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.

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The post Why Singapore’s Gen Z handles money differently and what it means for finance appeared first on e27.

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.

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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.

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