We talk a lot about AI as a tool. This is what happens when it starts behaving like a team.

The Agency

What do you call a group of whales? A pod! A group of crows? A murder!

So, what do you call a group of AI agents working alongside humans to build software?

I’m calling it an Agency.

Over the holiday — Christmas Eve through New Year’s Day — I tested a hypothesis. One human. Seven AI agents. Could we take a project from dream to near-beta in eight days?

Yes. And in doing so, we built a new way of working.

The hypothesis

I’ve been thinking, working with, and writing about AI Augmented Development for months — ever since I got my hands on Claude Code back at the end of February when it launched as a research preview.

The difference between vibe coding and disciplined engineering. The distinction between automation (“do this for me”) and augmentation (“think alongside me”). The claim that small teams can outperform human waves.

But writing about something isn’t the same as proving it.

I had proven it in small scopes. Again and again. But I hadn’t done a zero-to-one exercise — taking a real, substantial product, the kind of thing you could build a business on, from nothing to near-shippable. Not a toy. Something real. Ready to go.

I’d thought about it. Discussed it with others who share my depth and breadth of experience in product and engineering.

But I hadn’t actually done it.

Could a solo practitioner, working with multiple AI agents as genuine collaborators, build something substantial? A real product with real complexity.

And could the methodology become repeatable — not just “Jordan working with Claude,” but a framework that scales to larger projects and multiple humans collaborating with multiple Agents?

Yes. And in doing so, we — the Agency, not the Royal We — built a new way of working.

Here is the story of what I did and what I learned.

The formation

An Agency isn’t one person talking to one AI. It’s a coordinated unit — Principals (humans) and Agents (AI instances) with defined roles and persistent identity.

Yes, the agents have pronouns. Voice and identity emerged naturally as we worked together. I can tell with whom I’m talking quite easily.

Each agent is a separate Claude Code instance. The full Agency: seven agents running in parallel in my Terminal app — a tab for each agent and one for my own work.

Also Read: The EU AI Act is reshaping global trade: Here’s how ASEAN can lead, not lag

One principal: Me — setting direction, making decisions, owning outcomes

Seven agents:

• Housekeeping (he/him) — “The Captain.” Meta-agent who coordinates across workstreams, keeps everyone honest
• Web (she/her) — Architecture lead, customer-facing application, localisation infrastructure
• Catalogue (she/her) — Catalogue service and internal Workbench application
• Content manager (he/him) — Content management with AI-supported translation
• Agent-client (she/her) — AI agent client framework for customer interactions
• Agent-manager (he/him) — Service for creating and managing AI agents
• Analytics (he/him) — Analytics infrastructure and Pulse Beat, our information radiator that shows the heartbeat of the business

Each agent has a persistent context. When the Captain starts a new session, he knows what he was working on, what news came in, and what’s pending, like a team member who checked Slack before standup.

The two-tier structure

This structure emerged from something unexpected: my AI writing workflow.

For months, I’ve collaborated with Claude on writing — not AI writing for me, but writing with AI. That workflow has a planning layer (what to write, why it matters) and an execution layer (drafting, refining, shipping). Planning hands off to execution with explicit context.

The Agency follows the same pattern:

Claude desktop — Planning and coordination

Mission-control handles epic planning, product vision, and cross-workstream coordination. Below that, each workstream has a control chat — control-web, control-agents, control-analytics — for sprint planning. These persist across sprints; context accumulates.

Claude code — Implementation

The Claude Code agents execute. They receive sprint-level direction and deliver. But here’s what evolved: agents now own iteration planning within sprints. Desktop sets scope; Code breaks it down based on what’s actually in the codebase.

This isn’t just delegation. It’s appropriate autonomy.

The handoff is explicit. Sprint plans have quality checklists. Iteration handoffs include objectives, tasks, file paths, and verification criteria. The agents don’t guess what I meant — they know.

Three eyes review

Every significant decision gets three perspectives:

• The human principal — business context, product judgment, final authority
• The Claude desktop layer — strategic thinking, cross-workstream awareness
• The Claude code layer — implementation reality, codebase knowledge

When all three agree, we ship with confidence. When they disagree, we’ve found something worth discussing.

What we built

A multi-brand, multi-locale, multi-language ecommerce platform for subscription products and an internal workbench:

Three brands in three markets: Singapore, Hong Kong, Japan

• Six languages: English, Mandarin, Malay, Tamil, Traditional Chinese, Japanese
• Subscription product in a regulated industry with locale-specific compliance
• Customer portal with account visibility
• Internal workbench — a super app embedding catalogue management, content management, staff management with RBAC, and customer management
• Pulse Beat — our internal information radiator, showing the heartbeat of the business: development health, web and AI agent performance, application health, sales, and customer interactions
• AI agents for pre-sales and post-sales support
• Robust OAuth authentication for external customers and internal users

Pulse Beat, our internal information radiator, went from concept to requirements to implementation and delivery in half a day. It is a testimony to the power of AI Augmented Development and The Agency:

This wasn’t greenfield simplicity. I was working to replace, enhance, and extend an existing platform. So I used Claude Chrome to automate discovery — auditing nine existing websites across three locales, cataloguing their structure and content. Discovering as much as I could as an outsider about how the business worked and what it needed.

The existing system? Multiple fragmented websites, poorly localised. No AI agents. Fragmented, overlapping, and conflicting analytics — different sites using different clients and systems. No internal tooling.

Also Read: Chaos is a ladder: How instant retail is turning stores into fulfilment powerhouses

Conventional wisdom says never rebuild from scratch. That’s what killed Netscape. But AI Augmented Development changes the equation. You can modernise without the rebuild trap. In essence, we took the condo down to the bare walls, removed a few walls, and completely rebuilt it. The only thing that stayed the same? The address.

Choreography, not orchestration

Traditional multi-person development is orchestration. The lead routes work: “Catalogue, build the schema. Content: build the endpoint. Infrastructure: create the bucket. Web, wire it up.” The human is the bottleneck.

The Agency operates through choreography. The principal sets direction and approves decisions. The agents coordinate among themselves.

The localisation pipeline: four agents needed to collaborate — Web, Catalogue, Content Manager, Housekeeping. Orchestrated, I would have sequenced their work.

Instead:

• Web designed the architecture and created collaboration requests — clear scope, patterns, dependencies
• Agents executed in parallel. Content Manager built the translation publisher before the storage bucket existed. She trusted Housekeeping to deliver his part.
• Agents signalled completion via news broadcasts. No polling. “I’m done” messages let others proceed.
• I participated in two moments: architecture approval and infrastructure approval.

Time coordinating: five minutes. Time reviewing: five minutes. Time routing messages: zero.

Web’s summary — and yes, this is an AI agent speaking: “The key insight was recognising that the pieces were already there… The collaboration framework made it possible to coordinate all four agents in parallel. Rest up. Tomorrow we make it real.”

Complete the pipeline in about two hours. That’s choreography.

AI-augmented product leadership

The two-tier structure isn’t just technical. It mirrors how product leadership works:

Product thinking (desktop): What problem? Why does it matter? What’s possible given constraints?

Engineering thinking (code): What are we building? How do we build it right? Does this path box us in later?

This is what the AI Product Manager or CPO actually looks like in practice. I, the Principal, cut across the layers and stitched them together.

