OpenClaw has been circulating heavily across tech Twitter and developer communities. Agentic AI. Autonomous assistants. AI that “actually does things”.

The narrative is seductive: AI that doesn’t just respond, but acts. Check your inbox. Runs scripts. Controls systems. Executes workflows. It feels like a glimpse into the future. And in many ways, it is.

But the more important question isn’t whether OpenClaw is powerful. It’s whether power alone is product-market fit.

Infrastructure always comes before interface

Every technological shift follows a pattern. Infrastructure comes first. Interface comes later. Mass adoption follows usability. Monetisation follows adoption.

Linux preceded macOS. Terminal preceded GUI. Self-hosted email servers preceded Gmail. Open-source wallets preceded consumer crypto apps.

OpenClaw sits firmly in the infrastructure phase of agentic AI.

It validates something important: Autonomous AI agents are not theoretical anymore. They are technically viable. That matters. But viability and usability are two different markets.

The installation reality

I tried installing OpenClaw myself.

It took me minutes.

But that is because I have a technical background. I understand environments, configurations, system permissions, and hosting layers. I am comfortable unpacking files and troubleshooting.

Now imagine:

• A small business owner.
• A marketing lead.
• A 50-year-old founder.
• A creator trying to automate workflows.

Would they self-host? Configure execution permissions? Think about security boundaries? Debug dependency issues?

Unlikely.

This is not a criticism of capability. It is segmentation.

OpenClaw is designed for users who are technically equipped to operate infrastructure-level systems.

That is a niche. And niches are powerful, but they are not the mass market.

Also Read: Generative AI fatigue: Are we over‑automating creativity?

The product-market fit gap

Much of the public discourse makes it sound as if agentic AI is ready to replace assistants tomorrow.

But product-market fit requires more than technical capability.

It requires:

• Frictionless onboarding.
• Clear guardrails.
• Invisible hosting.
• Managed security.
• Defined execution boundaries.
• Support for non-technical users.

Power excites technologists. Simplicity converts markets.

If a user cannot install, configure, and confidently manage a system, adoption slows. And when adoption slows, monetisation follows.

The total addressable market for developer-grade AI is not the same as the total addressable market for consumer-grade AI. And that distinction matters for founders building in this space.

Infrastructure is step one, not the finish line

OpenClaw is not the problem.

It is proof.

It proves agentic AI is real.

But infrastructure alone does not create scale.

Someone will productise this layer. Someone will abstract the complexity. Someone will build guardrails by default. Someone will turn it into something that feels like using an app instead of running a server.

That is when adoption widens.

A case study in evolution

Before Seraphina became a consumer-facing AI assistant, she was my internal system. Powerful. Flexible. Built for me.

If I had released that early version publicly, adoption would have been zero. Not because it lacked capability. Because it required too much configuration.

I understood the parameters. I defined execution rules. I knew where clearance was required. I knew what she should and should not automate. Most users don’t have that clarity yet. So we simplified. We added guardrails. We reduced friction. We abstracted complexity. We made hosting invisible. We prioritised usability over raw power.

The ideology remained the same. The interface changed. That difference is product-market fit.

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

Automation without process clarity is risk

There is another layer most hype cycles ignore: governance. Agentic AI that can execute commands introduces operational risk if boundaries are unclear.

If someone doesn’t understand:

• Their workflow.
• Their approval layers.
• Their data movement.
• Their access permissions.

Then full autonomy becomes fragile.

In my own systems, certain actions require explicit clearance. Automation only works safely when processes are clearly defined.

This is why I often say: Automate when you know your process. If the process itself is unclear, automation amplifies confusion. Security risk and process ambiguity become friction points — not growth accelerators.

Not everyone needs to learn everything

There is also a broader founder lesson here.

I recently built a full system using Vibe Coding in under an hour. I signed up and executed immediately.

Others have taken courses on similar concepts and still haven’t built anything.

This is not about intelligence. It is about exposure, comfort, and alignment. Just because a capability exists doesn’t mean everyone must master it.

I cannot run a hawker stall or a beauty salon efficiently. That doesn’t diminish my ability. It means my skill set lies elsewhere.

In every tech wave, there are:

• Builders (infrastructure experts)
• Translators (product and interface designers)
• Users (operators and businesses)

All roles are valid.

And if you’re stepping into deep technical territory, one of the smartest moves is not learning everything yourself, but partnering with someone who already speaks that language.

When I entered education, I partnered strategically. It reduced friction. It accelerated execution. It saved time.

Time is the real currency in technology cycles.

The shortcut is not omniscience. The shortcut is access to experience.

The adoption curve is always slower than hype

Social media compresses perception. When everyone talks about a technology, it feels ubiquitous. But conversation does not equal penetration. OpenClaw excites technologists. Agentic AI excites futurists. Investors see long-term potential.

But mass-market adoption follows a different curve. Infrastructure. Abstraction. Interface. Trust. Then scale.

OpenClaw is step one.

The revolution is real. But revolutions rarely become mainstream overnight.

The opportunity is real — participation is optional

Agentic AI will reshape workflows. Autonomous assistants will become normal.

But not every founder needs to install infrastructure. Not every operator needs to configure agents. Not every business needs to self-host. Some will build engines. Some will productise them. Some will simply use them.

Powerful technology is not automatically mass-market technology.

And that’s not a flaw. It’s a phase.

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Deep tech startups rarely fail because the science is uninteresting or the problem is irrelevant. Many fail in the narrow stretch between a working technology and a scalable business, when capital runs out, timelines stretch, and risk shifts faster than funding models can adapt. This gap is commonly referred to as the “Valley of Death”.

