The retail industry is undergoing rapid transformation, driven by changing consumer expectations and the rapid expansion of the digital economy. This year’s e-Conomy SEA 2024 report highlights the region’s robust digital economy, which is projected to grow by 15 per cent year-over-year, reaching US$263 billion. Revenues have seen a 14 per cent increase, with forecasts indicating they will reach US$89 billion in 2024.

This growth underscores how digitalisation is reshaping the retail landscape, creating new ways for businesses to connect with consumers. For today’s shoppers, the ability to choose where and how they shop is non-negotiable.

Shoppers demand seamless, personalised experiences, and the lines between online and in-store shopping are fading fast. This new era of retail brings both opportunities and challenges as retailers strive to meet heightened expectations while maintaining operational efficiency.

A recent global shopper study by Zebra Technologies revealed a shift in shopper satisfaction. While consumer spending remains steady, shopper satisfaction is declining. In the Asia Pacific region, satisfaction with in-store shopping dropped from 81 per cent in 2023 to 78 per cent in 2024, and online satisfaction fell from 80 per cent to 75 per cent.

Meanwhile, 78 per cent of shoppers favour retailers with an omni-channel presence, and 78 per cent of shoppers prefer to shop with e-retailers that also have brick-and-mortar shops. Shoppers no longer see online shopping as a mere alternative but expect a seamless integration across different channels.

The new retail reality: Greater variety, greater complexity

Modern shoppers arrive at the store informed and expect retail associates to provide value beyond transactions through personalised insights and tailored recommendations. According to Zebra’s study, around 77 per cent of APAC shoppers are more likely to try and purchase items when retailers demonstrate an understanding of their preferences.

Offering consumers more choices come at a cost. Services like buy-online-return-in-store (BORIS), same-day locker pickups, and on-demand delivery have become essential offerings, but delivering these conveniences often complicates backend operations. With the proliferation of an increasingly popular ‘spend-and-return’ culture, retailers today need to spend more time and resources than usual to ensure that the back-of-house operations run smoothly.

Eighty-five percent of APAC retailers and associates reported challenges with both click-and-collect and returns options, citing challenges to confirm current inventory and pricing. Coupled with persistent labor shortages and inventory losses, these challenges risk slowing operations and disappointing customers.

Without better ways to handle this increased workload, organisations may end up experiencing stockouts, inefficiencies, and a loss of productivity—which ironically could lead to fewer choices and a poorer shopping experience.

Also Read: Cybersecurity for retail: How to avoid e-crimes

Bridging the gap with modern retail technology 

Retailers can address today’s toughest challenges with advanced retail technologies such AI-augmented mobile computers, connected RFID readers, and cloud-based workforce management systems without comprising efficiency. Research shows that modern RFID readers can improve inventory accuracy and reduce stockouts while lowering costs and boosting revenue.

Additionally, another research affirmed that retail management software enhances retail operations by providing businesses with automation capabilities, improved collaboration, and enhanced visibility.

Here’s how retailers can best use these technologies.

• Enhancing inventory visibility and fulfilment efficiency

Modern technologies like RFID scanners and mobile printers are transforming retail operations by dramatically accelerating productivity at the back-of-house, allowing businesses to better deal with the rapid pace of omni-channel sales and returns.

These devices collect data that provide complete, real-time view of inventories across the organisation, thereby optimising returns and ensuring products are where customers expect them to be, at all times.

Even taking baby steps with modern retail solutions can yield significant efficiency gains. For example, Malaysia’s largest prescription pharmacy chain, recently improved its capacity to fulfil e-commerce orders by five times and efficiency of fulfilment by 80 per cent just by deploying modern mobile computers and RFID sled readers.

• Empower retail associates with data-driven tools

Retail associates are pivotal to the omni-channel shopping experience, but they need the right tools to meet modern demands. Equipping them with modern devices like mobile computers and wearables, enables them to accelerate pickups and returns, reduce wait and checkout times, or make more informed decisions during service recovery.

According to Zebra’s study, 41 per cent of APAC retailers believe that Generative AI (Gen AI) will have an extremely significant impact on inventory management and demand forecasting.

Also Read: How to use Gen AI enabled chatbots for workplace safety?

Gen AI on these devices also further enhances productivity by providing employees with real-time personalised information. From summaries of return policies governing BORIS for specific lineups, to tailored product recommendations for in-store customers, Gen AI empowers retail associates to add a personal touch to every interaction.

