Sprouts.ai, a Palo Alto-based startup building AI agents for enterprise revenue teams, has raised US$9 million in pre-Series A funding, as investors continue to back software companies trying to automate parts of the B2B sales and marketing stack.

The round was co-led by True Global Ventures and Accel, with participation from Kickstart Ventures, the corporate VC arm associated with the Philippines’ Ayala group. The new round brings Sprouts.ai’s total funding to US$14 million.

Also Read: AI agents are already inside your systems, but who’s controlling them?

Founded in 2023 by Karan Chaudhry, Kapil Chaudhry, and Avinash Nagla, Sprouts.ai is building what it calls a Deep AI GTM Engine. The platform combines customer intelligence, account data, buyer committee mapping, relationship networks, product heatmaps, complex search, and AI-powered workflows to help enterprises identify, engage, and convert target customers.

The company says the funding will go towards improving its AI agent capabilities, deepening enterprise integrations, and expanding its platform.

A bet on AI-native sales infrastructure

Sprouts.ai is entering a crowded but active category: B2B revenue technology. For years, enterprise sales and marketing teams have stitched together customer relationship management systems, enrichment databases, sequencing tools, analytics dashboards, marketing automation software, and intent-data products. The result is often expensive, fragmented, and heavily dependent on manual data cleaning.

“The B2B revenue stack is broken. Sales and marketing teams operate across more than 20 tools, work off dirty data, and bolt AI on top of infrastructure that was never built for it,” said Karan Chaudhry, Co-founder and CEO of Sprouts.ai. “We built Sprouts.ai to replace that fragmentation with a unified data and agent layer that actually moves the pipeline.”

The pitch is timely. Enterprises are under pressure to show practical returns from generative AI after two years of experimentation. Revenue operations is one of the obvious targets: sales teams generate large volumes of structured and unstructured data, but much of it sits across CRM systems, email, call transcripts, spreadsheets, and third-party tools.

Sprouts.ai says its software connects with enterprise systems such as Salesforce and Microsoft Dynamics, as well as large language models including Claude. It aims to help teams move from prospecting and account research to workflow execution inside the systems they already use.

The company claims customers using its platform have reported a threefold increase in ideal customer profile-qualified leads, a 25 per cent lift in sales qualified leads, a threefold improvement in response rates, and a 35 per cent reduction in GTM tooling costs. These figures are company-provided and have not been independently verified.

Also Read: When AI agents start acting on our behalf, security gets more complicated

Its customers include Razorpay, Hewlett Packard, HighRadius, and Udemy.

The opportunities in Southeast Asia

Although Sprouts.ai is headquartered in Palo Alto, the Southeast Asian angle is not incidental. Kickstarts’s participation gives the company a regional investor with links to one of the Philippines’s largest conglomerates, and the problem Sprouts.ai is trying to solve is visible across the region.

Southeast Asia’s enterprises operate in fragmented markets with different languages, regulations, buyer behaviours, and levels of digital maturity. A regional B2B sales team may need to map accounts across Singapore, Indonesia, the Philippines, Vietnam, Thailand, and Malaysia, each with uneven public company data, inconsistent job-title structures, and different procurement norms.

This makes generic global go-to-market databases less useful than they appear on a slide. Many international sales intelligence tools have stronger coverage in North America and Europe than in Southeast Asia, where company registries, SME data, buyer contacts, and intent signals can be patchier.

Data readiness is also a broader barrier to AI adoption. Cisco’s 2024 AI Readiness Index found that only 13 per cent of organisations globally were fully ready to capture AI’s potential, with data infrastructure and governance among the main constraints. For Southeast Asian enterprises, those gaps are often compounded by legacy systems, business-unit silos, and markets where offline relationships still shape B2B sales.

That is the opening Sprouts.ai is targeting: not simply another sales tool, but an intelligence layer that can make AI agents useful because the underlying account and buyer data is cleaner.

“We’re entering an age where the businesses that win will be the ones who truly understand who their customers are,” said Joan Yao, General Partner at Kickstart Ventures. “As AI agents take on more of the work of finding, understanding, and engaging the right customers, that data advantage is what will set Sprouts.ai apart.”

Competition is already intense

Sprouts.ai will not have the market to itself. The B2B sales intelligence and revenue operations category includes established global players such as ZoomInfo, 6sense, Demandbase, Apollo.io, Lusha, Cognism, and Clearbit, now part of HubSpot. Clay has also gained attention among growth teams for combining data enrichment, prospecting workflows, and AI-assisted outbound execution.

Also Read: Adapting to the new B2B sales landscape: AI and beyond

Large enterprise software vendors are also moving down the same path. Salesforce, Microsoft, HubSpot, and Adobe are embedding AI assistants and automation into their revenue clouds and marketing suites. That creates a difficult strategic question for startups: can they build a defensible intelligence layer, or will incumbents absorb similar capabilities into existing enterprise contracts?

Sprouts.ai’s answer appears to be data depth and agentic execution. Instead of selling only a database or workflow tool, the company is positioning itself as a unified GTM intelligence layer that sits across the full funnel, from ideal customer profile definition to closed-won deals.

The approach could appeal to enterprises that are already paying for multiple revenue tools and now face pressure to rationalise software spending. It could also resonate in markets such as Southeast Asia, where companies want AI adoption but may not have the internal data quality required to deploy autonomous workflows reliably.

Funding follows a broader AI shift

The round also reflects a broader shift in venture capital. Investors are no longer backing generative AI only at the foundation-model layer. Capital is flowing into applied AI companies that target specific enterprise functions, including customer support, software development, legal operations, finance, HR, and sales.

For this part of the world, this is particularly relevant. The region is unlikely to produce many companies competing directly with OpenAI, Anthropic, Google DeepMind, or xAI at the infrastructure layer. But applied AI companies that solve local enterprise pain points may have a clearer path to adoption.

Google, Temasek, and Bain have estimated Southeast Asia’s internet economy at hundreds of billions of US dollars in gross merchandise value, but enterprise software adoption remains uneven across markets. That leaves room for vertical and workflow-specific AI products, particularly in areas where local data, integrations, and compliance requirements matter.

Sprouts.ai’s challenge will be to prove that its platform can deliver measurable revenue outcomes beyond early customer claims. Enterprise sales software is a category full of tools that promise better leads and cleaner workflows. Buyers will want evidence that AI agents can improve pipeline generation without creating compliance risks, inaccurate outreach, or another layer of software complexity.

Also Read: AI lead generation for B2B sales: A practical guide

For now, the company has secured credible investors and a problem large enough to justify attention. The harder part begins after the funding: showing that AI-native revenue operations can move from boardroom talking point to repeatable enterprise deployment, including in messy, multilingual, and data-fragmented markets such as Southeast Asia.

