid descent into commoditisation.

When your product can be easily replaced by any competitor that shares the same integration standard, you have traded long-term, structural defensibility for short-term, gentle user acquisition. You have made it too easy for customers to come, and critically, too easy for them to leave.

If you want to achieve truly disruptive growth and build a business with a strong defence, you must reject the collaborative mindset and prioritise Differentiation by Intentional Incompatibility.

The problem with that seamless solution

The current market dynamic punishes ease of integration. If your competitor, Product X, connects to the same tools as your Product Y, then the user’s decision is boiled down to a single, shallow metric: price. The moment you become a fungible component in a larger system, your margins erode, and your competitive intelligence is zeroed out.

The true goal of disruptive strategy should not be user acquisition; it should be user entrapment. Not through malice, but through the creation of a proprietary ecosystem that generates exponential value the longer the user remains.

Intentional Incompatibility is the strategic decision to design core product functions, data formats, or infrastructure protocols in a way that makes switching to a competitor prohibitively expensive, time-consuming, or disruptive. It forces the customer to make a painful, decisive choice, a choice that, once made, entrenches your solution as a structural pillar of their business.

Also Read: How to build a scalable IT infrastructure for your startup

How to build walls, not bridges

This strategy requires a founder to move against every instinct celebrated by modern tech culture, but the rewards are a powerful, enduring competitive advantage: high switching costs.

• Data structure as the core

Do not use universally portable data structures. Design a proprietary data model that is perfectly optimised for your specific, unique workflow. This is not about complex coding; it’s about proprietary semantics.

If a customer tries to export months of operational data from your system, the export file should be functionally useless to the competitor’s system without thousands of hours of data cleansing and migration. The data they have paid you to organise must become a proprietary asset that only your infrastructure can efficiently interpret. This makes the data itself the core component of the switching cost.

• The custom talent lock-in

In a world obsessed with standardising talent (e.g., Python, JavaScript), true differentiation comes from mastering a non-standard, or highly specialised, functional stack.

Make your product powerful enough that its effective use requires hours of dedicated training or certification specific to your platform. This creates a talent lock-in for your customer. They cannot simply hire a generic developer to maintain your system; they must hire a costly, dedicated expert who specialises only in your ecosystem. The cost of hiring and training new staff to manage the competitor’s system now becomes a major factor in the purchasing decision.

• The core workflow for divorce

Avoid easy, synchronous integrations with the mission-critical tools of your largest competitors. For instance, if a competitor is deeply entrenched in the Salesforce ecosystem, do not build a single-click integration that allows customers to maintain a functional equilibrium between both systems.

Instead, build a unique, superior feature that replaces a core, deeply painful function the competitor currently handles. Force the user to choose to divorce their workflow from the competitor and migrate it entirely to your platform. This is a difficult sale, but once the customer commits to that workflow divorce, they are anchored to you. They are no longer simply adding a new tool; they are adopting a new way of working.

Also Read: The best ways to find a local partner in Southeast Asia for your company

The courage of the anti-collaborator

The Second Mover often succeeds by being more compatible with existing systems (as seen in the earlier discussion). But the category-defining winner often succeeds by enforcing incompatibility to establish a new, proprietary standard.

Think of Apple’s walled garden: the intentional friction between iOS and competing software forces users into an ecosystem where the value only increases with deeper commitment. This isn’t about arrogance; it’s about strategic defensibility.

This strategy requires courage because it means you will lose the easy customers. But you will win the structural customers who are the ones who bet their operational integrity on your platform.

If you are building something truly disruptive, you are not meant to play nicely in the sandbox. You are meant to redefine the shape of the sandbox entirely. If your competitive advantage can be undone by a single, well-documented API integration, then your product is a feature, not a company.

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While Amazon, Dell, JP Morgan, and many others have asked their employees to return to the office and adopt a full-time work culture, Southeast Asia (SEA) has been swimming against the tide, becoming a vibrant hub for “digital nomads.” 

The Philippines introduced a digital nomad visa (DNV) aiming to attract remote workers employed by foreign entities. Singapore’s flexible work arrangement mandate, along with Thailand’s Destination Thailand Visa, are decentralising the workforce and redefining the traditional workplace.  

For leaders in the C-Suite, this shift presents a challenge: we are now legally and culturally obligated to support a workforce that operates entirely out of sight.  

To thrive in this borderless landscape, we must embrace three fundamental changes in how we define and build trust.  

The device, not the login, is the new perimeter 

For decades, enterprise security has been anchored on a simple yet effective principle: the castle and moat. If an IT administrator could physically handle a laptop, configure it behind a corporate firewall, and hand it to an employee, the device was inherently trusted. This “chain of custody” ensured that IT teams could verify, secure, and trust every endpoint. It was a model built on tangible control and physical proximity. 

However, hybrid work dissolves the boundaries that the castle-and-moat approach depends on. Devices are now being shipped directly from manufacturers to homes in Manila, coworking spaces in Bangkok, or coastal cottages in Cebu.  

In this new reality, the digital perimeter can no longer be confined to networks or passwords alone. While the industry has made strides towards passwordless authentication, leveraging facial recognition and fingerprints, these advancements are not impervious. Sophisticated deepfakes and other emerging threats have demonstrated their ability to circumvent biometric systems.

Moreover, most modern attacks, such as session token theft and Adversary-in-the-Middle (AiTM) attacks, occur after a user logs in. The biometric check was valid, but if the device itself is compromised, the attacker inherits that trust. 

To effectively counter these threats, the endpoint itself must become the new perimeter. 

