Enterprise AI has reached the point where hand-wringing is no longer enough. The urgent question is practical: what should organisations actually build if they want autonomous agents without autonomous chaos?

The “AI Agent Governance Gap” report by US-based API management company Gravitee offers a clear answer. It argues that the future lies in a unified AI identity and governance layer built around visibility, scoped access, runtime policy, and comprehensive observability.

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

That may sound like vendor language, but the underlying logic is hard to dispute. If AI agents are going to interact with large language models, APIs, databases, internal tools and emerging agent protocols, such as MCP, then those interactions need a control plane. Otherwise, enterprises will continue managing twenty-first-century automation with twentieth-century access assumptions and hoping luck remains employed.

The report says the three immediate priorities are inventory and visibility, governance primitives, and unified authorisation. Some 73 per cent of CISOs said API and workload identity discovery would be their top area of investment if budget were not a constraint. Another 68 per cent prioritised continuous monitoring and posture analytics. These are not cosmetic upgrades. They are the plumbing of governable AI.

Why the gateway is back in fashion

For years, API gateways were often discussed as middleware: useful, necessary, not especially glamorous. AI changes that. Once organisations connect internal agents to external models and internal systems, the gateway becomes the natural chokepoint where policy can actually be enforced.

Also Read: It’s not the chatbot but the access: Why AI agents are the real threat

Gravitee’s white paper makes this case directly. Instead of allowing agents to integrate independently with providers such as OpenAI, Bedrock, or Gemini, enterprises can proxy access through a central control point. That creates immediate benefits: authentication and authorisation can be standardised, token consumption can be monitored and limited, content can be inspected for sensitive data or prompt injection, and usage can be observed across providers in one place.

For Southeast Asia, this matters for three reasons.

First, cost discipline. Many regional startups and enterprises are enthusiastic about AI but deeply sensitive to runaway inference bills. Token-based rate limiting and usage observability are not just security features. They are financial controls.

Second, vendor flexibility. Companies across the region are increasingly wary of lock-in, especially as they balance global foundation models against local hosting, private deployments and open-source alternatives. A gateway layer makes it easier to switch, route or combine providers without rewriting every downstream integration.

Third, compliance. Centralising traffic makes it easier to apply rules about data handling, retention and model access. That is particularly useful for organisations operating across ASEAN markets with different expectations around privacy and sensitive data.

MCP and agent-to-agent traffic will need their own guardrails

One of the more forward-looking parts of the report concerns MCP, the emerging protocol layer that allows AI agents to discover and invoke tools in a more standardised way. Gravitee argues that enterprises should not treat MCP as a collection of point-to-point connections. They should govern it centrally.

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

That is a shrewd observation. The moment agents can discover capabilities dynamically, the old idea of static approved integrations starts to weaken. Security teams need to know which tools an agent can see, which prompts or methods it can invoke, which resources it can access and whether those permissions still make sense.

In practical terms, the report envisions protocol-aware proxying, a central registry of deployed AI agents, compliance with MCP authorisation flows and granular access policies controlling tool discovery and invocation. In less formal language: do not let agents wander the digital office unsupervised.

This is especially relevant in Southeast Asia because many businesses are trying to move fast with relatively lean teams. A standard way to expose internal capabilities to agents is attractive. But standardisation without governance simply scales mistakes more efficiently.

The winning model is governance without friction

Perhaps the report’s most commercially important insight is that security controls only work if they are easier to use than the unsafe alternative. This is the antidote to shadow AI. If developers and business teams can access approved models, tools and APIs quickly through a governed layer, they are less likely to bypass it.

That principle should resonate across Southeast Asia’s tech scene. The region’s best companies rarely succeed by saying “no” more loudly. They succeed by building faster, smoother systems that align business speed with operational discipline. AI governance will be no different.

A useful mental model is this: the goal is not to slow down agent adoption. The goal is to make compliant adoption the default path. That means provisioning agents with clear ownership, issuing short-lived tokens bound to specific resources, enforcing contextual policy at runtime and maintaining audit trails that can withstand customer scrutiny, regulator questions and incident response.

Also Read: Southeast Asia’s AI blind spot is getting bigger

For founders and product leaders, that may feel like heavy infrastructure. In practice, it is enabling infrastructure. Companies that solve this layer early will be able to deploy AI into revenue-generating and regulated workflows with far greater confidence.