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Talk to anyone in sales, marketing, or ops long enough, and the CRM conversation runs in a loop.

First, it’s “why are we paying this much for a system that basically does nothing?” Then the company adopts a new platform, and for a quarter or two there’s relief: the pipeline is clean, reports actually load, the rep dashboard is in one place.

Then come the integrations, the custom fields, the dashboards nobody asked for. A year later: “Why are we paying this much for a system that basically does nothing?”

The cycle keeps repeating because the conversation about CRM has been stuck in the wrong place. CRM, when it actually works, is one of the most useful pieces of software a company can buy. It pulls a sales team, a marketing team, customer success, and finance into the same set of facts about the same set of customers.

When it doesn’t work, it becomes the most expensive spreadsheet a company has ever owned.

The misconceptions that keep teams stuck

A few ideas about CRM survive longer than they should.

CRM is only for big corporations

Salesforce’s enterprise pricing set this expectation a decade ago, and it stuck even as smaller, more flexible products moved the floor.

A five-person team selling a US$40,000 product has every reason to track pipeline the way an enterprise team does. The math just runs differently. The era of paying enterprise prices for a CRM that barely does anything for you is over.

CRM is just a fancy contact database

This one is fading, but not fast enough. To some, CRM is still just a fancy contact database, a place where you keep all your connections stored but never actually take action on the leads you have.

Here’s where the difference shows up:

A contact list answers “who do we know?” A CRM answers “what should happen next, and who should do it?”

A contact list might only give you a name, a phone number, and an email. A CRM tells you what to do with that phone number, what emails to send, when to follow up, and how.

CRM is only for the sales team

Sales is where most companies start. But the record of who a customer is, what they bought, when they last talked to someone, and what they need next is also the foundation that marketing campaigns, support tickets, renewal forecasts, and finance reconciliation sit on top of.

Treating CRM as a sales-only tool turns every other team into a guest in someone else’s house. CRM systems are most helpful when they’re used across departments, all at once. Your marketing team logs prospects and assigns tasks, checking off requirements. Your sales team reaches out with outbound emails, runs cold calls or cold messaging sequences, and follows up on each action. Your legal team chimes in to check contracts and documents, calculate efforts, and break down costs.

And the list goes on.

Also Read: The problem with ‘PM as CEO of the Product’: A myth that hurts more than helps

Where most CRMs actually break down

Spend a few minutes on r/CRM, and the same complaints come up across companies, industries, and team sizes.

One recent thread on optimisation is a useful catalogue. The patterns, however, are remarkably consistent.

Data clutter and overcomplication

Most CRMs try to do everything, and the result is a homepage with seventeen widgets, a contact record with forty fields, and a sales rep who logs activity into one or two of them and ignores the rest.

And we haven’t even mentioned duplicate data. Duplicate records stored in your CRM lead to confusion, and they can seriously mess with your sales process. In fact, 15 per cent to 25 per cent of the data in a CRM is often duplicated. On top of that, 40 per cent of sales reps say they lack the data needed to effectively target leads.

The system optimises for completeness. The team optimises for getting through the day.

Too many clicks, too little flow

Logging a call should only take ten seconds.

For some reason, in most CRM platforms, it takes four clicks, two dropdowns, a free-text field, and a save button that occasionally fails silently. Multiply that across every rep and every interaction in a week, and you can predict where the data will be in six months: incomplete and quietly distrusted by everyone who relies on it.

Your CRM should work for you, in the most optimised way, on your own timeline. Spending real effort on what should be a simple, mundane task is not the goal of a CRM at all.

Expensive for what you actually use

CRM pricing has crept up faster than CRM functionality for years, partly because vendors keep moving features into higher tiers, and partly because the integrations and add-ons that make a platform usable get priced separately. Teams pay for the base seat, then again for analytics, then again for marketing automation, then again for the enrichment plugin that cleans up the data they were paying to enter.

So how much are we actually spending on CRM? It’s a question with no clean answer, because the add-on fees and extra tool charges keep piling on.

Choosing a CRM that holds up

A CRM you’ll still respect a year from now isn’t picked by a feature checklist. Features can always be upgraded, replaced, or even downgraded. What matters is what the CRM actually does for you, the customer journey it supports end-to-end, and whether it holds up in the long run beyond the flashy features.

Pick AI-native, not AI-bolted-on

A newer generation of CRMs has been built around the assumption that enrichment, summarisation, and follow-up drafting are part of the platform, not bolt-ons. That changes what a sales rep does in a typical hour. Less typing and less hunting for context, more time on the conversation. If a CRM still treats AI as a marketing slogan rather than a workflow primitive, the gap between it and the AI-native category will widen every quarter.

A real free trial, not some “14-day free demo”

Most free trials show a polished demo path and lock the rest behind a sales call. Ask for the full surface area before you sign. A platform that won’t let you stress-test it is telling you something about how confident the team is in the product outside of guided tours.

Check out everything the CRM has to offer. The question isn’t “can this CRM send a follow-up email?” The better one is “how many native integrations does this have to the systems my team already uses, and can I configure them without an admin certification?” Heavy manual overhead is usually a sign that the integrations were an afterthought.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

Audit the data, weekly or bi-weekly

CRM data decays. People change jobs, companies rebrand, deals stall and never get closed or lost properly. A short-standing review keeps the system trustworthy. A platform that surfaces stale records on its own, without a manual report, removes the meeting altogether.

Usability decides whether the rollout works

This is the unglamorous criterion and the one that quietly decides whether a CRM rollout works. If the interface is clunky, adoption stalls, and any feature on the brochure becomes irrelevant. The cleanest test is a five-minute walkthrough with a rep who didn’t pick the tool. Watch where they hesitate.

If you’re evaluating a CRM right now, the choice worth optimising for is AI-native and consolidated. Alano is one example of the category, designed so that a single workspace handles enrichment, outreach, and pipeline management without an integration layer between them. The point isn’t that any one product solves everything. It’s that consolidation has stopped being a “nice to have” and started being the difference between a CRM that earns its seat cost and one that doesn’t.

What CRM looks like next

The interesting shift in the next two years won’t be about features. It will be about who or what is doing the work inside the CRM.

The current model still assumes a person types most of what gets recorded and reads most of what gets reported. The next model assumes an agent is doing both. A call ends; the summary, the contact updates, the deal-stage change, and the follow-up draft are already in the record by the time the rep opens their laptop. Pipeline reviews stop being a weekly cleanup of bad data and start being a real conversation about strategy. Marketing stops asking sales for the latest contact list and starts triggering plays from the same source of truth.

That’s the version of CRM worth waiting for, and it’s the version a handful of platforms are quietly building toward right now. The companies that get the most out of it will be the ones that stop accepting friction as the cost of doing business and start asking, every quarter, the question this whole category should have been built around in the first place.