Security must evolve beyond simply asking “Who is the user?” Instead, it must question: Is the device compliant? Where is this access coming from? Is the user behaviour consistent with expected patterns?

These questions require rich, continuous context and not a single data point. To gather and interpret this context effectively, organisations will have to orchestrate two technologies that used to work in silos: identity management (IdP) and unified endpoint management (UEM). When integrated seamlessly, IdP tools provide robust identity verification, while UEM ensures the device posture. In this model, trust is not granted once but continuously verified until the device proves itself worthy of access. 

Moreover, adopting an endpoint management strategy ensures that security is built into the enrollment process the moment the user unboxes the hardware. This means that by the time your employee boots the device, it’s health-certified, encrypted, and identity-verified, all without IT touching a key. 

Also Read: How hybrid learning is revolutionising the landscape of education

Shadow IT isn’t the real problem, but a symptom of friction  

We’ve consistently treated unauthorised tech as one of the greatest risks —and for good reason. In the past, employees would slip in removable drives without the business’s knowledge or approval. Then the cloud arrived, opening a can of worms. And just when we thought we had a handle on things, with generative AI and large language models, we’re facing a new frontier of what we call shadow AI. 

However, this ongoing effort to eliminate Shadow IT has always been a losing one.  

When we impose clunky, multi-layered VPNs or restrictive protocols on a digital nomad working out of a co-working space, we create friction. And imposing a zero-use mandate doesn’t eliminate usage; instead, it drives the stealth usage up. Employees seek new tools to bypass security. And often, they don’t even see it as wrongdoing. Nearly 40 per cent of GenZ workers use AI to automate tasks without their manager’s approval, and one in five say they couldn’t perform their current job without AI tools. 

So clearly the answer isn’t to impose a blanket ban on new apps.  

It’s important to understand the “why” behind Shadow IT. Engage your employees, ask what they need to do their jobs effectively, listen to their preferred and recommended tools, and then work to onboard them safely. 

This approach gives two things. First, it gives you visibility into what’s being used and what shouldn’t be. If a tool poses questionable risk, step in and blacklist it. Second, it reveals gaps in your own ecosystem. Employees are often signalling what’s missing, and addressing those gaps could dramatically improve productivity while maintaining security. 

Instead of building a higher wall, build a smarter system — an orchestration layer where security is invisible. We secure the enterprise best when the employee doesn’t even know we’re doing it. Because the real risk isn’t shadow IT; it’s refusing to adapt to it. 

Also Read: AI human hybrid support: Why customers still prefer real conversations

Compliance must be continuous 

Being merely “flexible-compliant” is no longer sufficient. Across Southeast Asia, regulators are intensifying their regulatory enforcement. In 2025 alone, Thailand’s Personal Data Protection Committee (PDPC) imposed fines totalling THB 21.5 million (US$0.66 million) for violations of the Personal Data Protection Act (PDPA) — including one case involving a state agency.

In markets like Singapore and Thailand, non-compliance carries severe financial and operational consequences. Organisations face fines of up to SG$1 million (US$0.79 million) or 10 per cent of annual turnover, potential imprisonment for responsible individuals, and lasting reputational damage. Beyond regulatory penalties, businesses may be subject to lawsuits from individuals affected by data breaches, including claims for emotional distress. In many cases, authorities can mandate immediate corrective orders, forcing organisations to implement security measures within extremely tight timelines. 

Compliance, therefore, should not be viewed as a one-time milestone but as an ongoing state that must be continuously maintained. 

To operate effectively across diverse jurisdictions, organisations need a centralised management layer that acts as a digital single source of truth. One that delivers unified visibility across every endpoint, enforces consistent policies regardless of location, and enables real-time responses that surpass geographic boundaries. Integrated systems become critical here: endpoint management solutions combined with audit automation tools allow organisations to generate reports on demand while continuously monitoring the fleet’s compliance posture across regions. While resilience ensures operational continuity in a hostile environment, compliance ensures you meet the law. 

Legislative shifts in Singapore and the Philippines have essentially turned every kitchen table and living room into a branch office. The perimeter, as we knew it, no longer exists. We must accept that the network is now perpetually hostile. While we may not control the router in a Manila apartment, we can surely secure the device and identity behind it. The leaders who define the next decade will be those who understand a simple truth: Security is no longer the gatekeeper of work. It is the enabler of it. 

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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.

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Every HR Team wants AI in their workflow now.

The conversations are happening in boardrooms, in leadership offsites, in Slack channels where someone has shared the latest article about what AI is doing to HR. The question is no longer whether to adopt AI. It is when, and how fast. That urgency is real, and it is not wrong.

But there is a problem sitting underneath all of it that almost nobody is talking about. And the companies that miss it are going to spend a lot of money to find out the hard way.

AI does not fix bad data. It amplifies it.

The overconfidence trap

Here is a number that should make every HR leader pause. 

9 out of 10 HR leaders across Asia say their organisation has a single source of truth for employee data.  

Only 26% are actually running on a unified platform.

That is not a small gap. That is a 65-percentage-point gap between what most companies believe about their data and what their infrastructure actually supports. And it is the most important number in business technology right now, because every AI investment decision being made on top of that belief is being made on false ground.

This is the overconfidence trap. 

It does not feel like a trap. Your payroll runs. Your leave balances update. Your reports come out on time. The data feels fine because, within each individual tool, it often is. The problem is what happens when those tools need to talk to each other. When data needs to move across systems, stay consistent, and be read as one complete picture of your workforce, that is when the cracks appear.