The post Without governance, AI agents risk becoming enterprise chaos engines appeared first on e27.

Enterprise AI is entering a more dangerous phase, one where confidence is high, but control is dangerously low. A new report by Gravitee highlights a growing disconnect: while 82% of executives believe their AI security policies are robust, less than half of AI agents are actually monitored or secured.

This gap is especially concerning in Southeast Asia, where regulatory frameworks are fragmented and evolving. Companies operating across multiple ASEAN markets must navigate overlapping privacy, cybersecurity, and sector-specific rules; yet many still confuse compliance with actual control.

The real risk is not just flawed outputs, but unauthorised actions. As AI agents gain the ability to interact with systems, access data, and execute workflows, governance failures shift from theoretical to operational. A misstep is no longer a bad answer; it could mean data breaches, compliance violations, or financial exposure.

Compounding the issue, budgets for AI security are stagnating, signalling that many organisations are relying on policy rather than infrastructure. But regulation is unlikely to arrive neatly. Enforcement will come through audits, incidents, and customer demands.

For startups and enterprises alike, the message is clear: governance is no longer a checkbox. It is the foundation for scaling AI safely and competitively in a region where complexity is the norm.

Regional

SEA ecommerce hits US$157.6B GMV in 2025, up 22.8%: Shopee, Lazada, and TikTok Shop collectively held 98.8% of platform gross merchandise value, with Thailand and Malaysia recording the fastest growth at 51.8% and 47.6% respectively, per Momentum Works.

Temasek CEO takes helm at Vertex Venture Holdings: Dilhan Pillay succeeds Teo Ming Kian as chairman effective April 15, as assets under management grew from US$200M to US$7B under the outgoing chairman’s tenure since 2012.

OKX Ventures and HashKey back Vietnam’s regulated crypto exchange: The newly formed CAEX has raised its capital base to US$380M, meeting the threshold for Hanoi’s pilot programme. Vietnamese users moved around US$200B in digital assets through mid-2025, mostly via offshore venues.

Philippines orders Meta to act on disinformation within 7 days: Authorities warned that fake documents about President Marcos Jr. and misleading military and financial content violate the country’s penal code and cybercrime laws, threatening public order and national security.

OrtCloud bags US$1.7M pre-seed to fix cloud chaos for AI: Backed by Golden Gate Ventures and Antler, the Singapore-based startup offers fixed-resource virtual machines with deterministic performance, targeting a US$20B+ AI workload market in Southeast Asia.

GSM launches EV driver platform in Indonesia, Philippines: VinFast EV owners and renters can sign up as driver partners, keeping up to 90% of revenue. Free charging is offered until March 2029, with registration and insurance support added in the Philippines.

South Korea rolls out AI smart city pilots across SEA: Six projects under the 2026 K-City Network programme span Brunei, the Philippines, Vietnam, Thailand, and Malaysia, covering traffic, transport, disaster response, and building safety applications.

Singapore trials robotaxis in Punggol with Chinese AV firms: The government targets 100 to 150 autonomous vehicles by year-end, with Grab and ComfortDelGro cleared to partner WeRide and Pony.ai as Chinese AV firms push overseas expansion.

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Interviews & Features

The expensive middle: what career transitions really cost: A former Singapore Sports School GM spent three and a half years delivering food, crashing a motorcycle, and exhausting his savings before passing his real estate licence in 2022, exposing the hidden costs of career change that go beyond motivation.

Why financial and legal literacy is a founder’s survival skill: SEA founders routinely walk into five avoidable pitfalls, from messy cap tables and contractor misclassification to signing term sheets without understanding liquidation preferences, that only surface at the worst possible moments.

When north star metrics start narrowing a company’s vision: Scaling across Asia’s uneven markets, leaders often oversimplify dashboards until local signals stop surfacing and clean numbers mask an outdated picture of reality, the real risk isn’t bad data, it’s frozen judgment.

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International

Zepto trims cash burn ahead of planned US$1.2B IPO: The Bengaluru-based quick commerce startup has confidentially filed for an IPO and is pitching profitability by FY29 to institutional investors, with quarterly EBITDA losses narrowed to roughly US$6M.