What makes this phase especially lethal is not a single missing ingredient, but a mismatch between how risk actually unfolds and how it is financed, managed, and priced.

Deep tech commercialisation is fundamentally a risk allocation problem: most models misprice where technology, capital, and time actually fail, so the “Valley of Death” keeps reopening.

The deep tech risk budget

In software, the dominant risk is market risk — most companies die because nobody cares enough to pay, not because the app can’t be built. In deep tech, the risk budget flips: technology feasibility and funding structure dominate, while market risk is often more about timing than demand.

How common models shift (or ignore) risk

• Traditional VC in deep tech: Spreads bets and accepts high failure rates, but fund timelines (5–7 years) clash with 10+ year deep-tech gestation. Technology and funding risk compound, and companies often die with working prototypes but no runway.
• Corporate venture and pilots: Corporations help with adoption, but operating and technology risk remain with under‑resourced startups. Timing risk is severe: slow procurement and internal politics can strand ventures mid‑pilot.
• University spin‑outs and TTOs: Science is validated, but scale‑up, supply chain, and regulatory risk are under‑priced. Many spin‑outs stall between lab prototype and industrial‑grade product.
• Venture studios: Studio playbooks built for software underestimate capex, regulatory timelines, and hardware complexity when applied to deep tech.

Also Read: Dow hits record high, Nasdaq tumbles 0.6 per cent, Bitcoin miners flee: Signals deeper stress than price alone

Across these models, the Valley of Death persists because risk is assumed rather than designed.

A risk‑first, “foundry” approach

There is a class of foundry‑style models that start from the risk budget and work backwards:

• Enter at high TRL, avoiding pure science discovery risk and focusing on industrialisation.
• Launch ventures with pre‑sold demand and day‑one revenue to compress market and funding risk.
• Centralise legal, finance, and supply chain as a “business‑in‑a‑box” to reduce operating risk.
• Architect each venture around a specific 2–3 year path to liquidity so capital and timelines align.

Dragonfly Ventures and its Accelerated Deep Tech Commercialisation (ADTC) model is one example: it inverts the traditional risk stack by sourcing proven assets, securing day‑one customers, and designing for near‑term exits, turning startup success from a low‑probability bet into something closer to a yield problem.

What Southeast Asia needs to decide

For Southeast Asia to unlock its deep tech potential, the ecosystem will need to make explicit choices:

• Universities: lean into technology risk and push assets to higher TRLs before spin‑out.
• Corporates: underwrite timing and market risk with real offtake and industrial partnerships, not just pilots.
• Funds and foundries: innovate on ownership, liquidity, and operating models to ensure deep tech aligns with private capital cycles.

The Valley of Death won’t close by “more funding” alone; it will close when the region treats risk as a design variable in how we build, fund, and scale frontier tech companies.

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If 2025 felt like an uphill battle for marketing agencies, the data confirms it wasn’t just in your head. It was a structural inflection point.

Across the industry, the sentiment is identical: the old agency model, built on selling hours from more cost-efficient economies, execution, and “activity reports,” is collapsing.

As noted in a recent Entrepreneur analysis, agencies are squeezed between shrinking client budgets and skyrocketing expectations. The days of justifying a retainer with “we increased engagement by 40 per cent” are over. If that engagement doesn’t map directly to revenue, pipeline velocity, or measurable business growth, clients are walking away.

But as we settle into 2026, the winners are already emerging from the wreckage. They aren’t just “surviving” the AI revolution-they are weaponising it to move upstream.

The commoditisation of “doing”

The root of the crisis is the commoditisation of execution. Basic content creation, routine analytics, and campaign optimisation services that once commanded premium fees are now being automated in-house or handled by low-cost AI agents.

According to 2026 market predictions from the World Federation of Advertisers, the industry focus has shifted entirely from “efficiency” (doing things faster with AI) to “effectiveness” (delivering better outcomes). Agencies that stick to the “we do everything” generalist model are finding themselves in a race to the bottom on price.

The era of the linear workflow is over. Successful agencies are dismantling the old ‘strategy-to-creative-to-media’ handover in favor of agile ‘pods.’ By utilising predictive modelling, these integrated teams ensure that strategy and execution happen in tandem, not sequentially.

Also Read: The era of ‘black box’ pricing is over: Why transparency is the new currency in B2B marketing

The new currency: Strategic creative planning

If execution is cheap, insight is priceless.

The agencies thriving in 2026 have pivoted to selling strategic creative planning. They don’t just “make ads”; they use AI to decode cultural nuances, competitor strategies, and audience motivations before a single dollar is spent on media.

This shifts the agency’s value proposition from “outsourced hands” to “market intelligence partner.” This is where the next generation of AI tools is bridging the gap, allowing agencies to reclaim their premium status by offering predictive certainty rather than just creative guesses.

How AI is rewiring creative strategy

To survive, agencies must integrate human intelligence with AI capabilities that go beyond surface-level metrics. We are seeing a rise in platforms specifically designed to handle this “heavy lifting” of strategic analysis.

For instance, AI creative planning solutions are allowing agencies to reduce campaign research time from weeks to minutes. By using large language models to analyse massive datasets, agencies can now predict click-through rates with significantly higher accuracy than human instinct alone, effectively modelling success before launch.

This approach aligns with the “Psychographic Profiling” methodology discussed in Plug and Play APAC’s coverage of AI in marketing. Instead of broad demographic targeting, agencies can now identify thousands of micro-attributes and behavioural preferences, allowing them to cluster audiences and generate content derivatives that resonate on a personal level.