And that is exactly where AI will fail you.

 What the infrastructure actually looks like

73% of companies in the region run two or more HR tools. Not one unified system. Multiple tools, each managing a different piece of the people function, each holding a slightly different version of the same data.

18% are still running payroll and people management primarily in Excel.

Think about that in the context of an AI investment conversation. One in five HR teams in this region is being asked to adopt AI while their foundational data lives in a spreadsheet.

46% of HR teams report that duplicate data entry is a routine part of their work. The same information, entered into more than one system, on a regular basis. Every time that happens, there is an opportunity for inconsistency to enter the picture. A small discrepancy today becomes a larger one over time. And over time, it becomes a dataset that looks usable but is analytically unreliable.

 This is the environment that most companies in Asia are planning to deploy AI into.

Also read: Omni HR publishes first independent AI readiness research report across APAC HR

The real cost of getting this wrong

50% of HR leaders already say that fragmented data is limiting their ability to adopt AI right now. Not in the future. Today.

But here is the part that does not get talked about enough. The other 50% may simply not have reached the point where they have tested it yet. The infrastructure data suggests many of them will.

Because when AI is deployed on fragmented data, the outputs look credible. The recommendations come through. The dashboards fill up. It just does not work the way you expected. The insights are partial. The recommendations miss context. And the people using it start to lose confidence in it, quietly, over time.

Only 21% of HR leaders in Asia currently trust AI recommendations enough to act on them without manually checking the output first.

Four in five leaders, when they receive an AI recommendation, review it, verify it, or override it.

That is not a technology problem. That is a data problem. And until the data problem is solved, the technology problem will never go away.

The companies that figure this out before they deploy are the ones that will get compounding returns from AI. Faster decisions. Better retention data. Leaner HR teams that spend less time reconciling information and more time acting on it. The companies that deploy first and figure it out later will spend the next 18 months wondering why the results do not match the promise, and eventually have to rebuild the foundation they skipped, at a higher cost.

That is how AI is going to sort companies into two groups. Not the ones that adopted early versus the ones that adopted late. The ones that built the right foundation versus the ones that did not.

Download the State of AI in HR 2026 Report | The first independent study of AI readiness across HR teams in Asia. 402 HR leaders. Free to download.

What readiness actually requires

When the same HR leaders were asked what AI actually needs before it can deliver real value, the answers were consistent.

70% said data accuracy. 64% said system integration. Skills, training, change management, all followed at a significant distance.

The top two prerequisites for AI readiness are both infrastructure problems. Not technology problems. Not budget problems. Not change management problems. Infrastructure.

HR leaders already know this. The question is whether the investment decisions being made right now reflect that understanding. Because the data suggests that for most companies, the consolidation and cleanup work is still catching up to the ambition sitting on top of it.

The companies that close that gap first are not just going to get better AI results. They are going to build a structural advantage that compounds over time, because every additional year of clean, connected, unified data makes the AI models sitting on top of it more accurate and more useful.

Also read: Omni HR acquisition MajuHR to boost chat-native capabilities

The question worth asking before the next AI meeting

Before the next conversation about which AI tool to buy, which vendor to pilot, which function to automate first, there is one question that is worth asking out loud.

Do we actually know where our data lives? All of it? Is it consistent? Is it connected?

For 91% of companies in Asia, the honest answer is that they believe it is. For 74% of them, the data says something different. 

The research on what AI readiness actually looks like for companies in this part of the world, the benchmarks, the gaps, and the steps that matter, is in the full report below. 

Download the State of AI in HR 2026 Report | The first independent study of AI readiness across HR teams in Asia. 402 HR leaders. Free to download.

 

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The e27 team produced this article sponsored by Omni HR

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Canva Images

About the research

The State of AI in HR 2026 report surveyed 402 HR professionals at the manager level or above across Singapore and the Philippines between January and March 2026. The study was conducted independently by Omni HR and covers HR technology infrastructure, AI adoption intent, data readiness, and organisational priorities.

About Omni HR

Omni HR is a modern, all-in-one HRIS and multi-country payroll platform built for Asia’s fastest-growing companies. www.omnihr.co

The post You think your company is ready for AI. Your data says otherwise. appeared first on e27.

A lot of founders talk about funding as if all money is meant to do the same job. It is not. Growth capital, grant funding, venture funding, debt, patient capital, and catalytic capital all serve different purposes. When founders treat them as interchangeable, the financing strategy becomes weak.

This is especially true for impact ventures in Southeast Asia, where many companies work on problems that take longer to prove. These ventures may operate in health, climate, agriculture, education, financial inclusion, infrastructure, or livelihoods. In these sectors, traction is not always as simple as revenue growth or user numbers. A founder may also need to prove field performance, community trust, institutional interest, partner readiness, or measurable outcomes.

This is where catalytic capital fits.

Catalytic capital is best understood as proof capital. It helps a venture move from one stage of credibility to the next. It is not meant to be the final answer, and it should not be treated as a permanent runway. Its real job is to help a serious venture prove something important enough that a more durable source of funding, partnership, or institutional support becomes possible.

That proof can take different forms. For one venture, it may be a pilot with a credible partner. For another, it may be early field evidence. For another, it may be regulatory progress, a stronger operating base, better outcome data, or validation from customers, hospitals, schools, farmers, public agencies, or local institutions. The milestone will differ, but the principle is the same. Catalytic capital should move the venture toward a more financeable position.