Additionally, giving shoppers their own mobile shopping devices can elevate the customer experience. A Sri Lankan supermarket chain with 130 supermarkets conveniently located across the country, recently deployed such devices in-store, which enabled their shoppers to scan items as they shop and get recommendations on promotions – leading to reduced wait times and increased operational efficiency.

• Transform data into actionable insights

Businesses can leverage modern retail management software to fully harness the potential of the data collected by devices across the store. By using AI-driven predictive insights from historical data, retailers can better forecast demand, stay ahead of market trends, streamline stocktaking processes, and improve loss prevention.

Future-proofing for an evolving landscape

The retail sector continues to evolve, shaped by the expectations of emerging generations and their demand for hyper-personalised, seamless experiences. To stay ahead of these changes, retailers must future-proof their operations by adopting modern retail technologies, including AI-powered software, connected devices, and advanced data analytics.

These solutions not only enhance efficiency today but also enable retailers to anticipate and respond to the trends and preferences of tomorrow’s consumers. By embracing these innovations, businesses can remain agile, resilient, and prepared for the next wave of retail transformation.

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Former Indonesian Minister of Education, Culture, Research, and Technology Nadiem Makarim has been formally indicted over alleged corruption linked to a large-scale Chromebook procurement programme, with prosecutors claiming the scheme caused state losses of approximately US$125 million.

The indictment was read on 5 January 2026 at the Jakarta Anti-Corruption Court (Tipikor) and centres on the purchase of Chromebook laptops and Chrome Device Management (CDM) software as part of Indonesia’s national education digitalisation push during Makarim’s tenure from 2019 to 2024.

Also Read: Inside Indonesia’s US$610M Chromebook scandal: Raids, arrests, and Nadiem Makarim under scrutiny

Alleged state losses and procurement failures

According to prosecutors, the losses stemmed from two primary sources. About US$93 million was allegedly caused by inflated Chromebook pricing, while a further US$37 million was spent on CDM software that prosecutors said was unnecessary and delivered no tangible benefit to the ministry.

These findings were confirmed by an audit conducted in November 2025 by the Indonesian Financial and Development Supervisory Agency (BPKP).

The prosecution argued that the procurement process between 2019 and 2022 failed to meet basic planning and procurement standards. Crucially, the devices were found to be essentially unusable in Indonesia’s so-called “3T” regions (frontier, outermost, and remote areas) due to inadequate infrastructure.

The court heard that Chromebooks and the accompanying software were rolled out without a comprehensive needs assessment, reliable field surveys, or proper price benchmarking, particularly for schools in underserved regions.

Allegations of personal enrichment

Prosecutors further alleged that the scheme personally enriched Makarim, co-founder of Gojek, by approximately US$48.5 million. The procurement was carried out through official e-catalogues and the School Procurement Information System (SIPLah), but allegedly without mandatory reference pricing or robust price evaluations.

He is accused of acting in concert with several officials and external parties, including Sri Wahyuningsih, former Director of Primary Schools; Mulyatsyah, former Director of Junior High Schools; Ibrahim Arief, a consultant; and Jurist Tan, a former special staff member who is currently a fugitive.

Makarim faces charges under Articles 2 and 3 of Indonesia’s Anti-Corruption Law, in conjunction with the Criminal Code, which carry heavy penalties for abuse of authority resulting in state losses. Court observers noted that Nadiem appeared to smile while the indictment was read.

A case years in the making

The indictment marks a key milestone in a long-running investigation that intensified in mid-2025. Indonesia’s Attorney General’s Office launched a formal probe in May 2025, examining dozens of witnesses involved in Chromebook procurements valued at nearly US$600 million overall.

Indonesia names Nadiem Makarim a suspect in laptop procurement corruption case

Makarim publicly denied wrongdoing in June 2025, defending the use of Chromebooks as a cost-effective solution for remote learning during the COVID-19 pandemic. He was later questioned as a witness, barred from overseas travel, and named a suspect in September 2025 before being detained.

Multiple trial delays followed, including a postponement in December 2025 due to Makarim’s post-surgery recovery, before proceedings resumed in January.

Separately, Indonesia’s Corruption Eradication Commission (KPK) is reportedly examining a related procurement involving Google Cloud services, in which Makarim has been listed as a potential suspect.

The trial is set to continue with the examination of evidence related to weak data support, flawed procurement practices, and the failure of the digital education initiative to reach Indonesia’s most vulnerable students.

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Buy&Ship, a cross-border e-commerce enabler based in Hong Kong, has announced the first close of its Series C funding round, raising US$12 million.

The Series C round saw participation from a diverse group of investors, including MLC Ventures (Mitsubishi Logistics Corp. Ventures), DLK Advisory, and Hong Kong-listed company MemeStrategy. Existing investors Cool Japan Fund and Altara Ventures also provided strong follow-on support.