Meta set to surpass Google in global digital ad revenue in 2026: Emarketer projects Meta’s net ad revenue at US$243.46B versus Google’s US$239.54B, driven by 24.1% growth fuelled by WhatsApp, Threads, and Instagram Reels.

Anthropic hires Trump-linked lobbying firm after Pentagon clash: Ballard Partners was engaged days after the Pentagon designated Anthropic a supply chain risk. The AI firm spent US$3.1M on federal lobbying in 2025, up more than 330% year-on-year, as talks over government access to its tools broke down.

Crypto wallet firm Exodus sues W3C to close US$175M deal: Exodus alleges W3C and its CEO tried to avoid completing the acquisition of crypto card firms Baanx and Monovate, despite US$80M in loans already disbursed, including US$10M to the CEO personally.

Trump-linked crypto project faces investor revolt over token controls: Billionaire backer Justin Sun accused World Liberty Financial of secretly building controls to freeze token holders’ funds, as the firm also faces scrutiny over a US$75M stablecoin loan collateralised by its own tokens.

Japan’s SoftBank, NEC, and Honda form physical AI venture: The joint venture will develop AI for robots and vehicles, backed by Japan’s government plan to invest 1T yen (US$6.27B) in domestic AI projects over five years as Tokyo bids to close the gap with the US and China.

Chinese EV makers race to build in-house chips as rivalry deepens: Nio and Horizon Robotics are both launching proprietary intelligent driving chips this year as competition shifts from output volume to high-value technology, with semiconductor supply chain concerns escalating across the industry.

Bitcoin’s US$74K surge: institutional conviction or macro mirage?: Bitcoin climbed 5.38% to US$74,532, driven by spot ETF inflows of US$1.1B for the week, including US$612M into BlackRock’s iShares trust in a single day; yet a 94.5% correlation with the S&P 500 raises questions about its role as a hedge.

Crypto falls in lockstep with equities as geopolitical tensions rise: The crypto market fell 1.17% to US$2.42T after the collapse of US-Iran talks triggered a broad risk-off selloff, with digital assets showing a 94% correlation with the S&P 500 and 88% with gold.

Indian AI startups tackle science, engineering, and GPU reliability: Firms including ZeneteiQ, Oru’el, and HumanTronik are moving beyond app-layer products to build core scientific and physics-based models, though talent shortages and limited test environments remain key gaps.

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Cybersecurity

FBI and Indonesian police dismantle W3LL phishing marketplace: The operation targeted over 17,000 victims globally, selling kits for US$500 that enabled over US$20M in attempted fraud by generating fake login pages to steal passwords and multi-factor authentication codes.

Booking.com confirms unauthorised access to customer data: Personal details including names, phone numbers, addresses, and booking information may have been accessed by third parties, with at least one customer reporting a targeted WhatsApp phishing message containing their booking details.

Anthropic’s Mythos AI triggers cybersecurity alarm in India: The model’s ability to find software flaws in hours has prompted HDFC Bank and others to reassess exposure, with experts warning that legacy-dependent banks and telecoms are most vulnerable under India’s slower-paced cyber regulations.

SEA’s AI blind spot: executives overconfident, agents unmonitored: Gravitee research found that while 82% of executives report high confidence in AI security policies, only 47.1% of AI agents are actually monitored or secured — a dangerous gap in a region with fragmented regulatory frameworks.

How modern money laundering hides inside tech startups: The collapse of Builder.ai, a US$1.5B AI unicorn that used 700 engineers to fake automation, illustrates how inflated startup valuations and round-tripping schemes can serve as facades for financial crime, especially in loosely regulated innovation hubs.

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Semiconductor

Chinese EV chipmakers accelerate as supply chain fears mount: Nio’s in-house driving chips and Horizon Robotics’ upcoming cockpit chip signal that China’s EV sector is investing upstream into semiconductors, while global carmakers like Volkswagen deepen reliance on Chinese suppliers including CATL and Xpeng.

Nvidia denies report of talks to acquire Dell or HP: Shares of Dell jumped 6.3% and HP rose 2.3% after a SemiAccurate report claimed Nvidia had been in talks to reshape the PC market through a major acquisition, which Nvidia flatly denied.