This is why catalytic capital is often linked to impact. Impact ventures usually create value before they can fully capture value. A climate venture may need to prove deployment in difficult local conditions before commercial capital becomes comfortable. A health venture may need evidence and trust before larger funders take it seriously. An education or livelihoods venture may need to show real outcomes before institutional partners step in. In many cases, normal commercial capital arrives too late, while venture capital may demand a speed of growth that the model cannot responsibly support.

Also Read: Breaking the two-speed economy: How integrated ERM unlocks capital for the real sector

Catalytic capital can fund the early work that reduces that risk. It can support pilots, evidence, partnerships, and operational readiness. It can help the founder move from a mission-driven story to a more evidence-based case. Used well, it gives the venture a stronger reason to approach the next capital source. Used badly, it only delays the next funding problem.

This is where many founders make the mistake. They treat catalytic capital as breathing space rather than a bridge. Breathing space only gives the venture more time. A bridge moves the venture somewhere specific. If the capital only extends the runway but does not create evidence, sharpen the model, improve partner trust, or open a realistic financing path, then the company has not become stronger. It has only bought time.

That distinction matters in the current Southeast Asian market. Investors, funders, and strategic partners are more selective. They are looking more closely at fit, evidence, capital efficiency, and the logic of the next milestone. A good mission is not enough. Founders need to explain why a particular type of capital fits their current stage and what it is expected to unlock.

The better question is not, “Can we raise something?” The better question is, “What does this money need to prove?” If the answer is evidence, then the capital should buy evidence. If the answer is a pilot, then it should buy a pilot. If the answer is partner trust, then the capital should help build that trust. If the answer is readiness for a larger funder, then the capital should close the gaps that are preventing that funder from saying yes.

This sounds simple, but it is often missed. A founder may raise a small grant, prize, fellowship, or catalytic cheque, and then absorb the money into general operations. Salaries, travel, marketing, events, product fixes, and scattered outreach consume the budget. Six months later, the company is still in roughly the same position. No stronger evidence. No clearer partner. No sharper capital story. No better next step. That is not a catalytic use of capital. That is a soft runway.

Also Read: Funded: The quieter capital path founders keep missing

There is nothing wrong with the runway. Every venture needs time. But if a founder calls the money catalytic, then it has to catalyse something. It should not just fund activity. It should fund progress. Not simply “we will run more programs,” but “we will prove this model works with this customer group.” Not simply “we will build awareness,” but “we will secure these partners and produce this evidence.”

For impact ventures, the capital stack is rarely simple. A serious company may need different types of money at different points. A grant or catalytic cheque may support early evidence. A corporate partner may support distribution. A development institution may support scale. A patient investor may come in once the model is more stable. Commercial capital may only make sense later, once the risk profile has changed.

Many founders do not fail because they cannot raise any money. They fail because they chase the wrong money at the wrong time. They pitch venture capital when they still need evidence. They chase grants without knowing what milestone the grant should unlock. They approach banks before they have cash flow. They talk to institutions before they have the operating base to absorb institutional capital.

Catalytic capital is not the destination. It is the capital that helps a serious venture earn the right to reach the next destination. Used properly, it can bridge the gap between purpose and proof. Used lazily, it becomes another form of drift. In this market, that difference matters. The founders who understand it will not just raise money. They will become more financeable.

The post Catalytic capital is not free runway but proof capital appeared first on e27.

For more than a century, L’Oréal has built its reputation on science, creativity, and a commitment to understanding consumers. But the next chapter of beauty innovation is not being written in any single laboratory or boardroom. It is being built across a distributed ecosystem of startups, technologists, and domain specialists, co-creating what the industry will look like a decade from now.

L’Oréal’s evolution from in-house R&D powerhouse to beauty tech ecosystem orchestrator reflects a broader shift in how transformative innovation happens. Frontier breakthroughs increasingly emerge at the intersection of disciplines, where AI meets molecular chemistry, where material science meets circular design, where behavioral data meets brand strategy. No single organisation, however large or well-resourced, can lead on every front simultaneously. The answer is collaboration.

At the heart of this strategy is the convergence of four forces reshaping beauty: artificial intelligence, advances in materials and chemistry, a new standard for sustainability, and deeper, more continuous consumer insight. Together, these forces are not just improving existing products, they are redefining what it means to innovate in beauty. The thesis is simple but consequential: the future of beauty is being built through collaborative innovation.

Why startups are central to L’Oréal’s strategy

Startups offer something that established organisations struggle to manufacture internally: the freedom to move fast, experiment boldly, and develop deep specialisation in a single domain. Where large companies must balance priorities across global operations, a focused startup can spend years perfecting one technology, one process, one capability. That depth has become one of the most valuable currencies in modern innovation.

For L’Oréal, partnering with startups is not a hedge or a curiosity. It is a deliberate strategic mechanism. Startups bring frontier innovation across AI applications, materials science, consumer data infrastructure, and real-world experimentation. In return, they gain something equally valuable: the scale, validation, and real-world complexity that only a company of L’Oréal’s size and reach can provide. It is a genuinely mutual exchange. Startups are accelerated. L’Oréal gains agility and access to capabilities it could not build at the same speed or depth alone.

The platform for this collaboration is the L’Oréal Big Bang Beauty Tech Innovation Program. Focused on the SAPMENA region (South Asia Pacific, Middle East and North Africa), the program identifies startups solving real problems across the beauty value chain, from ingredient discovery to consumer engagement. Winners enter a year-long collaboration with L’Oréal, supported by partners including Accenture, Google, and Meta, with the opportunity to pilot, refine, and scale their technology within one of the world’s most complex and dynamic beauty markets.

