Exhibitions remain one of the most widely used channels for business development across industries. They provide concentrated access to potential customers, partners, and distributors within a short period of time.

Yet despite the scale of investment, the outcomes often fall short of expectations.

In many cases, the issue is not the quality of the meetings. It is what happens after the event.

Across multiple industries and regions, a consistent pattern appears: A significant portion of exhibition leads never progress into meaningful business conversations.

The gap is rarely strategic. It is operational.

The overlooked stage of the pipeline

Most organisations invest heavily in preparation.

They design booths, train staff, schedule meetings, and prepare marketing materials. Performance during the event is often measured by visible indicators such as:

• Number of visitors
• Number of business cards collected
• Number of product demonstrations conducted

These metrics provide a sense of activity. However, they do not necessarily reflect progress toward revenue.

The stage that determines long-term outcomes begins after the event ends.

Follow-up execution — not initial engagement — is where momentum is either sustained or lost.

Why follow-up breaks down

Through repeated observation in exhibition-driven sales environments, three structural causes appear consistently.

Information is captured without context

Teams often record contact details but fail to capture decision-relevant information.

For example:

• Level of interest or urgency
• Role in the decision-making process
• Specific needs discussed
• Agreed next steps

Without this context, follow-up communication becomes generic.

And generic communication rarely advances a business relationship.

Also Read: The real opportunity in ASEAN’s EV market lies in regional coordination

Responsibility becomes unclear after the event

During the exhibition, roles are well defined.

Sales teams engage visitors. Marketing teams manage materials. Operations teams coordinate logistics.

After the event, ownership of follow-up activities is often less clear.

Questions emerge:

• Who should initiate the next conversation?
• Who tracks the progress of each contact?
• Who ensures that commitments made during the meeting are fulfilled?

When responsibility is ambiguous, execution slows down.

Follow-up is treated as an action rather than a process

Many organisations view follow-up as a single step.

Send an email. Make a call. Share additional information.

In practice, effective follow-up requires a sequence of coordinated actions.

This typically includes:

• Prioritising contacts based on relevance
• Responding within an appropriate timeframe
• Personalising communication based on prior conversations
• Monitoring engagement and scheduling next steps

Without a structured workflow, even promising opportunities lose momentum.

Reframing exhibitions as part of an operational system

A useful shift in perspective is to view exhibitions not as isolated events, but as components of a larger pipeline.

The value of an exhibition is not determined solely by attendance or visibility. It is determined by continuity.

The first meeting establishes relevance. Subsequent actions establish trust.

Organisations that recognise this distinction tend to design their workflows differently.

Instead of optimising only for lead generation, they optimise for relationship progression.

Also Read: Beyond the booth: How tech companies can win at events

The role of collaboration in strengthening execution

Addressing post-event execution challenges often requires coordination across multiple areas of expertise.

Exhibition planning, on-site engagement, and follow-up management are interconnected activities.

When these functions operate independently, information fragmentation becomes more likely. When they are aligned, continuity improves.

For this reason, many organisations are beginning to integrate operational workflows more closely across teams and partners.

The focus is shifting from managing individual events to managing the full lifecycle of a business interaction.

Measuring what actually matters

As organisations refine their approach to exhibition-driven sales, performance measurement is evolving as well.

Execution metrics increasingly complement traditional activity metrics.

Examples include:

• Response rate to follow-up communication
• Number of second meetings scheduled
• Time elapsed between first contact and next action
• Conversion from conversation to qualified opportunity

These indicators provide a clearer view of pipeline health and operational effectiveness.

More importantly, they are actionable.

Teams can improve them through process design and disciplined execution.

Also Read: Building Web3 communities in Vietnam: Lessons from grassroots events

A practical implication for growing companies

For companies expanding into new markets or industries, exhibitions often represent a critical entry point.

They create initial visibility and open conversations that would otherwise take months to establish.

However, the long-term value of these interactions depends less on the event itself and more on the system that supports follow-up.