Nvidia acquisition rumour briefly lifts Dell and HP shares: A now-denied report of Nvidia pursuing a major PC maker acquisition briefly moved markets, underscoring investor sensitivity to consolidation signals in the semiconductor and hardware supply chain. Micron dipped 2.12% in the same session, signalling persistent unease.

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AI

Singapore’s AI ambition outpaces its governance foundations: PwC’s Global AI Survey 2026 found 67% of Singapore businesses have a higher appetite for AI risk than the global average of 41%, yet only 47% have documented responsible AI frameworks and 37% have redesigned workflows to genuinely embed AI.

Without AI governance, agents become enterprise chaos engines: Gravitee’s report argues that enterprises need a unified AI identity and governance layer built around visibility, scoped access, and runtime policy, with 73% of CISOs citing API identity discovery as their top investment priority.

Why AI agents need centralised control, not just policies: Agentic AI introduces a new risk category, unauthorised operational execution, that differs fundamentally from chatbot errors. For SEA businesses operating shared-service models across multiple jurisdictions, a single ungoverned workflow can cascade across systems and borders.

The right to AI explainability runs into a technical wall: Regulators increasingly expect decision traceability from AI systems, but foundation models generate outputs through probabilistic processes that their own builders cannot fully interpret, making system-level explainability, not model-level, the realistic compliance target.

South Korea deploys AI smart city tech across five SEA nations: The 2026 K-City Network selected six pilot projects spanning water management, traffic control, transport, and building safety, with Korean firms positioned to expand regionally through the initiative.

India’s AI model-builders go deep into science and engineering: Companies backed by the IndiaAI mission are developing physics-based, scientific, and GPU reliability models rather than consumer-facing apps, though founders cite engineer shortages and limited compute environments as ongoing constraints.

Japan bets US$6.27B on physical AI for robots and vehicles: A new joint venture by SoftBank, NEC, and Honda will develop physical AI for robotics and automotive applications, underpinned by a government funding commitment of 1T yen over five years aimed at narrowing Japan’s gap with the US and China.

AEO: the AI search strategy traditional industries are missing: Answer Engine Optimisation restructures content so AI systems cite it directly. Manufacturing, logistics, and legal sectors face a 20% year-on-year traffic drop from AI-powered search, yet fewer than 10% of sources cited by ChatGPT and Gemini rank in Google’s top 10.

If AI is changing everything, why does nothing look different yet?: AI’s labour market impact follows an S-curve — beginning with reduced junior hiring rather than mass layoffs, before reaching a Threshold Substitution point that forces competitive restructuring. Entry-level tech job postings have already plunged 50% from pre-pandemic levels.​

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Thought Leadership

Risk management is Southeast Asia’s secret 2026 growth engine: Beyond resilience, the highest-performing enterprises are becoming antifragile, using integrated Enterprise Risk Management to turn systemic volatility into a competitive advantage, unlock cheaper capital, and build a compliance moat against rivals still working manually.

How tiny daily habits secretly compound into company-defining wins: Breakthroughs rarely emerge from single events. Leaders who build organisational trust capital and cognitive momentum through small, consistent daily choices, gratitude notes, learning rituals, generosity defaults, compound advantages their rivals cannot easily replicate.

Governance without friction: the only AI control model that works: Security controls only work if they are easier to use than the unsafe alternative. Enterprises that make compliant AI adoption the default path,  through short-lived tokens, runtime policy enforcement, and audit trails, will outpace those still relying on governance theatre.

SEA founders must stop treating legal and finance as afterthoughts: Across post-mortems in the region, the most common thread is a financial or legal decision made early and casually that compounded into something irreversible, from SAFE misunderstandings to revenue recognition errors that sent investors walking.

Career transitions cost more than anyone admits. Here’s why: The sanitised narrative of clean career pivots ignores identity loss, decision fatigue, and relationship strain. Working parents face compounding pressures that solo founders never encounter, making the “fail fast” ethos genuinely dangerous in some contexts.

When metrics become referees, Asian companies stop seeing clearly: In markets as uneven as Asia’s, dashboards that once enabled growth can quietly suppress inconvenient local signals until the assumptions baked into the numbers no longer match the market they were built to measure.

The post Ecosystem Roundup: Confidence theatre meets AI reality appeared first on e27.

Singapore-based growth financing platform Choco Up has launched a US$30 million private credit facility in partnership with CHUAN, a tech-driven credit specialist focused on the digital economy, to put faster, more reliable working capital in the hands of SMEs across Asia Pacific.