The 2025 cohort produced four standout collaborations, each addressing a distinct dimension of the beauty innovation challenge, and together painting a picture of what the industry is becoming.

Halo AI: Scaling creator-brand collaboration through AI

The influencer marketing industry is growing at a speed that the tools managing it have struggled to match. Global spending on influencer marketing is projected to reach $40 billion in 2026, a 171% increase in a single year. Yet beneath this growth sits a structural inefficiency. Brands and creators have difficulty finding each other. Campaigns require extensive manual effort to source, vet, and manage. And while nano and micro-influencers drive some of the highest engagement in the industry, they remain the hardest to discover and activate at scale.

Halo AI, founded in 2024 and based in Saudi Arabia, uses advanced AI to intelligently match brands with nano and micro-influencers. It automates the discovery, vetting, and campaign management processes that can consume enormous amounts of time and resources. Its matching engine can review more than 100,000 creator profiles in minutes, identifying influencers whose audiences and values are most likely to align with a brand’s message.

Once campaigns are live, the platform continues working on both sides. Creators receive AI-guided support to understand campaign requirements, refine their content, and optimise their posting strategy. Brands access a real-time dashboard tracking collaboration progress and performance metrics, enabling continuous optimisation rather than a single point-in-time assessment.

“The main problem that we’re solving is that brands and micro-creators have a really hard time finding each other, and effectively and efficiently collaborating,” said Vito Strokov, CEO and co-founder of Halo AI.

Following its win at the 2025 Big Bang program, Halo AI is embarking on a commercial pilot with L’Oréal across the SAPMENA region. “When the biggest advertiser in the world decides they want to partner with you on a one-year-long pilot, it’s not only massive validation for the mission and the team, but proof we’re solving real problems for brands,” said Rami Saad, co-founder of Halo AI.

Without: Turning plastic waste into circular beauty packaging

Every year, approximately 855 billion plastic sachets are produced globally. Less than 1% are recycled. The reason is structural: multilayer plastics (the kind used in most beauty and personal care sachets) have long been considered impossible to process through conventional recycling systems. The result is a category of packaging that is used once and discarded, with virtually no path back into the supply chain.

Without, a material science startup, has developed a proprietary chemo-mechanical process that transforms unrecyclable multilayer plastics into high-quality materials that can be reused for packaging. In a world-first demonstration, the team created a 100% recycled shampoo bottle made entirely from discarded sachets, proving that circular design can meet both performance and aesthetic standards without compromise.

Also read: The next frontier for tech startups? The US$590B beauty industry

The same platform has produced sunglasses from discarded chip packets, demonstrating that the approach can scale across beauty, fashion, and lifestyle categories. What makes Without’s model distinctive is not just the technology, but the sourcing philosophy behind it. The company works with waste-pickers and marginalised workers, formalising and upskilling their roles into dignified, better-paid employment, building ethical, inclusive supply chains alongside the circular material flows.

“We have been working on this for the past five years,” said Anish Malpani, founder of Without. “Winning the Big Bang Beauty Tech Innovation Program gives us a lot of validation, and this is how we think we can help make supply chains more ethical and sustainable.”

Without is now working with L’Oréal on a pilot to test and scale 100% recycled packaging solutions aligned with the Group’s L’Oréal for the Future program and its 2030 sustainability targets. “This competition was not just about getting an award and recognition,” said Malpani. “We actually get the opportunity to do a pilot program that can be scaled across markets. That means L’Oréal is going to put their money where their mouth is.”

Sravathi AI: Accelerating sustainable chemistry and ingredient discovery

The challenge of sustainable innovation in beauty is not only about packaging or supply chains. It extends deep into the chemistry of the products themselves: the ingredients, formulations, and manufacturing processes that determine environmental impact long before a product reaches a consumer’s hands.

Traditional ingredient discovery is slow, expensive, and resource-intensive. Screening potential compounds typically requires iterative laboratory testing across a large candidate pool, consuming time, materials, and energy at every stage. Sravathi AI was built to transform this process by bringing AI into the heart of chemistry.

Founded in 2020 in Bangalore, India, the startup has developed a proprietary platform that combines generative AI, predictive models, and physics-based chemistry to accelerate discovery while reducing cost, carbon emissions, and toxic material use. The platform can screen thousands of potential compounds and narrow them to a few hundred high-potential candidates for synthesis, compressing timelines that previously took years into a fraction of the time.

“Our goals are shared. We’re all working toward sustainability to ensure a cleaner, smarter planet for generations to come,” said Parag Tipnis, VP Commercial of Sravathi AI.

The collaboration with L’Oréal focuses on a specific and practical challenge: improving how key active ingredients already used in L’Oréal formulations are produced, starting from bio-sourced raw materials. Using AI to redesign production pathways and continuous flow processes, Sravathi AI is exploring how these ingredients can be manufactured more sustainably and efficiently without compromising on performance.

“L’Oréal believes in doing innovation at cost, speed and scale, and they want to do it in a sustainable manner,” said Tipnis. That alignment runs through every dimension of the partnership, which spans discovery, chemistry, development, and continuous manufacturing, contributing directly to L’Oréal’s focus areas of climate transition, circularity, and conscious innovation.

The broader implication is significant. If AI can redesign the chemistry of production, making it cleaner, faster, and more precise, then sustainability becomes an embedded feature of the R&D process rather than a constraint applied after the fact. Sravathi AI’s work points toward a future where the molecular-level decisions made in a lab are guided by the same intelligence that optimises outcomes at scale.