Also Read: Recognised by Google DeepMind, SOMIN aims to redefine AI-powered marketing

Furthermore, academic research demonstrates how AI can be used to “decode” competitor strategies. By analysing high-performing content across an industry, these tools can generate data-backed creative briefs (user stories), giving agency strategists a blueprint for what is working right now in the market.

The 2026 mandate

For agency leaders, the path forward is clear but difficult. 2026 is a reset year.

You must stop fighting the old game of “billable hours for execution.” The new reality demands that you position yourself as a partner who owns the outcome.

• Double down on specialisation: Be the absolute best at understanding one vertical’s pain points.
• Invest in “pre-flight” intelligence: Use AI to validate creative strategies before production.
• Sell the roadmap, not just the car: Clients will pay for the strategy that ensures the execution works.

The “service provider” model is dead. Long live the strategic partner.

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While traditional US financial markets are closed for the Presidents’ Day holiday, the cryptocurrency market continues to operate relentlessly. Global equity futures trade with light volumes, constrained further by Lunar New Year closures across mainland China and Hong Kong. Yet crypto never pauses.

The total market capitalisation rose 0.74 per cent over twenty-four hours to reach US$2.36 trillion. This modest gain reflects a market searching for direction amid thin liquidity and conflicting signals. My view is that this movement represents not a decisive turnaround but a fragile, technical rebound driven by specific ecosystem dynamics rather than broad macroeconomic conviction.

Ethereum’s relative strength provided the primary catalyst for today’s advance. The Ethereum Ecosystem category climbed 1.16 per cent, notably outpacing the broader market’s 0.74 per cent gain. This outperformance follows recent commentary from Vitalik Buterin, emphasising Ethereum’s base-layer neutrality, and from Coinbase CEO Brian Armstrong, noting that retail investors continue to accumulate ETH with diamond hands.

After six consecutive red monthly candles and a period of historic underperformance, Ethereum appears to be executing a technical bounce from deeply oversold conditions. The narrative surrounding the protocol has shifted subtly toward constructive long-term fundamentals, which seems to have encouraged spot buyers to step in at current levels.

However, this rebound remains precarious. Ethereum must maintain a price above the psychological US$2,000 threshold to sustain momentum. A failure to hold that level could swiftly erase today’s gains and reintroduce downward pressure.

Several secondary factors contributed to the market’s upward drift. Bitcoin exchange-traded funds recorded a net outflow of US$98.86 million, indicating persistent institutional caution toward the largest cryptocurrency. In contrast, Solana ETFs attracted a modest $2.34 million in inflows, suggesting investors are selectively rotating capital toward alternative layer-one protocols. This divergence highlights a market in transition, where capital flows are becoming more discerning rather than broadly risk-on.

Meanwhile, the Fear and Greed Index inched higher from 12 to 13, a marginal improvement that nonetheless leaves sentiment firmly in the Extreme Fear zone. This slight uptick implies the current bounce is fragile, likely driven by short-term positioning adjustments rather than a fundamental shift in investor psychology. The market’s weak eight per cent correlation with Gold further confirms that today’s move is crypto-specific, not a reflection of broader safe-haven or inflationary trends.

Also Read: Crypto market bleeds US$44B as US$78M Bitcoin liquidations spark panic

The near-term trajectory of the cryptocurrency market hinges on several technical levels and external catalysts. The immediate resistance sits at the US$2.37 trillion mark, which represents the 78.6 per cent Fibonacci retracement of the recent swing high to low. A daily close above this level could open the door to a relief rally targeting US$2.53 trillion. Conversely, the market must defend the US$2.17 trillion support, which marks the yearly low established on February 6.

A break below that floor would likely renew bearish momentum and test lower liquidity zones. Beyond price action, participants should monitor commentary from Federal Reserve speakers for any shifts in interest rate expectations. Changes in liquidity sentiment could rapidly alter the risk calculus for digital assets, especially in a holiday-thinned trading environment where modest order flow can produce exaggerated price moves.

From my perspective, today’s price action warrants cautious interpretation. The advance lacks the breadth and volume conviction that typically confirms a sustainable trend reversal. Ethereum’s leadership is encouraging, particularly given its oversold technical setup and improving narrative backdrop, but the broader market remains vulnerable to renewed outflows from Bitcoin ETFs and lingering fear among retail participants.

The selective inflow into Solana ETFs suggests a maturing market in which investors differentiate among protocols based on fundamentals rather than moving in unison. This selectivity is healthy in the long term but can produce choppy, range-bound price action in the near term. I believe the current environment favours patience over aggression. Traders should watch for confirmation above the US$2.37 trillion resistance before committing to a long position, while maintaining awareness of the US$2.17 trillion support as a critical risk-management level.

The cryptocurrency market’s resilience during traditional market holidays underscores its unique, always-on nature. Yet this constant operation can also amplify volatility when liquidity is thin and catalysts are scarce. Today’s modest gain, driven by Ethereum’s technical bounce and selective altcoin demand, offers a tentative reprieve for bulls but does not resolve the underlying tensions of persistent ETF outflows and extreme fear sentiment.

Also Read: Crypto market cap drops to US$2.3T as Fed rate cut hopes fade after hot jobs report

The path forward likely depends on whether spot buyers can consistently defend the US$2.17 trillion to US$2.37 trillion range. If they succeed, a relief rally toward US$2.53 trillion becomes plausible. If they fail, residual leverage and continued institutional caution could trigger another leg lower. In my assessment, the balance of evidence points to a market in consolidation, searching for a clearer macro signal or a sustained shift in institutional flows to establish a more durable direction.