Also Read: The thesis for cross-border e-commerce in Southeast Asia

The investment is designed to accelerate the company’s mission to automate the global e-commerce value chain through the use of AI and pursue strategic mergers and acquisitions (M&A).

The funding arrives amid a period of significant growth for the startup, which focuses on eliminating the complexities of international shopping, such as customs documentation and high logistics costs. The company has reported a 100 per cent year-on-year growth in proxy-shopping revenue, primarily driven by its integration with Mercari, Japan’s largest C2C online marketplace.

In Southeast Asia, a key focus for the firm, Buy&Ship recorded more than 100 per cent year-on-year revenue growth in Singapore. Its presence in Taiwan also saw a revenue increase of over 60 per cent, bolstered by the introduction of direct shipping services from Japan to Taiwan.

To date, the platform claims to have amassed 3 million registered global users across 12 countries and regions, having handled more than 100 million packages.

Proprietary technology and AI integration

Established in 2014, Buy&Ship positions itself as a technology-first player rather than a traditional freight forwarder. Its integrated tech stack includes an AI-powered discovery engine that utilises Large Language Models (LLMs) to act as a virtual shopping assistant, surfacing global product opportunities for users.

Also Read: 5 trends shaping the cross-border trade landscape

The backend of the operation is supported by automated guided vehicle (AGV)-powered warehouses and intelligent logistics AI, designed to optimise delivery routes and auto-fill complex customs forms in real-time.

Buy&Ship, which operates 11 overseas warehouses, has so far raised approximately US$34.2 million across multiple rounds, which also include a US$16 million funding announced in June 2024.

Roadmap to public listing

The fresh capital will be utilised to deepen AI integration across the platform and embark on an aggressive expansion into the United States market. Furthermore, the company has confirmed it is initiating preparations for a public listing.

Sheldon Li, co-founder and CEO of Buy&Ship, stated: “This funding isn’t just capital; it’s rocket fuel for our mission to make the world’s products available to anyone, anywhere. With the strategic backing of investors, we will fast-track our AI optimisation to deliver a truly seamless cross-border shopping journey, enabling consumers to effortlessly access premium products. We are building the next-generation global e-commerce platform consumers deserve.”

Investor insights

Reflecting on the challenges of the current market, Dave Ng, General Partner at Altara Ventures, said: “Consumers are savvy global shoppers these days, with high expectations on the experiences they will receive when buying online. This makes cross border commerce even more challenging but presents a very exciting opportunity.”

Also Read: SEA’s e-commerce giants hit profitability: What it means for region’s digital future

“Buy&Ship is at the forefront of cross border innovation and they now play an even more important role in the brave new world of global tariffs. Integrating AI and technology into their business and operational expertise, Buy&Ship is leading the market in taking creative approaches to delivering delightful shopping experiences to users across Southeast Asia and beyond,” Ng added.

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The startup funding landscape has matured significantly over the past decade. Seed rounds, angel networks, and venture capital have become more accessible than ever before. Founders can pitch their way to initial capital with a compelling deck and a minimum viable product. Yet despite this abundance of funding opportunities, a new bottleneck has emerged that’s proving far more difficult to overcome: validation at scale.

Today’s startups with working products increasingly struggle not with building, but with proving their solutions work in real-world settings. They face a paradox: investors want traction before committing larger rounds, but achieving meaningful traction requires access to the very resources and partnerships that follow investment. The challenge isn’t about securing money to build. Instead, it’s about finding credible partners who can open doors to test, iterate, and demonstrate real impact.

The bottleneck isn’t money. It’s momentum.

Why funding isn’t enough

Most startups today can raise capital for MVPs and early development. The proliferation of early-stage funds, government grants, and angel investors means that promising ideas rarely die from lack of initial resources. The real challenge begins after the prototype is built: proving it works at scale with real users, under real constraints, generating real outcomes.

Startups face typical barriers that slow momentum even after they’ve secured funding. Enterprise decision cycles move slowly, often taking months or years to evaluate new vendors. Regulatory and compliance hurdles create friction, particularly in sectors like fintech, healthtech, and data-driven services. Perhaps most critically, startups lack access to large user bases or operational data that would allow them to validate their assumptions and refine their products meaningfully.

Without validation environments, startups cycle through iterations based on limited feedback, burning through runway while trying to convince potential customers that their solution works. They’re caught in a catch-22: they need proof to gain access, but they need access to generate proof.

You can’t pitch your way to product-market fit.

