Two weeks ago, I sat across from the new chairman of a cooperative in West Java. He had been appointed three months earlier under Indonesia’s Koperasi Merah Putih programme. He asked me, sincerely and without embarrassment, how he was supposed to know which risks his cooperative was actually carrying — and whether he had to file a report about it this quarter.

I have spent fifteen years inside Indonesian risk functions — banking, insurance, sharia microfinance — and I have heard versions of that question before. But never at this scale, and never with this little time to answer it.

That conversation is happening in 83,000 cooperatives across Indonesia right now. Each one has been required, under the Prabowo administration’s flagship Koperasi Merah Putih initiative, to implement formal risk management on day one — to identify the risks it is carrying, document the controls in place against them, monitor early warning signs, and record incidents as they happen. For most of these institutions, it is the first time anything that could be called governance has been written down.

The scale of the rollout is unlike anything ASEAN has attempted before.

The unprecedented scale

To put 83,000 cooperatives in context, it is roughly seventy times the number of commercial banks in Indonesia, and more institutions than there are public companies on the Indonesia Stock Exchange. The combined economic activity flowing through these cooperatives, even at modest per-unit volume, will touch tens of millions of households inside two years.

Indonesia has run financial inclusion experiments at scale before. Microfinance, sharia banking, branchless banking — each one produced lessons the region eventually absorbed. But none of them required formal enterprise risk frameworks on day one. The Koperasi Merah Putih programme is the first time a population-scale financial inclusion initiative has been launched with risk management embedded as a prerequisite, not as a maturity stage.

That decision is consequential. It is also extraordinarily ambitious.

Also Read: Business judgment on trial: Indonesia’s corruption courts are getting it backwards

What can go wrong

Three failure modes are predictable enough that they deserve to be named while there is still time to design around them.

Paper compliance. With deadlines this tight, the easiest response for cooperative leadership is to download a template, fill in the fields, file it, and move on. The documents exist on paper, but nobody on the ground is using them to make a decision. Within twelve months, they are stale, the staff have moved on, and the framework that was supposed to govern operations has become a binder in a drawer.

Supervisory dilution. Indonesia’s regulators — OJK, Kementerian Koperasi UKM — are themselves resource-constrained. Supervising 83,000 newly-launched institutions to a standard that takes years to build inside a commercial bank is, realistically, not going to happen at full depth. The risk is that the framework exists in policy but is enforced inconsistently, which is the worst of both worlds: cost without protection.

Loss-event blindspots. Cooperatives sit closer to their members than commercial banks do. They will be exposed to risks that traditional banking frameworks do not measure well — local social capital risk, agricultural cycle risk, informal credit chain contagion. A framework written in the language of banks will under-detect the things cooperatives are actually exposed to.

What needs to be in place

The next eighteen months will decide whether this becomes the largest financial inclusion success in Southeast Asia’s history or its most expensive policy lesson. Three things will determine which.

Training depth must outrun the deadline. Cooperative leaders need apprenticeship support, not certification cycles. Practical, hands-on coaching from people who have actually run risk frameworks inside real institutions — not slide decks — is what turns paper compliance into actual practice.

Also Read: Business judgment on trial: Indonesia’s corruption courts are getting it backwards

Tooling must be priced for the unit. The compliance software that costs ninety thousand dollars a year inside a large bank cannot be the model for a cooperative serving two thousand members. The tooling that works at this scale is cloud-native, modular, priced in tens of dollars per month, and operable by someone without a finance degree. The price point is, as it turns out, the easy part. The hard part is making the framework legible to someone who has never read a policy document before.

Reporting must aggregate upward. The supervisory burden cannot be solved by visit-each-cooperative auditing. It will only be solvable by data flowing up — from cooperative to district to regional to national — so regulators can spot anomalies at scale. That requires a deliberate reporting backbone, not an ad-hoc PDF submission system.

What ASEAN should learn from watching

The Philippines has cooperatives and rural banks facing similar inclusion-with-governance tensions. Malaysia and Thailand have parallel structures. Vietnam is beginning to formalise its financial cooperative sector. None of them has yet tried risk management at this scale on day one.

If the Indonesian experiment works, the model will be exported across the region within five years. The first institutions, supervisors, and operators to make it work at scale here will have an outsized influence on how the rest of ASEAN learns to do this.

If it fails, the lesson will be the more important one. Mandating governance at scale, without proportionate investment in capability and tooling, does not produce governance. It produces forms.

Either way, eighteen months from now we will know which of those outcomes Indonesia chose. The rest of ASEAN should be paying attention right now — because their version of this question is coming next.

—

The post The 83,000 experiment: Indonesia is running ASEAN’s largest test of risk management at scale appeared first on e27.

Manus’s reported buyback encapsulates a new reality: AI deals are no longer just commercial transactions but geopolitical chess moves with clear implications for Southeast Asia.

If Chinese investors repurchase Manus at the roughly US$2 billion price Meta paid, the outcome will reflect Beijing’s intent to retain control over advanced AI assets developed with Chinese capital, even when those assets are domiciled in neutral hubs such as Singapore.

That matters for Southeast Asia because the region is both a talent pool and a battleground for influence: Singapore remains an attractive incorporation point, but regulatory pressure from neighbouring powers can reshape ownership, governance and market access overnight.

The Manus story also exposes practical frictions for founders and VCs. Rapid revenue growth makes firms more strategically valuable, yet it also raises the stakes in national-security reviews. Western funds face difficult choices about exits and follow-on strategy; regional investors may see opportunities to repatriate value but must manage international credibility and market access.

For Southeast Asia’s startup ecosystem, the takeaway is twofold: structure deals with geopolitical scenarios in mind, and expect capital to flow along new corridors, including Hong Kong listings and China-centric joint ventures. Manus is not just one company’s reversal; it is a preview of how AI capital and control will be negotiated across Asia.

REGIONAL

Chinese investors move to unwind Manus’s Meta deal at US$2B: The attempted reversal reflects how US-China tech tensions are forcing Chinese-backed startups to choose sides and how deals once seen as validation are now becoming liabilities for founders navigating geopolitical crossfire.

BRI Ventures case: four executives jailed, no personal gain proven: The conviction despite no proven personal enrichment sets a troubling precedent for state-backed investors in Indonesia, where the line between bold investment decisions and criminal liability now appears dangerously unclear.

Carro acquires Australian firm CarPlace in cross-border push: The deal marks the Singapore-based used-car marketplace’s first move outside Southeast Asia, linking two fragmented automotive resale markets and raising questions about whether other SEA platforms will follow with similar outbound plays.

Vertex-backed ACRAB closes US$350M for agentic AI infra: One of the largest AI infrastructure deals in Southeast Asia this year, the raise positions ACRAB to compete directly with hyperscalers on compute supply for enterprise agentic workloads across the region.