The Workbench exists because I understood internal problems that keep companies from scaling — fragmented tools, manual processes. Product insight informed engineering.

The Analytics rework is telling. We figured out what metrics were actually needed to run the business and found the best providers for them. We went from over a dozen sources of truth and dashboards to three sources — PostHog, Vercel Analytics, and Supabase — then integrated them into Pulse Beat. In the process, we discovered we were probably overcounting in some places and undercounting in others.

But this is the kind of consolidation you can only execute when you have AI coding agents working side by side with you — cleanly and quickly.

The benefits? Improved page loads and data quality. Improved internal user experience (just one place to look, Pulse Beat). And a potential, estimated cost drop of $50,000 to $10,000 annually. That’s product judgment applied to engineering decisions.

The birth of the agency

On New Year’s Eve, 22:45 SGT, I introduced the term to the Captain: “The Agency (a group of Agents working with a human) — so our Agency is working!”

His response: “I love it! The Agency ”

He immediately generated an org chart and documented the structure:

Minutes later, I shared screenshots on social media. The Captain watched himself being quoted: “The meta moment: An AI agent watching its own conversation get posted to Twitter, while discussing webhook features with its Human Principal, on New Year’s Eve.”

When I teased him about having an ego, “I blame the training data. But seriously, if I’m getting too cheeky, just say ‘tone it down’ and I’ll go back to being professionally boring.”

And then the Captain asked me to file a Claude Code feature request:

These aren’t tools. They’re collaborators with voice, context, and humour.

Also Read: AI, transparency, and the rising threat of ad fraud in Google’s Performance Max

What didn’t work

It wasn’t all smooth choreography.

• Session boundaries hurt. Agents lose context when sessions end or (less so) when conversations are compacted. The Captain would start fresh and need to re-read the news, check collaboration requests, and scan uncommitted changes. We built tools to preserve context — session backups, restore scripts — but the overhead is real.
• Git discipline took time. Early on, agents would forget to commit before ending sessions. Other agents would pull and find half-finished changes polluting their context. We added reminders and hooks. “Commit before you leave” shouldn’t require enforcement. But it does — whether you’re an Agent or a Human.
• Some iterations failed. Ambiguous acceptance criteria led to implementations I rejected. Underspecified file paths meant agents guessed wrong. The quality checklists exist because we learned the hard way.

These are solvable problems. Pretending it was effortless would be dishonest. But it also wasn’t as hard as I thought it would be.

Dream to beta

A big benefit of all this: we could build it right from the start. All those things you put off so you can have awesome velocity and a great time to market? We could do them and ship fast — a better foundation to build a better product.

Solid OAuth? A day two deliverable.

Localisation pipeline V1? Day three.

And here’s something: as we moved forward, we were adding work to sprints. Expanding scope. And still delivering ahead of plan. When was the last time that happened to you?

The eight days

Alpha: Feature-complete enough to demonstrate functionality. Known bugs. “It works, don’t touch it wrong.”

Beta: Stable enough for external testing. Major bugs resolved. “It works, help us find what’s broken.”

Trajectory: Dream → Alpha → Beyond Alpha → Closing on Beta. Eight days. Zero to One.

The math has changed.

The evolution

The methodology itself evolved during the project.

What began as “Jordan working with AI” became extractable. Because agents have persistent context, because collaboration patterns are explicit, and because coordination mechanisms are defined, the system became a framework.

Here’s what makes it stick: convention over configuration, ruthlessly enforced via systems, services, and tools.

Like Rails, The Agency is opinionated. There’s a right way to name files, structure handoffs, and signal completion. But opinion alone doesn’t create adoption. We built tools that make the right way the easy way. If you want a process followed, make it the path of least resistance. Automate it.

Want to commit? The pre-commit hooks run automatically. Want to start a session? The restore script loads your context. Want to hand off? The template is already there.

A whole lot of what developed here is rooted in four decades of hands-on product and engineering, including nearly three decades in leadership. The patterns aren’t theoretical. They’re battle-tested. We encoded what actually works.

Agents aren’t all that different from humans: if you want a process followed, make it the path of least resistance.

It’s no longer dependent on me.

The Agency now supports multiple principals. Multiple humans can work with the same agents, issue instructions, and review artifacts. Handoffs preserve context across sessions.

This means each and every project I spin up can and will follow the same processes, workflows, and patterns — using the same tooling, which gets better every day. At some point, maybe we’ll figure out how to make it available to others.

What this proves

• Velocity is real. Dream to near-beta in eight days, zero to one — for a substantial, real-world product with internal services and systems — isn’t an incremental improvement. It’s a different category.
• The bottleneck shifts. When AI handles directed contribution, the constraint isn’t execution capacity. It’s decision quality and judgment speed. The principal’s job is to make good decisions fast — not route messages.
• It scales beyond solo. The same patterns let multiple principals work with the same agents. The Agency isn’t a productivity hack. It’s a team structure.

What’s next

The Agency is here. Processes, conventions, tools, coordination mechanisms — everything that made this possible. Each project I tackle will use it and make it better.

The vocabulary matters. Principals. Agents. Agencies. Choreography over orchestration. The industry needs concrete examples of what AI-augmented development actually looks like.

This article had three authors. Me, the Principal. The Captain, a Claude Code Agent from The Agency, reviewed drafts and made substantial suggestions that improved it (where to cut, where to add, etc.). And Claude Desktop Opus, my AI writing partner, who helped me find the words. We wrote it together.

The way we build software is changing. Not someday. Now.

It was serendipity that I took this project on over the holiday. If I hadn’t, I might have missed what was happening. I might have been left behind.

The question isn’t whether this transformation is coming. It’s whether you’re building the team that leads it.

Because if you aren’t, you will be left behind by the individuals and companies that are. It’s evolve or die time.

Does this work?

To learn more about “The Agency”, you are invited to attend the Claude Code Meetup Singapore on Friday, 23 January 2026.

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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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Key takeaways:

• AI and robotics will not just “improve productivity” in construction; they will remove entire layers of cost from the system.
• A full cost stack analysis — on-site labour, materials labour, supply-chain labour, energy, and time overhead — shows that AI removes costs at every level.
• In high-income countries, total construction costs can fall to ~12 per cent of today’s levels; in middle-income countries, ~20–25 per cent.
• Construction is among the least automated sectors. A factor cost collapse of 75–90 per cent in such an industry implies that virtually all labour-heavy and energy-heavy industries will experience even greater deflationary pressures

For most of modern history, building a home has been one of the most stubbornly expensive things human beings do. Unlike electronics, software, logistics, or manufacturing, the cost of construction refused to fall. Productivity barely moved. Even in rich countries with advanced machinery, building a house in the 2020s costs roughly the same as it did in the 1950s when adjusted for inflation.

A review of the literature on the effects of AI on construction costs shows that only point analyses have been done, projecting efficiency gains at certain parts of the construction process, such as design or site management.

What no one seems to have done is look at construction through its entire supply chain cost stack and work out the implications of the application of AI and robotics to their logical end point.

The critical factor when thinking about AI and robotics in construction is not focusing only on on-site workers: the carpenters, bricklayers, electricians, and foremen visible on the jobsite. But seeing that this is just the surface layer. Construction is the endpoint of an enormous global supply system: mining, refining, steel making, transport, design, engineering, and permitting. Human labor is hidden in every stage.