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Atome Philippines has secured a PHP 5 billion (~US$88 million) wholesale facility from Asia United Bank, giving the digital finance platform local-currency funding to expand its consumer credit business in one of Southeast Asia’s most underbanked large markets.

The facility will mainly support the Atome PayLater Anywhere Card, which the company says has now been issued to more than three million Filipinos.

Also Read: Atome lines up US$345M debt as Southeast Asia fintechs shun equity

According to Atome, up to 80 per cent of cardholders are first-time card users, while 65 per cent are women. Most use the card for recurring household spending, including groceries, food, household items, telecoms bills and utilities.

That usage profile matters. In the Philippines, credit access remains thin outside the traditional banking system, especially beyond Metro Manila. While digital payments have grown rapidly, formal credit penetration still lags behind demand, leaving fintech lenders, buy-now-pay-later operators and digital banks competing to serve consumers who are financially active but underserved by incumbent lenders.

Local funding for a local credit business

For Atome, the AUB facility adds a sizeable peso-denominated line to its funding base. That is more than a balance-sheet detail. Consumer lenders operating across Southeast Asia often face currency mismatch risks when they raise capital in US dollars but lend in local currency. A domestic funding line can help reduce that exposure, improve pricing discipline and support more predictable expansion.

Atome said adoption of its card has expanded beyond Metro Manila into Luzon, Visayas, and Mindanao. The company’s wider product portfolio in the Philippines includes lending, savings and insurance, positioning it less as a single-product BNPL provider and more as a digital finance platform targeting mass-market consumers.

“The closing of AUB’s PHP 5 billion facility validates Atome’s market position and delivers competitive, PHP-denominated funding at meaningful scale,” said Christian Quiros, President and Country Manager of Atome Philippines.

AUB framed the transaction as part of its support for fintech platforms operating within formal credit standards. “This partnership advances financial inclusion while maintaining rigorous credit standards,” said Ernesto Uy, Executive Vice President and Account Management Head at AUB.

Also Read: Atome defies market headwinds with 63 per cent income surge, US$4B GMV run rate

The phrasing is notable because BNPL and embedded credit players across the region have had to work harder to distinguish responsible lending from unchecked consumer credit growth. Regulators in markets such as Singapore, Indonesia and Malaysia have tightened scrutiny of lending disclosures, affordability checks and debt collection practices, even as they recognise that digital lenders can broaden access where banks have limited reach.

The Philippines remains a large inclusion opportunity

The Philippines has become one of Southeast Asia’s more active digital finance markets, driven by high smartphone usage, a young population, and persistent gaps in banking access. Bangko Sentral ng Pilipinas has reported strong growth in digital payments, with electronic transactions accounting for more than half of retail payment volumes in recent years. The central bank has also set financial inclusion as a core policy priority, particularly for women, micro-entrepreneurs and consumers outside major urban centres.

Still, access to credit remains uneven. Many Filipinos have digital wallets but limited access to formal revolving credit, cards or instalment products. That gap has created room for companies such as Atome, Billease, Home Credit Philippines, GCash-linked lending products, Maya, SeaMoney and other app-based lenders to build credit relationships with consumers who may not qualify for traditional bank cards.

The competitive field is crowded. Home Credit has long focused on point-of-sale consumer finance, particularly electronics and appliances. Billease has built a local BNPL and consumer lending business. GCash and Maya benefit from large wallet ecosystems and payments data. Regional players such as SeaMoney and Kredivo, meanwhile, have used e-commerce, payments and risk-scoring capabilities to push deeper into credit.

Atome’s card-led approach gives it a different route to consumer adoption. Instead of limiting usage to partner merchants or online checkouts, a PayLater card can become part of daily spending behaviour. That also raises the stakes on underwriting. Everyday-use credit products can scale quickly, but they need disciplined credit limits, repayment monitoring and collection practices if they are to avoid overextension among first-time borrowers.

BNPL evolves beyond checkout financing

Atome started as a BNPL platform but, like several players in the sector, has moved into a broader financial services model. That reflects the economics of the category. Pure BNPL margins can be pressured by merchant fees, funding costs, fraud risk and repayment behaviour. Platforms that can cross-sell lending, cards, savings or insurance may be better positioned to improve customer lifetime value, although they also face heavier regulatory and operational demands.

Across Southeast Asia, the BNPL sector has shifted from aggressive merchant acquisition to more disciplined credit growth. Rising interest rates over the past few years made wholesale funding more expensive, forcing lenders to pay closer attention to unit economics and asset quality. Investors have also become less tolerant of growth driven primarily by subsidies.

This is why the AUB facility is strategically useful for Atome. A large local bank facility suggests a degree of institutional confidence in its Philippine book, although the ultimate test will be portfolio performance as card usage expands beyond early adopters and urban customers.

Also Read: Atome secures US$75M facility to expand BNPL reach in Philippines

Atome is part of Singapore-headquartered Advance Intelligence Group, which is backed by investors including SoftBank Vision Fund 2, Warburg Pincus, Northstar and EDBI. The group operates across digital finance and risk technology, and Atome remains one of its more visible consumer brands in Southeast Asia.

For AUB, the deal gives it exposure to a fast-growing fintech credit channel without having to originate every end-borrower relationship directly. For Atome, it supplies domestic liquidity at scale in a market where demand for accessible credit is real, but where regulatory tolerance will depend on whether lenders can prove they are expanding access without encouraging unsustainable debt.

The Philippine opportunity is significant, but not uncontested. The next phase of growth will likely be defined less by card issuance numbers and more by repayment quality, customer retention and whether digital lenders can serve first-time borrowers without repeating the excesses seen in less regulated consumer credit markets.

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I want to tell you about a number that should make every climate founder in Southeast Asia angry.

US$37 billion.

That’s the combined JETP commitment across Indonesia and Vietnam alone. Indonesia signed for US$21.6 billion. Vietnam signed for US$15.5 billion. These aren’t projections or targets. These are commitments. Money that governments and international partners put their names on specifically to accelerate the climate transition in this region.

Now tell me how many founders you know who’ve seen a dollar of it.

I’ll wait.

The gap nobody is talking about

There’s a version of the SEA climate story that looks great on paper. Policy scaffolding going up. Carbon taxes rising. ASEAN sustainable finance taxonomy finally giving investors a common language. International capital showing interest. Conference panels full of optimistic people in linen shirts.

And then there’s the version on the ground.

Founders pitching VCs because they don’t know any other door exists. Climate ventures structured wrong for the instruments available. Development finance sitting in disbursement queues while startups run out of runway. A US$37 billion commitment slowly moving through bureaucratic channels while the companies that should be receiving it are busy preparing their fifteenth investor deck.

The money is not missing. The translation layer is.