The power of real-world pilots

Enterprise pilots provide what pitch decks and demos cannot: tangible proof. When a startup tests its solution within an actual enterprise environment, with real users and real constraints, it transforms theoretical value propositions into measurable evidence. Pilots allow startups to test solutions under authentic operational conditions, refine their technology with actual user data and feedback, and build credibility through documented outcomes that speak louder than any presentation.

This evidence becomes the currency that matters for scale. Investors pay attention to pilots that demonstrate retention, efficiency gains, or cost savings. Partners and customers trust solutions that have been vetted by credible organizations. Pilots turn assumptions into validated learning and speculation into track records.

For enterprises, pilots provide equally valuable benefits. They gain early access to innovation that fits their specific context, allowing them to evaluate emerging technologies without the risk of full-scale implementation. They can shape solutions to their needs while identifying promising partners before competitors do.

Real-world testing shortens the path from prototype to proof.

Also Read: From US$107M lows to a US$491M finish: SEA’s volatile 2025

Why test beds are the new startup infrastructure

Traditional accelerator programs have served an important function in the startup ecosystem, focusing on mentorship, initial capital, and pitch preparation. They help founders refine their thinking, build networks, and develop presentation skills. But for founders who already have working MVPs, these programs address yesterday’s problems. Founders with functional products don’t need more advice about business model canvases or pitch structure. Instead, they need execution environments where they can validate their solutions with real users and real data.

Test beds represent a fundamental shift in startup support infrastructure. Rather than offering guidance on how to build, they provide platforms for demonstrating that what’s been built actually works. Test beds offer access to enterprise infrastructure that would take years to build relationships to access independently, real user bases for validation rather than synthetic testing scenarios, and partnerships with organizations that have decision-making power and distribution capabilities.

This represents a maturation of the startup support ecosystem. The shift is from learning environments focused on preparation to execution platforms designed for validation. Where traditional programs focus on getting startups ready to build and pitch, test beds focus on helping startups prove and scale.

Where traditional programs end, practical collaboration begins.

How 917Ventures and Velocity enable momentum

917Ventures and Globe Group recognized this gap in the startup ecosystem and built Velocity as a response. It is a launchpad specifically designed for real-world validation rather than just preparation. Velocity operates on a fundamentally different model than traditional accelerators by connecting startups directly with enterprise partners who are ready to pilot solutions, not just mentor founders.

Through Velocity, startups gain access to enterprise partners actively seeking innovation in specific problem areas, infrastructure and operational environments for testing at scale, and support for navigating compliance, regulatory, and operational barriers that typically slow down enterprise partnerships. This isn’t about workshops on how to eventually approach enterprises. It’s about immediate engagement with organizations prepared to test solutions.

The program serves multiple startup profiles with different validation needs. Emerging startups with MVPs can use Velocity to gain their first enterprise validation, proving their technology works beyond controlled environments. Growing companies with early traction can leverage the program to build credibility and access distribution channels that would otherwise take years to develop. Regional or global players looking to enter or expand in the Philippines can use Velocity to localize their solutions and test them in a new market context with an established enterprise partner.

What makes the model effective is the alignment of incentives. Both startups and enterprises benefit from validated outcomes. Startups gain proof points that accelerate fundraising, partnerships, and customer acquisition. Enterprises gain evaluated access to innovations that address real business challenges. The shared focus on measurable results creates genuine collaboration rather than performative partnership.

Also Read: The three signals US investors actually look for (and why your startup keeps missing them)

From pilot to scale: The new growth model

Successful pilots become proof points that accelerate everything else in a startup’s journey. A documented pilot with measurable outcomes changes conversations with investors from speculative to evidence-based. It transforms customer acquisition from cold outreach to warm introductions built on credible references. It shifts partnership discussions from “would this work?” to “how do we expand this?”

Validation through enterprise collaboration is becoming the competitive advantage that separates startups that scale from those that stall. In increasingly crowded markets, the ability to demonstrate proven impact in real-world environments differentiates viable businesses from promising concepts. This matters not just for attracting capital but for building strategic partnerships and customer relationships that drive sustainable growth.

The model also benefits the broader innovation ecosystem. When enterprises actively participate in validation rather than waiting for fully mature solutions, they help shape innovations that better serve their industries. When startups gain structured access to validation environments, they can iterate more efficiently, reducing waste and increasing the likelihood of finding genuine product-market fit. This collaborative approach to innovation creates better outcomes for all stakeholders.

Innovation grows faster when tested in the real world, not just imagined on slides.