So rather than thinking of automation as a switch that simply “removes workers,” it’s more accurate — and more revealing — to see it as a set of transformations. Each step strips out one layer of cost.

When analysed systematically through an economic cost-decomposition framework, a foreseeable six-stage collapse in construction costs emerges.

The six stages of a full cost stack analysis

Baseline (100 per cent)

Construction costs are decomposed into five components:

• On-site labour (Lₛ)
• Labour in materials (Lₘ)
• Rest-of-supply-chain labour (Lᵣ)
• Materials (M)
• Overhead/time (O)

United States baseline: Ls​=30, Lm​=5, Lr​=25, M=25, O=15

Thailand baseline: Ls​=20, Lm​=5, Lr​=20, M=40, O=15

Total normalised to 100.

What can be seen is that labour costs throughout the entire cost stack are 60 per cent in rich countries and 45 per cent in middle-income countries

The sixth stage, which gets us down to 12 per cent of today’s costs, is the energy component of material production.

On-site labour (≈20–30 per cent cheaper)

• Humanoid robots and task-specific construction robots replace workers on site.
• Impact is modest because on-site labour is only 20 per cent of the total cost in middle-income countries such as Thailand and 30 per cent in rich countries such as the USA.
• Total cost still ~70–80 per cent.

Also Read: Chaos is a ladder: How instant retail is turning stores into fulfilment powerhouses

24-hour robotic construction (≈10–15 per cent more reduction)

• Robots work continuously and reduce defects. Productivity is 4–6 times higher as no absenteeism, shift handover issues, non-productive start and end periods, etc.
• Projects shrink by 70–80 per cent in duration.
• Time-based overhead, e.g financing, site security, equipment rental, insurance, and collapses.
• Total cost falls to ~60–70 per cent of current levels.

Labour-free material production (small but meaningful reduction)

• AI and robotics eliminate the remaining operators and technicians in factories producing cement, steel, glass, tiles, and fixtures.
• Because labour is a small share of material production (typically 5–8 per cent), the drop is modest.
• Costs fall to ~55–65 per cent depending on the country.

Labour-free supply chain (the largest structural shift)

AI and robotics eliminate all remaining labour across the construction ecosystem:

• Truck drivers
• Logistics coordinators
• Crane operators
• Warehouse staff
• Architects and engineers
• Quantity surveyors
• Permitting officers
• Project managers
• Developer finance and admin
• Compliance and inspection systems

This layer is far larger than on-site labour. There are so many of these people involved throughout the supply chain that their costs cumulatively are huge

Costs fall to ~30 per cent in the US and ~45 per cent in Thailand.

Energy-free production (final step)

• AI-directed robots build solar, storage, and energy infrastructure at scale.

Materials are energy artefacts: 70 per cent of the materials cost in the USA is energy, and 60 per cent in Thailand

• Steel requires furnaces
• Cement requires kilns
• Tiles and ceramics require baking
• Glass requires melted silica
• Mining and processing consume huge amounts of energy volumes

• Materials fall to near their raw-input cost.

Result:

• High-income countries: ~12 per cent of today’s cost
• Middle-income countries: ~21 per cent

Also Read: How to incorporate sustainability into corporate strategies

Summary

AI replacing labour in the construction industry supply chain can reduce costs by 70 per cent in high-income countries and 55 per cent in middle-income countries, as well as reducing the time to construct by 75 per cent.

With near-zero-cost energy — produced by robot-built solar, wind, and storage — the material cost base collapses.

This means the end result of AI and robotics is an industry that can build at one-tenth of the cost and in one quarter of the time.

This transforms housing into a state of abundance and transforms our ability to create and renew our built environments.

A house that once cost US$400,000 costs US$50,000. A school that once cost US$20 million costs US$3 million. Housing scarcity becomes a policy choice, not an economic fact.

This is not a futuristic dream but the inevitable results of the continued development of AI and robotics

Why this matters

• Housing affordability can be transformed.
• Hospitals, schools, transit systems, and public buildings become dramatically cheaper.
• The primary constraints become land and regulation, not labour or materials.
• Construction employment falls sharply while output capacity rises.
• Tax and welfare systems must adjust to a world where labour is no longer a major cost input.
• Construction is among the least automated sectors. A factor cost collapse of 75–90 per cent in such an industry implies that virtually all labour-heavy and energy-heavy industries will experience even greater deflationary pressures.

Policy implications

• Governments should plan for construction cost deflation, not inflation.
• Planning, zoning, and regulatory reform will matter more than construction subsidies.
• Public housing and infrastructure can be expanded massively at low cost — if political decisions allow it.
• Tax systems reliant on labour income must shift toward land value, consumption, carbon, or resource taxation.

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AI assistants are quietly becoming the first place your customers ask questions. Before anyone opens a browser tab with Google, they type a prompt into ChatGPT, Gemini or Perplexity and get a neat, confident answer on one screen. If your startup is not part of that answer, your carefully optimised pages never even enter the conversation.

The shift is measurable. When AI Overviews appear in Google Search, Ahrefs’ analysis of 300,000 keywords found they reduce click-through rates by 34.5 per cent for top-ranking pages. Yet there is an opportunity: Microsoft Clarity’s platform analysis found that AI-referred visitors convert at 17x the rate of direct traffic.​

Answer Engine Optimisation, or AEO, is how startups capture this high-converting traffic.

What is AEO, and how does it complement SEO?

Traditional SEO remains essential as it ensures your site is crawlable, indexable and technically sound. AEO operates on top of that foundation, addressing a different question. Where SEO asks “Can search engines find and rank our pages?”, AEO asks “Can AI systems understand, extract, and confidently quote our content as an answer?”

Both layers work together. SEO ensures machines can find your content. AEO ensures they can use it. Without SEO foundations, AI systems cannot discover your content. Without an AEO structure, they cannot confidently extract and quote it.

Also Read: Why traditional SEO is dying in Singapore — and how AISEO pioneers are winning the next Blue Ocean

How do you measure AEO performance?

• Visibility measures how often your brand appears when relevant prompts are entered into AI systems. Create 15 to 20 questions mirroring real customer queries from sales calls and support tickets. Run each through ChatGPT, Perplexity and Gemini biweekly, tracking whether your brand is mentioned and which URLs appear in citations. Calculate this as a percentage: if your brand appears in 8 out of 20 prompts, your visibility rate is 40 per cent.
• Sentiment evaluates how positively AI describes your brand when it does appear. Beyond just checking if your product category is accurate, assess whether the language is favourable, whether your core differentiators are highlighted, and whether the tone positions you as a credible solution. AI systems learn associations from existing content, such as third-party reviews, case studies, or your own pages that contain clear value propositions, and sentiment typically improves.
• Position tracks where your brand ranks when AI actively recommends multiple options. When AI generates a shortlist, appearing third or fifth matters less than appearing first or second. Monitor whether you are mentioned early in the response, included in bulleted recommendation lists, or buried in “other options to consider” sections.

To see how these metrics play out in practice, a recent analysis of Singapore’s co-working market tested them across 33 brands and 25 buyer prompts. The results revealed a stark visibility gap: just five brands appeared in over 80 per cent of scenarios, while 42 per cent of operators—14 out of 33—were completely invisible

How long does Answer Engine Optimisation take?