Organisations that invest in structured post-meeting workflows, including clear ownership, defined timelines, and consistent communication, are more likely to convert early conversations into sustained partnerships.

Conclusion

Exhibitions will continue to play an important role in business development.

But their effectiveness increasingly depends on operational discipline rather than promotional effort.

The most decisive moment is not when a conversation begins. It is when the next step happens.

Companies that design for continuity, not just visibility, are better positioned to translate meetings into measurable outcomes.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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When was the last time you visited a website to shop or learn something, and felt left out because you couldn’t access it? For many people with disabilities, this is an everyday reality.

Eliminating this exclusion is why accessibility should matter to businesses. Today, we will explore what business accessibility really means – why it matters, what the ROI looks like, and how organisations can build more inclusive experiences for everyone.

With the rise of AI and automation, accessibility is no longer as complex or resource-intensive as it once was. What used to be seen as a compliance burden is increasingly becoming a design and product decision, embedded into how digital experiences are built, scaled, and improved over time.

Why does accessibility matter?

When a product or service is accessible, it means everyone, including people with disabilities, can engage with it fully and independently. Hence, operating an accessible business is not just about complying with regulations but about meeting the needs of a broader customer base in a digital-first world.

Key reasons accessibility matters:

• Inclusivity builds trust: A more accessible product reaches a broader audience and fosters brand loyalty.
• Technology supports accessibility: AI and automation are making accessibility more scalable and efficient to implement.
• Legal requirements are increasing: Many countries now mandate accessibility compliance, especially for digital platforms.

With advancements in AI and assistive technology, accessibility is becoming easier to implement and more impactful than ever. Businesses that embrace it are not only doing the right thing, they are also positioning themselves for long-term success.

Also Read: Empathetic software development: Creating accessibility-first apps for greater inclusivity

The ROI of accessibility

At its core, accessibility drives profitability for businesses. Investing in accessibility yields measurable returns across multiple areas. Here’s how:

• Increased market reach

Around 1 in 4 adults in the US lives with a disability. Increasing accessibility in your business helps you reach a larger audience and invites them in for more engagement. This expands your market reach.

• Improved SEO

Accessible websites follow a clear structure, include alt text, and offer easier navigation, all of which Google loves. What helps a screen reader also helps a search engine. In other words, accessibility and SEO don’t compete; instead, they complement each other beautifully.

• Reduced legal risk

Digital accessibility lawsuits are rising every year. From small startups to global brands, no one is immune. There is also an increase in measures taken to focus on businesses becoming more accessible. Hence, proactive accessibility protects your business from costly legal action and positions your brand as responsible and forward-thinking.

• Better user experience

One last thing to remember is that accessible businesses not just benefit people with disabilities, but they benefit everyone. Features like clear navigation, readable fonts, and video captions enhance usability across the board, think about people with slow internet. Having accessible features improves satisfaction for all users, regardless of ability or context.

How can businesses be more accessible?

Recognising the value of accessibility is the first step. But what does it look like in action? These practical strategies will help you understand how businesses can be more accessible, both sustainably and meaningfully:

• PDF or document accessibility

Documents, especially PDFs, are often where accessibility breaks down. Without proper tagging and formatting, screen readers cannot interpret them, leaving users frustrated.

A dedicated PDF accessibility software can help ensure your digital documents are inclusive, readable, and compliant, without slowing down your workflow.

• Website accessibility

Your website is your storefront. If someone cannot navigate it with a keyboard, read it with a screen reader, or understand its structure, they are more likely to leave and not return due to a poor user experience.

To tackle this website accessibility issue, use clear headings, alt text, accessible colour contrasts, and ensure WCAG compliance from the ground up.

Also Read: Accessibility requirements every startup should know

• Physical accessibility

Accessibility isn’t limited to screens. Think about the layout of your physical space. Are entrances, pathways, and restrooms usable for everyone? Every ramp, every sign, every widened door should send a message: you belong here.