Building for momentum, not just funding

The next wave of startup success will be defined not by access to capital, but by access to validation. The startups that thrive will be those that can efficiently prove their solutions work at scale, demonstrating traction through credible partnerships and measurable outcomes. This shift requires new infrastructure in the startup ecosystem. There is a need for infrastructure focused on execution rather than preparation, on validation rather than education.

Enterprise partnerships and test beds represent this new infrastructure for startup growth. Programs like Velocity signal an evolution from accelerating ideas to enabling execution. They recognize that the most valuable support for many startups isn’t more mentorship or demo days, but direct access to the environments where they can prove their value and build momentum.

This represents a recognition of where the real bottlenecks in startup growth have shifted. As capital becomes more accessible, as technical talent becomes more distributed, and as tools for building become more powerful, scarce resources become validation opportunities. The ability to test with credible partners, to iterate with real users, and to prove impact on operational environments. These capabilities now determine which startups can move from concept to scale.

Startups don’t lack funding. They lack the environment to prove what works.

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The modern founder narrative is a story of heroic, singular effort. It’s about the visionary genius toiling away in the garage, clinging to every share of equity and every ounce of control. We celebrate the lone wolf who builds an empire against all odds.

This narrative, while dramatic, is a dangerous trap. It reinforces the most toxic instinct of a growing business: the impulse to hoard. Founders hoard credit, hoard decision-making power, and, most disastrously, hoard the feeling of ownership.

If your goal is merely to build a business that serves you, congratulations, you’ve succeeded. If your goal is to build a company capable of achieving exponential, unassailable growth, you must immediately discard the selfish notion of “mine.” The fastest way to scale is not through singular effort, but through mass distributed motivation. You must learn to make your success everyone else’s victory.

The myth of singular control

The desire for total control is often rooted in fear, not strategy. Founders fear that if they relinquish power, the vision will be diluted or the company steered off course. In reality, attempting to maintain absolute control over a growing organism is the surest way to stunt its growth.

The moment a company engages with the outside world, whether it is with a customer, a supplier, or an employee, it ceases to be a solo project. It becomes a network of self-interested actors. The brilliant strategic move is to align those self-interests with the company’s ultimate success through sophisticated stakeholder management.

This isn’t just about sending a few newsletters or holding quarterly meetings. It’s about genuinely giving stakes to the critical participants in your ecosystem. Not just equity to your executives, but psychological and transactional stakes to everyone who interacts with your product.

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

Distributing the equity of success

Consider the three most vital external actors: the user, the customer, and the client. Are you merely selling them a product, or are you enrolling them in a joint venture?

• The user: You need them to feel not just satisfied, but invested. Companies that scale fastest empower their users to be co-creators. They don’t just ask for feedback; they clearly show how user suggestions directly influenced the roadmap. This transforms the user from a passive consumer into an unpaid evangelist whose reputation is now tied to your success. You gave them a piece of the creative equity.
• The customer (and client): This goes beyond the transactional purchase. The most powerful relationships are those where your client’s growth is inherently and demonstrably linked to yours. This is the integration of shared destiny. You shouldn’t just sell them software; you should integrate your processes so deeply that your mutual success is contingent on the other. Your client isn’t just buying your service; they are becoming a strategic growth partner. They should see you not as a vendor, but as an integral department of their own company.

When you allow users and clients to grow with you, when their own professional or personal success hinges on the success of your platform, their loyalty becomes unbreakable. They become fiercely protective of your brand because protecting it means protecting their own investment of time, reputation, and resources.

The power of relinquished control

The ultimate failure of the “mine” mindset is its effect on your internal talent. You cannot hire exceptional people (the kind of talent that makes exponential growth possible) and then tell them your company is a machine where they are only a cog.

You must build a culture where control is deliberately and transparently relinquished. Give your key leaders genuine agency over their domains. Ensure they feel the profound responsibility of stewardship, not just the task of execution. When they make a mistake, they own it, but when they succeed, they feel the full, unshared weight of the victory.

Also Read: Fractional CFOs: The missing link for startups struggling with finance

The founder’s greatest role is not to be the smartest person in the room, but to be the Chief Stakeholder Manager, who ensures that every essential person is maximally motivated because they know, without doubt, that your company’s success translates directly into their success. This generosity of control and credit creates a force field of loyalty that no amount of competitor funding can penetrate.

Stop trying to wear every hat. Stop seeking every ounce of credit. Your success will be far larger, faster, and more enduring when it is built on the combined, decentralised efforts of a network of people who believe they are building their own empire right alongside yours.

If the growth of your business requires ten people to do the work, why are you insisting that only one person gets to feel the triumph?

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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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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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