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.

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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.

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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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