• Closed captions and transcripts

Words matter, especially when they are visible. Closed captions and transcripts not only support users with hearing impairments but also boost comprehension, engagement, and even searchability. This feature makes a business accessible by being a simple fix that creates a big impact.

• Employee training

Tools and checklists only go so far. Real change happens when people understand the “why” behind accessibility. Training your team builds a culture of inclusion and empowers them to create with empathy, not just for efficiency.

Continue monitoring and updating

Making a business accessible isn’t just a one-time project. It is a continuous process that requires regular auditing to stay current with evolving standards and user feedback. Moreover, it is also not just about making your business legally compliant, but also about aligning your brand with ethical values so that all individuals feel included.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Building a startup in Southeast Asia today can feel like surviving in a vast, deep ocean. The ecosystem is growing, capital is selective, regulatory scrutiny is sharper, and competition is higher.

Founders focus on product, fundraising, and growth loops. That is necessary. But many ignore one of the most reliable growth levers: reputation. And building it does not require a big budget. It requires intention and consistent communication.

According to Burson, companies with strong reputations can realise up to 4.78 per cent in additional unexpected annual shareholder returns. Other studies show that startups with consistent media coverage attract venture capital faster, close funding rounds efficiently, enjoy lower customer acquisition costs, and outperform weaker brands financially.

Reputation in 2026 measurable. And it is a growth advantage. The question is not whether you can afford to invest in it. The question is whether you can afford not to.

Reputation as a tangible asset: The business case for startups

Reputation is no longer a soft concept; it has financial value. Burson estimates the global Reputation Economy at US$7.07 trillion. 

We have seen this clearly in Southeast Asia. When Grab acquired Uber’s Southeast Asia operations in 2018, part of its advantage was reputation. Grab had a long-term commitment to working with regulators. It was trusted. That credibility mattered in a fragmented regulatory environment.

An inverse example is the low-trust environment of e-commerce. Carousell was an early leader. But when security concerns hit the industry amidst the rise in scams, Shopee’s reputation for buyer protection insulated it. Today, Shopee is exponentially larger.

Reputation shapes outcomes. It protects, attracts, and compounds. Yet many founders treat it as a luxury, or only as a checklist item.

Also Read: Why startups should prioritise brand reputation from day one

Why communications cannot be an afterthought

Many founders delay communications because it feels cosmetic, expensive, or premature.

In reality, communications is tightly linked to core business outcomes. How a company positions itself influences fundraising conversations, hiring quality, partnership opportunities, and even how regulators interpret its intent.

Left unmanaged, perception forms anyway. The risk is not a lack of visibility, but misalignment between what a company is building and how it is understood.

Early-stage communication should focus on clarity. What problem is being solved, why it matters, and why this team is credible. When done well, visibility becomes a byproduct of coherence, not the objective.

Start early: Consistency beats perfection

Many startups wait until they feel “ready” to communicate. That threshold is often unclear and constantly shifting.

Start earlier than feels comfortable.

Progress builds trust more effectively than polished narratives. Sharing what is being built, along with the challenges and trade-offs involved, creates a more credible signal than occasional, highly curated updates.

Silence, on the other hand, creates ambiguity. And ambiguity rarely works in a startup’s favour.

Messaging matters: Balancing the three C’s

Execution without messaging clarity creates noise. A useful way to structure communication is across three dimensions:

• Corporate: business fundamentals, vision, governance. Why should stakeholders take this company seriously?
• Concept: product, technology, differentiation. Why does this solution matter now?
• Community: human connection, brand story, ecosystem. Why should people care?

Startups often over-index on product. But technology without credibility feels fragile, while credibility without connection feels distant.

Reputation sits at the intersection of all three.

Also Read: With AI comes huge reputational risks: How businesses can navigate the ChatGPT era

Adapt to context: Southeast Asia is not a single market

Communications is context-dependent.

Southeast Asia is frequently treated as a single region, but media environments, regulatory sensitivities, and audience expectations differ significantly across markets. What works in Malaysia may not translate in the Philippines. What resonates in Singapore may fall flat in Indonesia.