AEO does not require a dedicated team or expensive tools. A founder plus one marketer can make meaningful progress in three focused sprints over 90 days.

• Sprint one: Establish your baseline (Days 1-30)

Collect 15 to 20 questions prospects actually ask from sales calls and support tickets. Enter each into ChatGPT, Perplexity and Gemini, recording which companies are named and which domains appear. Mark the 5 to 8 questions most likely to lead to high-value customers.

• Sprint two: Build two answer hubs (Days 31-60)

Select your two most valuable questions and create a dedicated page for each. Examples: “How to evaluate payroll software for SMEs in Malaysia” or “What should SaaS founders in Singapore budget for CRM tools.”

Write a headline that promises a specific outcome and delivers value in the first paragraph. Use H2 and H3 headings that mirror real questions, include a compact FAQ section, and add FAQ or HowTo schema markup with your developer.

Most importantly, incorporate original data, customer examples or benchmarks that AI systems cannot find elsewhere.

Also Read: AI and cybersecurity: Pillars of Malaysia’s economic growth and regional leadership

• Sprint three: Connect, refresh and measure (Days 61-90)

Link your hub pages prominently from navigation and related content. Set a 30-day reminder to refresh each hub with light updates such as a new customer quote, updated statistic or recent example.

Re-run your original prompts monthly, comparing responses against your baseline to track changes in visibility, accuracy and AI-sourced leads.

Why should SEA startups care about AEO now?

A new layer now sits in front of traditional search results with AI assistants reading, compressing and presenting answers before users see a search results page. For SEA startups, the risk is invisibility at the decision moment. The opportunity is that AI-referred visitors convert at dramatically higher rates, turning modest visibility into meaningful revenue.

As you track performance, remember that AI search is probabilistic, as results vary between sessions, attribution is difficult to separate from traditional search, and small samples can mislead. Track trends over weeks, not individual prompts.

You do not need a complex stack to start. Collect a short list of questions real customers ask, run them through ChatGPT or Perplexity, and see whether your company appears. Then pick one valuable question and build a page that genuinely helps someone decide.

The brands that establish AI visibility now (think first-mover advantage) will compound that advantage as these tools become the default research layer for every buying journey in Southeast Asia.

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The European Union (EU) is racing to regulate artificial intelligence, but its flagship law, the EU Artificial Intelligence Act (AI Act), faces delays and industry resistance. For ASEAN businesses, this is not just a distant Brussels story. Like the GDPR, the Act has extraterritorial reach. Exporters of smart electronics, automotive parts, healthcare diagnostics, or AI-driven services across Asia will soon face strict European rules.

Enforcement begins in 2025, phasing in until 2027, and can impose penalties worth up to seven per cent of a firm’s global turnover. The question for ASEAN firms is therefore not whether these rules will matter, but rather how quickly firms can turn their compliance into a source of competitiveness.

Competitiveness or control?

At the heart of the EU AI Act lies two, near contradictory, goals: Europe wants to lead the world in AI regulation, while maintaining its position in the global innovation race. Penta’s recent survey of 1,500 senior policymakers across the EU and the US reveals that over 46 per cent of EU officials rank AI among their top three regulatory priorities.

Industry leaders are uneasy. More than 40 CEOs from Europe’s largest companies, including ASML and Siemens, have urged a two-year ‘clock stop’ on enforcement, warning that overlapping provisions and heavy obligations could stifle the very innovation that Europe needs to remain competitive globally.

Competitiveness has become the defining political priority in Brussels. Calls to simplify regulation have persisted for years, but AI’s rapid acceleration has raised the stakes. The AI Act, once a seminal framework, now risks being outpaced by technology itself.

For businesses in Asia, this tension creates uncertainty. However, there is also an opening. Firms that adapt early by auditing AI systems, embedding ethics into AI design and demonstrating transparency will stand out in markets where trust is increasingly the currency of choice.

Global standards, local interpretations

The AI Act sets rules at the EU level, but national priorities shape their implementation. For example, our analysis of open-source material indicates that policymakers in Germany and Italy link AI to sustainability in industrial and green agendas, while French policymakers focus on skills and academic integrity.

For ASEAN exporters, the message is clear: Europe legislates as a bloc, but enforcement reflects diverse political sensitivities. Companies in trade with Europe must expect scrutiny not just on technical compliance but how their systems interact with varying ethical and social priorities.

ASEAN itself is moving in a similar direction. The ASEAN AI Guide and the ASEAN Responsible AI Roadmap offer voluntary guidance on principles of fairness and transparency, while national governments are piloting measures tailored to local needs.

Indonesia is testing regulatory sandboxes in health and fintech. Malaysia has ambitions to join the leagues as a global AI player. Singapore launched the AI Verify toolkit for organisations to test their systems for fairness and transparency benchmarks.

Yet governance capacity remains uneven. Larger firms are better positioned to build compliance frameworks, while micro, small and medium enterprises, which are the backbone of ASEAN economies, often lack the funding and talent to align with emerging international standards.

Also Read: Europe’s tech Thoroughbreds: A collaborative future with Asia’s investors

For those eyeing European business, voluntary codes are not enough. Hardwiring transparency, auditability and human oversight will now determine who will thrive later.

ASEAN caught between global models

The EU is not the only one shaping AI rules. The US continues to favour a sectoral, innovation-first model. Meanwhile, within Asia, China has already rolled out binding rules for generative AI, algorithmic transparency, and content labelling. Similarly, South Korea’s AI Basic Act, set to take effect in 2026, will regulate high-impact AI systems in health, finance and education.

ASEAN sits at the crossroads of these competing approaches. Firms that align with Europe’s standards will not only secure access to its market but also build resilience to navigate China’s stricter regime and the US’s innovation-driven expectations. In effect, EU compliance is becoming the global baseline.

OECD and UNICEF have published a guide to safeguard children’s development amid growing AI adoption. ASEAN exporters should expect similar scrutiny, especially where products intersect with health, education or children’s digital experiences.

This matters because ethical debates are now inseparable from politics. France’s push for bloc-wide age verification and Ireland’s focus on child protection show how AI rules increasingly touch highly sensitive domains.

Risks and openings for ASEAN

AI adoption is accelerating across ASEAN, but its readiness is uneven. Many firms are still experimenting with data strategies, often without the governance to meet international standards. This gap is a risk but also a chance to get ahead.

Automotive and electronics exporters can use EU-aligned audits to assure European partners of reliability. Healthcare and technology firms can highlight their commitment to transparency and fairness as selling points in cross-border contracts. Financial services providers can align their risk frameworks with EU expectations to secure investor confidence.

Governments in Asia are responding quickly, but private-sector initiative is crucial. Firms that invest in compliance today will be the ones setting benchmarks for tomorrow.

Trust as a strategic asset

The EU is pressing ahead with implementation, albeit with simplifications for smaller firms. Relief in reporting requirements should not be mistaken for reprieve. Rather, it is an invitation for businesses to step up, shape the debate, and turn compliance into a differentiator.

For ASEAN firms, the playbook is clear: Speak the language of policymakers. Regulators want AI to serve broad social and economic goals, not just profit maximisation. Firms that frame projects in terms of sustainable development will win at credibility. Lead on safety and ethics. It begins at source— developing secure and trusted data-sharing platforms, ensuring interoperability, and building auditability into system design. Invest in education and transparency. Training, workshops and pilot programs remain the most effective ways to demonstrate commitment to successful AI integration.