Also Read: Funded: I keep a notebook by my bed with one question about SEA climate

Why the capital isn’t moving

JETP money doesn’t flow like VC money. It moves through governments, multilateral institutions, development banks, and implementing agencies before it ever gets close to a founder. Each layer has its own compliance requirements, reporting standards, and risk appetite. By the time it reaches the ground, it looks nothing like what a climate startup can actually absorb.

Development finance institutions want projects at a certain scale. Foundations want specific proof points. Grant programmes want reporting frameworks that most early-stage founders have never heard of. The instruments being offered and the ventures trying to receive them are speaking completely different languages.

This is not a criticism of the institutions. They’re doing exactly what they were designed to do. The problem is that nobody is sitting in the middle translating.

What the best climate funds understand

The funds that have stayed consistent in the SEA climate, and there are very few of them, understand one thing clearly. Commercial viability and emissions impact are not in conflict. The best climate companies create real economic value for their customers first. The impact follows from the business working, not the other way around.

That framing is what makes a climate venture legible to multiple capital sources simultaneously. A venture that creates genuine value can absorb VC, attract development finance, qualify for catalytic grants, and access JETP-linked programmes. But only if it’s structured correctly from the start.

Most aren’t. Not because the founders are wrong. Because nobody showed them the full map.

Also Read: Funded: AI is having its moment, climate is having a crisis. SEA can’t afford to confuse the two

The US$37 billion translation problem

Here’s what the translation layer actually looks like in practice.

A climate founder in Indonesia building in solid waste or energy efficiency has potential access to multiple capital sources. JETP-linked programmes for energy transition. Foundation capital for proof of concept. Development finance for scale. Equity for growth. Each instrument has a different entry point, different evidence requirements, different timeline.

A founder who sequences these correctly can build a genuinely well-capitalised company without giving away equity too early, without taking on the wrong kind of debt, and without spending two years pitching VCs who were never the right fit to begin with.

But the sequencing requires someone who knows all the rooms. Most founders only know one.

The real opportunity

US$37 billion committed to SEA climate is not a problem. It’s an infrastructure waiting for founders who know how to access it and intermediaries who know how to connect them.

The next wave of SEA climate companies won’t be built by founders who pitched their way to a VC term sheet. They’ll be built by founders who understood the full capital landscape, sequenced it intelligently, and used the right instrument at the right stage.

The money is already here. It has been for a while.

The question is who’s going to help founders find the door.

—

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

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

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After more than 16 years working as a financial advisor in Singapore, I’ve noticed a change that I didn’t expect to see this early. Some of the most financially engaged conversations I’m having today are not with people approaching retirement or preparing for their children’s education. They’re young adults in their late teens and early twenties.

When I began my career in finance, it was common for people to start thinking about financial planning only after significant life events like getting married, buying a home, or becoming a parent. However, I’ve noticed a shift with many Gen Z Singaporeans initiating these important discussions much earlier in their lives. 

At first, it might seem like this trend is driven by social media, financial influencers, or easier access to information. While those elements do play a part, I sense there’s something more profound at work. The young adults I encounter today are navigating a very different economic landscape compared to what my generation faced.

A generation responding to economic reality

Today, a significant number of young Singaporeans are grappling with a genuine housing challenge. According to transaction data from 2025, the average resale HDB flat in Singapore is priced around SG$652,000 (US$504,039), while a typical condominium costs about SG$2.13 million (US$1.65 million), and a landed property can reach nearly SG$5.93 million (US$4.59 million). This stark reality has made housing affordability a central topic in financial discussions.

The broader market reflects this pressure. According to Reuters, Singapore’s HDB resale prices rose 9.6 per cent in 2024, nearly double the growth recorded the year before. For many young adults, homeownership remains achievable, but the financial runway required to get there has become significantly longer.

During the COVID-19 era, Gen Z stepped into adulthood under unique circumstances. Unlike earlier generations, many faced layoffs, business closures, and economic instability while growing up. These experiences have undoubtedly shaped their perspectives. What truly fascinates me is not just that Gen Z is starting to plan for their futures sooner, but also the reasons behind this proactive approach.

What Gen Z is actually asking

Younger consumers are often thought to be mainly focused on investing, trading, or quickly finding ways to increase their wealth. However, my observations tell a different story.

Also Read: How tech startups can attract Gen Z and millennials seeking flexibility and purpose

The questions I hear most often are surprisingly practical:

• Should I build an emergency fund before investing?
• How much insurance do I actually need?
• How do I prepare for buying a home in Singapore?
• What happens if I lose my income unexpectedly?
• How do I enjoy life now without compromising my future?

These are not questions about getting rich. They are questions about creating stability. That aligns with broader research. Deloitte’s 2025 Gen Z and Millennial Survey found that the cost of living remains one of the top concerns among Singapore’s Gen Z, while younger workers are increasingly prioritising financial security, well-being, and sustainable career growth over traditional status markers.

It’s fascinating to see that younger Singaporeans are starting to build their financial habits much earlier than many might think. According to a SingSaver survey, a remarkable 85 per cent of Gen Z participants began saving before turning 22, in stark contrast to only 41 per cent of Millennials. The study also revealed that Gen Z individuals are more inclined to adhere to a budget compared to their older counterparts.

These insights truly resonate with my daily experiences. I’ve noticed that many young adults are engaging in financial planning not out of a desire for wealth, but rather to cultivate a sense of resilience in their lives.

The rise of the “invisible financial cage”

As I reflect on my journey, I’ve come to realise that financial planning transcends mere monetary concerns; it’s fundamentally about the choices we can make. I often refer to the concept of the “invisible financial cage.” This describes individuals who, despite seeming successful outwardly, lack the freedom to make choices that truly enhance their lives. They may find themselves stuck in jobs detrimental to their well-being, delaying significant life decisions, or enduring tough situations simply because they feel trapped by their financial circumstances.

Throughout my career, I’ve had the privilege of working alongside senior bankers, business owners, and executives who, despite their impressive earnings, often feel confined. It’s important to recognise that earning a high income doesn’t necessarily equate to true financial freedom.

Early in my career, I encountered a 29-year-old accident victim whose insurance payout fell short for long-term disability support. Witnessing the real-life impact of poor planning profoundly shifted my perspective on this profession. It taught me to see financial planning not just as a means to accumulate wealth but as a way to build a protective safety net for individuals and families. This understanding also influences my views on Gen Z and their financial needs.

What I see is not a generation obsessed with wealth. I see a generation trying to build resilience.

Also Read: A millennial’s perspective on working with Gen Z

What businesses often misunderstand about Gen Z

A common misconception about Gen Z is that they are reckless with their finances or solely focused on making quick money. In reality, my experiences reveal a different perspective. Many young individuals approach their financial matters with care and consideration. They dedicate significant time to understanding their options, seeking out educational materials, and comprehending the motivations behind their financial choices before taking action.