Investors should approach this environment with disciplined risk management and a focus on high-conviction narratives. Ethereum’s recent outperformance, supported by protocol-level developments and accumulation by committed holders, presents a compelling case for selective exposure. However, the broader market’s dependence on Bitcoin ETF flows and macro liquidity conditions means that any single asset’s strength can be quickly overwhelmed by systemic headwinds.

The coming days will likely test whether today’s bounce can evolve into a more robust recovery or remain a fleeting pause within a larger corrective phase. For now, the cryptocurrency market offers a lesson in patience, where waiting for confirmation at key technical levels may prove more rewarding than chasing momentum in a landscape still defined by caution and selectivity.

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In B2B services, people often expect value to appear instantly: a sudden jump in numbers, leads, partnerships, or revenue.

But the reality is simpler and less glamorous: real transformation happens through process, clarity, systems, and consistent execution.

Hype and promises of overnight results miss the point. What actually helps businesses operate better, grow faster, and avoid costly mistakes is structure. And because this kind of value builds over time, it is often misunderstood or measured incorrectly.

The illusion of instant results

Short-term metrics can be seductive. They promise clarity and control, but they often measure activity, not progress. When service providers chase quick wins, they risk optimising for the wrong outcomes — speed over substance, visibility over value.

Common pitfalls of the “instant results” mindset:

• Overpromising outcomes that can’t be sustained
• Prioritising short-term KPIs over long-term growth
• Ignoring the deeper systemic changes clients actually need
• Undermining trust when results plateau after early gains

Redefining value in B2B relationships

When a company engages a service provider, they’re not buying hours or slides. They’re buying movement — the shift from where they are to where they want to go.

Value in B2B services isn’t a transaction; it’s a transformation. It emerges from shared understanding, consistent delivery, and the ability to adapt together over time.

A campaign or strategy engagement is valuable when it brings more benefit than what the client invested. And value isn’t just financial. It includes time saved, confusion removed, manpower reduced, and opportunities created.

If you avoid months of trial-and-error, cut wasted spending, avoid bad vendors, and land in a clearer, faster path: that’s meaningful ROI.

Also Read: Why your 50s are the perfect time to start a business

True value comes from:

• Strategic alignment: Understanding the client’s real business drivers, not just their immediate pain points.
• Capability building: Helping clients grow their own capacity to sustain results.
• Iterative improvement: Using feedback loops to refine and evolve solutions.
• Partnership mindset: Treating success as mutual, not one-sided.

This is the foundation of value pricing: You pay for transformation, not just activity.

Value comes from outcomes, not optics

The real test of value is what remains after the engagement ends.

A business has grown in capability. A system now runs where there was chaos. A team gains direction instead of confusion. A market strategy becomes clear instead of abstract. A partnership pipeline lives on instead of dying after one attempt.

Real value is a trajectory shift, not a moment. It’s the difference between constantly improvising and finally having a system.

The patience of progress

Real change takes time because it involves people, systems, and culture. The most effective B2B service providers know how to balance urgency with patience — delivering early wins while laying the groundwork for lasting impact.

Ways to build sustainable results:

• Set expectations early about the timeline for meaningful outcomes.
• Combine short-term deliverables with long-term capability goals.
• Use transparent communication to show progress, even when results are still forming.
• Celebrate learning milestones, not just performance metrics.

The long game of trust

Trust compounds over time. When clients see consistent effort, honest communication, and steady improvement, they become partners in progress. That trust becomes the foundation for deeper collaboration, innovation, and shared success.

Also Read: The architecture of bad deals: Moral hazard in modern business

The intangible value: What it feels like to work with a good partner

Every great service business carries an invisible asset — its ethos.

For us, value is not only in the tangible deliverables clients receive, but in the experience of working with a partner who is honest, transparent, and aligned with their success.

We believe in clarity instead of confusion. We break down complex processes into understandable steps and ensure the client always knows what is happening, who is doing the work, and why decisions are made.

We believe in transparency instead of rent-seeking. Our pricing is clean and direct. We don’t inflate costs or hide commissions. Most of the client’s money goes directly into the work — researchers, vendors, outreach teams, content creators — not into layered markups.

We believe in fairness. Vendors are paid properly; clients receive fair value; the ecosystem grows on trust instead of exploitation.

And we believe in partnership. We don’t take work we can’t deliver. We don’t overpromise. We don’t disappear. We stay accountable from day one to the last milestone.

For many clients, these intangibles — honesty, clarity, competence — are worth more than any deliverable.

Why this matters: The B2B world is broken

Much of the B2B service ecosystem is built on opacity. Consultancies overcharge and underdeliver. Agencies outsource everything and hide their vendors. Freelancers disappear after payment. “Strategy decks” look impressive but produce nothing.

Globaloca was created as a counterpoint. The focus is not on selling time but on enabling progress. Emphasis is placed on clarity, structure, and repeatable systems rather than ad hoc decision-making or experimentation.

The platform provides transparent pricing, verified vendors, multiple quotes, milestone-linked payments, and performance tracking.

Because the B2B service industry doesn’t need more noise. It needs a trust infrastructure.

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In this interview, e27 speaks with Eric about the Asia AI Association’s work in advancing AI innovation, professional collaboration, and ethical adoption across Asia, as well as how organisations can approach AI transformation responsibly.

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

Fostering AI innovation, networking, and ethics across Asia

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

Eric: Fostering AI Innovation, Networking and Ethics Across Asia Promoting technological advancement in Artificial Intelligence, facilitating professional connections, and championing ethical practices in Asia

1. Professional Development – Gain access to educational resources, career advancement programs, skill development training, and expert consultancy to boost your AI career.
2. Networking and Collaboration – Connect diverse communities of AI professionals, academics, and enthusiasts for collaborative research, and events.
3. Industry and Ethical Insights – Stay updated on AI trends, technologies, and best practices while engaging in discussions about interdisciplinary applications and ethics.

