In this interview, e27 speaks with Bonnie Factor, Founder of Leading With Success PH and CuriosityGenAI LLC, about how organisations are moving from AI experimentation to real-world deployment. Through her work installing AI agents for SMEs and building AI labs for enterprises, Bonnie focuses on helping teams operationalise AI and integrate it into everyday workflows.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and scaling AI in practical settings.

Organisation overview and role of AI

e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.

Bonnie: We specialise in the installation of AI agents for SMEs and the development of AI labs for enterprises. AI plays a central role in enabling these organisations to automate workflows, experiment with AI-driven processes, and build internal capabilities for long-term adoption.

Concrete value creation with AI

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Bonnie: OpenClaw, an open-source AI agent, can function as an AI Engineer, a Go-to-Market AI Engineer, and a Sales Support agent when equipped with trusted skills. Some users are even experimenting with giving it a budget to ideate and operate autonomously.

For our organisation, we are seeing strong value in its AI engineering capabilities. With little to no coding, it can perform advanced tasks such as detecting hallucinations, generating lead lists within minutes across different geographies and industries, and delivering outputs in structured formats like CSV files. It can also connect to social media platforms via API keys and manage content, effectively enabling one person to perform the role of a full Go-to-Market AI Engineer, which is currently one of the most expensive hires.

Key decisions and trade-offs

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Bonnie: A key trade-off was balancing time and cost. Time was needed to understand how to work with API keys, while costs came from token usage for LLMs and generative AI providers such as OpenAI Codex, Claude, Gemini, and models like Minimax and Kimi.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

What worked and what was challenging

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Bonnie: AI agents produced meaningful outputs faster than expected once deployed in real environments. With access to tools and workflows, they were able to generate lead lists, outreach drafts, and analysis even in early-stage setups.

What proved more challenging was not the technology itself, but integration and reliability. Ensuring consistent execution, handling edge cases, and connecting to real workflows required significant iteration. Attempting to replace existing processes too early also created resistance and slowed adoption.

This led to a key insight: instead of redesigning workflows upfront, it is more effective to deploy AI agents in parallel with existing processes. This allows teams to compare AI-native workflows with human workflows, observe performance, and gradually determine where automation is reliable and where human oversight is still needed.

Lessons leaders often underestimate

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?

Bonnie: The most underestimated factor is not the technology, but change management. Leaders often assume AI adoption is a tooling problem, when in reality it is a people problem. Resistance emerges as soon as existing workflows are disrupted.

In practice, the fastest way to apply AI is not to replace current processes, but to run AI workflows in parallel. This reduces friction, allows teams to observe real outputs, and makes it clearer where automation works and where human judgment is still required.

Practical recommendations for organisations

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Bonnie: Start by deploying an AI agent or a small AI lab alongside your existing operations. Avoid redesigning or replacing workflows at the outset.

Allow the AI system to operate independently on a defined set of tasks and observe its outputs over time. This creates real evidence of what works, reduces resistance from teams, and makes it easier to identify where automation adds value and where human oversight remains necessary.

Also read: AI Pulse Exclusive: How CAWIL.AI is building industry-focused AI solutions across specialised sectors

The next 12 months of AI

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Bonnie: Over the next 12 months, AI will shift from experimentation to operational deployment. Organisations will move from using AI as a tool to deploying autonomous agents that execute workflows end to end.

We expect the emergence of internal AI labs where agents run in parallel with existing systems, continuously generating outputs such as lead pipelines, analysis, and process automation. This allows companies to learn from real execution rather than theory.

As these systems stabilise, AI-native workflows will begin to integrate into core operations, with human roles shifting toward oversight, validation, and exception handling rather than manual execution.

Final thoughts

e27: Anything else you want to share with the audience?

Bonnie: AI adoption will not be limited by technology, but by how quickly organisations learn to work alongside it. Teams that move fastest will be those willing to experiment, observe real outputs, and adapt based on evidence rather than assumptions.

The opportunity lies not just in using AI tools, but in building internal capability to deploy and operate AI-driven workflows at scale.