Singapore AI startup K25 closes US$10M pre-Series A: With pre-A funding secured and Series A already underway, K25 AI is moving quickly, a sign that enterprise AI in Singapore is still attracting early-stage capital despite a broader global venture slowdown.

Respond.io raises fresh capital in latest funding round: The customer messaging platform will use the new capital to deepen its foothold among Southeast Asian enterprises, competing in a market where fragmented messaging channels remain a persistent pain point for regional businesses.

Golden Gate Ventures opens office in Uzbekistan: The Singapore VC’s first Central Asian outpost is a bet that the region’s emerging tech firms are ready to scale into Southeast Asia, and that capital flows between the two corridors can be meaningfully accelerated.

100×100 bets US$100M on 50 climate startups in SEA, India: The fund is structured as a startup factory, backing 50 companies at early stage across two of Asia’s most climate-vulnerable markets, where institutional climate capital has historically been thin.

Singapore leads APAC in AI agent rollouts and rollbacks: The findings suggest deployment speed is outstripping governance readiness, a pattern that could expose enterprises to operational and compliance risk as agentic AI moves deeper into business-critical functions.

MAS chief flags AI risk even as Singapore’s economy holds firm: The central bank governor cautioned that AI-related financial risks , including model opacity and systemic concentration, could undermine stability even as Singapore’s near-term economic outlook remains relatively resilient.

Singapore urges ASEAN to pursue AI without ceding data sovereignty: At a regional forum, Singapore pushed fellow ASEAN members to adopt AI collectively while guarding against the data dependency and sovereignty risks that come with over-reliance on a small number of foreign AI infrastructure providers.

Singapore workers adopt AI faster than their bosses: The gap threatens to create a two-tier workforce where employees build AI fluency that management cannot evaluate, undermining the strategic oversight needed to deploy these tools responsibly at scale.

Singapore launches US$29M scheme to fund digital media content: The programme targets media professionals transitioning into digital content creation, reflecting the government’s broader push to future-proof creative industries as traditional media revenue models continue to erode.

Vietnam and Singapore lead Southeast Asia in construction tech: Both markets are deploying construction technology at a pace that outstrips the rest of Southeast Asia, driven by large-scale infrastructure pipelines and a growing base of proptech and built-environment startups.

Indonesia runs ASEAN’s largest risk management experiment: The 83,000-participant pilot has direct implications for insurtech and financial inclusion models across ASEAN, where underinsurance and informal labour markets make population-scale risk programmes both necessary and enormously difficult to execute.

Vietnam’s growth hinges on structural dependence on private capital: Unlike peers that treat foreign investment as supplementary, Vietnam has built its economic growth model around it, a distinction that raises the stakes considerably if investor sentiment shifts or geopolitical conditions tighten.

---

INTERVIEWS & FEATURES

Tin Men Capital on backing unglamorous but durable SEA bets: The firm argues that Southeast Asia’s most defensible businesses are not the most visible ones; its portfolio strategy deliberately targets sectors with high switching costs and low VC competition, where patient capital can compound quietly.

How Marsham Edge is rethinking AI anomaly detection: The startup’s approach treats anomaly detection as a continuous learning problem rather than a rules-based one, targeting financial and logistics clients in Southeast Asia where irregular patterns often go undetected until material damage has occurred.

Vietnam’s biggest PE deal of 2025 was a food company: The outcome challenges the tech-first narrative that dominates SEA investor conversations; patient capital is quietly finding its best returns in essential consumer goods, not software.

---

INTERNATIONAL

OpenAI recruits senior talent ahead of anticipated IPO: The hires span finance, legal, and communications — roles that reflect a company shifting its centre of gravity from research output to investor relations, regulatory compliance, and public market readiness.

PayPal Ventures shuts down amid broader company restructure: The closure ends a funding channel that had backed fintech startups across Asia and Latin America, reflecting a wider retreat by corporates from venture activity as balance sheet discipline takes precedence over strategic portfolio plays.

Telegram ban in India drives users to VPNs and rival apps: The crackdown is already reshaping messaging app dynamics across South Asia, with spillover effects likely in Southeast Asia where Telegram remains a primary channel for communities, traders, and political organising.

Waymo recalls 4,000 robotaxis over highway construction flaw: The software error caused vehicles to navigate into active construction zones, an edge case failure that regulators in multiple markets are likely to cite as evidence that autonomous vehicle certification standards need tightening.

AI pressures could force Apple to raise iPhone prices: Rivals are shipping more capable on-device AI while Apple plays catch-up, and the cost of closing that gap may be passed directly to consumers through higher handset prices, a significant risk in price-sensitive Southeast Asian markets.

---

CYBERSECURITY

Cybercriminals breach tens of thousands of Fortinet firewalls: The alleged intrusion targeted enterprise-grade perimeter security deployed by major global corporations, exposing a systemic vulnerability in firewall infrastructure that many Southeast Asian enterprises rely on as their primary network defence.

---

SEMICONDUCTOR

Renesas acquires Pictorus to accelerate embedded software development: The deal plugs a browser-based behavioural modelling tool into Renesas 365, enabling engineers to generate Rust, C/C++, and Python-compatible embedded code from block diagrams, strengthening the platform’s pitch to automotive and robotics developers.

ASML denies EUV breach after US raises China export control fears: Commerce Secretary Howard Lutnick confronted ASML leadership over whether its most advanced lithography equipment had circumvented export controls, a more serious allegation than its routine China business, which already bars EUV shipments under Dutch rules.

Apple-Intel chip deal is thinner than Trump’s announcement suggests: Reported details point only to a preliminary manufacturing arrangement, not a design partnership, but it still hands Intel’s floundering foundry business a credibility boost, backed by the US government’s 9.9% stake and US$8.5B in prior grants to the chipmaker.

Amazon moves to sell its own AI chips to challenge Nvidia: Selling Trainium and Inferentia externally would let Amazon monetise its chip investment beyond its own cloud, directly threatening Nvidia’s grip on the accelerator market at a moment when enterprises are actively seeking supply alternatives.

---

AI

AI did not kill creativity; it just raised the bar: The argument is that generative tools have commoditised baseline creative output, making distinctly human judgement, taste, and originality more valuable, not less, for individuals and teams willing to develop them.

Only 16% of Americans expect AI to benefit society: The data points to a widening trust deficit that tech companies and policymakers have yet to address meaningfully, a sentiment gap with real consequences for AI product adoption, regulation, and public legitimacy.

---

THOUGHT LEADERSHIP

The higher you rise, the less you hear, and the more it costs: Organisations systematically filter out uncomfortable truths as they move up the chain, leaving senior leaders making high-stakes decisions on increasingly sanitised information, a structural failure, not a personal one.

Bitcoin’s 81% gold correlation signals a new macro identity: The data suggests Bitcoin is being absorbed into institutional portfolios as a macro hedge rather than a speculative asset, a shift with significant implications for how crypto fits into diversified portfolio construction.