This requires adaptation at multiple levels, from messaging to channel selection to how success is measured.

A single approach may be efficient, but it is rarely effective.

From episodic visibility to sustained credibility

Not all external communication is equal. One-off announcements can generate attention, but they do not build a reputation on their own.

Reputation is cumulative. It is shaped by consistency, clarity, and how a company shows up over time.

Startups that treat communication as an ongoing function, rather than a series of campaigns, tend to build stronger credibility. This has practical implications. Investors engage faster, customers convert with more confidence, and external stakeholders require less persuasion.

Building a startup will always involve uncertainty. Reputation does not remove that uncertainty, but it changes how others respond to it.

In Southeast Asia’s current environment, that shift can be a meaningful advantage.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Southeast Asia’s AI story is often told as a race to build models or launch chatbots. The more immediate opening may be smaller and more practical: software agents that reduce fraud losses, clear operational backlogs, and automate repetitive casework inside large local industries. That matters in a region where the digital economy reached US$263 billion in gross merchandise value in 2024, yet trust, labour constraints, and uneven software adoption still slow execution, according to Google, Temasek, and Bain.

The first real gap is not intelligence but operational drag

The strongest case for AI agents in Southeast Asia is not that companies suddenly need more ideas. It is that many still run important processes through spreadsheets, email chains, call-centre queues, manual reviews, and fragmented software.

That makes payments operations, scam detection, BPO workflows, and hospital administration better targets than general-purpose assistants. These are environments where tasks repeat at scale, errors are expensive, and buyers can measure whether a product actually reduces handling time, fraud leakage, or case backlog.

Google, Temasek, and Bain say AI adopters in the region are already realising returns within 12 months. That is a useful signal for founders: in Southeast Asia, the first winning agent startups are likely to sell cost reduction and process reliability before they sell autonomy.

Fraud and compliance may be the clearest wedge

If one category looks especially underbuilt, it is digital trust. Singapore’s annual scam brief put 2024 scam losses at about S$1.1 billion, while the same e-Conomy SEA 2024 research found that half of digital users had already fallen victim to online scams despite feeling confident they could detect them.

That creates room for agentic products that sit inside existing financial and commerce workflows: transaction monitoring, merchant-risk review, scam-pattern escalation, claims triage, customer outreach, and internal investigation support. These are not glamorous use cases, but they map directly to budgets because the cost of doing nothing is already visible.

Also Read: Hospitality needs to treat AI agents like a new channel, not a new feature

The region’s policy conversation also points in this direction. ASEAN’s expanded guidance on generative AI highlights deepfakes, inaccurate outputs, privacy, and malicious activity as material risks, which means products that improve verification, auditability, and safe human handoff may have a more credible path to adoption than agents that try to operate unchecked.

The Philippines shows why back-office agents matter

The second major opening sits in business-process work. Reuters reported that the Philippine IT-BPM industry was on track for US$38 billion in revenue and 1.82 million jobs in 2024, with an industry roadmap targeting up to US$59 billion by 2028. That is exactly the kind of large, process-heavy market where agents can create value without needing fully autonomous decision-making.

A credible startup here would not try to replace entire teams. It would target narrower pain points such as case summarisation, post-call compliance checks, workflow routing, multilingual knowledge retrieval, or quality assurance for voice and chat operations. In those settings, the product succeeds only if it works with existing CRMs, ticketing systems, and service-level agreements.

This is also why the market remains underexploited. Local buyers may be interested in AI, but deploying agents inside live customer operations requires strong integration, careful change management, and clear accountability when outputs are wrong. Even in Singapore, IMDA said only 14.5 per cent of SMEs adopted AI in 2024, up from 4.2 per cent a year earlier, which suggests willingness is rising faster than readiness.