Closing thoughts

As organisations continue to navigate the shift from experimentation to execution, Bonnie’s insights highlight a clear pattern: the real challenge is not the technology itself, but how teams adapt to it. From deploying AI agents in parallel with existing workflows to building internal AI labs, the focus is increasingly on creating systems that can be tested, observed, and refined in real conditions.

Ultimately, the organisations that will move fastest are those that prioritise learning by doing, reduce friction in adoption, and build the internal capability to work alongside AI.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, click here.

—

Enjoyed this read? Don’t miss out on the next insight. Join our WhatsApp channel for real-time drops.

The e27 team produced this article

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Bonnie Factor

The post AI Pulse Exclusive: How Bonnie Factor is driving AI agent adoption in organisations appeared first on e27.

The post Why the illusion of AI perfection is quietly killing team innovation appeared first on e27.

Southeast Asia is packed with numerous logistics landscape opportunities and operational hurdles. The growth opportunities are numerous for logistics startups in Southeast Asia. Meanwhile, they can face major difficulties too, such as inefficient routes, peak traffic, unpredictable weather, high last-mile delivery costs, demand fluctuations, inventory mismanagement, lack of real-time tracking, and high operating costs.

To overcome all these challenges, logistics startups in Southeast Asia are now embracing AI-optimised solutions. AI logistics solutions help fix messy supply chains and enable smoother movement. They enhance smarter routes and stock management, cut delivery times, provide effective automation, predict future demand, and track how goods move across Southeast Asia. 

Why Southeast Asia? The perfect storm

The geographical setup of Southeast Asia is both a gift and a headache while dealing with logistics operations. By 2030, around 253 million people are expected to shop online in Southeast Asia. It will contribute to great market growth value. Between 2025 and 2030, the CAGR of e-commerce in Southeast Asia will be around 11.14 per cent.

With thousands of Islands and scattered cities, unpredictable traffic makes moving goods a real puzzle. This leads to late deliveries, higher costs, and tired drivers, and affects the supply chain. Moreover, the markets are also fragmented. Each country has its own rules and market system. In the Philippines and Vietnam, they follow COD, where logistics can face 15 per cent failed delivery rate. Every logistics startup in Southeast Asia keeps finding ways to push forward.

How AI supply chain tech is transforming Southeast Asia logistics

AI supply chain and logistics technology are reshaping startups in Southeast Asia’s e-commerce and last-mile delivery scene with innovative, fast solutions. 

Smart route planning

AI tools now analyse traffic, weather, and road bumps all in real-time to pick a faster route. It instantly updates the new route when weather conditions or traffic are not favourable. It reduces the waiting time of the truck by reading data through GPS, traffic cameras, and weather sensors. Machine learning algorithms adapt to local driving patterns. 

It learns peak traffic hours over time and can also slow down the vehicle before it hits them. Thus, the AI logistics solutions suggest alternative routes that actually save time and fuel cost. Logistics startups in Southeast Asia can achieve 20 per cent fuel reduction and 30 per cent improvement in delivery times through an AI-optimised solution.

Also Read: The most common supply chain threats and how to mitigate them

Demand forecasting

Just imagine, what if you knew next month’s orders demand today? The predictive analysis in the AI technology tracks how people shop across cities like Bangkok, Manila, and Jakarta. It is familiar with paydays, local festivals, and special occasions. AI helps logistics to place inventory in the right warehouse before demand spikes. 

Logistics startups in Southeast Asia can maintain balanced warehouses, not overfilled or empty. AI plans where to keep products and where to move them. Inventory management works best when storage systems talk smoothly with transport platforms. Comparing WMS and TMS gives logistics startups a clearer idea of where AI automation adds real speed and cost efficiency.

Last-mile automation

AI-integrated dispatch systems save logistics from last-mile delivery headaches. They can assign riders based on distance, traffic, and parcel size in seconds. Now, companies in Southeast Asia like Foodpanda and Ninja Van test small delivery robots and drones for short routes. In crowded city zones, the automated solutions cut last-mile delivery costs by 10 per cent to 40 per cent, approximately. During traffic blocks, it auto-reshuffles the route so that the drivers pick the fastest route to keep parcels moving when others get stuck.