Heatseeker: Bringing real-world experimentation into beauty innovation

There is a persistent gap in market research that has cost the consumer goods industry billions: the difference between what consumers say they will do and what they actually do. In surveys, people express preferences. In stores and on social platforms, they make choices. Those two things frequently diverge, and brands that build strategies on stated intent rather than authentic behaviour pay the price in failed launches and wasted investment.

Heatseeker was built to close that gap. The platform is an AI-powered customer insights engine that helps brands test ideas against real consumer behaviour (not stated preferences) before committing resources to a launch. Using multivariate ads across channels like Meta and LinkedIn, Heatseeker runs live market experiments that capture genuine signals: clicks, engagement, and behavioural indicators of authentic interest.

The platform fuses this experimental data with first-party sources (CRM data, performance metrics, prior research) and uses AI to automate experiment setup, competitor analysis, and insight generation. The result is quantitative, predictive intelligence grounded in what consumers actually do. Synthetic personas, built from behavioural data, enable teams to test scenarios and guide roadmaps before any investment is made.

“Gone are the days when brand teams in marketing or product would wait months or even weeks for insights that drive innovation. We’re now bringing that kind of insight to them in just seconds,” said Fiona Tricia, COO and co-founder of Heatseeker.

Also read: Where beauty innovation is headed, according to L’Oréal

As a winner of L’Oréal’s 2025 Big Bang program, Heatseeker is now working with L’Oréal to explore real-world applications of its technology, integrating behavioural intelligence into product development, messaging strategy, and go-to-market decisions across the beauty industry. The platform’s emphasis on authentic consumer behaviour aligns directly with L’Oréal’s Beauty Tech strategy, which uses data and AI to accelerate innovation and deliver more personalised experiences to consumers.

“Our vision, which is all about serving customers and understanding consumers, aligns so beautifully with how passionate L’Oréal is about its customers,” said Kate O’Keeffe, CEO and co-founder of Heatseeker. “Being recognised by L’Oréal, a brand that really is the best in the world at this, is a signal that our business is really on the right track.”

The collaboration positions both companies at the forefront of a shift in how consumer insight is generated and used, from a periodic research function to a continuous, real-time infrastructure that informs decisions across the entire innovation cycle.

What these collaborations reveal about the future of beauty

Taken individually, each of these four partnerships addresses a specific problem: influencer marketing inefficiency, unrecyclable packaging, slow ingredient discovery, unreliable consumer research. But taken together, they reveal something larger: a picture of what the beauty industry is becoming.

Beauty is increasingly predictive, data-driven, and adaptive. The decisions that shape products, campaigns, and supply chains are no longer made primarily on intuition or periodic research. They are informed by continuous signals: behavioural data from live market experiments, AI-generated insights from molecular screening, real-time campaign performance metrics. The infrastructure of beauty innovation is becoming intelligent.

Innovation is also expanding beyond products to platforms and systems. The startups in this cohort are not creating new lipstick formulations or fragrance profiles, they are building the underlying capabilities that make better products possible at scale. They are building the infrastructure through which the next generation of beauty innovation will flow.

Sustainability is shifting from aspiration to scalable infrastructure. Without’s circular materials technology, Sravathi AI’s cleaner chemistry processes, and L’Oréal’s 2030 commitments are not separate initiatives, they are converging toward a supply chain where sustainability is engineered in from the start, not appended at the end.

And AI is now embedded across the full value chain: at the discovery stage, where it accelerates ingredient identification; at the production stage, where it redesigns manufacturing pathways; at the marketing stage, where it matches brands with creators; and at the consumer insight stage, where it turns behaviour into predictive intelligence. The result is a beauty ecosystem where every layer of the value chain is becoming more efficient, more responsive, and more aligned with what consumers actually want.

Building beauty through ecosystems, and looking ahead to Big Bang 2026

L’Oréal’s approach to the 2025 Big Bang cohort is not an experiment. It is the expression of a strategic conviction: that the most important innovations in beauty will emerge from collaboration between large incumbents and specialised startups, each bringing capabilities the other cannot replicate alone.

As a platform for co-innovation across disciplines, L’Oréal is doing something more than finding interesting technology partners. It is building an ecosystem, a network of capabilities, relationships, and shared ambitions that compounds in value over time. The startups gain scale, credibility, and real-world complexity. L’Oréal gains agility, specialised expertise, and early access to the technologies that will define the next decade of the industry.

The future of beauty is interconnected, tech-enabled, and continuously evolving through collaboration. That future is not waiting to arrive, it is already being built, one partnership at a time, through programs exactly like this one.

And that mission continues. With Big Bang 2026 on the horizon, L’Oréal is once again opening the door to the startups, technologists, and innovators who believe that beauty’s next breakthroughs belong to those bold enough to build them together.

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The e27 team produced this article sponsored by L’Oréal

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: L’Oréal

The post Inside the beauty innovation ecosystem building L’Oréal’s next breakthroughs appeared first on e27.

Money is one of humanity’s most powerful technologies. Every time it evolves, economies don’t just grow — they reset.

At its core, money is trust made tangible — a shared belief that a piece of paper, a seashell, a token today will hold value tomorrow. Across history, every major shift in money has been tectonic, reshaping societies, redistributing power, and unlocking entirely new economic behaviours.

We are standing at another such moment.

We are entering the Fourth Industrial Revolution — and money must evolve to match it. Tokenisation is that evolution.

Money through history: Scaling trust

Every evolution of money has solved one problem: how to scale trust.