The first drawdown has already been completed, a signal that, at least for now, the market appetite is real.

Also Read: Understanding private credit: Filling the gaps left by banks

The funding gap nobody is solving fast enough

For all the noise about Asia’s startup boom and venture capital frenzy, the region’s backbone — its tens of millions of SMEs — remains chronically underfunded. Banks demand collateral, long operating histories, and months of paperwork. The result is a structural mismatch between when SMEs need money and when they can actually get it.

This is not simply a capital availability problem. It is a timing problem. A manufacturer that has shipped goods but is waiting 90 days for payment cannot afford to wait six months for a bank loan to be approved. A digital commerce seller facing a seasonal demand spike needs funding decisions measured in hours, not quarters.

At the same time, investor appetite has historically skewed towards startups rather than SMEs, and with good reason, from a returns perspective. Startups offer the possibility of exponential growth, equity upside, and portfolio-defining outcomes. SMEs, by contrast, tend to grow linearly, generate steady but unspectacular returns, and offer little of the asymmetric payoff that venture investors seek.

The result is a two-tier capital market where high-risk, high-reward bets attract institutional attention while profitable, established small businesses are left to scrape together funding from overdrafts, trade credit, government grants, and family networks.

Private credit is increasingly positioned as the answer to this structural gap, but understanding what it actually offers and where it falls short matters.

What private credit can and cannot do for SMEs

Private credit refers to lending provided outside of traditional banking and public debt markets, typically from institutional investors such as asset managers, family offices, credit funds, and insurance companies. For SMEs, it can offer a meaningful alternative when banks won’t lend quickly enough, or at all.

The advantages are real. Private credit facilities can move significantly faster than conventional bank loans, with approval timelines collapsing from months to days or even hours when real-time data powers underwriting. Facilities are often structured with greater flexibility than rigid bank products, with repayment tied to business performance rather than fixed schedules.

Also Read: Choco Up, Wonder Capital join forces to launch US$50M private credit funds for APAC SMEs

Crucially, unlike equity financing, private credit does not dilute founders’ ownership stakes, a significant consideration for SME owners who have spent years building their businesses and have no interest in giving away a slice of them.

Choco Up’s partnership with CHUAN leans into these strengths. By combining CHUAN’s access to institutional capital with Choco Up’s AI-driven credit assessment, which draws on real-time business performance data, the facility promises funding approvals in as little as a few hours. Capital providers, meanwhile, gain near real-time visibility into the underlying asset performance, a level of transparency that has historically been absent from SME lending.

“SMEs today don’t just need access to capital. They need financing that keeps pace with how their businesses operate,” said Percy Hung, CEO and founder of Choco Up.

But private credit is not without its complications. The cost of capital is typically higher than a bank loan, reflecting the risk premium demanded by non-bank lenders operating in a less regulated space. SMEs that rely too heavily on private credit facilities without a clear path to profitability can find themselves in a cycle of rolling debt at increasingly punishing rates. Transparency on fees and terms can also vary significantly between providers, leaving less sophisticated borrowers exposed.

The governance and oversight frameworks around private credit markets in Asia are also still developing. Unlike bank lending, which is heavily regulated across the region’s major jurisdictions, private credit operates with considerably more latitude, which cuts both ways. For nimble operators, it is a feature. For borrowers who do not fully understand the terms they are signing, it can become a liability.

Private credit versus venture debt: not the same animal

It is worth drawing a clear distinction between private credit and venture debt, two instruments that are sometimes conflated but serve very different purposes.

Venture debt is designed specifically for startups, typically those that have already raised equity funding from venture capital investors. It is structured as a complement to equity rounds, providing additional runway without further dilution. Lenders price venture debt on the assumption that the borrower has VC backing as a credibility signal, and deals often include warrant coverage, the right to buy equity at a fixed price, as additional compensation for the lender’s risk.

Private credit, as deployed through the Choco Up-CHUAN facility, is aimed squarely at operating businesses with real revenue, not high-burn startups chasing growth at any cost. The underwriting is based on demonstrated business performance: cash flows, transaction data, and operational metrics, not the identity of a startup’s investors or the promise of a future funding round. The repayment structure reflects this too, with facilities designed to align with how a business actually generates and collects cash.