Healthcare administration is a harder market but a real one

Healthcare is often discussed as a moonshot AI category, but the nearer opportunity in Southeast Asia is less about diagnosis than administration. Philips, citing WHO estimates, said the region could face a 6.9 million health-worker shortage by 2030, while surveyed professionals pointed to burnout from non-clinical work and concern about growing patient backlogs.

That makes scheduling, referrals, documentation, discharge coordination, and claims preparation more plausible starting points. An agent that reduces clerical load for nurses, front-desk teams, or care coordinators may be easier to validate and regulate than one that makes frontline clinical judgments.

Also Read: The one-person company was always possible. AI agents make it probable

The same constraint applies across the region: the best products will be the ones that respect low-trust environments. Founders need to assume patchy data, mixed-language workflows, limited technical staff, and buyers who want human oversight embedded from day one.

Early signals are real, but the bar is higher than hype suggests

There are signs that agent-native companies are starting to form. Tech in Asia reported that 35 Southeast Asian startups had raised at least US$1 million for agentic AI products by early 2026, while Singapore-based Level3AI announced a US$13 million seed round to expand enterprise voice and chat automation. Antler separately said it invested US$7.4 million across Southeast Asian AI startups in the first half of 2025, including seven companies through an AI residency in Singapore.

Still, these are early signals, not proof that the region has found its defining winners. The agent startups most likely to work in Southeast Asia will be narrow, local, and deeply operational: they will sell into industries with measurable pain, build around regulation instead of around it, and keep a human in the loop where trust is low.

For founders, investors, and ecosystem builders, the call is straightforward. Stop asking where the region’s AI equivalent of a universal assistant will come from, and start backing teams that can remove a costly hour, a missed payment flag, or a hospital admin bottleneck from a real workflow in Southeast Asia. That is where the gold rush is likely to become a business.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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Around a week before I started writing this, I saw a job post on social media that was everywhere. It got a lot of attention, so I clicked to see why. The requirements were:

• Minimum bachelor’s degree.
• More than 15 years of work experience.
• Under 25 years old.

There were plenty of other detailed requirements, too, all tied to the role itself.

My first thought was that it had to be a typo. But after a few minutes, I started thinking about it differently. What if it was not a mistake? What if the recruiter was genuinely looking for someone young, flexible, and still able to deliver senior-level output?

It sounds extreme, but plenty of companies are looking for someone like this.

Why SMEs create unrealistic roles

Most SMEs do not create impossible roles because they like setting impossible standards. They do it because they are under pressure. They need growth, speed, and solid execution, but they often cannot afford multiple hires or senior-heavy teams.

The work still needs to get done. Marketing, operations, customer support, hiring, and admin all have to keep moving. But instead of building a team with clear roles, they squeeze several responsibilities into one position. They look for someone who can think strategically, move fast, adapt easily, and work with very little support. Sometimes they are not hiring for one role at all. They are really asking one person to carry the weight of several jobs.

At the core, they are trying to solve a real business problem with limited resources. 

Why AI agents make this fantasy feel possible

This is where AI agents start to change the picture.

The idea of one person doing the work of several people no longer feels unrealistic. When one employee has AI tools that can draft emails, summarise documents, organise information, suggest ideas, and automate repetitive tasks, that person can suddenly work much faster.

That is a big part of why AI agents are so attractive, especially for lean businesses. They feel efficient, flexible, and scalable. They can help marketers come up with content ideas, summarise campaign results, and prepare reports. They can help recruiters screen CVs, draft outreach messages, and manage interview schedules. In short, they can take a lot of repetitive work off people’s plates.

Also Read: Hospitality needs to treat AI agents like a new channel, not a new feature

And this is not just hype anymore. McKinsey’s 2024 global survey found that 65 per cent of respondents said their organisations were already using generative AI regularly in at least one business function. That does not mean AI is replacing entire roles, but it does show that companies are starting to treat it as a practical tool for everyday work, not just an experiment.

From a business point of view, the appeal is clear. AI agents look like a way to increase output without immediately adding headcount.

The real barrier is not only technology, but trust

The hardest part of using AI agents is not just the technology itself. It is trust, judgment, and the discipline to use them well.