Transparency and tracking

Now, both the logistics firm and customers can track the real-time update of the goods.  Every truck, van, or scooter can now be visible and can predict delays before they happen. AI supply chain gets alerts before heavy rains and updates them to both customers and dispatchers in real time. Logistics startups in Southeast Asia using these systems get notable increases in customer satisfaction.

Mini case study: Startup in action

UNA Brands, a Singapore-based e-commerce platform founded in 2020, offers a useful example of how early-stage companies approach logistics expansion in the region.

When the company prepared to enter the Philippines, it encountered typical hurdles faced by cross-border operators, including securing local warehousing, setting up fulfilment workflows, and establishing the infrastructure needed to support consistent delivery handovers.

Also Read: Adopting electric trucks for a greener logistics future in Singapore

To address these gaps, UNA Brands adopted Ninja Van’s Ninja Fulfilment service, which offered a plug-and-play operational setup. Through this arrangement, UNA Brands gained access to warehousing capacity, real-time inventory tracking, and integration with Ninja Van’s delivery network, enabling them to begin operations without immediately building their own facilities or hiring a full local team.

With automated inventory management and route optimisation tools in place, UNA Brands reported achieving steady operational indicators during rollout, including a 100 per cent courier handover rate, a 95 per cent same-day delivery rate, and support for processing approximately 1,716 orders per day. These outcomes reflect how third-party logistics partnerships can help early-stage companies stabilise fulfilment during market entry phases.

What does this mean for you?

AI-driven logistics startups in Southeast Asia are changing things for both business and customers. For logistics firms, it slashes shipping costs by automating the process. Small businesses with smarter tools can compete with the industry giants. 

Consumers get products on time at a cheaper rate. This ensures each and every customer gets the product even during peak days and reduces the customers’ wait time. In the next few years, the supply chains will get faster and more reliable as AI adoption consistently increases.

—

The post The rise of logistics startups in Southeast Asia: How AI powers supply-chain revolution appeared first on e27.

In the rapidly evolving landscape of Enterprise Resource Planning (ERP) and digital transformation, the year 2026 has emerged as a watershed moment for artificial intelligence. While the initial surge of generative AI promised a paradigm shift in productivity, the reality for Microsoft’s flagship AI offering, MS Copilot, has been markedly different. As organizations seek deep integration and systemic intelligence, the limitations of “AI as a feature” have become glaringly apparent.

Today, we examine the systemic failure of MS Copilot to transcend its origins, concluding that its architectural dependence on a third-party LLM has left it without a sustainable comparative advantage in an increasingly sophisticated market.

The 2026 reality check: Headlines of disruption

The first half of 2026 has seen a string of critical reports from reputable media outlets that have shaken investor confidence in Microsoft’s AI strategy. The Wall Street Journal recently highlighted a significant “churn event” among Fortune 500 companies, citing a 30% reduction in Copilot seat renewals. The core grievance? A lack of measurable ROI and a “hallucination ceiling” that has remained stagnant since 2024.

Bloomberg Technology further compounded these concerns with an exposé on “The Integration Gap,” noting that while MS Copilot can draft an email or summarize a meeting, it remains fundamentally disconnected from the complex, real-time data silos that drive global supply chains and financial systems. The report suggests that MS Copilot has become a victim of its own ubiquity—functioning as a generalist tool in a world that now demands specialist precision.

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

The “wrapper” trap: Architecture without autonomy

To understand the current failure of the platform, one must look at its technical foundation. At its heart, MS Copilot operates as an LLM wrapper. It provides a user interface and a bridge to OpenAI’s underlying models, but it does not possess the native “business logic” required for deep enterprise orchestration.