For Lin Tun, founding partner and chief investment officer of CHUAN, the institutional opportunity here extends beyond any single market. “This partnership is central to CHUAN’s strategy of curating a network of proven tech partners, providing global investors with access to diversified credit assets with attractive yields that have largely been untapped by the capital markets,” he said.

A platform play with regional ambitions

Choco Up brings more than technology to the table. The company claims to have enabled over US$1 billion in gross merchandise value across its portfolio, giving it a meaningful track record in flexible, equity-free financing across Southeast Asia and beyond. CHUAN provides capital markets infrastructure for aggregating and distributing credit assets at scale, alongside a global investor network.

Also Read: Choco Up, Set Sail AI forge partnership to help businesses grow through Gen AI adoption

The combined pitch to institutional investors is essentially this: SME credit in Asia, structured with the kind of data transparency and underwriting rigour that have historically been reserved for larger corporate borrowers, is now accessible as a diversified, relatively short-duration asset class.

Whether that pitch translates into sustained capital deployment at scale will depend on whether the technology infrastructure can withstand stress, and whether SMEs across the region can access the facility on terms that genuinely help rather than merely substitute one form of financial pressure for another. For Asia’s US$2.5 trillion funding gap, a US$30 million facility is a start. It is a long way from a solution.

The post Choco Up taps US$30M to tackle Asia’s SME funding squeeze appeared first on e27.

Navigating manufacturing HR challenges in Singapore (2026)

As we move through 2026, manufacturers in Singapore are facing a transformative yet volatile landscape. The primary challenge lies in the acute shortage of specialized technical labor, compounded by stricter foreign workforce quotas and the rising levies associated with the COMPASS framework. Furthermore, the push toward “Industry 4.0” has created a digital divide; many firms struggle to integrate legacy shop-floor machinery with modern data-driven management systems. Rising operational costs—driven by fluctuating energy prices and high land premiums in Singapore—demand unprecedented efficiency. Manufacturers are also under pressure to implement real-time workforce tracking to manage complex shift rotations and ensure compliance with evolving Ministry of Manpower (MOM) safety and welfare regulations in a post-automation era.

Why specialized HRMS trumps conventional software

HRMS software for manufacturers is fundamentally different from standard commercial HR tools because it bridges the gap between administrative personnel management and the physical reality of the factory floor. While standard software treats employees as static entries, a manufacturing-centric HRMS views the workforce as a dynamic component of production capacity.

• Complex Shift & OT Management: Handles 24/7 rotating shifts, overnight patterns, and complex overtime calculations that standard software cannot process.
• Production Linkage: Integrates with shop-floor data to track labor costs per project or production line.
• Skills & Certification Tracking: Automated alerts for expiring safety certifications or specialized machine operating licenses.
• High-Volume Transaction Handling: Designed to process thousands of clocking records daily from various biometric points without latency.

Unique system requirements for Singapore manufacturers

Singapore’s regulatory and geographical context imposes specific demands on HRMS architecture that are rarely found in global “one-size-fits-all” solutions. The integration of localized statutory requirements with Singapore’s specific banking and digital infrastructure is non-negotiable for 2026.

• MOM & CPF Integration: Seamless, automated API hooks for CPF contributions, AIS for tax filing, and foreign worker levy (FWL) calculations.

• Skillspark & Government Grant Tracking: Capability to track training hours and claimable expenses under various Enterprise Singapore (ESG) or WSQ grants.

• Multi-Location Biometrics: Support for geo-fencing and facial recognition across multiple Tuas or Jurong-based facilities, integrated into a single database.

• Public Holiday & Rest Day Logic: Specific handling of Singapore’s Employment Act regarding work on rest days and public holiday substitutions.

Also read: AI agents and ERP: Why Singapore businesses must act now

The hidden cost of “Accounting Package + Customization”

Many manufacturers attempt to save costs by adding HR modules to a general accounting package. In 2026, this approach often leads to “Digital Debt.” General accounting systems lack the granular database schema required for complex manufacturing payroll. Customizing these packages usually results in a “Frankenstein” system that is difficult to upgrade. When the MOM changes a regulation, a customized accounting package requires expensive manual recoding, whereas an industrial-fit HRMS is updated via standard patches. The result of using a generic package is typically a loss of data integrity, inaccurate labor costing, and a high risk of non-compliance fines that far outweigh the initial “savings.”