It is fairly easy to trust AI with first drafts, summaries, or simple admin work. It gets much harder when the task involves customer communication, compliance, or decisions that depend on context. That is usually where hesitation starts.

I do not fully trust AI agents all the time. Some tasks that seem simple enough to hand over still do not come back as well as I expect. Sometimes the output is wrong. Sometimes the judgment is off.

And businesses know those mistakes are not always minor. A weak internal summary can be fixed. A poor customer response can damage trust. A compliance mistake can create much bigger problems.

You can see this gap between experimentation and real confidence at the company level, too. McKinsey reported in 2025 that while almost all companies are investing in AI, only one per cent believe they are truly mature in how they use it. The same report says the biggest barrier to scaling AI is not employees, but leadership not moving the change forward fast enough. That matters because many companies talk about AI as if it is just a tool issue, when in reality it is also a management and workflow issue.

So the real question is not just whether AI agents can do the task. It is whether businesses are comfortable relying on them when accuracy, accountability, and judgment really matter.

What AI agents might quietly take away from workers, especially junior talent

This is the part of the conversation I think we should pay more attention to.

A lot of the work AI agents are starting to handle is repetitive, low-risk, and operational. But that is also where many junior employees used to learn. That first layer of work was never just simple work. It was often where people built judgment, picked up context, and grew professionally.

Think about a junior marketer who no longer has to come up with content ideas, write campaign summaries, or prepare monthly reports, or a junior recruiter who no longer needs to screen resumes, schedule interviews, or manage candidate documents. A lot of junior employees used to learn through exactly these kinds of tasks. By drafting, organising, following up, researching, and handling simpler work first, they built pattern recognition, confidence, and judgment over time. 

Also Read: AI agents and the new rules of business execution

If AI agents absorb too much of that layer, companies may become more efficient in the short term, but weaker in the long term. If junior employees do not get enough real experience, where will future managers and specialists come from?

That is why AI adoption should not only be framed as a productivity story. It should also be part of a capability-building conversation. A company can save time today and still create a talent problem for itself tomorrow.

What companies should do instead

This is why businesses should stop thinking about AI agents only as a replacement tool and start treating them as a work redesign tool.

The better question is not, “How many people can AI replace?” It is, “Which tasks should AI take on, which should stay human, and which should be done together?”

AI agents are useful for repetitive admin, first drafts, summaries, sorting information, and process support. Humans are still better at relationship-building, final decisions, ethical judgment, and high-stakes communication. In most cases, the strongest model is not AI alone or human alone, but humans working with AI.

AI can also raise the floor for less-experienced teams. It can help people perform more consistently in areas like drafting, research, reporting, coordination, and execution. But exceptional talent will still stand out. Judgment, creativity, systems thinking, leadership, and taste are still much harder to compress.

That matters because it changes what company-building can look like. A business in Singapore or Australia, for example, may not need one person to do everything locally. It may make more sense to keep the most strategic roles close to home, build human teams in emerging talent markets like Indonesia or the Philippines, and let those teams work alongside AI agents.

That model can work across many functions, from software development to finance and accounting to sales and marketing. The goal is not to replace people. It is to build a more realistic and scalable way of working around them.

For SMEs especially, this matters. The answer is not to keep searching for one magical employee who can do everything. The smarter move is to build better systems around real people.

Also Read: The one-person company was always possible. AI agents make it probable

The end of the all-in-one employee

AI agents will absolutely change the way companies work. They will raise expectations around speed, output, and how much one person can get done. But they should not become an excuse for unrealistic hiring or weak team design.

The real opportunity is not to expect humans to do even more just because AI exists. It is time to rethink how work is shared, where people add the most value, and how teams are built more realistically from the start.

AI agents will not replace workers. But they may replace the fantasy of the all-in-one employee that so many SMEs have been searching for. And honestly, that may not be a bad thing. That fantasy was never a sustainable way to build a business in the first place.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The post AI Agents and the end of the all-in-one employee appeared first on e27.