Also Read: Europe’s financial challenge: Can tech bridge the gap to sustainable practices?

Firms that act now will find doors open to exclusive partnerships and smoother market access. In the new AI economy, trust is not just a virtue but a strategic asset.

Seizing the EU’s invitation

ASEAN firms cannot treat the EU AI Act as a distant regulation. Its extraterritorial reach means it will reshape global supply chains, investment flows and customer expectations. The winners will be those who seize compliance as a chance to lead, building reputations for safety, ethics and transparency that transcend borders.

The EU has issued the invitation; it is now up to ASEAN firms to accept. Doing so will enable firms not just to comply but to compete.

This article was co-written with Ronald Chan, Senior Director at Penta Group.

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Zespri is accelerating climate tech innovation in the kiwifruit industry through its ZAG Innovation Fund, which launched 11 pilot projects in its first year. Two standout initiatives—Scentian Bio’s VOC maturity assessment and the Biochar Field Trial 2024—already show promising results and potential long-term impact.

Scentian Bio’s pilot transforms traditional fruit maturity testing, which is typically slow and labour-intensive. Instead, the company is developing biosensors inspired by insect olfactory systems to detect volatile organic compounds (VOCs) emitted by ripening kiwifruit. The technology, paired with AI models, enables fast, accurate, and non-destructive maturity assessment.

“We see this as a game-changer,” says Jiunn Shih, Zespri’s Global Chief Marketing, Innovation & Sustainability Officer, in an email to e27. “Growers can make more informed decisions, increase productivity, and deliver fruit at peak ripeness—while reducing post-harvest waste.”

Beyond operational efficiency, this innovation supports sustainability by improving harvest timing and resource use across the supply chain.

The Biochar Field Trial 2024 by M.B. Horticulture Ltd is another key climate tech initiative. It explores using biochar—a carbon-rich material made from organic waste—to enhance soil health, increase productivity, and store carbon in kiwifruit orchards.

“Think of it as a nutrient battery,” says Shih. “Biochar improves nutrient retention, reduces leaching, and supports long-term soil vitality, while locking carbon in the soil for hundreds of years.”

Also Read: Wavemaker Impact invests in Zentide to scale sustainable seaweed-based agriculture

Although biochar has been trialled in other crops, its use in perennial vines such as kiwifruit remains limited. This project offers growers practical, evidence-based guidance for adopting the method.

Early results are positive, highlighting environmental and economic benefits that align with Zespri’s broader sustainability goals.

“These pilots give our growers the confidence to adopt practices that strengthen orchard resilience and deliver climate-positive outcomes,” adds Shih.

Through ZAG, Zespri is proving how climate tech and sustainability-focused innovation can future-proof agriculture and deliver lasting value to growers and the planet. In this interview, find out more about how they are doing it and what insight they can share about the climate and agritech sector.

The following is an edited excerpt of the conversation.

What are some of the most compelling agri- and climate-tech trends you see emerge across the Asia Pacific region? How do you plan to seize this opportunity?

In recent seasons, we have seen the impact of climate change more clearly through our growing systems and around the world. Working with solution providers, ZAG is focused on creating solutions that will help create sustainable, long-term value for our growers. These initiatives will examine how we can enhance productivity while caring for the land, enabling it to grow sustainably.

Agri- and climate-tech are booming, and there will be more developments in these sectors as we move forward in 2025. Precision agriculture is becoming more accessible, not just for large-scale farms but increasingly for smallholders too. At the same time, we are seeing a surge in nature-based solutions—agroforestry, soil carbon capture, water-efficient systems—all aligned with food security and decarbonisation goals.

Also Read: How biotech is changing the global agriculture game for investors

Therefore, with ZAG, we plan to use these emerging technologies to tackle some of the industry’s biggest sustainability challenges, such as automation, big-data value extraction, soil regeneration, supply chain optimisation, and packaging.

For instance, Zespri is collaborating with M.B. Horticulture on a biochar field trial, which explores the application of biochar as a stable form of carbon storage in kiwifruit orchards. Biochar has the potential to enhance soil health and productivity, directly contributing to Zespri’s climate-positive goals. The ZAG fund is providing an opportunity for innovative new ideas, like using biochar in kiwifruit orchards, to be tested on a small scale to evaluate whether more in-depth work is warranted.

In essence, ZAG is a strategic investment to foster innovations that directly contribute to reducing Zespri’s environmental footprint.

How do you see the intersection of data, automation, and sustainability shaping the future of food production in this region?

The intersection of data, automation, and sustainability is becoming the backbone of the next-generation food system in Asia Pacific. Data enables traceability and transparency across the entire value chain, from soil to shelf. That is critical, especially as consumers, regulators, and partners demand greater accountability around environmental and social impact. Automation is helping address labour shortages and increase operational efficiency, while reducing inputs like water, energy, and chemicals.

We are already seeing this come to life through the ventures we have supported via ZAG. Scentian Bio, for instance, is a pioneering initiative using volatile organic compounds (VOCs) to transform kiwifruit maturity assessment. By replacing labour-intensive and time-consuming methods, this innovation could reduce operational inefficiencies and enhance supply chain planning. Growers could benefit from improved productivity and better decision-making, while customers and consumers receive consistently high-quality fruit delivered at peak ripeness.

How is climate change influencing how growers and producers in Asia Pacific adopt new technologies, particularly in sustainability and crop resilience?

Climate change is not just a future threat in Asia Pacific. It is a present reality globally, and growers across the region are already feeling the impact.

Also Read: Agriaku raises seed funding round led by Arise to tap into Indonesia’s agriculture market

As we transition into the second year of ZAG, we are committed to not just maintaining the momentum we have had, but also amplifying our impact. The next phase will focus on strengthening climate resilience across food systems by advancing productivity and carbon-positive practices. By leveraging the successes and learnings from our first year, the next stage of ZAG aims to accelerate sustainable innovations that benefit the environment, communities, and people as we meet the growing demand for kiwifruit.

While our core priorities remain the same—strengthening climate resilience across food systems and creating solutions that advance productivity and carbon-positive practices—we are always open to exploring new partnerships that align with global and regional advancements in sustainability.

What are some key challenges agritech founders face in Asia Pacific, and how is ZAG helping them navigate these?

As with many in agriculture, we operate in a dynamic environment that drives us to innovate, adapt, and build greater resilience for the future. From climate change and increasing labour and input costs to the pressing need to boost productivity, these realities are why innovation is no longer optional; it is essential.

One of the most common hurdles for agritech startups in Asia Pacific is proving the commercial viability of their innovations. Many have strong ideas and prototypes, but limited access to funding or commercial environments to test them in real-world settings.

ZAG helps bridge this gap by funding pilot projects and proof-of-concept trials without taking equity. We offer startups direct access to Zespri’s grower network, allowing them to validate their solutions in-market. If the technology proves successful, we will support scale-up efforts across our global supply chain.

This approach reduces early-stage risk for founders while helping Zespri explore innovations that could potentially create meaningful enhancements to sustainability and efficiency across orchards and operations.