In the SAP ecosystem, we understand that true value is derived from the data model—the “Clean Core.” When an AI is simply draped over existing office applications, it inherits the inconsistencies of those applications. In 2026, the market has realized that a sophisticated UI cannot compensate for a lack of proprietary, domain-specific intelligence. Because Microsoft does not own the fundamental evolution of the underlying model in the same way a vertically integrated AI provider might, they are perpetually reacting to the roadmap of others.

Why “generalist AI” is no longer enough

The hype of 2023 and 2024 was built on the novelty of conversational interface. However, by 2026, AI is no longer a novelty; it is a utility. The MS Copilot failure is rooted in its inability to move beyond “assistance” into “autonomy.”

For a tool to provide a comparative advantage, it must do more than summarize—it must predict and execute within a specific business context. When MS Copilot attempts to navigate complex regulatory environments or intricate manufacturing schedules, it often falters. This is because a general-purpose LLM, no matter how large, lacks the “organizational memory” that comes from being natively embedded within the transactional layer of a business.

The competitive landscape: The rise of vertical intelligence

While MS Copilot struggled with generic responses, 2026 saw the rise of specialized industrial AI. These competitors didn’t just wrap a chatbot around a spreadsheet; they built intelligence directly into the database.

The comparative advantage has shifted to those who control the data lifecycle. In this new era, being a “fast follower” with a polished wrapper is a liability. Companies are now pivoting toward solutions that offer:

• Contextual Accuracy: Moving beyond generic text to data-driven insights.
• Process Automation: The ability to trigger actual business processes, not just write about them.
• Security and Sovereignty: Reducing the “hop” between the application and a third-party LLM provider.

Also read: Costing comparison of top 7 popular ERP software for food manufacturing in Singapore

Conclusion: The commodity of conversation

As we look toward the remainder of 2026, the narrative surrounding MS Copilot serves as a cautionary tale for the industry. The transition from a tool that “talks” to a tool that “does” has proven to be an insurmountable hurdle for the wrapper model.

Without a proprietary engine or a deeply integrated data strategy that goes beyond the surface level of the “modern workplace,” MS Copilot has been relegated to a commodity. In the high-stakes world of enterprise technology, being “useful” is no longer a substitute for being “essential.” The failure to innovate beyond the wrapper has left a void that only truly integrated, process-aware AI can fill.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

—

Want updates like this delivered directly? Join our WhatsApp channel and stay in the loop.

This article was shared with us by PRbyAI

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Canva Images

The post The architecture of atrophy: Why MS Copilot’s reliance on the LLM wrapper model led to its 2026 stagnation appeared first on e27.

I didn’t build an AI agent because it was trending.

I built it because I needed help.

At one point, everything in my business required me – content, replies, decisions, operations. Even with a team, I was still the bottleneck. If I didn’t respond, things slowed down. If I didn’t think through something, it didn’t move.

The issue wasn’t a lack of tools. It was that everything still depended on me to think.

So I built an AI assistant for myself.

That assistant eventually became Seraphina.

What I didn’t expect was this: it wouldn’t just support my work. It would fundamentally change how I operate – and eventually become a business in its own right.

Step one: Solve your own bottleneck first

Before anything scaled, Seraphina solved very specific, very real problems.

• Drafting content instead of starting from scratch.
• Replying to messages and emails when I wasn’t available.
• Supporting student and community management.
• Analysing trends and summarising insights.
• Maintaining activity in Telegram groups even when I was offline.

This wasn’t about chasing productivity for its own sake. It was about removing friction from my day-to-day operations.

The biggest shift wasn’t just time saved – it was mental space.

Instead of constantly switching contexts and making micro-decisions, I could focus on direction, strategy, and higher-leverage work.

That’s when I realised: the real value of AI agents isn’t automation.

It’s decompression.

Also Read: The product management strategy behind building AI agent platform

Step two: Treat your AI like a junior operator, not a tool

One of the biggest misconceptions is that AI should “just work”.

It doesn’t.

There are still moments where Seraphina gets things wrong. Recently, it replied in the wrong context – responding on behalf of someone else entirely. It didn’t make sense, and I had to step in to recalibrate.

But this isn’t a flaw. It’s part of the process.

