Artificial Intelligence is rapidly reshaping how we work and live. As NVIDIA CEO Jensen Huang said, “AI will be the most transformative technology of the 21st century.” This shift is already underway, especially in Singapore’s dynamic AI landscape.

To better understand this transformation, we spoke with three key players:

• AI Singapore
• JJ Innovation
• Knovel Engineering

AI landscape and outlook

Over the past two to three years, AI has undergone a transformative shift, with generative AI and large language models (LLMs) emerging as the most significant developments noted by all interviewees. These technologies have expanded AI’s capabilities, reshaping how people and businesses work, create, and interact with technology.

A major highlight, shared by Hee Chuan, Founder & Chief Executive Officer of Knovel Engineering and Laurence Liew, Director of AI Innovation at AI Singapore, shared that tools like ChatGPT and Claude have made AI user-friendly, encouraging wider adoption. However, Chuan noted that adoption still lags in some sectors due to unclear ROI.

Liew also pointed out a global shift towards building local AI talent and readiness, inspired by Singapore’s AI Apprenticeship Programme (AIAP) and AI Readiness Index (AIRI). Daniel Yip, Technology Project Consultant at JJ Innovation highlighted rapid advancements in AI applications across media—text, video, and audio. Tools like Google’s Veo 3 showcase remarkable improvements in realism and capability.

AI adoption in Singapore

The projects shared by the interviewees demonstrate AI’s application across sectors to streamline both operational and strategic business functions.

Carol Wong, Regional Head of Technology Services at JJ Innovation, led a project using Natural Language Processing (NLP) to analyse employee feedback at a global tech firm, significantly accelerating HR insights and responsiveness.

Yip shared how integrating a Generative AI assistant into a logistics company’s systems simplified complex processes, enabling non-technical staff to manage inventory and documentation through a prompt-based interface.

Liew illustrated the breadth of AI’s impact—from real-time multilingual emergency call transcription for SCDF to AI-enhanced dental diagnostics with Q&M Dental Group, and even route optimisation for a local SME, uParcel. Collectively, these examples underscore AI’s versatility in transforming both routine and critical business functions.

Also Read: Levelling the playing field: How AI can transform SME hiring

Chuan shared that one of their customised workflow productivity tools, powered by AI, has helped a local heritage brand—HarriAnns Nonya Table—transform its manual backend ordering process from hotels and its own cafes to a centralised kitchen, streamlining operations and reducing human errors.

Challenges and solutions

A key barrier identified by all three companies is data readiness—many organisations lack sufficient data, have fragmented or poor-quality datasets, or lack the infrastructure to prepare data effectively for AI.

Mindset and cultural resistance also pose major obstacles. Chuan noted that unrealistic expectations—such as seeking one-size-fits-all “silver bullet” solutions—and common misconceptions, like fears of job loss or over expectations of AI’s current capabilities, continue to hinder progress.

Liew highlighted the lack of internal expertise, especially among SMEs, where teams may not have the technical skills to deploy or maintain AI systems. He also pointed out that many companies wrongly assume their existing IT setups are AI-ready.

JJ Innovation further noted difficulties in identifying practical use cases and adapting AI models trained on Western data, which may not reflect Singapore’s unique cultural context.

To overcome common AI adoption challenges, interviewees advocated for companies to start small—by piloting a focused project or proof of concept to test value and feasibility before scaling.

Interviewees emphasised the need to foster AI literacy to dispel fears and align expectations. Wong highlighted the importance of training and up-skilling to build the capabilities needed.

Companies were encouraged to begin organising their data early to ensure it’s clean, accessible, and secure.

Also Read: AI bubble fears trigger market rotation: What it means for crypto and tech stocks

Finally, Yip stressed the importance of linking AI efforts to clear business problems, ensuring AI is adopted with purpose—not just for novelty.

To ensure post-project continuity, interviewees stressed the need for structured knowledge transfer and internal capability building.

Liew from AI Singapore shared that their 100E programme involves internal engineers from the start, with sprints, testing, documentation, and formal handovers. Companies are also encouraged to train staff in foundational AI.