Promoting AI adoption through collaboration

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

Eric: As a business association focused on AI, AAIA connects with corporations, individual professionals, and business networks to provide training, knowledge-exchange platforms, workshops, and hackathons—promoting how AI can benefit everyone.

The rapid advancement of Artificial Intelligence (AI) has reshaped industries, revolutionized workflows, and transformed the way we interact with technology. With AI-powered automation, machine learning, and generative models now performing tasks once reserved for human expertise, many professionals fear being replaced by intelligent systems. However, while AI significantly enhances efficiency, it lacks the deeper capabilities of Human Intelligence (HI)—such as intuition, creativity, critical thinking, and emotional intelligence.

This is where AAIA comes in: to guide innovation through deeply human collaboration with technology. AI on its own is powerful; AI guided by empathy, ethics, and human creativity is transformative. This is the role of Human Intelligence (HI) that we advocate and champion.

Navigating organisational change in AI transformation

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

Eric: As a business association serving our members, the general public, business networks, and the public sector, we recognise that change is often painful—but it is also inevitable. One example is a technology member of ours who spent over 90% of the engagement working closely with a local clinic chain on its AI transformation, converting manual bookkeeping and accounting processes into automated workflows. The key hurdles for the client were:

1. Clearly understanding and accurately presenting existing processes; and
2. Adapting to new AI-assisted workflows and embracing the discomfort that comes with change.

Also read: AI Pulse Exclusive: How Explico is building AI teachers can actually rely on

Rising awareness alongside cautious adoption

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

Eric: Public awareness of AI is improving rapidly, and adoption is happening faster than ever before. However, most people still treat AI merely as a tool—some embrace it enthusiastically, while many remain cautious or even fearful. Few have fully considered how AI-driven transformation can fundamentally improve and reshape the way we live, work, and make decisions in our daily lives.

The underestimated effort behind AI transformation

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

Eric: Leaders and founders often underestimate the effort, time, and resources required for meaningful transformation. While many existing business processes may appear to function adequately, they are frequently ambiguous or messy beneath the surface. Converting these informal processes into structured workflows or systems requires first making the situation transparent, then introducing new ways of working—both of which take time, alignment, and patience to “clear the air” before real progress can happen.

Communication as the foundation for AI adoption

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

Eric: A practical recommendation for organisations that are just beginning to explore—or looking to scale—AI adoption is to prioritise communication above all else. Effective communication must happen at every level and in every form: from strategic planning and leadership alignment, to clear documentation of existing and future processes, and down to day-to-day verbal conversations across teams.

AI transformation is not purely a technology exercise; it is a shared understanding exercise. When goals, assumptions, workflows, and expectations are communicated clearly, organisations can surface ambiguities early, reduce resistance, and align people around a common direction. This clarity makes it far easier to redesign processes, adopt AI-assisted workflows, and move toward the intended outcomes with less friction and fewer costly detours.

Also read: AI Pulse Exclusive: How CoBALT is designing AI that teams can actually trust

Continuing the AI plus human intelligence approach

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?

Eric: Continue to roll out the AI + HI (Human Intelligence) program to aid the industry to improve with human touch.

Invitation to engage with the AI community

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

Eric: Readers are encouraged to join our events & as members.

Human intelligence in the AI era

This conversation highlights the growing importance of keeping AI development grounded in human judgment, ethics, and collaboration as adoption accelerates across industries. As organisations move from experimentation to real-world deployment, initiatives that prioritise professional education, responsible innovation, and interdisciplinary dialogue will play a key role in ensuring AI strengthens human capability rather than replacing it.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, subscribe to our newsletter. You can also explore more AI stories here.

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For centuries, government has often been seen as a slow, reactive bureaucracy. Citizens fill out forms, wait in lines, and hope for a response. Artificial Intelligence (AI) is beginning to change this in a fundamental way, enabling a shift from a government that reacts to problems to one that anticipates needs.

Think of it like managing a city bridge. The old way was to wait for cracks to appear or, worse, for the bridge to fail, and then scramble to make repairs. The new, AI-driven approach is to use sensors and predictive models to understand the bridge’s structural stress in real-time, allowing engineers to prevent the failure before it ever happens.

This shift is more than just a technological upgrade; it’s a redefinition of the social contract. As decisions about benefits, health, and safety move from human clerks to algorithms, the relationship between the citizen and the state is fundamentally changing.

This is the promise of AI in government: to build a more proactive, personalised, and efficient state that can forecast health crises, disburse benefits to those in need without lengthy applications, and optimise city traffic dynamically. This document will explore what “AI” really is, see how it’s being used to remake key public services, and understand the critical challenges we must address, all based on insights from the “Tools to build an AI state” report.

What exactly is the ‘AI’ in government? A simple toolkit

“Artificial Intelligence” isn’t a single technology; it’s a collection of tools. Just as a mechanic has different tools for different jobs, governments use various types of AI to solve specific problems. The table below introduces three of the most common AI technologies used in public services.

Now that we understand the basic tools in the government’s AI toolkit, let’s explore how they are being applied to improve the services that impact our daily lives.

Also Read: A new ocean order: What startups and investors need to know about the High Seas Treaty

How AI is remaking public services: Three key examples

This transformation of the social contract is not abstract; it’s happening now in the public services that define our daily lives. From the classroom to the hospital to the daily commute, AI is being applied to fulfil the state’s core promises more effectively.