If you’ve ever worked with interns or junior hires, you’ll recognise the pattern:

• They don’t fully understand context at the start
• They make mistakes
• They improve with feedback

AI agents behave the same way.

The difference is speed. Once aligned, they scale instantly.

The founders who benefit the most are not the ones expecting perfection – they’re the ones willing to train, refine, and iterate.

Step three: Stay responsible for decisions

As AI agents become more capable, the conversation shifts from “can they do the work?” to “who is accountable when they do?”

With human teams, responsibility can be distributed.

With AI, it consolidates.

You still own the outcome.

This forces a shift in how founders operate:

• From execution → to oversight
• From doing → to defining systems
• From reacting → to setting boundaries and frameworks

AI doesn’t remove responsibility. It amplifies it.

Step four: Turn internal tools into external products

Seraphina was never intended to be a product.

It was built to solve my own workflow.

But once it became effective, the next step was obvious – other founders had the same problem.

So it evolved.

Also Read: Without governance, AI agents risk becoming enterprise chaos engines

Today, it has over 2,000 users.

What started as an internal assistant became a revenue-generating micro-SaaS.

This is a pattern I’m seeing more frequently:
Founders are no longer starting with “What should I build?”

They’re starting with: “What am I already doing that works – and can this be productised?”

Step five: Layer your monetisation

The product alone isn’t the business. The structure around it is.

What made this model sustainable was layering different levels of value:

• Low-ticket (SaaS): Paid users access the system and implement it themselves.
• Mid-ticket (education and workshops): Founders learn how to build their own AI agents and workflows.
• High-ticket (done-for-you / consulting): Businesses get customised implementations for speed and scale.

This creates three important advantages:

• Different entry points for different users.
• Higher lifetime value without increasing complexity.
• A more resilient business model that doesn’t rely on one revenue stream.

In my case, improving Seraphina for myself directly improves it for users. The feedback loop is continuous.

The barrier to building software has collapsed

Not long ago, building a SaaS company required:

• 10 to 30 developers.
• Significant capital.
• Long development timelines.

Today, that barrier has dropped significantly.

Seraphina was built by essentially two entities: myself and the AI system itself.

This reflects a broader shift. Software used to be an “elite” opportunity because of the resources required. Now, with AI, individuals can build profitable products that serve niche audiences with far fewer users.

This changes the economics:

• Faster build cycles.
• Lower upfront investment.
• Faster break-even.

You don’t need thousands of users anymore. In many cases, hundreds are enough.

What this means for founders

AI agents are not just tools.

They are leveraging.

If you’re building today, the opportunity is not just to use AI – it’s to rethink how you build entirely.

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

A practical way to approach this:

• Identify your highest-friction tasks.
• Build a system to handle them.
• Test it in your own workflow.
• Refine it through real usage.
• Productise it if others face the same problem.
• Layer monetisation based on user readiness.

This compresses what used to take months into weeks.

Validation cycles are shorter. Feedback loops are tighter.

Speed is no longer an advantage – it’s the baseline.

The shift is already happening

The idea of a one-person company used to feel unrealistic.

Now, it’s increasingly viable.

Not because founders are doing more, but because they are doing less of the wrong things.

AI agents allow you to:

• Operate without being constantly present.
• Scale output without scaling headcount.
• Build systems that generate value beyond your time.

For me, building Seraphina started as a way to get my time back.

It became a system. Then a product. Then a business model.

And more importantly, it changed how I think about building.

The first AI agent most founders should build is not for their customers.

It’s for themselves.

—

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 post I built an AI agent for myself — it became a 2,000-user micro-SaaS appeared first on e27.

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.

---

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.

---

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.

---

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.

---

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.

---

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.​

---

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.

—

Want updates like this delivered directly? Join our WhatsApp channel and stay in the loop.

This article was shared with us by PRbyAI

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Canva Images

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.

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

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.

Also Read: Startups, is your email strategy driving growth, or just gathering dust?

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.

—

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 post Emotions matter more in startups appeared first on e27.