When gold, stocks, and crypto fall together, nothing hedges: Classic diversification theory assumes low correlation between asset classes, but simultaneous declines across all three expose a structural flaw in modern portfolio construction that neither retail nor institutional investors have adequately prepared for.

Why tech giants are crashing as Bitcoin surges past US$67K: The divergence challenges a long-held assumption that risk assets move in tandem, suggesting capital is rotating out of equities and into crypto as a distinct macro hedge, not merely following the same sentiment cycle.

Why Pop Mart’s Labubu loyalty is not the same as brand loyalty: Consumers are devoted to the character, not the company — a vulnerability that leaves Pop Mart exposed if Labubu’s cultural moment fades, with no deeper brand architecture to fall back on.

The post Ecosystem Roundup: Manus buyback is a geopolitical wake‑up call for SEA’s AI ecosystem appeared first on e27.

Singapore-based enterprise AI company fileAI has received a strategic investment from JRE VENTURES, the corporate venture capital arm of Japan’s JR East Group, marking a significant step in the company’s expansion into the Japanese market.

The investment comes alongside a deployment partnership in which fileAI’s governed AI platform will be rolled out across JR East Group companies. The JR East Group operates one of Japan’s largest and most complex rail and transportation networks, making the partnership a high-profile test of the platform’s enterprise capabilities.

fileAI’s platform deploys proprietary AI agents to convert legacy contracts and documents into structured, searchable knowledge assets. The tech spans document digitisation, AI-powered data extraction, centralised repositories, and contract intelligence — addressing a longstanding challenge for large organisations that hold vast archives of paper-based and legacy digital records.

Under the partnership, fileAI and JRE VENTURES aim to digitise historical contracts and documents, automatically extract key terms, clauses, obligations, and metadata, consolidate that knowledge into a centralised intelligent repository, and generate contract analytics including risk insights and renewal forecasting.

Also Read: The 83,000 experiment: Indonesia is running ASEAN’s largest test of risk management at scale

The long-term ambition is to establish what fileAI describes as a “living contract intelligence layer” across organisations, reducing manual document handling and enabling more informed operational decision-making.

Christian Schneider, chief executive of fileAI, described Japan as a pivotal market for the company. “Their appetite for innovation, combined with the scale of their operations, makes this the perfect proving ground for what AI agents can do for enterprises,” Schneider said in a statement. fileAI is currently building a dedicated local team in Tokyo, with hiring under way across sales, engineering, and customer success.

Junichi Eto, managing director of JRE VENTURES, said the partnership would help validate practical use cases for enterprise AI in Japan and the broader Asia-Pacific region. “fileAI’s approach to AI-driven file processing represents a meaningful advancement in how enterprise data can be structured and utilised,” he said.

The investment was facilitated through 1982 Ventures, a Singapore-based fund manager focused on enterprise AI, fintech, and private markets across Asia. The firm positioned itself as a bridge between high-growth Asian technology companies and Japanese corporate investors.

Also Read: Chinese backers move to buy Manus from Meta in potential US$2B reversal

Herston Powers, founding managing partner at 1982 Ventures, said the JR East Group deployment offered strong market validation. “Every large enterprise sits on a goldmine of trapped data. Seeing them deploy inside the JR East Group — a massive, complex environment — is the best kind of validation,” he said.

fileAI did not disclose the financial terms of the investment.

—

Image Credit: fileAI

The post fileAI secures strategic investment from JR East Group’s venture arm to expand in Japan appeared first on e27.

The AI hype cycle loves a clean split: innovators and laggards. Southeast Asia’s story is messier and more interesting.

A study titled “AI in Southeast Asia: An era of opportunity” by McKinsey and the Singapore Economic Development Board surveyed 330 respondents across six economies — Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam — and found the region is nudging ahead of the global average in moving beyond experimentation.

That is the good news.

Also Read: Southeast Asia’s AI boom is built on steel, not startups

The bad news is that the region’s economic backbone—micro, small, and medium-sized enterprises (MSMEs)—could be priced out of the next productivity leap unless AI becomes cheaper, simpler, and more local.

The adoption numbers: slightly ahead of the world, behind the US

According to the report’s regional breakdown, AI adoption in Southeast Asia is now heavily weighted toward scaling rather than dabbling. The data shows:

• 8 per cent fully scaled
• 38 per cent scaling
• 35 per cent piloting
• 19 per cent experimenting
• effectively negligible “no use at all” in the dataset

In other words, 46 per cent are beyond pilots (fully scaled + scaling). That edges the global composite in the report, and signals that “AI in enterprise” is no longer exotic in the region’s more digitally advanced markets.

Yet the US still leads, with higher fully scaled and scaling shares. Southeast Asia is not winning on maturity; it is winning on momentum.

Size matters and it’s not subtle

The report slices adoption by company revenue. The pattern is predictable, but the gap is still meaningful:

• Large firms (annual revenue more than US$250 million): 56 per cent
• scaling or fully scaled
• Medium firms (US$100 million–US$249 million): 47 per cent scaling or fully scaled
• Small firms (less than US$100 million): 42 per cent scaling or fully scaled

That is the real divide: not country, not sector, but organisational capacity. Larger enterprises have deeper data pools, more stable infrastructure, and budgets that can absorb mistakes. Smaller businesses have less room to “learn by doing” when the learning curve costs money.

MSMEs are in the region. AI pricing could decide who wins

Southeast Asia has 70 million MSMEs, the report says, representing about 97 per cent of the workforce and a large share of GDP. That makes AI adoption for small firms less a technology question than an economic one.

If AI tools remain priced and packaged for enterprise procurement teams, the region gets an ugly outcome: big firms compound their productivity advantages while small firms fall further behind, even if the technology itself is “available”.

Also Read: Rethinking AI adoption: Why Southeast Asia’s businesses must transform to thrive

The report calls out what MSMEs need from providers:

• low-cost entry options
• local currency pricing (or at least predictable usage-based pricing)
• bundled packages (collaboration tools + data + model access + onboarding)
• guided adoption to reduce complexity

That is basically a demand for AI as a utility, not AI as a bespoke transformation programme.

The sector leaders are what you’d expect and that’s the point

AI maturity varies by industry. The report highlights technology, media, telecommunications, and advanced industries as the leaders, with around six in ten firms scaling or fully scaled. Energy and materials also show substantial progress, with around half of them scaling.

In contrast, the public sector, healthcare, travel, and infrastructure remain earlier-stage, with over six in ten still piloting or experimenting. This is not because those sectors lack use cases. It’s because they have nastier data environments, heavier regulation, and higher consequences when models hallucinate or leak.

Real adoption is changing job expectations — not just dashboards

The report includes a candid Grab quote that reveals what “AI adoption” actually looks like inside a scaled platform.