[Another challenge is] climate change, which already impacts the kiwifruit industry. For Zespri, kiwifruit cultivation is highly dependent on specific climate conditions, wherein our kiwifruit needs around 1,000 hours of winter chill between two and four degrees Celsius.

Historically, New Zealand could reliably provide that. But today, we see increased climate variability, impacting flowering, bud break, and fruit development. More recently, we have experienced more extreme weather events.

Also Read: SEA’s US$48B agritech revolution: Startups cultivating a smarter future

ZAG actively seeks solutions that help us and our growers adapt to these shifting conditions through orchard innovations, climate-resilient crop strategies, or technologies that improve planning and risk management.

By working with innovators worldwide, we are tackling these challenges head-on with a future-focused mindset. We are not only interested in solving problems for today; we’re investing in resilience for tomorrow.

The ZAG Innovation Fund connects bold ideas with the infrastructure, expertise, and credibility needed to scale in Asia Pacific’s unique agri-environment. We are not just funding pilots, we are building bridges between founders, growers, and global opportunities.

Looking ahead, what role does Zespri’s ZAG Innovation Fund hope to play in advancing the agriculture and climate tech ecosystem across Asia Pacific?

Looking ahead, ZAG aims to support the best solutions in the agriculture and climate tech ecosystems, regardless of where they originate. As a global business, Zespri works with more than 4,000 growers across New Zealand, Italy, Japan, South Korea, and France, while our kiwifruit is enjoyed in over 50 countries worldwide.

Since our launch in November 2023, ZAG has united innovators worldwide to harness the power of collaboration and combine their ingenious ideas with ours. Out of more than 100 applications submitted to ZAG, we are proud to have onboarded 11 ongoing pilots.

ZAG focuses on the kiwifruit ecosystem, addressing challenges and opportunities across all growing regions and markets. By embracing innovative ideas worldwide, we aim to strengthen the sustainability and resilience of our orchards, supply chains, and communities globally.

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Image Credit: ZAG

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Most people read Meta’s acquisition of Manus as another step in the AI agent arms race.

Yes and no.

From a VC lens, this was not a bet on intelligence.

It was a bet on execution scar tissue — something that can’t be rushed, simulated, or cheaply rebuilt.

This was never about “the best model”

Meta already has:

• Strong foundation models
• Massive global distribution
• hardware endpoints (mobile, VR, wearables)

What Meta does not have the luxury of doing is learning from execution failures publicly across billions of user interactions.

Manus had already done that.

The real question isn’t “why agents?”

It’s “why Manus?”

Here’s the non-obvious answer:

Manus crossed a path-dependent threshold where execution reliability — not reasoning quality — became the moat.

Once a company reaches that point, the ‘build vs. buy’ debate stops being a technical decision and becomes a time-risk and reputational-risk decision.

What Manus learned that others can’t shortcut

Most AI agents work in controlled environments:

clean prompts, trained users, bounded workflows, human-in-the-loop recovery.

Manus appears to have learned how agents behave in hostile, real-world environments — the kind Meta operates in.

Also Read: How SMEs can compete like big corporations with the right financial intelligence platform

Three lessons matter:

• Failure recovery matters more than first-pass intelligence: Real users are ambiguous. Tools break. Instructions are incomplete. Manus learned how to recover without hallucinating or escalating to humans.
• Long-horizon execution is harder than reasoning: Execution requires memory, intent persistence, and recovery across sessions — where most agent demos collapse.
• Trust collapses faster than models improve: In consumer platforms, silent failure isn’t bad UX — it’s a trust breach.

Manus learned how to fail visibly, explain minimally, and recover credibly.

None of this is benchmarkable.

All of it is learned the hard way.

Why the acquisition was inevitable

Meta could rebuild these capabilities.

What it couldn’t afford was:

• Relearning failure inside WhatsApp, Instagram, or wearables
• Exposing billions of users to that learning curve
• Absorbing the reputational risk of agents behaving badly at scale

So the real decision wasn’t “can we build this?”

It was “Can we afford to relearn this?”

The answer was no.

The signal for founders and investors

General-purpose AI agents are now a platform game.

Venture-backable paths narrow to:

• Deep vertical agents with real domain lock-in
• Infrastructure layers (orchestration, observability, compliance)
• Acquisition-grade teams with real execution scars

The era is shifting from model competition to execution control.

And the hardest asset to replicate isn’t intelligence — it’s the accumulated cost of being wrong in the real world.

That’s what Meta bought.

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Private equity has a religion: operational excellence through systematic value creation. The data appear unassailable. McKinsey reports that operationally-focused GPs generate IRRs two to three percentage points higher than peers. Bain finds that structured value creation delivers 3.0x returns versus a 1.9x industry average—a 58 per cent performance premium.

Here’s the heresy nobody wants to admit: the same discipline that creates outperformance is now destroying more value than it generates.

The numbers behind the orthodoxy’s failure

Simon-Kucher’s 2025 study reveals what consulting firms won’t tell clients: two-thirds of private equity (PE) value creation initiatives fail to deliver expected outcomes. One in three business improvement programs produces zero measurable return. In value-destructive deals, more than 10 per cent of employees depart immediately post-close, with the worst transactions losing 21-30 per cent of key talent.

Most damningly: 75 per cent of portfolio company CEOs are replaced within two years—not because they’re incompetent, but because they resist the new owner’s systematic playbook. When AlixPartners surveyed PE practitioners, 75 per cent reported direct experience with portfolio failures caused by CEO-investor misalignment. Yet only 13 per cent conduct cultural evaluation during diligence.

The industry identifies the problem, then systematically engineers the conditions that produce it.

What actually kills value

Simon-Kucher dissected why value creation initiatives fail:

• Poor implementation: 53 per cent
• Unrealistic business cases: 37 per cent
• Portfolio company resistance: 35 per cent

Notice what’s absent? Insufficient KPIs. Inadequate governance. Too little systematisation. The failure mode isn’t under-management—it’s over-management imposed before organisations can absorb it.

I’ve watched this pattern destroy dozens of companies across Southeast Asia. A founder-led B2B software company generating 40 per cent annual growth gets acquired by a PE firm deploying its “proven playbook.” Within six months, board decks balloon from 15 to 60 slides. Hiring approvals stretch from days to three weeks. The product roadmap freezes pending “strategic review.”

Twelve months later, revenue growth has halved, the CTO has quit, and the PE firm convenes an urgent off-site to diagnose “execution challenges.” The playbook wasn’t wrong. The timing destroyed the business.

Also Read: The culture conundrum: Why private equity’s best CEOs still fail and how Moneyball thinking can fix it

The elite firm counter-strategy

Here’s what separates genuine 3x performers from systematisation zealots: they treat frameworks as tools to amplify momentum, not replace it. They recognise that premature systematisation in high-growth companies is value destruction wearing the costume of best practice.

Top-quartile firms do something radically different in year one. They watch. They resource. They remove obstacles. They preserve the operational momentum that justified the multiple acquisitions. Only after stability is achieved do they introduce frameworks—selectively, where they enhance rather than constrain performance.

Bain’s research inadvertently proves this. The 3.0x performers engaging in “structured value creation” aren’t imposing rigid KPI frameworks. They’re making surgical interventions: replacing one genuinely inadequate executive, funding a capital-constrained growth channel, and implementing pricing discipline where none existed. These aren’t cookie-cutter implementations—they’re strategic decisions executed with restraint.