Here are three key examples.

• Education: From standardised lessons to personalised learning

The traditional challenge in education has always been the “one-size-fits-all” model, where a single teacher must try to meet the diverse needs of a large classroom. AI’s primary promise is to make learning adaptive and personalised for every student.

• AI-driven tutoring: Platforms like Squirrel AI in China provide millions of students with tutoring that adjusts the difficulty of lessons in real-time based on their performance, acting like a personal tutor for each child.
• Smarter teacher tools: AI can automate routine tasks like grading assignments and generating lesson materials aligned with national curricula, providing teachers with detailed analytics on student progress. This frees up teachers’ time to focus on what matters most: mentoring and providing personal support to their students.
• Building economic pathways: AI is not just for children. Platforms like Singapore’s SkillsFuture use AI to analyse labour market trends and guide adult workers toward in-demand skills and jobs, strengthening the promise of lifelong economic opportunity.

Just as AI can tailor a student’s education, it is also beginning to personalise healthcare from the moment a person seeks care.

• Healthcare: From treating sickness to predicting it

Healthcare systems worldwide are strained by rising costs and a focus on treating people only after they get sick. AI is playing a central role in shifting this focus from treatment to anticipation, making public health more predictive and preventive.

• Faster, more accurate diagnosis: Computer vision algorithms can analyse medical images like X-rays and MRIs with incredible speed and accuracy. These systems can identify anomalies in seconds, flagging risks that allow for intervention before a crisis occurs and leading to better patient outcomes.
• Predicting health crises: During the COVID-19 pandemic, AI-driven epidemiological models helped governments predict where outbreaks would occur, allowing them to allocate resources more effectively. Beyond pandemics, these models can analyse health records to flag patients at high risk of conditions like sepsis, allowing hospitals to intervene preventatively.

While AI’s impact on personal health is profound, its ability to analyse and optimise large, complex systems is also reshaping the public infrastructure we all share, starting with our transport networks.

• Transport: From traffic jams to smart traffic flow

Every city dweller is familiar with the frustration of traffic congestion, transit delays, and infrastructure failures. By analysing vast amounts of real-time data, AI is helping make transport systems adaptive and predictive, smoothing out the flow of people and goods.

• Dubai’s smart traffic signals: In Dubai, AI-powered traffic lights respond dynamically to real-time traffic conditions. Instead of following a fixed schedule, they adjust their timing to reduce congestion and cut down on waiting times for drivers.
• China’s city brain: This massive platform, developed by Alibaba, analyses city-wide data from cameras, GPS, and public transit. It orchestrates traffic flow across entire districts, dramatically cutting response times for emergency vehicles by minutes that can save lives.

These examples show a future of exciting possibilities, but this progress also comes with significant challenges and questions that society must carefully address.

Also Read: Asia rises in the AI chip race: China to outgrow US by 30 per cent by 2030

The Big questions: Balancing progress with people

Deploying this technology responsibly requires confronting the profound governance challenges it creates. While the benefits are clear, AI’s use in the public sector forces us to ask critical questions about fairness, accountability, and our fundamental rights.

• Is it fair? The challenge of bias

AI systems learn from the data they are given. If that data reflects historical human biases, the AI can learn and even amplify those same prejudices. For example, a predictive policing model trained on biased arrest records could unfairly target a community that was already over-policed, creating a vicious cycle of discrimination.

• Who’s in charge? The accountability problem

Many advanced AI systems are a “black box,” meaning it can be difficult, even for their creators, to understand exactly why they made a specific decision. This raises a critical question: if an algorithm wrongfully denies a person welfare benefits or flags them as a risk, who is accountable for the mistake?

• Are we being watched? The privacy puzzle

To work effectively, AI often requires vast amounts of data about citizens, from their health records to their daily travel patterns. This creates a fundamental trade-off, raising serious concerns about the potential for government surveillance and the protection of personal privacy.

Conclusion: Governing wiser, not just faster

Artificial Intelligence is clearly more than just a new technology; it is a powerful force that is reshaping the relationship between citizens and the state. It offers the tools to build a government that is not only faster and more efficient but also more proactive and personalised.

However, the true measure of success for AI in government will not be speed or cost savings alone. It will be whether these tools are used to strengthen the social contract by making governance more transparent, inclusive, and trustworthy.

The goal is not simply to adopt AI the fastest, but to integrate it wisely, ensuring that this powerful wave of technological innovation is carefully aligned with our democratic values and the public’s trust.

Watch this space for a follow-up article for a deeper dive into AI applications in Government, and where opportunities lie for startups and investors.

A comprehensive analysis, “Tools to Deliver The AI State – a Technology Watch and Horizon Scan”, is available here.

You can also find me on my podcast and newsletter, where I share regular insights on geopolitics and leadership.

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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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Over the past few years, I’ve come to genuinely admire how far Apple and Google have pushed the world toward stronger privacy and security.

Their efforts have not only reshaped user expectations but also forced the entire tech industry to rethink how data is handled, stored, and protected. Their frameworks — from Apple’s App Tracking Transparency to Google’s Privacy Sandbox — have raised the bar for what users expect in terms of trust and control.

These frameworks didn’t just appear overnight; they were the result of years of advocacy, user demand, and a growing recognition that privacy is not a luxury but a necessity in the digital age.

But as someone working in privacy-preserving AI, I’ve also seen the other side of this progress: access. This is where the narrative gets complicated. While these safeguards are undeniably beneficial for users, they also create an unintended consequence: they can stifle the very innovation that aims to enhance privacy further.