• Barter: Local and personal, but limited by coincidence of wants
• Commodity money: Shells, salt, gold — portable, but inefficient
• Coinage and empires: Standardised, backed by central authority
• Paper money: Trust abstracted into institutions
• Digital money: Fast and global, but still centralised
• Cryptocurrencies and tokens: Trust embedded in code — programmable and decentralised

The pattern is consistent: Money evolves to match the scale and complexity of the economy it serves.

Today’s economy is becoming always-on, digital, and increasingly AI-driven. Traditional money — built for batch processing and intermediaries is increasingly misaligned.

Tokenised money is not just an upgrade. It is a re-architecture.

Tokenisation and the combinatorial economy

Tokenisation does more than split assets into smaller units. It transforms value into programmable building blocks.

A fraction of a solar panel. A streaming royalty. A carbon credit. A loyalty point. These are not just more efficient assets — they are composable primitives.

The real shift is this: when assets become atomic, they can be recombined.

A fraction of property can merge with revenue streams, identity layers, or incentive systems — forming entirely new financial structures. What emerges is not just a more efficient market, but a new kind of economy.

This is not financial innovation. This is a financial composition.

More tokens create more combinations. More combinations create more markets.

Previously unviable ideas become economically feasible. Innovation shifts from creating standalone assets to recombining them dynamically.

The winners will not be those who simply own assets — but those who orchestrate them.

Also Read: Asia’s US$4T tokenisation boom: Why the region will lead the global financial revolution by 2030

Why 2026 is the inflection point

Blockchain is nearing two decades of development, but the last few years have seen an acceleration across all fronts.

• Economic: Institutions like BlackRock and JPMorgan are deploying capital and infrastructure
• Social: A digital-native generation expects seamless ownership and transactions
• Technological: Blockchain infrastructure, wallets, and APIs have matured significantly

For the first time in history, money is not constrained by technology or demand.

It is constrained by policy.

Regulation — the Political dimension of the PEST framework — is now the gating factor. Frameworks like the CLARITY Act in the US and global policy developments are beginning to define digital assets within existing systems.

The shift is subtle but critical: Policy is no longer asking whether to allow digital assets — but how to integrate them.

The bottleneck is no longer innovation. It is permission.

When that clears, adoption will not be gradual. It will be exponential.

Crypto and tokenisation adoption: Moving across all layers

What makes this moment different is not isolated progress — it is the synchronic movement across the entire system.

• Infrastructure:
  Apr 2026 — SWIFT, backed by BBVA, BNP Paribas, and Citi, has launched a blockchain-based cross-border payment ledger integrated with digital asset custody — bringing tokenisation into global financial rails.
• Regulation:
  Mar 2026 — US SEC issued updated interpretations on how securities laws apply to crypto asset-related products, signalling a shift from ambiguity to structured oversight.
• Banking:
  Feb 2026 — Bank Negara Malaysia is piloting stablecoins and tokenised deposits with major banks like Standard Chartered, Maybank, and CIMB, while DBS had earlier tokenised structured notes on Ethereum.
• Ecosystems:
  Nov 2025 — Singapore’s MAS is advancing Project Guardian, a coordinated push between policymakers and financial institutions to unlock asset tokenisation.
• Sovereigns:
  Jan 2026 — Philippines became the first country to publish its national budget on a public blockchain.
• Markets:
  Sep 2025 — Across APAC, on-chain transaction value has tripled in 30 months, from $81B to $244B — signalling real transactional demand from India to Indonesia, Japan to South Korea.

This is no longer speculative momentum. When infrastructure, regulators, banks, and sovereigns move in parallel, the convergence of adoption is inevitable.

Also Read: Real world tokenisation fireside chat with Anndy Lian: Unpacking the landscape

Opportunities for founders and startups

A tokenised, combinatorial economy rewires the playbook for entrepreneurship.

Here’s where founders should be building now:

• Financial infrastructure: Build the rails — compliance, custody, and tokenisation platforms. The opportunity is a “Stripe for tokens” — APIs that turn any asset into a programmable financial object.
• Granular business models: Move beyond subscriptions into real-time economics — per-second billing for compute, pay-per-use APIs, streaming salaries, and dynamic incentives.
• Platforms as economies: Turn products into ecosystems — enabling revenue sharing, creator royalties, and user-owned marketplaces powered by tokens.
• AI + money: Autonomous agents will transact — paying for APIs, data, and compute. They will need wallets, identity, and financial rails. This stack is still largely unbuilt.
• Interface innovation: Wallets and onboarding remain friction-heavy. The winners will abstract complexity — embedding identity, custody, and payments into seamless user experiences.

Every token is a new canvas. Every split creates new building blocks.

Tokenisation isn’t just creating more opportunities — it is creating exponentially more ways to construct them.

Closing thought

From seashells to smart tokens, the history of money is simple: trust keeps scaling — faster, farther, and smarter as well.

In 2026, clarity may be the switch. When the US embarks, the rest of the world will follow — and the floodgates will open.

The rails are built. The users are ready. The institutions are moving.

What remains is the switch.

The question is — will you build before it flips, or after?

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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 post From seashells to tokens: Why 2026 could be the inflection point for money appeared first on e27.

It’s OK to talk about your strengths, but it’s so much better when others do the talking for you.

Earned media (unpaid publicity gained organically through third-party endorsement) has become one of the factors most strongly linked to AI search visibility. And in B2B companies in ASEAN, most marketing strategies haven’t caught up.