Grab’s group head of data and analytics, Nikhil Dwarakanath, says: “We have several implementations that are running at scale, such as our merchant AI assistant, now rolled out to over 1.2 million merchants…”

He adds: “AI is helping to improve top-line growth. For example, merchants using the merchant assistant have seen their business grow by about 10 per cent.”

That is a direct claim of revenue impact from a scaled AI product. It also hints at a regional opportunity: platforms that serve MSMEs can act as AI distribution rails, delivering AI benefits to small businesses that would never build these systems on their own.

People are unusually optimistic about AI here. That’s an advantage

One of the report’s more striking societal stats: 70 per cent of the population in Southeast Asia regard AI as a societal benefit, compared with 44 per cent in Japan and 42 per cent in the US (as cited in the report).

This matters because adoption is not just about budgets and infrastructure; it is about trust and willingness. A region that is culturally open to AI products may see faster consumer uptake and less friction in deploying AI-enabled services—especially in mobile-first markets.

The real bottleneck is not curiosity — it’s operational discipline

Southeast Asia’s AI adoption is no longer stuck at “pilot theatre”. But scaling beyond pilots is not the same as scaling impact. The next stage will be determined by whether companies can:

Also Read: From hesitation to action: How SMEs in Southeast Asia can start AI adoption

• integrate AI into messy legacy systems
• build or buy the right talent (especially MLOps and applied engineering)
• prove ROI beyond productivity anecdotes
• manage risk without paralysing deployment

The region’s momentum is real. But momentum alone does not produce winners. Pricing models, packaging, and platform distribution—especially for MSMEs—could decide whether Southeast Asia’s AI wave becomes broad-based growth or just another round of consolidation for the biggest players.

—

The image was created using AI.

The post Momentum without maturity: Southeast Asia’s AI reality appeared first on e27.

K25.ai, an APAC-focused startup attempting to fuse live streaming, creator monetisation, and prediction markets, has closed a US$10 million pre‑Series A round at a US$100 million valuation.

With this deal, Nasdaq-listed NewGenIVF Group completed a US$4 million tranche that brings its aggregate commitment in the AI firm to US$10 million. Once closing conditions are satisfied, NewGen’s ownership in K25.ai is expected to rise to roughly 10 per cent.

Also Read: K25.ai bags strategic funding from Nasdaq-listed NIVF at US$100M pre-A valuation

K25.ai has already begun raising a Series A to fund commercialisation and regional expansion.

A product for creator-first Asia

K25.ai bills itself as a fusion of Twitch, prediction markets such as Polymarket, and generative-AI assistants. This way, it provides an experience that lets creators host livestreams while audiences “watch-to-predict” outcomes of sports, e-sports and entertainment events. The startup says AI will help create and resolve markets, while enabling real-time community participation.

For a region where livestream commerce, creator monetisation, and e-sports are booming, the proposition is timely. Southeast Asia’s internet economy continues to expand, fuelled by mobile-first consumption and rising creator activity across platforms, such as TikTok and YouTube. Localised content, live engagement features and novel monetisation mechanisms matter more here than in many mature markets, a dynamic that plays directly into K25.ai’s stated strengths.

Asia Pacific is both culturally receptive to live, communal viewing experiences, and regulatory-complex when it comes to information markets. If they can thread the needle between product-market fit and compliance, there is a clear path to scale.

Strategic stake and regional agency rights

NewGen’s additional investment follows a US$2 million commitment in May and a further US$4 million announced on June 4, completing its headline US$10 million backing. Beyond a financial stake, NewGen has secured exclusive Asia Pacific agency rights with K25.ai, a commercial arrangement that grants it distribution and partnership opportunities across permitted markets.

That dual arrangement of equity plus agency rights is notable. It gives NewGen both an economic upside and a route to monetise the product regionally through local partnerships, distribution deals, and go-to-market activities.

Also Read: Streaming the dream: How live streaming technology can increase access to brands

For K25.ai, partnering with an investor that already has regional ties can accelerate market entry, particularly in jurisdictions where navigating regulatory regimes and building creator ecosystems are resource‑intensive.

NewGen’s chairman and CEO Alfred Siu framed the move as strategic: the firm is seeking exposure to “a differentiated platform operating at the convergence of artificial intelligence, livestreaming and prediction-market infrastructure.”

K25.ai’s CEO Andy Cheung said the pre‑A close is “strong strategic validation” and that the Series A will accelerate product launch and regulatory licensing in selected Asian markets.

Regulatory obstacles and the compliance imperative

Prediction markets and wagering-adjacent products face a patchwork of legal regimes across Southeast Asia. Countries such as Singapore and Malaysia impose strict rules on gambling and speculative betting, while others adopt more permissive frameworks for information markets or skill-based prediction activities. K25.ai says it will not operate in jurisdictions where such activities are restricted or prohibited, and it is pursuing applicable regulatory licensing in selected markets.

That cautious stance is necessary: several startups in the prediction and betting space have run into regulatory pushback when launching without sufficient local licences or when their product crossed into gambling territory. For K25.ai, the technical promise of AI-assisted market resolution needs to be matched by clear legal boundaries and robust age‑gating, geofencing and compliance tooling, especially if creators in multiple countries can host events that attract cross-border audiences.

The Series A will likely be as much about compliance and localisation as it is about marketing and creator acquisition. Investors in the region will want to see concrete plans for regulatory approvals, partnerships with licensed operators where required, and product controls that demarcate entertainment from gambling.

Monetisation and creator economics

K25.ai’s model ties revenue potential to creator engagement and the liquidity of prediction markets: higher viewership and more active markets can translate into fees, sponsorship deals and potentially secondary markets for data and signals. Southeast Asia’s creators are expert at converting live engagement into commercial outcomes — think live commerce on Shopee or TikTok — but prediction-based monetisation is newer.

Monetisation hurdles include user education, trust in market settlement and the need to seed liquidity so markets feel meaningful. AI can help standardise market creation and speed up resolution, but user-facing clarity and transparent settlement mechanics will be crucial to adoption. Given the region’s appetite for esports and fantasy sports, these categories could be early wins if regulatory fit is achieved.

Why the Southeast Asian angle matters

Southeast Asia represents a large and young digital-native audience, a flourishing creator economy and high mobile engagement, all tailwinds for a live-streaming, prediction-driven product. Local languages, cultural nuances in content, and varying regulatory regimes mean success will depend on granular market-by-market strategies rather than a one-size-fits-all regional roll‑out.

NewGen’s agency rights across Asia-Pacific suggest K25.ai will lean on partners with established regional networks. That could speed creator recruitment, secure local licences faster and build a distribution play that a pure-play Silicon Valley investor might struggle to execute.

What to watch next

In the coming months, the market will be watching for K25.ai’s product launch cadence, the specifics of its Series A valuation and investor mix, and the company’s progress on regulatory approvals in key Southeast Asian markets. Execution on creator partnerships, user safety and market liquidity will determine whether the startup can turn its concept into a viable, scalable business.