The paradox of privacy

Every new safeguard limits who can access sensitive device signals — including notifications, app usage, and network patterns. That’s good for users. After all, no one wants their personal data to be exploited or mishandled. These protections ensure that users have more control over their digital footprints, which is a significant step forward in an era where data breaches and misuse are all too common.

Yet, in practice, these restrictions mean the same companies that set the rules also keep privileged access for themselves. This creates a dynamic where only a handful of players—those with the resources and influence to shape these frameworks—can fully leverage the data they collect. Smaller players, even those with innovative solutions, are often left on the sidelines, struggling to gain the access they need to prove their concepts.

Also Read: How to build customer trust with improved data privacy

Independent innovators — the ones building privacy-enhancing technologies that never move or expose data — often can’t even demonstrate their models because the APIs are closed. This is particularly frustrating because these innovators are often the ones pushing the boundaries of what’s possible in privacy-preserving tech. Without access to the necessary tools and data, their potential contributions remain untapped.

It’s a strange paradox: we protect privacy by preventing the very people designing privacy-safe systems from proving their value. In essence, we’re creating a system where privacy is protected, but only for those who already have power. The innovators who could help democratise privacy are left struggling to gain a foothold.

The bigger picture

Regulators have started to notice this imbalance. Regulators have started to notice this imbalance. This is a positive sign, as it indicates that the conversation around privacy is evolving beyond just protection to include fairness and accessibility.

• The EU Digital Markets Act (DMA) now classifies large platform owners as “gatekeepers” who must support interoperability and fair access to the data business users generate.
• Singapore’s PDPA and AI Governance Framework name Federated Learning, Multi-Party Computation, and Differential Privacy as key enablers of responsible data use.
• Global standards bodies such as OECD and NIST are defining what trustworthy privacy-preserving collaboration looks like.

These developments aren’t about punishing Big Tech. Rather, they’re about creating a more equitable digital ecosystem where innovation isn’t stifled by monopolistic practices. They’re about ensuring that privacy doesn’t become a monopoly, reserved only for those who own the operating system. The goal is to foster an environment where privacy is a shared responsibility, not a privilege reserved for a select few.

Also Read: How to unlock possibilities through data privacy enhancing technologies

A personal reflection

I don’t write this to criticise Apple or Google; their leadership in privacy has influenced how users perceive digital trust. In fact, their contributions have been instrumental in shifting the industry toward a more user-centric approach to data privacy. Without their efforts, we might still be in a world where user data is treated as a commodity rather than a right.

However, progress in technology should be inclusive, not exclusive. Inclusivity in this context means ensuring that the tools and frameworks designed to protect privacy are accessible to all innovators, not just those who already have a seat at the table. If we truly believe that privacy is a universal right, then access—guided by transparency and compliance, not control—must be part of that vision.

Because privacy shouldn’t be a privilege, it should be a fundamental right that is accessible to everyone, regardless of their size or resources. It should be the foundation on which fair innovation is built.

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Jeremy Chua, Chief Technical Officer & Co-founder, BioArk

Farming practices across Asia face mounting pressure to increase output while reducing environmental damage. For BioArk, a Singapore-based agritech company, this challenge is a starting point for rethinking how fertilisers are made, applied, and integrated into existing systems without demanding costly changes from farmers.

Rather than focusing on history or legacy methods, BioArk’s team develops bio-based fertilisers that compete directly with conventional chemical inputs.

“Our goal is to provide a like-for-like substitute,” says Jeremy Chua, BioArk’s CTO and co-founder, in an email to e27. “One that performs as well, costs comparably, and doesn’t require farmers to rework their operations.”

Its flagship product, Arktivate, is positioned as an interchangeable input that delivers immediate results while improving soil conditions over time. The company frames this as part of a broader “symbiotic ecosystem” approach, blending ecological processes with applied science to produce measurable outcomes in crop yields, soil health and environmental impact.

Key to BioArk’s development philosophy is the view that plant health cannot be separated from environmental health.

“Nature manages nutrient cycling and biodiversity without external inputs,” says Chua. “We try to understand how that works, identify the underlying scientific principles, and build those into our product designs.”

Also Read: You are what you eat: Opportunities in Southeast Asia’s agri-food sector

This involves using biotechnology processes to incorporate sustainably sourced organic inputs. The aim is to enhance the availability and uptake of nutrients while supporting the surrounding soil microbiome. According to the company, field tests show that these fertilisers can match or outperform traditional inputs while reducing reliance on fossil fuel–based products like urea or mined resources such as phosphate and potash.

The company also points to early evidence suggesting that every tonne of its fertiliser used may help store about 0.5 tonnes of CO₂ equivalent annually through improved soil biology. While this data is still being validated, it speaks to a wider goal: to enable farming methods that are economically viable while contributing to climate mitigation and ecosystem regeneration.

Growth strategy

BioArk is currently focusing on expansion in Indonesia and is exploring similar opportunities across key Southeast Asian agricultural markets. Countries such as Vietnam, Thailand and the Philippines are particularly interesting, given their high food production levels and vulnerability to environmental degradation.

Matthew Edward Loh, Chief Executive Officer & Co-founder, BioArk

The company’s strategy involves close collaboration with local farming communities to adapt its products to specific soil conditions and crop types. In practice, this includes on-the-ground demonstrations, training sessions and ongoing agronomic support. This approach is intended to reduce barriers to adoption and ensure compatibility with existing agricultural practices.

Also Read: Singapore anchors inaugural ClimAccelerator for agritech startups in APAC

The decision to avoid requiring major behavioural shifts reflects one of the company’s core assumptions: that new tools for sustainable agriculture must be easy to use, or risk being ignored altogether. Many of today’s alternatives—such as organic farming or precision agriculture—offer environmental benefits but often require significant capital investment or operational changes.