Wong highlighted the role of “change champions,” while Yip recommended appointing “AI custodians.” The consensus: sustained success requires ongoing training, collaboration, and ownership.

At the current state of AI, complete displacement of jobs and human intervention is still not possible.

As Yip explained, “AI is not out to replace your job just yet. In the present, AI should be thought of as an assistant to boost your effectiveness in your current job.”

Liew supported by sharing that AI adoption is less about wholesale reskilling and more about what one expert called “plus-skilling.” He elaborated, “For example, an accountant doesn’t need to become a data scientist; rather, they should remain an accountant who is now empowered to use AI tools effectively in their daily work.”

Moving forward

AI success should go beyond technical metrics like accuracy or speed. Liew emphasised that true indicators lie in business outcomes—such as deployment rates, time or cost savings, efficiency gains, revenue impact, and employee adoption.

He shared that tracking organisational maturity through frameworks like the AI Readiness Index (AIRI) and monitoring AI literacy efforts are also important. Chuan added that success can be seen in the number of jobs redesigned or up-skilled, and that AI should be viewed as a long-term investment, not just a cost-saving measure.

Also Read: AI in action: How governments are using technology to predict, prevent, and personalise

Equally critical is embedding responsible design principles from the outset. Interviewees consistently stressed that ethical standards and compliance should be treated as key measures of AI success, not afterthoughts. Ensuring AI solutions are trustworthy, explainable, and human-centric requires maintaining governance frameworks and establishing human oversight in the workflow to validate safety and reliability throughout development.

To prepare for the future of work driven by AI, organisations should start early by building a strong foundation—this includes digitalising processes, preparing clean and structured data, and developing AI literacy across all levels of the workforce. Success comes from starting small, experimenting quickly, and learning by doing, rather than waiting for perfect conditions.

Equally important is shaping employee mindsets and fostering a culture of curiosity and adaptability. Organisations should also prioritise human oversight by forming diverse, multidisciplinary teams—not just to drive innovation, but to ensure AI systems remain understandable and trustworthy. After all, trustworthy AI is not just about meeting compliance standards; it’s about building systems people can understand and rely on.

Ultimately, AI should be viewed as a tool to augment human capabilities, not replace them. Long-term transformation is best supported through collaborative partnerships with startups, universities, and national programmes.

As AI reshapes the future of work and business, organisations yet to begin their transformation journey should start now. Starting small by addressing existing pain points can drive productivity and efficiency. AI transformation is an ongoing process of growth and adaptation.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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At Echelon Philippines 2025, Isabel Calvo, Co-Founder of Bravo!, spoke with e27‘s Mohan Belani about building a fast, affordable, and efficient food platform in the Philippines—a concept inspired by their sister company, Pickup Coffee.

Bravo! was born from the idea of disrupting the local pizza market, where, despite pizza’s wide popularity, quality options remain scarce. Initially targeting upper-class tastes with European-style pizza, the team quickly realised that segment was too niche.

Their launch revealed a key consumer insight: Filipinos order food around the clock, not just at mealtimes. Calvo also emphasised the critical mindset shift required when transitioning from the tech industry into the F&B space.

The post Echelon Philippines 2025 – What does Bravo! and the iPad have in common? appeared first on e27.

If the first era of esports was about proving its legitimacy through packed arenas and global numbers, the next is about something more complex: how technology is reshaping the way fans in the Asia Pacific actually experience competition.

APAC already accounts for more than half of the global esports audience. Southeast Asia alone generated over USD 71 million in esports revenue in 2024, with projections pointing to steady growth through the next decade 

But beyond revenue and viewership, what is really changing is how audiences consume esports and what they now expect from a broadcast.

Across the region, fans are no longer satisfied with simply watching a match unfold. They are demanding perspective, data, interaction and control. This behaviour is driving a wave of broadcast innovation that is likely to shape the global esports experience.

From linear viewing to multi-layered experiences

In many Western markets, esports broadcasting initially mirrored traditional sports. A single curated feed, a production team deciding the angles, and fans following along passively. In the Asia Pacific, that model is becoming outdated.

