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Singapore-based SimpleAI has secured a US$10 million debt facility to acquire accounting and fund administration firms across Asia Pacific, as the startup shifts from selling automation software alone to owning the service businesses where that software can be deployed.

The company has also announced a US$5 million seed round, with a lead investor already committed, following an earlier US$500,000 pre-seed investment from strategic backers.

Also Read: The future of numbers: Automation’s transformative impact on accounting jobs

SimpleAI did not disclose the names of the investors or the terms of the debt facility.

Founded in 2023 by Roger Tan, Shim Youngjun, and Bryan Sng, SimpleAI builds automation agents for accounting and finance teams. Its software reads ledgers, charts of accounts, and existing workflows, then proposes accounting actions while a deterministic module handles calculations. Accountants can review and approve entries before they are posted.

The funding marks a change in strategy. Rather than relying solely on organic software adoption, SimpleAI now wants to acquire established accounting and fund administration firms, keep their client relationships intact, and introduce AI into their workflows.

A roll-up model for professional services

SimpleAI is targeting firms with annual revenues between US$500,000 and US$5 million, although it said it may consider larger transactions above US$20 million. Its immediate focus is Singapore and Australia, with Hong Kong and Mauritius also under consideration. The company said it has more than US$25 million worth of potential deals under review across Singapore and Australia.

Acquired companies will operate under what SimpleAI calls a Partner-and-Operator model. In practice, this means the acquired firms continue serving existing clients while SimpleAI provides technology and operational support. The company said it will assess acquisition targets based on unit economics, cultural fit, and whether its AI agents can be embedded into the firm’s workflows.

That approach reflects a wider trend in vertical software and professional services, where startups are increasingly trying to control both the software layer and the operating business. The model has already been tested in fragmented sectors such as dental clinics, legal services, bookkeeping, and insurance distribution.

In accounting, the thesis is straightforward: many smaller firms have recurring clients, predictable revenues, and labour-intensive processes, but lack the capital or technical capacity to automate quickly.

For SimpleAI, acquisitions could offer a faster route to distribution than selling software firm by firm. The risk is that running services businesses is operationally heavier than selling software, especially across jurisdictions with different tax, compliance, and reporting requirements.

Why Southeast Asia matters

The Southeast Asian angle is central to the story. Singapore has pushed aggressively to digitise financial infrastructure through initiatives such as InvoiceNow, the nationwide e-invoicing network based on the Peppol framework. SimpleAI has partnered with the Singapore Business Federation and SESAMi in support of the Infocomm Media Development Authority’s InvoiceNow initiative, extending its automation agents to help small and medium-sized enterprises adopt e-invoicing.

This matters because accounting automation depends on the quality and structure of financial data. E-invoicing reduces manual entry, improves audit trails, and gives software platforms cleaner transaction data to process. Singapore has been ahead of much of the region on this front, while markets such as Malaysia, Indonesia, Vietnam, and Thailand are also moving towards more formal digital tax and invoicing systems.

Also Read: How Transparently.AI uses Artificial Intelligence to detect accounting manipulation, fraud

Across the region, the broader digital financial services market has continued to expand even as venture funding has tightened. The Google, Temasek, and Bain e-Conomy SEA report estimated that digital financial services revenue in the region could reach around US$60 billion by 2025, driven by payments, lending, insurance, and wealth products. While accounting automation is a smaller segment, it sits underneath many of these activities, particularly for SMEs, funds, and corporate service providers.

The opportunity is also shaped by a funding environment that has become more disciplined. After the 2021 peak, Southeast Asian startup funding fell sharply, forcing companies to show clearer paths to revenue and profitability. In that context, SimpleAI’s acquisition-led strategy is notable: it is using debt to buy revenue-generating firms rather than relying only on venture-backed software growth.

AI in accounting is crowded but still early

SimpleAI is entering a competitive market. Global accounting software incumbents such as Xero, Intuit QuickBooks, Sage, and Oracle NetSuite have been adding AI and automation features to their platforms. In Southeast Asia, SMEs often rely on a mix of cloud accounting tools, outsourced bookkeepers, corporate secretarial firms, and local tax software providers.

The fund administration market is also competitive, with global players such as Vistra, Apex Group, TMF Group, and Tricor serving private funds, special purpose vehicles, and corporate clients across Asia Pacific. SimpleAI’s appointment of Otto Von Domingo as Chief Revenue Officer points to this segment as a priority. Domingo has more than 20 years of experience in private markets and corporate services and previously helped grow Vistra’s funds business in Singapore and Asia-Pacific.

SimpleAI said its platform is deployed across more than 10 markets, supporting more than 1,500 entities and over 2,000 users across more than 18 industries. It is also an app partner of Xero and Intuit QuickBooks, and says it is ISO-certified.

Bryan Sng, co-founder and Chief Operating Officer of SimpleAI, said the company was formed after the founders saw how much accounting work still depended on repeated manual reviews and corrections.

“What struck us was how painful that simple need actually was,” he said. “The endless loop of sending a report, catching an error, amending it, reviewing again, and how familiar the excuses had become for late submissions.”

The execution question

SimpleAI’s ambition is not modest. The company said it plans to strengthen its presence in Singapore and Australia, expand into Hong Kong, Mauritius, and Europe, and consider a public-market exit, including a possible IPO, within 24 months.

That timeline will invite scrutiny. Roll-up strategies can look compelling on paper but often depend on disciplined acquisition pricing, smooth integration, staff retention, and consistent service quality. In professional services, client trust and regulatory accuracy matter as much as automation.

Roger Tan, founder and CEO of SimpleAI, said the company would remain selective. “As an AI-native company, our M&A facility lets us acquire businesses where we can deploy our agents into the workflow immediately,” he said. “We are being disciplined and will only move forward when the economics, talent, and cultural fit are right.”

Also Read: Why the future of AI automation belongs to builders who ship

For now, SimpleAI’s bet is that accounting and fund administration firms will not be replaced by AI so much as reshaped by it. If the company can combine automation with acquired distribution, it may build a defensible services platform. If integration proves harder than expected, it will face the same problem as many roll-ups before it: buying revenue is easier than improving the business behind it.

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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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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.

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.

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.