Top 5 popular HRMS software

Selecting the right Human Resources Management System (HRMS) is critical for maintaining a competitive edge in Singapore’s manufacturing sector. Below are the top five solutions currently leading the market.

1. Multiable

A. Pros

• Seamless integration between payroll and complex manufacturing shift rosters.
• Highly scalable architecture that supports rapid regional expansion.
• Multiable HCM offers advanced AI-driven predictive analytics for manpower planning.
• Full compliance with Singapore MOM, CPF, and IRAS regulations out-of-the-box.
• High level of configurability without requiring core code changes

B. Cons

• Support service in weekend or public holiday will incur extra charge.
• Only suitable for mid-sized or large enterprise. Price may be out of touch for mom-and-pop business.
• Implementation phase requires a dedicated internal project team due to system depth.

C. How the vendor meets the unique requirement

• Features a dedicated Singapore-specific statutory engine for CPF and FWL.
• Built-in module for tracking WSQ training grants and Skillspark integrations.
• Supports high-frequency biometric data sync from multiple factory sites in Singapore.
• Learn more about Multiable HCM

2. SAP SuccessFactors

A. Pros

• Global standard for enterprise-grade human capital management.
• Deep integration with SAP ERP manufacturing modules (PP/MM).
• Robust self-service portal for a diverse, multilingual workforce.

B. Cons

• Long and costly implementation cycles.
• Complex user interface that may require extensive employee training.
• High total cost of ownership including maintenance and consultant fees.
• High resource consumption on local infrastructure.

C. How the vendor meets the unique requirement

• Provides localized payroll clusters specifically for Singapore tax laws.
• Extensive reporting tools for foreign worker quota management.
• Secure cloud hosting options compliant with Singapore’s PDPA.

3. Oracle Cloud HCM

A. Pros

• Strong focus on data security and high-availability architecture.
• Comprehensive talent management and succession planning tools.
• Built-in AI for resume screening and candidate matching.

B. Cons

• Often viewed as too rigid for highly specific local manufacturing workflows.
• Integration with third-party biometric hardware can be challenging.
• Significant learning curve for HR administrators.
• Frequent update cycles can occasionally disrupt custom workflows.

C. How the vendor meets the unique requirement

• Offers a localized Singapore Legislative Data Group (LDG).
• Automated updates for Singapore Budget changes (e.g., CPF rate adjustments).
• Global platform that manages Singapore-based headquarters with regional factory oversight.

Also read: The architect’s mandate: Building a resilient foundation for the intelligent enterprise

4. Workday

A. Pros

• User-friendly, modern interface that encourages high adoption.
• Continuous innovation with frequent, seamless cloud updates.
• Strong “Power of One” single-data-source architecture.
• Excellent mobile capabilities for workers on the move.

B. Cons

• Premium pricing model.
• Less flexibility for highly niche, manual shop-floor work rules.
• Heavy reliance on stable internet connectivity for all functions.

C. How the vendor meets the unique requirement

• Certified for Singapore AIS (Auto-Inclusion Scheme) for employment income.
• Robust diversity and inclusion tracking relevant to Singapore’s multi-ethnic workforce.
• Visit Workday

5. Clockgogo

A. Pros

• Specialized in high-accuracy time and attendance tracking.
• Innovative “CWS” technology to prevent “buddy punching.”
• Cost-effective for companies focused primarily on attendance and payroll.

B. Cons

• Narrower focus; lacks full-suite talent management features.
• May require integration with a separate system for full ERP functionality.
• Limited advanced predictive analytics compared to larger suites.

C. How the vendor meets the unique requirement

• Clockgogo is specifically designed for mobile workforces in Singapore’s urban environment.
• Direct API links to local payroll providers for instant attendance-to-pay processing.

Also read: Why Singapore manufacturers must embrace MES for the future

Precautions for decision makers in 2026

Selecting a system today requires a forward-looking lens that accounts for the rapid shift in the technological ecosystem.

1. Avoid Windows-Only Ecosystems:

Decision makers cannot select a system which is bound to the Windows Server ecosystem. Since all popular Large Language Models (LLMs) and agentic AI tools are running natively on Linux, systems which cannot run on Linux may become obsolete in the near future. Compatibility with containerization (like Docker) and Linux-based environments is now a prerequisite for AI readiness.