Also Read: Live-streaming done right: How brands can turn viewers into loyal customers

For incumbents and investors, the combination of AI, live video and prediction mechanics is a fresh experiment. If K25.ai and NewGen can navigate regulatory complexity and prove a compelling creator revenue path in Southeast Asia, they could lay the groundwork for a new digital entertainment category across the region.

The post NewGen doubles down on K25.ai as Asia-focused AI livestreaming platform eyes commercial launch appeared first on e27.

Carro, the Singapore-headquartered online automotive marketplace that has built a significant presence across Southeast Asia, has made its first major move into Australia with the acquisition of CarPlace, a used-car platform operated by Australian automotive group Autoleague.

The deal gives Carro a physical presence in three of Australia’s four largest markets in one go. It signals a strategic effort to export its technology-rich approach beyond Southeast Asia. For an industry accustomed to fragmented dealer networks and opaque pricing, Carro is pitching transparency, end-to-end workflows and a stronger wholesale pipeline, including vehicles from Japan, as its differentiators.

Also Read: Profitable, AI-driven, and IPO-ready: Inside Carro’s next chapter

A foothold Down Under

CarPlace, positioned among Australia’s more prominent used-car platforms, will remain linked to Autoleague, which becomes a strategic shareholder in the combined business. Autoleague’s continued involvement provides Carro with an immediate network of dealerships and operational scale that would take years to build organically.

Aaron Tan, Carro’s co-founder and chief executive, framed the move in blunt commercial terms: Australia, he said, is “one of the largest used car markets in Asia Pacific, with a consistent annual sales volume of 2.3 million used cars and fast-growing EV penetration.” He argued the market is “ripe for a platform like Carro to transform the used car landscape and deliver a better, convenient, more transparent customer experience that is powered by technology and AI.”

Tan’s pitch rests on a two-pronged strategy:

1. Strengthen retail pre-owned operations using Carro’s proprietary tools, such as vehicle inspections, inventory tracking, asset and lead management.
2. Grow wholesale activity, leveraging Carro’s Japan presence, to serve the Japan–Australia import corridor.

The latter is notable because Japanese models remain popular in Australia, and a reliable cross-border wholesale channel could be a lucrative niche.

What Carro brings: technology and dealer relationships

Carro’s operating model in Southeast Asia blends marketplace listings with back‑end services for dealers, combined with data and AI to reduce information asymmetry. In its markets, Carro has emphasised standardised inspections and clearer pricing, both of which can shorten transaction times and reduce post-sale disputes.

Also Read: Carro acquires Beyond Cars, bets big on Hong Kong’s strong EV growth

“Building and maintaining a strong dealer network has always been key for all the markets we’re in,” Tan said. “Carro has a track record of partnering with local dealers to support their growth, and we welcome ‘win-win’ partnerships in Australia. We’re confident in our strong Wholesales capabilities, thanks to our presence in Japan.”

For Australian dealers, access to Carro’s inspection protocols, lifecycle management tools, and potential new supply from Japan may help both sourcing and remarketing. For consumers, the promise is greater transparency on vehicle condition and pricing, a recurring sore point in used-car markets globally.

Autoleague’s backing reduces execution risk. Dan Kawai, Autoleague’s managing director and CEO, welcomed the partnership: “We’ve seen Carro’s technology infrastructure, streamlined operations, and unwavering commitment to transparency within the industry, and we’re confident in their goal to become a leading player in Australia.”

Southeast Asian angle and regional implications

Carro’s expansion is relevant to Southeast Asia’s increasingly competitive used-vehicle ecosystem. Over the last decade, regional players have experimented with a range of models: pure marketplaces, trade-only exchanges, and vertically integrated services that include financing, repairs and logistics. Carro’s Australia move demonstrates a reverse flow of scale: a Southeast Asian-grown company exporting its playbook to a developed market.

That matters for several reasons. First, it underscores the maturing capabilities of Southeast Asian tech startups, many of which have moved beyond consumer-facing apps into complex logistics and asset-heavy categories. Second, if Carro successfully integrates Australian dealers and the Japanese supply chain, it could create a blueprint for other regional expansions, including more formalised import/export routes between Japan, Southeast Asia, and Australia.

Finally, the move could stoke competition in Australia from both local incumbents and other regional challengers. Sellers and dealers in Southeast Asia may face new competition for Japanese used-vehicle stock if Carro scales its wholesale operations across borders.

Challenges ahead

Carro’s promise of technology-driven clarity is persuasive on paper, but execution risks are real. Australia’s market is dispersed and regulated at the state level for vehicle inspections, registrations, and consumer protections. Winning consumer trust will require consistent inspection standards and reliable post-sale support, areas in which established local players have credibility.

Moreover, scaling a Japan-Australia import corridor means handling customs, compliance, logistics, and model homologation issues, tasks that can be capital-intensive and operationally complex. The economic case depends on margins after shipping and compliance costs, especially for lower-priced models.

Also Read: Carro invests in digital content, marketing services agency Driven Communications

There is also the question of brand recognition. While Carro is well known in Southeast Asia, Australian consumers and dealers may initially view the company as an outsider. Close cooperation with Autoleague and local dealers could mitigate that, but it will take time to convert partnerships into sustained market share.

What to watch next

In the near term, Carro will likely focus on plugging CarPlace’s dealer network into its tech stack, standardising inspections, and piloting Japan-sourced inventory flows. Observers should monitor three metrics: dealer uptake of Carro’s platform features, wholesale volumes moving from Japan to Australia, and post-purchase consumer satisfaction scores.

If Carro succeeds, the acquisition will mark a pivotal moment for Southeast Asian mobility tech firms: proof that their operational models can scale into developed markets and that regional cross-border automotive supply chains are commercially viable.

For Australia’s used-car market, the arrival of a technology-led, cross-border operator could accelerate transparency and digitisation to the benefit of buyers and dealers alike, but only if the promises match the execution. The partnership with Autoleague gives Carro a bridge; the rest will depend on how quickly it turns that bridge into reliable, everyday commerce.

The post Singapore’s Carro expands regional footprint by acquiring CarPlace: what it means for SEA, Australia markets appeared first on e27.

Acrab Inc., a Singapore-headquartered frontier tech company, has announced it has secured more than US$350 million in cumulative financing since its founding in 2024, as it looks to establish itself as a foundational infrastructure provider for the next generation of agentic AI.

The company, which had previously operated in stealth, said the financing was raised across multiple rounds and includes participation from global venture capital firms and strategic industry investors. Vertex, the global venture platform backed by Singapore state investor Temasek, was among the earliest backers through its Vertex Ventures SEA & India and Vertex Growth funds, and has continued to increase its commitment as Acrab reached key technological milestones.