They tend to be more careful with their finances compared to earlier generations. This change is significant for financial institutions, insurers, and fintech companies. Many young consumers prioritise understanding over simply seeking out products.

Young consumers are seeking knowledge before receiving suggestions. They desire a deeper understanding before making commitments. Building trust is becoming essential in today’s market. Companies that thrive with Gen Z will be those that empower individuals to make informed choices, rather than just pushing more products on them.

Why this matters beyond financial services

For employers, there’s an important takeaway. Financial well-being is becoming a significant concern in the workplace. Employees grappling with worries about housing costs, debt, healthcare bills, or their future security carry these burdens with them, even into the office.

As organisations continue investing in employee well-being initiatives, financial education and planning support may become increasingly important components of that conversation. The scale of the challenge facing younger Singaporeans is evident in the housing market. Reuters reported that Singapore recorded a record number of million-dollar HDB transactions in 2025, highlighting how dramatically financial expectations and planning timelines have shifted compared to previous generations.

Looking ahead

Having spent 16 years in this field, I’ve come to realise that Gen Z’s increasing focus on financial planning goes beyond just finance. It’s about their need to adapt. With rising costs, extended financial timelines, and more uncertainty than earlier generations faced, young Singaporeans are stepping up to take charge of their financial futures sooner than ever.

This could lead to a generation that transforms the very essence of financial planning. From my perspective, that might actually be a positive change.

—

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

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

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An AI model judged too dangerous to release has exposed an uncomfortable truth: the legacy systems quietly running our banks, hospitals, and power grids are far more vulnerable than we let ourselves believe. The threat isn’t old code — it’s abandoned code. And the response isn’t a patch. It’s a fundamental rethink of how we build, defend, and value software.

For forty years, the smartest thing you could say about a critical system was four words: it works, don’t touch it.

That was wisdom. Stillness was a virtue. The system that ran untouched for a decade had earned its keep. Every night it read a number, did some math, wrote the number back, a million times without a mistake. You didn’t audit it. You didn’t rewrite it. You built a glossy app on top and left the engine alone, the way you’d leave a load-bearing wall alone.

I want to argue that something just quietly reversed that logic. The very stillness that earned our trust — unexamined, unchanged, unread — is now precisely what makes a system dangerous. The code didn’t move. The world around it did.

What changed

In April 2026, Anthropic announced a model it decided the public could not have.

It’s called Claude Mythos Preview, and the company chose not to release it. Instead, they handed it to a coalition of roughly fifty critical-infrastructure organisations — AWS, Google, Microsoft, Apple, Cisco, JPMorganChase, the Linux Foundation among them — under a program called Project Glasswing. The idea was to let the defenders patch before the rest of the world caught up.

Read that again. A technology company built something and then concluded the responsible move was to withhold it. That alone should make you sit forward.

What does Mythos do? It reads code and finds the ways code breaks. Pointed at OpenBSD — an operating system hardened by some of the best security minds alive for decades — it surfaced a flaw that had been hiding for twenty-seven years. Pointed at a ubiquitous video library, it found a bug in a single line that automated tools had run past five million times. And when asked not merely to find a weakness but to weaponise it, the model that came before it succeeded almost never. Mythos succeeded most of the time.

The reaction told you everything. Within weeks, the US Treasury Secretary and the Fed Chair pulled major bank CEOs into a room. India’s Finance Minister convened the RBI and the heads of the banks. The European Central Bank called an urgent meeting. Central bankers across the world held emergency sessions about a model they are not even allowed to use.

And the containment failed almost immediately. The model leaked — not through some cinematic hack, but through borrowed credentials from a contractor and a guessed web address. Even fifty handpicked partners couldn’t hold it for a day. Anthropic itself estimates that comparable capability, including in open form, becomes broadly available within roughly twelve to eighteen months.

So that’s the clock. Eighteen months to confront what we’ve avoided for thirty years.

It was never really about COBOL

The easy version of this story is about old banking mainframes. By widely-cited if aging industry estimates, COBOL still underpins something like 95 per cent of ATM transactions, and hundreds of billions of lines of it remain in production. Some single banks run three hundred million lines at their core, written in the 1980s, last understood by people who have since retired or died.

That story is true. But it’s too small.

Mythos doesn’t read COBOL. It reads code. It does not care whether the lines were written in a language older than the moon landing, or in a framework that felt modern in 2012. What it hunts is not age. It is abandonment — code that nobody owns, nobody reads, nobody fully understands anymore, still quietly wired to the network, still moving something that matters.

Also Read: Why the US tech rebound matters for SEA’s AI and venture ecosystem

And here is the trap most leaders will walk straight into: the belief that building something new makes them safe.

It doesn’t. Newness isn’t a state of safety. It’s the first day of a countdown. That twenty-seven-year-old flaw was, once, a fresh commit written by a careful engineer who believed it was correct. Every piece of legacy code was somebody’s clean, modern, well-intentioned new code. Age didn’t make it vulnerable. Time merely revealed what was always there, while nobody was looking again.

In one specific way, new software is more exposed, not less. The old mainframe was a windowless bunker — dangerous because nobody had the map, but also sealed, air-gapped, sitting in obscurity behind decades of forgetting. The thing we build today is a glass house. It lives on the public internet by default. It speaks through a hundred APIs. And it is assembled — not written, assembled — from a thousand prefab parts shipped in from open-source factories none of us inspected. The infamous Log4Shell crisis wasn’t old code failing. It was modern code importing a tiny utility nobody had read, inside nearly everything. We didn’t write that bug. We installed it.

Then comes the sharpest irony of this exact moment. The same AI revolution that produced Mythos is also flooding the world with machine-generated code faster than any human can read it. We are manufacturing tomorrow’s abandoned systems today, at industrial scale, and calling it productivity. The gap between lines written and lines understood has never been wider. That gap is the attack surface.

So the variable was never age. It was attention.

Where stillness is most sacred

Now widen the lens past banking to where “don’t touch it” is treated as scripture.

Utilities run operational technology often older than the banking code, because you do not casually reboot a power grid to install a patch. Hospitals run frozen embedded systems inside MRI machines and infusion pumps, certified once and never touched again. Logistics, water, energy, public records — the systems a country actually rests on — much of it held together by the quiet assumption that nobody was looking.

This isn’t hypothetical. We’ve already seen it at human speed. A piece of malware once erased a global shipping giant’s entire logistics backbone in hours; the company survived partly because a single server in Ghana happened to be offline. A worm walked into the national health service through unpatched machines and turned ambulances away.

Those attacks were carried out by people. Slow, tired, fallible people. Now imagine the same intent, equipped with something that never sleeps, never retires, and no longer needs a hunch.

The honest comparison, and where it breaks

The instinct is to call this Y2K again, and that instinct is half right.