2. The Rise of Asian ERP Value:

While AIs in Asia start to catch up with those in the US, Asian ERP vendors also start to provide better ROI than household ERP names from the US or EU. These regional vendors often offer deeper localization for Asian labor laws and faster response times for local regulatory changes at a more competitive price point.

3. Agentic AI Readiness:

Ensure the HRMS has an open API architecture. The next wave of productivity will come from “AI Agents” that perform tasks across systems. If your HRMS is a “closed shop,” it will be unable to participate in the automated workflows of 2027 and beyond.

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The post Top 5 popular HRMS software for manufacturers in Singapore appeared first on e27.

Startups amplify everything-uncertainty, pressure, ambiguity. And with that amplification comes something less often discussed: the emotional load of working in these environments. Every decision, every interaction, every outcome feels larger because the margin for error is small, and the stakes are intensely personal. In a corporate setting, failure is often buffered by systems, processes, and teams. In a startup, it lands squarely on your shoulders, often faster than you realise.

I’ve noticed that the moments when my emotions are hardest to manage often align with the moments that test me most as a founder or early employee. A minor disagreement in a meeting, an overlooked task, or a shifting process can trigger frustration that grows faster than logic can keep up. In corporate roles, I had the structure to absorb it; in startups, there is little buffer. That intensity makes emotional self-awareness not just valuable-it’s essential.

Over time, I’ve learned to adopt a simple, practical approach: pause and process. It’s not a perfect system, and it doesn’t eliminate frustration, but it allows me to step back and consider what’s happening before reacting. This habit has helped me navigate three recurring challenges that seem to define the startup experience.

The first is process frustration

Startups are fluid by design. Rules are undefined, priorities shift constantly, and what works one day may be irrelevant the next. Coming from a structured corporate background, this initially felt uncomfortable-almost disorienting. Instead of reacting with immediate frustration, I began asking a different question: What is this environment trying to teach me? That shift from resistance to curiosity has opened more doors than I expected. It allowed me to participate constructively in shaping processes rather than getting caught in a loop of complaint.

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The second is people frustration

Early-stage teams often bring together individuals with widely different backgrounds, experiences, and ways of thinking. Alignment doesn’t come naturally, and miscommunication is inevitable. When tensions arise, I’ve found it helpful to reframe the situation internally: from “Why isn’t this done my way?” to “What might their approach reveal that mine doesn’t?” This doesn’t remove friction, but it transforms it into a productive force, encouraging me to understand rather than resist, to adapt rather than criticise.

The third is outcome frustration

In startups, the consequences of failure are often immediate, visible, and personal. A delayed product release, a missed target, or a misjudged strategy can feel like a reflection of your own capability. In those moments, it’s easy to spiral into self-doubt or overcorrection. Having a space, whether through reflection, journaling, or talking with a trusted sounding board, helps me regain perspective. Even small reframing exercises can make the difference between dwelling on setbacks and taking constructive action.

The common thread across these challenges is that unmanaged emotions don’t just affect you-they ripple out to teams, decisions, and the overall trajectory of the startup. Emotions themselves are not the enemy of professionalism. The real challenge is unprocessed emotions. When we ignore or suppress them, they have a way of leaking into our work, our decisions, and our interactions in ways that can be damaging.

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The practical takeaway is simple: you don’t need to be emotionless to be effective. In fact, acknowledging and understanding emotions can be a competitive advantage. But we do need mechanisms to process before projecting. Reflection, conversations, and intentional pause create space to make sense of what we feel and why. That space allows us to turn emotions into clarity, empathy, and better decision-making.

In a startup environment, this ability isn’t a luxury-it’s a survival skill. It helps you navigate ambiguity, work better with diverse teams, and maintain perspective when outcomes don’t go as planned. Most importantly, it allows you to stay grounded, remain engaged, and continue growing without being derailed by the intensity that is inevitable in early-stage ventures.

Emotions are amplified in startups, but they don’t have to be destructive. Managed well, they become signals, guides, and even sources of energy. And when you learn to listen, process, and respond thoughtfully, those emotional moments stop being obstacles-they become tools for better work, stronger teams, and longer-lasting engagement.

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