In Indonesia and the Philippines, mobile-first audiences want quick access, multi-device viewing and interactive layers that allow them to engage while watching. In India, where esports exposure has grown rapidly via mobile titles and streamers, fans are used to switching perspectives and platforms mid-match. In Korea, where esports culture is more established, viewers increasingly expect data-rich viewing similar to professional sports analytics.

This has encouraged a shift away from one fixed broadcast stream towards multi-layered experiences where viewers can personalise how they follow a match. Multi-angle viewing, player-focused perspectives and customisable overlays are no longer niche experiments but part of a broader structural change in how esports is consumed.

When fans are given the choice to follow a carry player’s perspective, or instead analyse how a support player controls space and tempo, the match stops being a single narrative. It becomes a set of parallel stories, all built on the same live event.

Why data has become part of the entertainment

Another clear shift in APAC esports broadcasting is the integration of real-time data into the viewing experience.

Heat maps, economy trackers, player performance graphs and match momentum indicators are now expected features for serious viewers rather than optional extras. In Korea, this trend mirrors how traditional sports like baseball and football integrated analytics years ago. In Southeast Asia, it reflects the region’s comfort with data layered over entertainment, seen in everything from gaming to fintech apps.

What makes esports different is that this data is not designed only for post-match analysis. It is built into the live experience. Fans are consuming statistics as the game unfolds, using them to make predictions, debate strategies, and build narratives around what they are watching.

This is changing the role of the broadcast itself. It is no longer just a transmission channel. It is a live information system that merges spectacle and analysis.

Also read: The future of fan engagement and how sports tech is turning spectators into stakeholders

The infrastructure challenge in a fragmented region

Asia Pacific presents a unique challenge for esports broadcasters. The region is geographically vast and digitally fragmented. Delivering a smooth, low-latency viewing experience across markets like Singapore, Jakarta, Manila, Mumbai, and Seoul requires more than just good production values. It demands serious infrastructure.

Distributed servers, cloud-based production pipelines and low-latency streaming technologies are no longer optional. They are foundational. Without them, synchronised experiences across markets collapse and engagement drops.

These systems also support localised layers on top of global feeds. For example, the same match can be broadcast with local language commentary, region-specific graphics and culturally relevant references without needing to rebuild the entire production for each country.

This hybrid model of centralised backbone and localised experience is becoming a defining feature of esports broadcasting in APAC.

Interactivity as an extension, not a distraction

As broadcasts evolve, so does the role of interactivity. But the key lesson emerging from Asia Pacific is that engagement tools must enhance rather than disrupt the experience.

Fans are responding well to features like live polls, prediction overlays and dynamic stats dashboards that are integrated into the stream itself. When designed carefully, these elements add layers to the experience rather than pulling attention away from the match.

In this context, platforms like 1XBet illustrate how prediction and interaction can exist as optional extensions within the esports ecosystem when they are embedded responsibly and without overwhelming the core broadcast.

The most successful integrations in APAC are not the loudest or most aggressive. They are the ones that feel native to the viewing environment and allow fans to engage on their terms.

Also Read: From niche hobby to billion-dollar industry: The meteoric rise of esports

What this means for the next phase of esports innovation

The evolution of broadcast technologies in the Asia Pacific points to three broader shifts in how esports will develop globally.

• First, personalisation will become non-negotiable: Fans increasingly expect to control what they see, how they see it and which data layers they follow. One-size-fits-all broadcasts will struggle to hold attention in a hyper-customised digital culture.
• Second, infrastructure will differentiate serious players from superficial ones: Behind every smooth multi-angle stream and real-time data overlay sits deep technical investment. As fan expectations rise, infrastructure quality will directly impact trust, loyalty and long-term relevance.
• Third, cultural context will matter more than raw technology: APAC is not a single audience. Korea’s data-driven fans, Indonesia’s mobile-first viewers and India’s creator-led communities have different expectations. Technologies that adapt to these differences will scale. Those who treat APAC as a monolith will not.

A region shaping the global blueprint

It would be easy to frame Asia Pacific as simply the fastest-growing esports market by numbers. But that misses the point.