Y2K is the right metaphor for the mobilisation. A vast inventory of legacy code, a global scramble, a deadline, and an enormous surge of demand for people who could go in and fix it. That surge is, quite literally, what built the modern Indian IT industry — Infosys, TCS, and Wipro booked the work, earned the trust of Western clients, and never looked back.

But Y2K is the wrong metaphor for the threat. Y2K was bounded, dated, and deterministic. Everyone knew the deadline, the failure, and the fix. You could declare victory at one minute past midnight and go home.

This has no midnight. It is open-ended and adversarial. There is no single patch, no finish line, no moment when you are done. The discovery engine keeps improving while you sleep. So if you take only one lesson from Y2K, don’t take “there will be a project.” Take “there will be a permanent capability — and someone will own it.”

Also Read: The sovereign AI moat: Why integrated risk is the only way to scale intelligence in 2026

There is no fix, there is a posture

This is the part nobody wants printed on a slide. There isn’t a fix — because we’ve been misnaming the problem the whole time. We thought we had a maintenance problem. We have a metabolism problem.

A building, once built, can stand untouched for a century. We quietly borrowed that mental model for software — construct it, certify it, occupy it, walk away. But software was never architecture. It’s closer to something alive. And living things that stop renewing don’t hold steady; they rot. We simply couldn’t see the rot because nothing was poking at the body. The system looked healthy because no one was testing whether it still was.

So if there’s no fix, what’s left? A change of posture. From building to tending. From done to alive.

That sounds soft until you make it concrete, and then it turns brutal. You can no longer answer a sleepless adversary with a quarterly patch committee. The attacker works in hours; a defender who works in months has lost the arithmetic before anyone arrives. The only thing that collapses that asymmetry is symmetry — attention as continuous as the attack. And it begins with the most unglamorous act of all: knowing what you own. You cannot defend what you cannot see, and most organisations genuinely do not have a complete list of what runs inside their own walls, who wrote it, or who still holds a key.

Here is the inversion that is the answer. For forty years, we optimised software for stability — its highest virtue was that you never had to touch it. The new world flips the virtue. The system that survives is not the one that never moves. It’s the one that can be moved safely every single day. Changeability becomes the security property. The organisation that can rewrite, redeploy, and re-examine a component on an ordinary Tuesday without fear is the one that outruns the threat — not because it’s invulnerable, but because it heals faster than it can be wounded.

Stop chasing invulnerability; it was always a fantasy. Chase resilience.

The roles ascend

A living system needs organs — and that is what our software roles are quietly becoming.

Start with a reframe. The engineer was never valuable because they could type. They were valuable because they understood — and code was simply the only interface we had for expressing that understanding to a machine. Now the machine can take intent more directly. So the typing falls away, and what’s left standing is the thing we were paying for all along: the judgment. The job was never the code. The code was the proxy.

So the builder ascends. The question shifts from did I write this correctly? to is this what we meant, and can I prove it does that? Quality stops being the people who find bugs after the fact and becomes the people who author the intent and the test of the intent — then validate that the generated thing honors it. We used to pay people to write the answer. Increasingly, we pay them to know whether the answer is true.

The same inversion hits security, and it’s the sharper one. The old model was the firefighter: sit in the station, wait for the alarm, run toward the smoke. But when an unsleeping engine surfaces weaknesses every week, incidents stop being events and become weather. You cannot staff for weather with a fire brigade. The role becomes the gardener — continuous, vigilant, tending both the known cracks and the ones still surfacing. And the scarce skill is no longer the patch. The machine can produce ten thousand findings; the irreplaceably human act is deciding which of them matters, what is worth defending, and how. Not coding the defense. Discerning it.

Be honest about the cost, though, because it’s the opposite of comfort. This is a higher bar, not a lower one. To author intent, you must actually know what you want — precisely enough to specify it, precisely enough to test against it. And most organisations have never had to. The slow act of writing code let them discover their intent by trial and error, hiding the fact that they often didn’t know what they meant. Take the slow part away, and you expose the gap. The hardest thing in the new world isn’t the machine. It’s learning to say clearly what we want.

The opening for India and the Philippines

Here is where I stop describing weather and start arguing.

Notice that every shift in this essay is the same movement at a different scale: value is leaving production and migrating to intent, judgment, and care. It’s true for the individual engineer. It’s true for the organisation. And it’s true for entire nations.

The countries that built their software industries on cost arbitrage are standing at a fork — because the same AI that creates this mountain of remediation work is also eroding the bodies-on-seats model that historically did the remediation. India’s IT industry is approaching three hundred billion dollars in revenue. The Philippines, my home base, crossed thirty-eight billion in IT-BPM exports and is climbing toward security, modernisation, and engineering rather than seats.

Also Read: When startups fail, should VCs go to jail?

The wrong response is to wait for the tickets to arrive and bill by the hour. The right response is to own the capability — to build the AI-augmented modernisation platforms, the secure-code practices, the disciplined incremental migration (the strangler approach that drains a legacy system one capability at a time rather than the big-bang rewrite that has wrecked more than one bank) — and to do it as intellectual property made here, not labour rented from here.

Y2K rewarded whoever showed up with hands. This one rewards whoever shows up with a system. For a region with deep engineering talent and a thirty-year track record of doing exactly this unglamorous, mission-critical work, that is not a threat to survive. It is the largest opening in a generation — if we choose to build the tools instead of waiting to be handed the tickets.

What this asks of you

If you run anything that matters — a bank, a utility, a hospital network, a government platform — the comfortable sentence has expired. It works, don’t touch it is no longer a caution. It is exposure.

A few questions worth sitting with before the clock runs out:

• What are you protecting with stillness that you should be protecting with attention?
• If a machine can now write the answer, what exactly were you being paid for — and are you ready to do that instead?
• Can your organisation say clearly what it wants, or has it only ever discovered its intent by accident, one line of code at a time?
• Are your defenders still waiting for the alarm, in a world where the smoke never stops?

The work was never the keystrokes. It was always the knowing. We just couldn’t see it because the keystrokes were in the way.

Magicians conceal. Builders reveal. The old systems kept their secrets because no one was asking. That era is ending — so turn on the lights, look hard at what you’ve been afraid to touch, and decide now whether you’ll be the one who builds the response or the one who rents it.

You have about eighteen months before someone else looks first.

The post “It works, don’t touch it” is now the most dangerous sentence in tech appeared first on e27.

Every week, a new AI tool launches, promising to transform marketing. One that writes content, another that generates videos, a third that automates outreach. Finally, one that builds reports.

For a while, it felt like the companies that adopted the most AI would win. But something interesting has happened over the last two years.

Many businesses have successfully reduced the cost and time required to execute marketing activities. They can produce more content, launch more campaigns, and generate more reports than ever before.