Singapore ranks second globally for AI adoption, with 60.9 per cent of its working-age population already using generative AI tools. Google’s e-Conomy SEA 2024 report placed Singapore, the Philippines, and Malaysia among the top 10 countries globally for AI-related searches.

The brands appearing often have built an editorial presence that these systems could find, verify, and cite. The companies that earn media coverage strategically can, sometimes rapidly, take pole position.

How does AI search decide which sources to recommend?

AI search tends to recommend sources that appear consistently and credibly across multiple independent publications. That’s why your editorial presence typically matters more to these systems than your on-site SEO (what’s on your website, socials, etc.).

Traditional search returns a list of links; AI search reads multiple sources and synthesises a single answer, and AI/LLM platforms need to judge which sources can be trusted before they can write that answer.

Ahrefs’ analysis of 75,000 brands found that being mentioned on other websites matters roughly three times more to AI search visibility than how many sites link to you (backlinks). Brands with few web mentions are largely invisible to these AI systems, no matter how well their website ranks, and brands with the most mentions earn up to 10x the AI Overview references than the next closest group.

The factors these systems weigh include:

• how often your brand is mentioned independently across the web
• how recently that coverage was published
• whether it includes named expert quotes
• whether the content opens with a direct answer rather than burying the point
• whether specific data backs the claims.

Backlinks, strong blog content, and good service pages still matter plenty, but considerably less than mentions alone.

Why does AI search favour earned media over owned content?

AI systems favour earned media because these systems can’t independently verify what a brand says about itself, but they can check whether outside sources say the same thing. When your brand writes its own content, AI systems see your own version of events – useful, but unverified.

Also Read: Thailand is suddenly on the frontline of a new ransomware wave

When Nikkei Asia, The Straits Times, South China Morning Post, or an analyst report mentions your brand, a credible outside source has made a deliberate choice to include you, and that carries a different weight.

Muck Rack’s What Is AI Reading? report analysed over one million links cited by AI search tools and found that 94 per cent of AI citations come from non-paid sources, with earned media alone accounting for 82 per cent. Generic corporate blogs and lightly produced branded pages rarely make the cut. Well-researched, authoritative owned content performs better – but the bar is higher than most brands realise.

Half of all AI citations come from content published within the last 11 months, which means coverage from three years ago has little sway (unless it’s highly original or authoritative), and a consistent PR program seeking earned media can keep your brand popping up again and again.

How does public relations coverage influence what AI search recommends?

PR builds the independent editorial record that AI systems draw on when deciding which brands to recommend. Every journalist feature, analyst mention, and trade publication article gives these systems another viable reference when someone asks a relevant question.

The specific activities most likely to help include:

• media relations
• thought leadership and bylined articles
• press releases built around data
• analyst relations
• original research
• executive profiling

i.e., anything that puts a named, quotable person and verifiable facts into an independently published source.

That record only helps if you’re building it in the right places. That means a PR team that knows the terrain and isn’t working on now-ancient paradigms.

Getting your brand into those publications, or the trade outlets AI engines trust in your sector, requires choosing them deliberately, not running broad outreach campaigns.

What makes content more likely to get cited by AI search?

The content most likely to get cited has three qualities. It:

• quotes named experts
• opens with a direct answer to a clear question
• includes specific data

Named expert quotes get cited more often. A direct opening matters because AI systems pull from the first paragraph more than anywhere else in a piece. Specific numbers and verifiable facts consistently outperform general storytelling in citation rates.

Where you publish matters as much as what you publish. A Stacker/Scrunch study of 87 stories across 30 brands, tested across 8 AI platforms, found that placing the same content across multiple trusted news outlets more than tripled how often AI systems cited it, compared to publishing only on the brand’s own site. I’ve seen this in practice, working with a Japan-based SaaS company and a Japanese medical device manufacturer, building editorial coverage across regional and international business press with named expert commentary and specific data in every placement.

Also Read: Hiring creatives in the AI age: Skills over titles

How do you measure whether public relations is working for AI search?

Citation presence – how often your brand appears as a named or linked source in AI-generated answers – is the right thing to track, because traffic alone no longer tells the full story. AI Overviews reduce clicks by 34.5 per cent compared with standard search results, so your brand can be the cited answer to a buyer’s question and generate no trackable visit at all.

ChatGPT gives you the clearest attribution, as it automatically labels the traffic it sends to your site, so you can see it as a separate source in your analytics. For Google, map Search Console impression data against your PR campaign timelines and look for patterns. For Claude and Perplexity, watch your branded search volume – how often people search directly for your company name – since it tracks closely with AI visibility.

Some tools now measure citation share directly. Manual testing across the major AI platforms with prompts relevant to your category is a practical starting point if you don’t have one yet.

How should ASEAN B2B brands use earned media to improve AI search visibility?

Start by treating PR as part of your AI search strategy. Find out which publications AI tools actually cite for your category and build relationships with those outlets. Pitch stories that include named experts, real and compelling data, and specific angles – not general company news. Stay consistent, because AI search rewards brands that show up regularly in their industry’s conversation.

Most in-house PR teams (if they even exist) are completely missing the boat on this.

ASEAN’s AI market is projected to grow from US$12 billion in 2025 to nearly US$80 billion by 2031, a 37 per cent annual growth rate. Boston Consulting Group has projected that AI and generative AI will contribute US$120 billion to the region’s GDP by 2027. The buyers and decision-makers in this market are already using AI search as a primary research tool.

Brands that build a consistent, deliberate earned media presence as a central part of their marketing mix are giving themselves a real advantage over those that treat PR as an afterthought.

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