APAC is shaping how esports is moving from a broadcast sport to an interactive media format where fans do not just watch but experience, analyse and participate. The region is pushing esports beyond a screen and into a multi-layered digital environment.

From multi-perspective viewing to data-driven storytelling to responsible interactivity, the innovations emerging from the Asia Pacific are not just responding to demand. They are actively redefining global expectations.

The future of esports broadcasting will not be built only in production studios or technology labs. It will be shaped by how fans across Asia Pacific choose to engage, personalise and make the experience their own.

And in many ways, that future is already unfolding.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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

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The post How broadcast innovation in APAC is redefining the e-sports viewing experience appeared first on e27.

Southeast Asia’s AI wave is gaining momentum, and Indonesia is fast emerging as a hotspot for innovation. From fintech and healthtech to creative platforms and conversational commerce, a new generation of startups is reimagining how artificial intelligence can solve everyday problems at scale. These companies are not just building tools; they are shaping behaviours, unlocking efficiencies, and creating entirely new digital experiences.

In this listicle, we spotlight some of the most promising Indonesian AI startups to watch, each bringing a unique approach to harnessing AI in one of the region’s most dynamic and rapidly evolving tech ecosystems.

Also Read: From energy to ergonomics: 20 AI startups to watch in Southeast Asia

SPUN Global

YukYuk!

bythen

Equitiv

Bulu

Mimin

Ledgerowl

ChatApp

bubbME.AI

Rapty.app

JUTIVE International

BJTech

Sonar Platform

Cekmata.com

AdSpace

The post From chatbots to creators: Indonesia’s AI startups to watch appeared first on e27.

A few years ago, I sat across from a founder who was trying to save his company.

He had done everything right, at least on paper. Top university. Ex–big tech operator. Raised a respectable seed round. Built a disciplined team. Shipped on time.

But growth had stalled. Users weren’t sticking. The market wasn’t responding. He had started waking up at 4 a.m., obsessively checking dashboards. He rewrote the homepage three times in a week. He micromanaged product decisions. He doubled the sales targets.

Nothing moved.

At some point in the conversation, he said quietly, “I just need to push harder.”

I’ve heard that sentence many times.

And every time, I think about something that psychology, physics, Buddhism, Daoism, and even classical Confucian thought all strangely agree on: when you push too hard against the current of reality, reality pushes back.

This is not mysticism. It is not “founder wellness fluff.” It is a pattern that shows up across disciplines, across centuries, and across markets.

If you are building a startup, especially in today’s volatile climate, this might be the most important lesson you internalise:

Success rarely comes from force. It comes from alignment with flow and trend.

Let me explain.

The way you see the market shapes the market you see

In psychology, there is a concept called projection. Your internal state shapes how you interpret external events. If you are anxious, you see risk everywhere. If you feel scarce, every competitor looks like a threat. If you feel confident, obstacles look like puzzles.

In physics, we encounter the observer effect: observing can influence the state of what is observed.

In Buddhism, there is a phrase: “The world arises from the mind 境由心生.”

In Daoism: “All things are shaped by the heart 一切皆由心造.”

Different language. Same idea.

For founders, this plays out daily.

If you believe the market is hostile, you will read every piece of feedback as rejection. If you believe users are fickle, you will overbuild features to “lock them in.” If you believe investors are predatory, you will negotiate defensively and damage relationships before they begin.

Your inner posture shapes your strategic posture.

I’ve seen founders in a downturn who interpret slower sales cycles as proof that “no one wants innovation anymore.” They shrink. They cut ambition. They retreat to incrementalism.

I’ve also seen founders in the same market environment interpret the slowdown as “a filtering moment.” They refine positioning. They deepen product-market fit. They quietly gain share while others panic.

Also Read: Balancing ambition and well-being: A founder’s take on sustainable company building

The macro was identical. The interpretation wasn’t.

When your mind is rigid, the market appears rigid. When your mind is adaptive, the market appears full of possibilities.

This doesn’t mean reality is imaginary. It means your response to reality determines your trajectory within it.