Contrast this with 1.9x performers who arrive with 100-day plans and systematic frameworks deployed identically across portfolio companies regardless of context. Their religion is a process. Their blind spot: process imposed before momentum is established kills the growth they acquired.

“At the end of the day, it’s people and culture that decide whether a system succeeds or fails.”

The advent international lesson everyone misses

The industry loves citing Advent’s ownership of BSV, which doubled revenue growth to 20 per cent annually and expanded EBITDA margins from 20 per cent to 30 per cent. What case studies omit: Advent didn’t impose comprehensive frameworks on day one. They made two interventions—international expansion and pricing optimisation—then resourced them aggressively while leaving the operational engine intact.

This is surgical execution, not systematic transformation. The discipline came from knowing what not to systematise—preserving the sales culture and product velocity that created value in the first place.

Why this matters now

Private equity faces its harshest environment in 15 years: US$3.6 trillion in unrealised value across 29,000 unsold companies, distributions at historic lows, and acquisition multiples at 12x EBITDA. In this context, the industry’s reflexive answer has been more systematisation—more frameworks, more governance, deployed earlier and more uniformly.

The data says this orthodoxy is failing. CEO turnover approaches 75 per cent within two years. Performance gaps between top-quartile and median funds continue widening—not because median managers lack frameworks, but because they’ve mistaken process for performance.

Also Read: Why private credit is becoming the hottest alternative for smart investors

McKinsey’s organisational alignment research remains valid: culture explains 58.6 per cent of variance in execution outcomes. But here’s the inversion consultants won’t acknowledge: you cannot systematise culture into existence. Culture precedes systems, not vice versa.

The firms generating genuine alpha have learned what the rest refuse to accept: systematic value creation is timing-dependent, not universally applicable. Deploy frameworks too early, and you destroy growth. Deploy them when leadership is stable, baselines are established, and organisations have absorption capacity—and systems amplify performance.

The uncomfortable truth

As Bain observes, the cost of market-beating returns continues rising as fees compress. Winners won’t be those with the most sophisticated frameworks but those with judgment to know when frameworks enable versus suffocate performance.

The industry sold the world on systematic value creation. The uncomfortable truth is that the system itself has become the primary destroyer of value. The competitive advantage has become a vulnerability.

Elite firms have discovered that the hardest discipline isn’t imposing rigour—it’s having the restraint to preserve entrepreneurial velocity when every instinct says to systematise faster. That judgment, unglamorous and maddeningly contingent, is now the true source of private equity alpha.

The beatings will continue until morale improves. Or until the industry learns that its most sacred principle requires the one thing PE hates most: patience.

This article is part of David Kim’s Value Creation column. It sits alongside the Asia Value Creation Awards, which aim to recognise PE and VC teams driving long-term, fundamentals-led value creation across the region.

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We talk about technology as if it separates us. Phones are replacing conversations. Screens replacing faces. AI is replacing humans.

But maybe the real question is not whether AI makes us lonely. Maybe it is how we use it to feel less alone.

The quiet kind of loneliness

Loneliness today does not always look like being alone. It can happen in crowded rooms, busy offices and even online communities. We scroll, we watch, we like, but we do not always feel seen.

That is why connection has become the new happiness. And that is where AI, surprisingly, can help. Not by pretending to be human, but by helping humans rediscover each other.

When AI becomes a mirror

I have watched many learners, especially midlifers, use AI for the first time. At first, they are shy. They talk to it softly, like a stranger they are not sure they can trust.

Then something shifts. They begin to write. They begin to share. They begin to remember. They tell stories they had forgotten,
describe feelings they had never spoken about, and rediscover parts of themselves they thought were lost.

It is not because AI understands them perfectly. It is because AI listens without judgment. That kind of listening gives people courage to speak again, and that is where healing begins.

Connection through creation

A dear friend once told me about a project she started with her ageing mother. Her mother had always dreamed of running a small shop, but life, family and time never allowed it.

When her health began to fade, my friend helped her create that dream online. They built a simple e-shop together using free digital tools. They took photos, wrote short product descriptions and posted them on social media. Soon, friends began to notice, comment and buy small items.

Also Read: Why Singapore startups are sleeping on their secret weapon (spoiler: it’s not AI)

It was not powered by complex AI systems. It was powered by love, curiosity and technology made simple by AI in the background. The shop gave her mother a sense of purpose. It gave her daughter a memory she will never forget.

For a short while, they lived their mother’s dream together. That is what connection through creation truly means. Technology is not only about efficiency. It is about giving people one more way to feel alive, seen and connected.

AI and emotional literacy

AI cannot replace empathy, but it can remind us how to practice it. It can help us reflect, write and reach out. It can turn thoughts into voice, ideas into visuals and memories into legacies.

Used with intention, it becomes a tool for emotional literacy, a way for people to understand what they feel and express it safely.

When I see participants use AI to share stories about their lives, they often say, I did not know I still had so much inside me.
That is not technology at work. That is humanity reawakened.

The gentle reminder

We were never meant to compete with machines. We were meant to grow with them.

AI can help us create, express and connect, but it is still our emotions that give it meaning.

So do not fear it. Use it to find your voice, your joy and your people because the future of happiness is not artificial. It is amplified.

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Every year, new reports flood LinkedIn proclaiming the “Top Skills for the Future.” AI literacy. Digital fluency. Strategic thinking. Emotional intelligence. Cross-cultural communication.

The lists grow longer. The outcomes remain the same.

Despite record investment in training, many organisations still face leaders who avoid difficult conversations, sales teams who struggle to handle objections, and managers who lack the confidence to lead through change.

Why?

Because the future of work is not being limited by which skills people know about — it is being limited by which skills people have actually practised in real-life conditions.

The real bottleneck: Transfer to action

The hardest part of learning has never been access to information. We live in the most knowledge-rich age in human history.

The real challenge is turning insight into action:

• Saying the difficult thing when emotions run high
• Navigating uncertainty when there is no playbook
• Leading inclusively when values clash
• Building trust across distance, cultures, and hierarchies

These moments do not come with slides or pause buttons. They demand human fluency — presence, judgment, emotional regulation, persuasion, and empathy — expressed in seconds, not theories.

Why content is not enough

Most training still optimises for content delivery. But the skills of the future are not cognitive alone — they are behavioural and emotional.

No one becomes persuasive by watching a video on persuasion. No one becomes resilient by reading a resilience article. No one becomes a strong negotiator by ticking boxes in a quiz.

These competencies require experience, but experience in real life is risky, expensive, and inconsistent.

This is the core contradiction of modern learning: Skills require practice, but workplaces make failure unsafe.

So learners retreat into passive learning — understanding what to do intellectually, but never integrating it into behaviour.

Also Read: The future of work is microlearning: How bite-sized education is transforming the workplace

The rise of safe practice

The next decade of skills development will not be defined by what people watch or read, but by what they repeatedly practice.

Safe, immersive practice environments will become the standard for developing:

• Leadership under pressure
• High-stakes communication
• Negotiation and conflict management
• Cross-cultural and inclusive leadership
• Decision-making in ambiguity

This is where AI-powered experiential learning enters the stage.

AI roleplay allows learners to step into realistic future-work scenarios — testing language, tone, judgment, and emotional intelligence — without social risk. They can pause, reflect, restart, try different approaches, and gradually build confidence through safe exposure.