Yet, many struggle with the same business problems: Revenue growth has slowed, customer acquisition costs remain high, and retention rates haven’t improved.

Marketing teams are busier than ever, but leadership teams often have less confidence in the numbers they’re looking at. The problem isn’t a lack of AI but a lack of systems.

The hidden cost of too many tools

Most companies didn’t intentionally design a fragmented technology stack. It happened gradually. A CRM was added to manage leads, an analytics platform to measure performance, a customer support tool was introduced to handle tickets and so on.

Then AI tools arrived and were layered on top of everything else. Individually, each decision made sense. Collectively, many businesses ended up with data spread across multiple platforms, teams working from different reports, and leadership making decisions based on conflicting information.

There seems to be no tally between marketing reports one customer acquisition cost and the numbers the finance is look at. Product analytics tells a different story about user behaviour while attribution changes depending on the platform being used.

The same customer often exists across multiple systems with different histories attached to them. This creates a dangerous situation. Companies become highly efficient at producing activity while becoming less effective at understanding what is actually driving growth.

According to Gartner, organisations use only around one-third of their marketing technology capabilities despite continuing to invest in new platforms. At the same time, the marketing technology ecosystem has expanded to more than 14,000 products.

The challenge for modern businesses is no longer access to technology but creating clarity.

Also Read: AI and accessibility: The untapped solution to the cybersecurity skills gap

What makes a marketing stack AI-native?

Most conversations about AI marketing focus on tools; a better approach is to focus on outcomes. Every marketing system, regardless of industry, needs to answer five questions.

• Do we understand our customers?

Before creating campaigns, businesses need a reliable way to understand customer behaviour, objections, motivations, and buying triggers. AI can now analyse interviews, support tickets, reviews, sales calls, and survey responses at a scale that would have been unrealistic a few years ago. The value is in reducing assumptions in addition to analytics.

• Can we create and test ideas faster?

AI has dramatically lowered the cost of content production. Articles, advertisements, videos, creative concepts, and landing page variations can now be produced significantly faster than before.

This matters because growth is often a function of experimentation. The companies that can test more ideas typically learn faster.

• Can we reach the right people consistently?

Distribution remains one of the most overlooked growth challenges.

AI can assist with segmentation, personalisation, and campaign execution, but distribution still requires a system that ensures the right message reaches the right audience at the right time.

• Can we measure what matters?

This is where many AI implementations break down. The purpose of measurement is not reporting but decision-making.

If leadership cannot confidently answer where customers come from, which channels generate profit, or which activities drive retention, then adding more AI tools rarely solves the underlying problem.

In fact, many companies end up solving the wrong problem entirely. What appears to be an acquisition issue may actually be poor activation. What looks like a retention problem may be a pricing issue. Sometimes the bottleneck isn’t growth at all, but inconsistent measurement across teams.

Understanding where growth is breaking down is often more valuable than buying another tool.

• Can information move across the business?

The most valuable AI systems are often the least visible. They’re the automations that eliminate manual work, connect disconnected platforms, and ensure information flows seamlessly across teams. When customer data moves effectively between systems, businesses spend less time managing tools and more time making decisions.

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

The minimum viable AI-native stack

Customer understanding

• ChatGPT or
• Perplexity

Content and creative

• ChatGPT
• Claude
• Nano Banana
• HeyGen

Measurement

• Google Analytics 4
• PostHog

CRM & lifecycle

• HubSpot

Automation

• n8n

The goal is to ensure every tool contributes to a clearer understanding of customers and a better customer journey.

The real question founders should be asking

When evaluating AI, most companies ask: “What tools should we use?” The better question is: “What is currently preventing growth?”

If activation is weak, no content tool will solve it. If customers are churning, another automation platform won’t fix it. If pricing is wrong, more traffic won’t help. If reporting is fragmented, additional dashboards will only create more confusion.

AI amplifies whatever system already exists. Strong systems become faster. Weak systems become harder to diagnose. That’s why the companies creating sustainable growth in the AI era are not necessarily those with the most sophisticated technology stacks.

They’re the ones that have built systems capable of turning customer data into decisions, decisions into action, and action into measurable business outcomes.

The future of marketing isn’t more AI tools. It’s a better system. This is where many businesses get stuck.

Identifying that growth has slowed is relatively easy. Identifying why it has slowed is significantly harder.

A company may assume it has an acquisition problem when the real issue is activation. Others invest heavily in new channels when customer retention is actually the bottleneck. In some cases, the issue isn’t growth at all, but measurement, where different teams are making decisions using conflicting data.

—

The post The AI stack trap: Why more AI tools aren’t translating into more growth appeared first on e27.

Mid-2026 has been a turning point for marketing. Technology, commerce and creative craft are jostling for equal billing, and the conversation has shifted from “what’s possible” to “what actually moves the business.” Below are the trends that are reshaping how brands plan, produce and prove marketing impact this year.

AI moved from theory to practice

Generative AI is transitioning from an experimental gimmick to an operational tool. Teams are using models to prototype concepts, produce rapid creative variants for A/B testing, automate localisation, and generate personalised assets at scale. Successful organisations treat AI as part of a workflow that increases testing velocity. New processes now pair human curators with machine outputs and tighter measurement so that speed doesn’t come at the cost of brand risk or relevance.

At the Marketing Leadership Summit at Cannes Lions, Melody Lee of Mercedes-Benz USA and Nicole Guess from Perion held a measured discussion on storytelling and creativity amid the rise of AI. They highlighted a common industry dilemma: AI speeds up production and enables mass personalization, yet it can’t substitute for the human creative judgment that gives luxury brand communications their depth.

Shoppable digital formats are now a creative imperative in e-commerce

Commerce-first creative is now table stakes. Short-form video with embedded checkout, virtual try-ons linked to instant purchase, and livestream commerce are proliferating across platforms, forcing creative briefs to include distribution and conversion mechanics from day one.

TikTok Shop remains the leader in viral discovery and impulse buying — the 2025 Sprout Social Index found TikTok is Gen Z’s primary product discovery destination (49 per cent go there first) and that 55 per cent of Gen Z engage with brand content there at least once a day. That behaviour changes how campaigns are engineered: creative work is increasingly judged on whether it can translate attention directly into measurable transactions.

Also Read: The future of marketing isn’t about AI, it’s about judgment

Brand management and sustained loyalty remain central

Despite the noise around new technologies, brand management and customer retention remain foundational. Building and protecting brand equity and turning customer acquisition into retention are central priorities.

Leandro Barreto, Global CMO for Unilever Beauty & Wellbeing, and Harry Kargman, Founder & CEO of Kargo, discussed a simple but powerful idea at the Marketing Leadership Summit at the Cannes Lions Festival 2026: that meaningful growth comes from brands that stay true to their values while creating ideas that travel and endure.