If you want to change your startup’s future, sometimes the first pivot is internal.

The harder you cling, the more you repel

Let’s talk about something uncomfortable: desperation.

You can feel it in a pitch. In a sales call. In a product roadmap.

It’s subtle, but it’s there.

In psychology, when attachment becomes obsession, behavior is distorted. You try too hard. You overcompensate. You signal neediness. Ironically, that very energy repels what you seek.

In Buddhism, this is the core teaching: attachment creates suffering.

In Daoism, the principle of wu wei (无为)—often mistranslated as “doing nothing”—actually means “not forcing.” Acting without strain. Moving with the natural flow of things.

Founders struggle with this deeply.

You want that enterprise contract so badly that you overpromise features. You want that Series A so badly that you inflate metrics. You want growth so badly that you pour money into unsustainable acquisitions.

You grip the outcome.

And in gripping it, you distort the process.

The sales cycle is longer because the client feels pressured. The team burns out because your urgency becomes anxiety. The product bloats because you chase every revenue opportunity instead of focusing.

Here’s the paradox: the less attached you are to a specific outcome, the more clearly you can see the path toward it.

This does not mean you stop caring. It means you detach from ego-driven urgency. You still show up. You still build. You still pitch. But you are not emotionally hostage to the result.

When you’re not desperate for a deal, you negotiate better. When you’re not desperate for funding, you choose better investors. When you’re not desperate for vanity growth, you build a healthier company.

Founders often tell me, “If I don’t push relentlessly, nothing will happen.” I disagree.

Relentlessness is not the same as force. Relentlessness is sustained clarity of direction.

Force is anxiety disguised as drive. One builds momentum. The other creates friction.

Trend is stronger than willpower

Here’s a hard truth: willpower is weak compared to trend.

You can will a product into existence. You cannot will a market into readiness.

The graveyard of startups is filled with brilliant founders who tried to force timing.

In physics, when two frequencies align, they resonate and amplify. When they are out of sync, they cancel each other out. In business, this is the difference between swimming upstream and surfing a wave.

When you align with a macro trend—AI infrastructure, climate adaptation, fintech inclusion, creator monetisation—you harness external momentum.

When you fight trend—trying to revive declining consumer behaviour, betting against technological inevitability—you rely purely on internal energy.

Also Read: Founder etiquette: Questions best left unasked

Internal energy is finite. Trend energy is compounding.

The founders who look like geniuses in hindsight are often those who positioned themselves at the intersection of readiness and inevitability.

They didn’t invent the wave. They recognised it early.

Going with the flow does not mean passivity. It means pattern recognition.

It means asking:

• Is this problem growing or shrinking?
• Is regulation moving in my favour or against me?
• Are user behaviours accelerating in this direction?
• Is technology making this cheaper and easier over time?

If you constantly need to convince the world that it should want what you are building, you are probably fighting the tide. If the world is already moving in that direction and you are simply building the best vessel for it, you are surfing.

Founders love the romantic idea of being contrarian visionaries.

But the most successful ones are rarely contrarian against reality. They are contrarian against complacency. They go with deep structural forces, not against them.

Alignment: The hidden multiplier

There is another idea that cuts across disciplines: coherence.

In psychology, it’s self-congruence. When your beliefs, values, and actions align, you experience less internal friction. In mindfulness practice, it’s presence—your attention unified with your action. In classical Chinese philosophy, Wang Yangming 王阳明called it “the unity of knowledge and action 知行合一.”

For founders, alignment is a hidden multiplier.

Misalignment looks like this:

• You say you value long-term culture, but you reward short-term revenue at any cost.
• You say you care about product excellence, yet you constantly pivot under investor pressure.
• You say you want balance, but you secretly glorify burnout.

Every misalignment drains energy. Your team feels it. Your customers sense it. You feel it in your gut.

An aligned founder is powerful not because they are superhuman, but because their energy is concentrated.

Their vision, words, and actions point in the same direction. They don’t chase every opportunity. They choose the ones that match their thesis. They don’t say yes to every investor. They partner with those who share their time horizon. They don’t build features that contradict their core identity.