The announcement also marks the commercial debut of GΞLIX, Acrab’s first-generation compute platform designed to support local LLM inference and agentic AI workloads at the edge. The company said GΞLIX has been validated in demanding real-world deployment environments and is progressing toward first industry adoption and mass production.

Acrab’s full-stack architecture spans AI silicon, local LLM inference, operating systems, multi-modal human-machine interface (HMI), and agent orchestration technologies. The company positions its approach around heterogeneous computing — specifically, the coordinated interaction between central processing units (CPUs) and neural processing units (NPUs) — which it describes as central to the performance demands of agentic AI systems.

Also Read: Top 4 Best ERP for Large Enterprise in Malaysia

“CPUs are becoming increasingly important as AI systems evolve into heterogeneous computing environments, where execution depends not only on NPU performance, but on the seamless coordination between CPUs and NPUs,” said Dr. Ken Phua, CEO of Acrab. “Delivering this new generation of agentic experiences calls for a fundamentally new compute foundation.”

Phua brings extensive experience from the semiconductor sector, including a career at Arm UK, where he led Asia Applications Engineering and co-led global IP strategies, before serving as co-CEO of Arm China.

Vertex’s Kee Lock Chua, CEO of Vertex Holdings, said the firm’s conviction in Acrab rests on a belief that the next wave of AI will run at the edge rather than in the cloud. “Our confidence has only deepened as the team has translated that thesis into a validated platform, and we’ve increased our commitment at every step,” he said.

Acrab’s target markets span personal AI PCs, home hub devices, in-vehicle intelligence, industrial operations, and general robotics — environments the company believes will increasingly require private-by-design, context-aware computing that can carry out tasks in real time without relying on cloud infrastructure.

Also Read: Vietnam isn’t just inviting private capital in. It is structurally dependent on it

The broader thesis underpinning Acrab’s strategy is a distinction between generative AI, which responds to prompts, and agentic AI, which the company characterises as systems capable of inferring intent, co-ordinating tools, and executing tasks on behalf of users — a shift that demands a new class of compute infrastructure.

Acrab said it will use the new capital to accelerate platform development, expand partnerships with global technology firms, and strengthen its presence in key international markets.

The post Acrab raises US$350M to advance Agentic AI compute infrastructure appeared first on e27.

A Jakarta courtroom delivered a verdict on Thursday that is already reverberating far beyond Indonesia’s borders.

Four senior executives from two of the country’s most prominent state-backed venture capital firms, MDI Ventures and BRI Ventures, have been sentenced to prison over investments made in the now-defunct agritech startup TaniHub.

Also Read: Indonesia detains 3 more suspects in TaniHub investment fraud case

The ruling has sent a chill through the regional startup ecosystem, prompting an immediate and forceful pushback from the defence and igniting a broader debate about the criminalisation of business failure.

The sentences

The Jakarta Corruption Court sentenced former MDI Ventures CEO Donald Wihardja to five years in prison and fined him US$28,000, with a substitute sentence of six months in detention should the fine go unpaid. His former vice president of investment, Aldi Adrian Hartanto, received a two-year prison term and a US$14,000 fine.

On the BRI Ventures side, former CEO Nicko Widjaja was handed a three-year sentence and fined US$19,600, with 110 days of additional imprisonment as a final substitute if asset seizure proves insufficient. Former vice president of investment William Gozali received two years and a US$14,000 fine.

Prosecutors alleged that all four approved investments in TaniHub without performing adequate due diligence, leaning too heavily on data supplied by the startup itself. Authorities put MDI Ventures’s losses to the state at approximately US$20 million and BRI’s at around US$5 million.

How a farming unicorn hopeful unravelled

Founded in 2016, TaniHub was once among Indonesia’s most celebrated agritech bets. The company raised US$92.5 million in total disclosed funding over its lifetime, including a high-profile US$65.5 million round in 2021 in which both MDI Ventures and BRI Ventures participated alongside other investors. It positioned itself as a transformative platform connecting smallholder farmers to buyers and, eventually, consumers, scritping compelling story in a country where agriculture employs nearly a third of the workforce.

The story, however, did not end well. TaniHub ran into severe financial difficulties, carried out sweeping layoffs, and eventually wound down much of its operations. By September 2023, the value of TaniHub shares held by BRI Ventures had fallen to approximately US$380, a near-total write-off on what had been a US$5 million position.

Also Read: “Special Projects” and shady metrics: TaniHub whistleblower speaks as top execs detained

It was that collapse in value that prosecutors and, ultimately, the court treated as evidence of state financial loss.

What the court found

In the case against Widjaja, the Panel of Judges found that BRI Ventures’s investment process, split between a US$2 million Series A+ round and a US$3 million convertible note round, violated the prudential principle on multiple counts.

The court found that the deep feasibility study relied too heavily on TaniHub-supplied data, without sufficient independent verification, and that the analysis was based in part on unaudited financial statements. The outstanding receivable concerns were not adequately interrogated.

The panel also noted that the investment committee at the time consisted only of the president director, and that the oversight function of the board of commissioners had not operated optimally.

On the question of personal enrichment, a standard element in Indonesian corruption cases, the court acknowledged that Widjaja received no personal benefit whatsoever. Nevertheless, it held the element fulfilled because the US$5 million flowed to TaniHub as a third-party corporation. The court further held that cooperation in the investment decision-making process was sufficient to establish joint participation without requiring proof of an explicit agreement.

The defence fires back

Widjaja’s legal team, led by Ditho Sitompoel, did not mince words in their response.

“The line between a failed investment decision and a criminal act must be carefully preserved, so that criminal law is not used to judge a business decision based solely on its outcome — that is, hindsight bias,” Sitompoel said in a statement.

The defence raised six pointed objections to the court’s reasoning. Chief among them was the argument that the panel’s prudential standard was simply wrong for the asset class. Venture capital, by its very nature and by the explicit mandate of POJK 35/2015 (Indonesia’s regulatory framework for the industry), requires firms to take measured risk on high-growth companies that are often pre-revenue, unaudited, and operating at a loss. Holding a VC firm to the verification standards of a bank extending credit to a mature business, the defence argued, would render nearly every venture investment in Indonesia legally suspect.

The team also highlighted that BRI Ventures had, upon identifying deteriorating conditions at TaniHub, halted Series B funding and pursued divestment, hardly the conduct of a reckless actor. The panel acknowledged these steps but held that they did not negate the original unlawful act.

Perhaps most significantly, Sitompoel pointed to the systemic implications. “If investment failures that went through proper approval, review, and governance can still be criminalised, this creates legal uncertainty with the potential to produce a chilling effect on investors, directors, commissioners, and professionals who are required every day to take the legitimate business risks needed to drive economic growth.”

A verdict with consequences beyond one courtroom

The concern is not abstract. Indonesia has spent years trying to build itself into a credible destination for venture capital, and state-backed funds like MDI Ventures and BRI Ventures have been central to that ambition, deploying government-linked capital into the startup ecosystem at a time when private capital was still finding its footing.