Practically, marketers are investing in customer loyalty and subscription offers that prove an impact on repeat behaviour and lifetime value. The recurring insight: purpose and values matter only when embedded in systems and operations that produce measurable customer outcomes, not just campaign headlines.

The creator economy — enthusiasm meets scrutiny

The creator economy remains influential but is under growing scrutiny. While creator-led formats and talent-driven activations continue to reach audiences, several senior marketing leaders told me they are scaling back influencer spend after mixed results. In a private exchange with a C-level marketing executive at a long-established global brand, candid feedback was blunt: recent collaborations with creators produced uneven outcomes, with several projects failing to meet expectations and a noticeable decline in effectiveness from social media bloggers. The executive traced this to shifting audience behaviours, platform algorithm changes, measurement blind spots, and an over-reliance on one-off activations.

Also Read: The playbook for going global: What C-dramas teach us about market entry

External data echoes the caution. Kantar’s research finds that fewer than one in 15 pieces of creator content delivers both strong audience engagement and ROI, which helps explain why brands are recalibrating toward longer partnerships, clearer KPIs, and closer integration of creators into distribution and commerce plans. In short: creators still matter, but their place in marketing strategies — and the metrics — needs rethinking.

A final observation

This is less a moment of disruption and more a period of consolidation. Tools and platforms are maturing; the premium now is on disciplined use of technology, clearer lines between creative idea and commercial outcome, and measurement that ties marketing to business results. Brands that balance digital agility with careful stewardship of their identity, translate experimental wins into repeatable systems, and build creator and commerce strategies around measurable outcomes will lead the next phase of growth.

The post How creativity, commerce and AI collide in mid-2026 marketing mix appeared first on e27.

It was a Sunday afternoon. A founder I know, running a company with 100 employees, revenue above US$50 million, was mid-pickleball when a message arrived: “Can you speak at our conference Tuesday morning?”

He agreed. The conference was two flights away.

What followed was two and a half hours across six browser tabs. Flight aggregators. An airline app. Hotel comparison sites. His loyalty portal. Google Maps for the airport transfer. A WhatsApp message to a friend asking which hotel was actually good near the venue.

By the time he’d confirmed everything, he’d made fourteen booking decisions, compared forty-seven options, and completed a job that neither his role nor his Sunday afternoon were designed for.

That’s not a story about bad travel tools. It’s a story about a category that was designed, from its inception, to serve someone else.

We assumed the problem was price, we were wrong

When we started Bliink, the working thesis was simple: the economics of corporate travel management were broken. Legacy TMCs like Amex GBT, CWT, and BCD require minimum annual travel spend of US$500,000 to access managed services. That threshold eliminates virtually every SME in Southeast Asia before the conversation begins.

So we built for the SME tier. And then we started listening.

After working directly with companies across Indonesia and Singapore, the answer that kept surfacing wasn’t price. It was time. Convenience. And fragmentation.

Companies weren’t failing to book travel because they couldn’t afford a TMC. They were losing hours every week because no product had ever actually taken the job away from them.

Also Read: Why the US tech rebound matters for SEA’s AI and venture ecosystem

Three solutions, three failures

The corporate travel industry has had thirty years to solve this and has produced three categories of response. Each one fails differently.

• Legacy TMCs price you out.  Amex GBT runs an 80 per cent opex ratio, US$479 million in operating costs against US$597 million in revenue, managed by 19,000 people. That cost structure doesn’t support SME pricing. It never will. For Southeast Asia’s 60 million-plus SMEs, these platforms are structurally inaccessible.
• Self-serve tools still make you do the work.  TravelPerk, Navan, and SAP Concur gave companies a better interface and called it a solution. But a faster booking tool is still a booking tool. You still search, filter, compare, approve, reconcile, and report. The friction moved from phone to screen. The labour did not move at all.
• The SME market was never the target.  This is the one the industry has never said out loud: no travel management company was ever designed for companies with 20 to 500 employees booking in rupiah, using Lion Air, needing WhatsApp approvals, and managing travel across multiple time zones and currencies. Southeast Asia’s SMEs weren’t underserved. They were structurally excluded. There is a difference.

Why the window just opened

Three things have converged that have not converged before.

AI has made fully managed travel economically viable at SME scale for the first time. The 1:120 service ratio that legacy TMCs achieved by employing thousands of agents can now be delivered by a single AI system. What required a US$500,000 minimum spend in 2022 can be delivered at the 50-employee level in 2026.

Also Read: The future of marketing isn’t about AI, it’s about judgment

The legacy players are collapsing under their own weight. CWT filed for bankruptcy. Amex GBT is being taken private in a US$6.3 billion deal, which is a cost restructuring play, not a growth strategy. The managed travel market for SMEs is, for the first time, structurally undefended.

Southeast Asia’s SMEs have already digitised their behaviour. WhatsApp penetration exceeds 90 per cent in Indonesia. Mobile corporate expense adoption has tripled since 2022. The behavioural foundation that makes AI-native corporate travel workable exists in the market right now. It didn’t three years ago.

What the data reveals

We built Bliink to test this thesis. Eight months in, the metric that surprises people most isn’t revenue. It’s retention.

We’ve had 100 per cent client retention since the beta launch. We’ve never run a marketing campaign. Every client arrived through a referral.

I’m careful about what conclusions to draw from early data. But 100 per cent retention across eight months of operation is not a product metric. It’s a signal about what category you’re actually in.

There’s a difference between software that people use and a service that people keep. Corporate SaaS benchmarks 5-10 per cent monthly churn as normal. The gap isn’t in features. It’s whether the job belongs to the product or to the person.

When a platform handles the trip, preferences recalled, policy applied, itinerary sent, receipts filed, the work is no longer the traveller’s. That’s not a UX improvement. It’s a category shift. And it turns out that when you actually complete the job for someone, they stop looking for alternatives.

The actual opportunity

The opportunity in corporate travel intelligence for Southeast Asia is not better booking software. It’s institutional memory.

Every trip a company takes encodes information: traveller preferences, policy boundaries, pricing benchmarks, vendor performance, patterns across teams and time. That data doesn’t exist in any structured form for the 60 million-plus SMEs in this region. It’s scattered across email threads, WhatsApp chats, and booking confirmation PDFs that nobody reads twice.

The company that captures and structures that data, not for MNCs, not for Fortune 500 procurement teams, but for the mid-tier Indonesian consultancy and the Singapore regional distributor and the Jakarta family office, builds a moat that no generalist AI model or offshore OTA can replicate.

The Internet gave companies infinite choices and left them flying blind. AI is finally giving them back the trusted advisor the Internet took away.

The question for Southeast Asia is who builds it first, and whether they build it from here.

The post Corporate travel in Southeast Asia was never broken, it was never built appeared first on e27.