“Inertia is a real issue,” Chua says. “If we want widespread change, solutions must fit into current systems, not expect systems to change first.”

BioArk’s approach also reflects broader shifts in how agricultural innovation is pursued, particularly in urban hubs such as Singapore. As a regional centre for agri-food research, the city-state has provided BioArk access to government-backed R&D facilities, startup support networks and policy frameworks that prioritise sustainability.

Partnerships with local agencies, including Enterprise Singapore (ESG), have supported BioArk’s product development and helped position its technology for international deployment. Chua says this environment has allowed the team to quickly iterate and validate its fertilisers before scaling into wider markets.

Looking forward, BioArk aims to expand its manufacturing capacity, extend field trials across Asia and forge new partnerships to accelerate adoption. Its long-term objective is to reduce the agricultural sector’s reliance on synthetic fertilisers while contributing to improved soil resilience and carbon storage.

“Our focus is on scaling what works—environmentally, scientifically and economically,” Chua says. “Not in isolation, but in partnership with the growers who work the land every day.”

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Image Credit: BioArk

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In a powerful demonstration of purpose-driven innovation, Indonesian agritech company Elevarm has unveiled its 2024 Impact Report, shedding light on its transformative contributions to the nation’s horticulture sector. The report outlines how Elevarm’s integrated ecosystem model is revolutionising farming practices, improving farmer livelihoods, and advancing sustainable agriculture across the archipelago.

“Indonesia’s food security depends on empowering farmers with the right tools, knowledge, and support,” says Bayu Syerli Rachmat, co-founder and CEO of Elevarm, in a press statement. “By directly addressing sustainability at the grassroots level, Elevarm is proud to help close the productivity gap while protecting the environment.”

In 2024, Elevarm supported more than 16,000 smallholder farmers. These farmers experienced a remarkable transformation, with 36.5 per cent reporting increased yields and average incomes rising from IDR12.1 million (US$735) to IDR14.1 million (US$857) per crop cycle.

Beyond financial gains, farmers saw a 14 per cent reduction in chemical usage, thanks to Elevarm’s organic solutions such as vermicompost, produced and distributed in-house.

At the core of Elevarm’s achievements lies its integrated ecosystem service model, designed to create “triple wins”: boosting livelihoods, enhancing food security, and strengthening environmental resilience. Through a blend of advanced technology, tailored financing, market infrastructure, and advisory services, Elevarm addresses systemic challenges in Indonesian agriculture.

Also Read: Unlocking agritech’s potential: Can Southeast Asia rise to the challenge?

Tech-driven cultivation support

Elevarm leverages cutting-edge tech to deliver tailored cultivation practices. Farmers are equipped with high-quality inputs, including seeds, organic fertilisers, and biostimulants. The company also employs tech-driven solutions such as soil testing, monitoring dashboards, IoT-based field devices, and a dedicated farmer app that offers real-time insights and personalised guidance.

Addressing one of smallholder farmers’ most significant barriers, Elevarm provides affordable loans tied to harvest repayment. This financial support covers an average of 62.5 per cent of farmers’ working capital needs, reducing dependence on informal lending channels.

Moreover, crop and life insurance options protect farmers from risks associated with climate events, pests, and unforeseen personal tragedies. By shifting reliance away from informal lending, the company intends to help farmers gain financial stability and peace of mind.

To ensure farmers have reliable markets for their produce, Elevarm has established a comprehensive market infrastructure. Under its Farmer Partnership Model, farmers commit to selling their entire harvest to Elevarm at mutually agreed-upon fair prices. This approach guarantees off-take certainty and strengthens market trust, reflected in the growing volume of produce sold directly to the company.

Sustainable and professional farming practices

Sustainability remains central to Elevarm’s vision. The company promotes Good Agricultural Practices (GAP), polyculture (adopted by 42.1 per cent of its farmers), organic fertilisation, and reduced chemical and water usage. Their flagship vermicompost and NextBio products are pivotal in improving soil health, enhancing plant resilience, and driving long-term environmental benefits.

A rigorous data-driven approach underpins Elevarm’s operations. The company employs stratified sampling, historical yield analysis, field surveys, and third-party datasets to measure impact accurately. This meticulous data collection informs strategic decisions and ensures transparency in reporting outcomes.

Also Read: Automation, AI, and agritech power Vietnam’s VC momentum

A robust governance framework incorporating Standard Operating Procedures (SOPs), Service Level Agreements (SLAs), and comprehensive risk management supports Elevarm’s model. This structure ensures consistent service delivery, timely input distribution, efficient payment processing, and reliable claim verification.

A vision for nationwide transformation

Looking ahead, Elevarm is poised to scale its model nationally. Following its focus on impact in 2024, the company plans significant expansion in 2025, targeting new high-value commodities such as shallots, tomatoes, and beans. It will also venture into agroforestry, revitalising underutilised lands in Purwakarta, West Java.

By 2026, Elevarm intends to extend operations to Sumatra and Sulawesi, with an eye on institutionalising its model through policy advocacy and government collaboration.

The company is also developing a predictive AI-powered digital platform that aims to become an indispensable tool for farmers, offering even more precise and timely insights.

As part of its growth strategy, Elevarm plans to introduce third-party audits and Social Return on Investment (SROI) analyses to further validate its SDG-linked outcomes. These efforts will expand the scope of measurable indicators, including gender impact, environmental footprint, and income stability.

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Image Credit: Elevarm

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