Also Read: Nicko Widjaja’s legal defence team on the prospect of winning: “We are confident enough”

The TaniHub verdict raises an uncomfortable question: if a VC fund manager at a state-linked institution can be imprisoned when a portfolio company fails, even absent any personal gain or proven misconduct, who in their right mind will take the job?

The four convicted executives are widely expected to appeal. How Indonesia’s higher courts handle those appeals may well determine the country’s investment climate for years to come.

The post Four VC executives. Zero personal gain. Three years in prison appeared first on e27.

Most voice AI demos are built for perfect conditions.

The room is quiet. The Wi-Fi is stable. The user speaks clearly, waits for the response, and follows the script. In that setting, many modern voice agents can sound impressive.

The real test begins outside the demo room.

A user in Jakarta speaks over mobile data during a commute. A child in Manila interrupts an AI tutor halfway through an explanation. A customer in Ho Chi Minh City switches between English and Vietnamese. A smart device in Thailand moves from stable Wi-Fi to weak 4G.

Suddenly, the AI no longer feels intelligent. It feels delayed, stiff, and difficult to trust.

For teams building voice AI in Southeast Asia, this is the uncomfortable lesson: the issue is not always intelligence. It is whether the full voice experience can survive real-world conditions.

Physical AI needs more than a voice

Physical AI is not just AI inside a device.

It is AI that lives in the user’s environment and becomes part of daily life. It may appear as an educational toy, a wearable translator, a companion robot, an in-car assistant, a smart camera, or a home device that can talk, listen, and respond.

That changes the standard for user experience.

When people type into a chatbot, waiting feels normal. The interaction is already asynchronous. A short delay may be acceptable because the user is looking at a screen and expecting software to process a request.

When people speak to a device in the room, the expectation is different. They expect rhythm. They expect responsiveness. They expect the device to understand when a conversation has started, shifted, or ended.

This is why physical AI has to be evaluated differently from text-based AI. It is not enough for the answer to be correct. The interaction has to feel natural in the moment.

The bot feeling usually starts with latency

When users say a voice AI “feels like a bot,” they are often reacting to timing before they are reacting to content.

A pause after every sentence makes the experience feel mechanical. A delayed answer makes the user wonder whether the device heard them. A voice agent that continues speaking after the user has moved on feels disconnected from the conversation.

This sensitivity is not new. In real-time voice traffic, Cisco cites the ITU G.114 recommendation of less than 150 milliseconds of one-way end-to-end delay for high-quality voice. Voice AI adds more layers on top of that, including speech recognition, model response, speech output, and routing between services.

Also Read: To Voice AI or not – The changing face of customer experience

Human conversation depends on small timing cues. We pause, overlap, correct ourselves, interrupt politely or impatiently, and change direction mid-thought. These are not edge cases. They are the normal shape of how people speak.

Voice AI breaks when it treats conversation as a clean sequence: user speaks, machine processes, machine replies.

A more natural system needs a fluid loop. It has to listen, process, generate, and speak with minimal delay. It also has to adapt when the user changes direction. That requires real-time audio transport, streaming speech recognition, response generation, speech output, and interruption handling to work together.

For builders, latency is not just an engineering metric. It is part of the product’s personality.

Southeast Asia turns weakness into a market problem

Southeast Asia is an important region for voice-first AI because the use cases are practical.

The region’s digital economy is already large enough for these experiences to matter at scale. Google, Temasek, and Bain estimate that Southeast Asia’s digital economy is set to surpass US$300 billion in gross merchandise value by 2025. The same report frames the region’s next phase around AI adoption, after years of growth in digital services.

The opportunity is clear: mobile-first users, multilingual households, growing demand for education technology, rising adoption of connected devices, and many situations where voice can make technology easier to use. A screenless device, a voice tutor, or a smart assistant can be valuable when typing is inconvenient, when users move between languages, or when a product is used by children, older adults, or workers on the move.

But the same conditions also make the region difficult.

Indonesia shows the scale and complexity. DataReportal counted 212 million internet users in Indonesia at the start of 2025, along with 356 million cellular mobile connections, equivalent to 125 per cent of the population. Yet Ookla data cited by DataReportal showed a median mobile internet download speed of 29.06 Mbps. For voice AI, that gap between massive connectivity and uneven consistency is where user experience problems appear.

The fragmentation is regional, not just local. Data found that mobile-only smartphone users make up less than 10 per cent of users in markets such as Vietnam, Brunei, and the Philippines, but more than a quarter in East Timor, Laos, Thailand, and Malaysia. In Laos, Thailand, Malaysia, Cambodia, and Indonesia, more than 40 per cent of smartphone users have no or very limited Wi-Fi use.

This turns voice quality from a technical detail into a market expansion issue.

If a product only works in controlled conditions, it cannot scale confidently across the region. If it struggles with accents, unstable networks, or mixed-language behaviour, users will not wait for it to improve. They will stop using it.

Also Read: How voice AI is revolutionising the fintech scene

The stack needs to be built for interruption

A strong physical AI product needs more than a model and a synthetic voice.

• The device needs reliable audio capture: If the microphone hears too much background noise or misses the wake word, the experience fails before the model is involved.

• The voice pipeline needs low-latency transport: Audio has to move quickly between the device, the cloud, and the AI services without adding noticeable delay.

• The system needs interruption handling: Humans do not wait politely for a machine to finish talking. They correct it, interrupt it, and change direction. A natural voice agent must be able to stop, listen, and respond without making the user repeat everything.

• The AI needs memory and context: This is where physical AI starts to feel different from a basic voice bot. A companion device that remembers preferences, routines, or past interactions can create a sense of continuity.

• The product needs a persona: Not every device should sound friendly. Some should sound calm, professional, playful, or neutral. A toy, a healthcare assistant, and an in-car agent should not share the same personality.

The “soul” of physical AI comes from this full stack. The model matters, but it is only one part of the experience.

Builders should measure the conversation, not just the model

Many teams still evaluate voice AI by asking which model is the smartest.

That is too narrow.

A better question is: what does the full conversation feel like in the user’s actual environment?

For teams building in Southeast Asia, that means testing on mobile data, not just office Wi-Fi. It means testing noisy rooms, not just quiet meeting spaces. It means testing repeated use, mixed-language behaviour, unstable networks, and users who do not follow a script.

Product and procurement teams should ask practical questions before committing to a voice AI stack:

• What does the experience feel like on a real 4G connection?
• How quickly can the agent respond during natural turn-taking?
• Can it handle a user changing direction mid-conversation?
• What happens when the network becomes unstable?
• Can the stack support different models, speech providers, and deployment needs?
• Can the product preserve useful context across sessions?
• Can the voice persona be adapted for different markets and product categories?