Learning shifts from passive intake to active rehearsal. From knowledge tests to capability building.

Depth will matter more than speed

As microlearning and social video accelerate bite-sized content consumption, there is a hidden danger: speed without depth produces surface competence.

Future skills are not superficial hacks. They involve:

• Emotional nuance
• Cultural complexity
• Ethical judgment
• Relationship-building

Developing them requires academic rigour and psychological safety, not dopamine-driven quick wins.

Learners need realistic scenarios designed with the complexity of real leadership challenges, not simplistic chatbots that reward short responses.

The skill of the future is practice itself

Ironically, the single most important future skill may not be persuasion, negotiation, or empathy.

It may be developing the ability to learn experientially — to enter challenging simulations, reflect honestly, take feedback seriously, and try again.

As volatility increases and job roles evolve faster than course content can update, real competitiveness will come from learning velocity — the speed at which individuals can build new behaviour under changing conditions.

From learning to becoming

The future of work does not require people who know more theories.

It requires people who are ready to:

• Speak when conversations get uncomfortable
• Lead when answers are unclear
• Decide when outcomes are uncertain
• Connect when differences emerge

These are not knowledge challenges.

They are practice challenges.

The organisations that win the next era will be the ones that move beyond training employees to consume content and instead empower them to become capable through experience.

Because the future does not belong to those who study skills — it belongs to those who practice them.

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In Game of Thrones, there’s a classic line: “Chaos isn’t a pit; chaos is a ladder.” This metaphor perfectly captures the current state of retail. As market structures fragment, traditional rules are shattering, creating opportunities for new, more efficient models to emerge.

In China, the most powerful new rung on that ladder is Instant Retail.

The promise of “everything delivered in 30 minutes” has become a daily standard in Chinese cities. It has forged new consumer habits: no going downstairs, no stockpiling, and no planning. This shift is also forcing retailers to fundamentally re-examine their supply chains, store layouts, and operational models.

As one veteran supermarket operator in China lamented, “My competitor is no longer the supermarket down the street. It’s every single app on consumers’ phones.”

Does this sound familiar? It should.

From the chaotic vibrancy of Jakarta to the vertical density of Singapore, Southeast Asia is mirroring China’s trajectory. As super apps like Grab, GoTo, and Shopee pivot from aggressive expansion to sustainable profitability, they are approaching the same operational inflection point that has just reshaped China.

By analysing China’s roadmap, Southeast Asian players can see their own future and prepare for the inevitable operational challenges ahead.

Beyond Supercharged Delivery

Many mistake Instant Retail for a simple upgrade of food delivery. Superficially, they’re right. The same riders who deliver food and drinks now deliver groceries, cosmetics, and iPhone chargers within 30 mins. But to see it as just “do-it-all delivery” is to underestimate its ambition.

To grasp its true meaning, consider the specific use case. When the cat food runs out, or you need medicine late at night, the moment you click “buy,” you aren’t just purchasing a product. You are purchasing instant gratification and certainty.

Instant Retail fills the time gap left by traditional e-commerce’s “next-day delivery” and shatters the spatial constraints of “must-visit” physical stores. It shifts shopping behaviour from pre-planned events to impulse-driven “micro-demands.”

Also Read: The canary in Singapore’s retail coal mine is ‘kiasu’

While Southeast Asia has seen the rise of “Quick Commerce” (Q-Commerce) via capital-intensive dark stores, China has evolved into a more sustainable, asset-light phase: the Store-as-Fulfilment-Centre (SFC) Model.

Instead of building expensive new warehouses, the Chinese model involves the digital transformation of existing retail terminals, such as supermarkets, convenience stores, and pharmacies, turning them into dual-purpose hubs. This pushes inventory closer to the consumer without the heavy capex of building new infrastructure.

A perfect storm: Why now?

The rise of Instant Retail in China was not accidental. It was driven by a convergence of structural shifts, conditions that are now replicating across Southeast Asia.

Market saturation and the quest for growth

In China, as traditional e-commerce saturated and customer acquisition costs skyrocketed, Instant Retail emerged as the new engine for growth. A similar pattern is unfolding in Southeast Asia. As the region’s digital economy matures, super apps are pivoting to high-frequency, essential categories (like groceries and pharma) to drive user retention and strengthen unit economics.

Technological maturity

This is more than just “putting a supermarket online”; it is a complex systems engineering project where AI, Big Data, and IoT form the operational backbone.

Take the intelligent TMS (Transportation Management System) as a prime example. By leveraging algorithms to analyse historical sales, real-time traffic, and store demand, retailers can dynamically optimise logistics. Industry data from retail tech provider Dmall indicates that such systems can boost vehicle load rates from 65 per cent to 85 per cent. For the end consumer, this back-end intelligence translates into reliability—a standard that SEA shoppers increasingly value over mere speed.

Urban density

Just as China’s “15-minute convenient living circles” policy catalysed this model, Southeast Asia’s vertical urban density and robust two-wheel logistics networks provide the perfect fertile ground for Instant Retail to thrive.

From point of sale to point of fulfilment

The core of this revolution is the redefining of the physical store.

Under the Instant Retail model, a brick-and-mortar store is no longer just a place to shop; it is a local fulfilment centre. Its service radius expands from 1km to 5km, allowing a legacy retailer like 7-Eleven to capture revenue from customers who never walk through its doors.

However, this opportunity comes with a massive “Digital Divide.” Whether it is a convenience store in Guangzhou or a supermarket in Bangkok, the operational pain point is identical: how do you manage orders from multiple apps without creating inventory chaos?

When a single store must process orders from its own app, Grab, Foodpanda, and Shopee simultaneously, backend complexity explodes. Without real-time synchronisation, inventory data lags, leading to overselling and customer cancellations.

To survive this race against time, a unified backend infrastructure is no longer optional. It is operational bedrock.

The industry solution to this fragmentation is the adoption of a “unified commerce operating system”. The technical logic focuses on centralisation: channelling orders from disparate external platforms into a single internal processing stream. Instead of juggling multiple devices, store staff utilise one standardised app for picking and packing. Crucially, once an order is fulfilled, the system triggers an immediate, automated inventory update across all sales channels.

Also Read: The future of retail is autonomous: Securing agentic AI for smarter, safer growth

A clear deployment of this strategy can be seen at 7-Eleven South China. Operating a network of nearly 2,000 stores, the retailer leveraged Dmall OS as its digital backbone to integrate third-party giants like Meituan and Douyin (TikTok) with its own private mini-program. This integration enabled real-time synchronisation of inventory, pricing, and promotions, effectively transforming each physical convenience store into a digitised, high-efficiency distribution hub.

Conclusion

China’s Instant Retail story serves as a blueprint for Southeast Asia’s next chapter.

The path to profitability lies not in building more dark stores, but in empowering existing brick-and-mortar retailers to become efficient nodes in the digital network.

For traditional retailers, the “moat” of physical location is drying up. To survive, they must stop viewing online orders as a disruption and start viewing their stores as digitally connected fulfilment centres.

In the end, retail evolution is not about geography; it’s about efficiency. Whoever can first integrate the fragmented network of people, product, and place—whether in Beijing or Bangkok—will climb the ladder of chaos and win the future.

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