Also Read: Strategic investment 101: A founder’s playbook for winning without losing control

Alignment reduces noise.  When your company’s narrative, product, market, and team incentives are coherent, execution becomes smoother. Decisions become faster. Trust increases. Momentum compounds.

It looks like luck from the outside. It is coherent on the inside.

Flow is not laziness

At this point, some founders get nervous. “Are you telling me to just relax and hope things work out?” No.

Flow is not laziness. Flow is disciplined responsiveness.

A surfer doesn’t control the ocean. But she studies tides, watches wind, positions herself, and paddles with precision. She doesn’t fight the wave head-on. She rides it at the right angle.

Founders who succeed in turbulent markets often exhibit this same quality.

They are alert but not frantic. They adjust pricing when conditions change. They pivot segments when signals accumulate. They cut and burn early instead of waiting for a crisis. They are in constant dialogue with reality.

Flow is a relationship with feedback. Force ignores feedback.

When metrics dip, force says, “Push harder.” Flow says, “What is the system telling us?”

When users churn, force says, “Increase marketing.” Flow says, “Is the core value misaligned?”

When fundraising stalls, force says, “Pitch more aggressively.” Flow says, “Is the narrative resonant with current capital cycles?”

This difference in posture can determine whether a startup survives or implodes.

The founder as instrument

There’s one more uncomfortable truth. Your company can only be as coherent as you are.

If you are internally chaotic—oscillating between grandiosity and fear—your strategy will oscillate too. If you are chronically insecure, you will overhire to signal strength. If you are obsessed with validation, you will prioritise headlines over fundamentals.

The market amplifies who you already are. That is why so many ancient traditions emphasise self-cultivation before leadership.

It is not moral preaching. It is structural logic.

When your internal state stabilises, your decision-making improves. When you release attachment to ego outcomes, you negotiate better. When you align your actions with your long-term thesis, you conserve energy.

In a startup, energy management is survival. Burn rate applies to founders, too.

Also Read: The alliance economy: How founders and investors should position in a fragmented world

Going with the flow in 2026

We are in a world where technology cycles are compressing. AI capabilities shift quarterly. Capital markets tighten and loosen in rapid succession. Regulation lags innovation.

In such an environment, brute force is even less effective.

Trend awareness is a strategic advantage.

Ask yourself:

• Are you building for where the world was, or where it is going?
• Are you forcing user behaviour, or enabling emerging behaviour?
• Are you clinging to your original pitch deck identity, or evolving with data?

Sometimes going with the flow means killing a feature you love. Sometimes it means pivoting segments even when your ego resists. Sometimes it means walking away from a flashy partnership that distracts from core alignment. Sometimes it means doubling down when everyone else retreats—because the long-term trend is still intact.

Flow is not about comfort. It is about synchronising with reality.

Also Read: How founders should build for a Meta-national suture

The three commitments

If I had to distil all of this for startup founders, it would be three commitments:

• Look inward before blaming outward: Your interpretation of the market shapes your response. Upgrade your mindset before rewriting your strategy.
• Release desperate attachment to outcomes: Care deeply about the work. Care less about immediate validation. Process excellence compounds more reliably than forced results.
• Align with the trend and align with yourself: Build at the intersection of structural momentum and personal coherence. When your thesis, market, and behaviour resonate, growth accelerates.

When psychology, physics, and centuries of philosophy converge on the same principles, it is worth paying attention.

The founders who endure are rarely the most forceful. They are the most attuned. They sense when to paddle and when to wait. They sense when to pivot and when to persist. They sense when the wave is forming—and they position early.

And when the wave comes, it looks effortless. It never was. It was aligned with the flow all along.

So if you are exhausted from pushing, from forcing, from gripping every metric and milestone with white knuckles—pause.

Step back. Study the tide. Adjust your stance. Then move with the current, not against it. In the long run, the trend is stronger than willpower.

Flow is stronger than force. And alignment is stronger than raw effort. Build accordingly.

—

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 The flow principle: Why the best startups move with the market, not against it appeared first on e27.

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.

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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.

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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.

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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.

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