Also Read: Never fear, AI is here: Helping midlife artists build their social media voice

The next AI device will be judged by how it feels

The next wave of AI will not stay inside chat windows.

It will live in toys, robots, wearables, cars, cameras, appliances, and industrial devices. In those environments, users will judge AI less like software and more like something sharing their space.

They will notice whether it listens at the right moment. They will notice whether it talks too much. They will notice whether it remembers. They will notice whether it is helpful or frustrating when the environment becomes noisy and unpredictable.

For Southeast Asia, this is both a challenge and an advantage. The region is difficult to build for because of its network complexity, language diversity, and mobile-first behaviour. But any physical AI product that performs well here will be stronger in many other markets.

The question for builders is no longer whether voice AI can speak. The question is whether it can stay useful when the real world gets messy.

If your physical AI device had to hold a natural conversation today on a crowded Southeast Asian mobile network, would it still feel alive?

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post Give physical AI a soul: Why your voice AI still feels like a bot appeared first on e27.

Banks are still talking about AI governance as though it belongs mainly to policy teams, future regulation, and committee oversight. That view is already out of date. AI governance is now an operating model issue.

The real danger is not only that a model makes a poor recommendation or that an employee shares sensitive information with the wrong tool. Those risks matter, but they are also the easiest to recognise. The deeper problem is that AI adoption is spreading faster than many banks can govern judgment, accountability, customer treatment, and operational response.

That matters because banking is not judged only by the quality of its policies. It is judged by whether decisions can be explained, defended, and corrected when customers are affected. If AI use spreads faster than the control model around it, the consequences will not stay inside technology teams. They will surface in complaint handling, operations, fraud casework, collections, service recovery, and exceptions, which are all areas where judgment carries regulatory and reputational weight.

The wrong starting point

A great deal of discussion still begins with model risk, privacy, or compliance. These issues matter, but they are not the most useful place to begin. A better question is this: what happens to banking operations when employee judgement is partly assisted by systems that are fast, persuasive, difficult to challenge, and unevenly understood across the organisation?

That question changes the conversation. It moves the bank away from abstract talk about AI innovation and towards the daily reality of operational decisioning. It forces leaders to look at how a complaints handler uses AI to summarise a case, how an operations analyst drafts an exception rationale, how a collections agent shapes a customer conversation, or how a fraud investigator relies on AI driven prioritisation.

In each case, the issue is not simply whether the tool exists. The issue is whether the bank can still stand behind the quality, fairness, and traceability of the resulting action. That is the point where AI governance stops being a technology topic and becomes a banking discipline.

Banks are governing two different problems

One reason many governance frameworks remain vague is that banks often blend together two different forms of AI use.

The first is employee AI use. This includes drafting, summarising, searching, translating, analysing, and preparing work. On the surface, that can look like a productivity issue. In practice, it is also a judgment and control issue. Once employees rely on AI to shape internal outputs, customer responses, escalation summaries, or operational recommendations, the work is no longer purely human in the old sense. It becomes a blended output. That means the bank needs clear rules on what can be delegated, what must be checked, and what remains fully owned by the employee.

Also Read: The shadow ledger: Why AI governance is the new architecture of brand trust and enterprise revenue

The second is AI-assisted business decisioning. This sits much closer to customer outcomes, prioritisation, approvals, exceptions, operational triage, and vulnerability handling. Here, the stakes are higher because AI is not only improving speed or language. It is influencing actions that can shape fairness, access, treatment, and trust.

These two categories require different controls. Too many governance models treat them as one broad AI issue and end up being both too loose and too blunt.

The first failures will be quiet

Banks often expect AI risk to arrive as a major breach, a headline incident, or an obvious model failure. That expectation is misleading. The first operational failures are more likely to be quiet and cumulative.

Sensitive data starts appearing in prompts because employees are under pressure, and approved workflows are slower. AI-generated summaries begin to flatten nuance in complaint files, which weakens later review. Decision support outputs become trusted because they sound coherent, even when the reasoning beneath them is thin. Frontline teams rely on wording that is technically acceptable but badly judged for vulnerable customer situations. Managers approve outputs without really knowing how much human challenge has been applied. Control teams discover that the audit trail feels sufficient for internal reassurance but is not strong enough for real scrutiny.

None of these issues looks catastrophic in isolation. Together, they create a more serious pattern. The bank begins to lose clarity about where human judgment ends, and AI-shaped judgment begins.

Why traditional model governance is not enough

Banks already know how to govern models in a traditional sense. They have approval forums, validation standards, documentation requirements, monitoring routines, and control owners. Those disciplines still matter, but they are not sufficient for the current AI wave.

What is needed now is governance of assisted judgment.

That means understanding not only whether a model is technically sound, but also how AI-shaped outputs enter workflows, how much challenge is realistically applied by employees, where reliance becomes routine, how explanations are captured, how errors are detected, and how customer harm is identified when the decision was not fully automated yet was no longer fully human either.

Also Read: Why emerging markets need AI governance infrastructure before AI scale

This is where current governance language can become misleading. Many organisations take comfort from calling a tool assistive rather than determinative. In practice, assistive systems can heavily shape outcomes because they influence what employees see first, how they frame the case, what options appear reasonable, and how quickly they move.

Complaint handling is the real test

Many banks will discover the quality of their AI governance not through a model review, but through complaints.

Complaint handling is where customer harm, operational judgement, fairness, explanation, and record quality all come together. If AI is being used poorly elsewhere in the operation, complaint handling is often where the weaknesses become visible. Cases become harder to assess. Summaries lose context. Root cause becomes harder to establish. Responses sound polished but unconvincing. Reviewers struggle to tell whether the original decision reflected genuine consideration of the customer’s circumstances or simply repeated an AI-shaped conclusion.

This is why complaints should be treated as a primary governance lens. If a bank cannot defend AI-influenced operational decisions inside its complaints process, it is not governing them properly. If complaint handlers do not know where AI was used earlier in the customer journey, they cannot properly assess fairness. If complaint responses themselves are AI-assisted, the bank needs very clear boundaries on what can and cannot be delegated.

A better model

A stronger governance model starts with use case classification. Banks need to separate low-consequence productivity support from high-consequence decision support, while also recognising the large middle ground where AI is not making the final decision but is materially shaping human judgment.

From there, governance needs to move into workflow design. Where is AI allowed? What data can be used? What outputs are acceptable? What level of verification is required? What must be recorded? What escalation is needed if the AI output conflicts with case evidence or customer context?

After that comes monitoring. Not only whether a tool is secure and available, but how it is actually being used, where teams are becoming over-reliant, where outcomes are drifting, and which workflows are creating defensibility problems.

Then comes response capability. If something goes wrong, the bank needs to know how to pause usage, investigate the impact, identify affected customers, explain what happened, and remediate quickly. Governance without response capability is only paperwork.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post AI governance in banking operations and decisioning appeared first on e27.