GoTo’s first quarterly profit is less a triumphant endpoint than a revealing pivot point for Southeast Asia’s tech narrative. After years of subsidy-fuelled expansion, the company has finally demonstrated that scale can translate into earnings, but only after becoming a markedly different business.

The headline numbers — positive net income, rising EBITDA, and strong cash flow — signal a long-awaited shift in discipline. Yet beneath them lies a more nuanced reality: profitability is being driven disproportionately by fintech, not by the ride-hailing and delivery engine that built GoTo’s brand. Payments and lending, with their superior monetisation dynamics, are now doing the heavy lifting, while on-demand services mature and grow more slowly.

This evolution raises a strategic question. Is GoTo still a super app, or increasingly a fintech platform with logistics attached? The answer matters because fintech brings both higher margins and higher risks, especially around credit quality, where disclosures remain thin.

Equally notable is what enabled this milestone: fewer incentives, tighter pricing, and a leaner corporate structure. Profit has arrived not through expansion, but through restraint.

For investors, the signal is clear. The era of growth at any cost is over. The next test is whether this new, narrower GoTo can sustain profitability without sacrificing relevance in an intensely competitive market.

REGIONAL

GoTo’s first quarterly net profit driven by fintech arm: Indonesia’s biggest digital ecosystem posted a first-quarter net profit of US$10.2M, reversing a US$21.8M loss a year earlier, with fintech revenue surging 58% to US$113.6M, now carrying the bulk of the investment case as on-demand growth slows.

eFishery founder handed nine-year jail sentence: Gibran Huzaifah was convicted of corporate deception, embezzlement, and money laundering after eFishery’s alleged revenue manipulation erased US$300M in investor value, rattling confidence in private-market due diligence across Southeast Asia.

TrueMoney Philippines winds down after a decade: The digital payments and remittance firm is ceasing all commercial operations in the country, having served over 5M customers and 20,000 SMEs across payments, lending, and insurance since its launch.

Indonesia mandates suspension reporting for under-16 accounts: Platforms must now publicly disclose how many accounts belonging to under-16 users they suspend, with rules affecting 70M children, though digital rights groups warn age checks expose sensitive data and are easily bypassed.

TikTok Go by Tokopedia targets Indonesia’s F&B market: Launched on April 29, the local services feature bridges content and offline visits via videos, live streams, and location-based discovery, as daily dine-in merchant orders reportedly rose more than 20 times in 2025.

Roblox moves to comply with Indonesia’s under-16 curbs: The US gaming platform has begun rolling out compliance measures following Jakarta’s tighter online rules for minors, though specific steps were not disclosed by the country’s communications minister.

Malaysia’s Qarbotech wins Grand Prix at SusHi Tech 2026: The agritech startup beat 820 applicants from 60 countries to claim JPY10M (US$62,000) for its nanocarbon photosynthesis-boosting agent, signalling growing global resonance for tropical-climate agritech solutions.

Cata raises US$5.3M to democratise F&B app tech: The Singapore-based consumer app platform closed an oversubscribed seed round led by Portage, enabling independent F&B and retail operators to launch branded apps with loyalty, payments, and CRM within days, with Germany as its first international market.

Cube raises US$3.7M to solve e-commerce data chaos: The Bangkok-based market intelligence startup secured Series A funding led by Betatron Venture Group to expand AI-enabled product tagging into North Asia and Latin America, targeting brands flying blind on fragmented digital shelf data.

FORMAS.AI closes US$3.98M pre-seed for AEC design platform: Led by Vertex Ventures Southeast Asia & India, the oversubscribed round will fund a platform orchestrating 60-plus AI models for architects, with users across 135 countries already generating over 500,000 designs organically since November 2025.

SiamDL raises US$7.8M to expand AI lending in Thailand: The Bangkok-based lender secured an oversubscribed Series A from international investors to scale its proprietary AI credit-scoring system, targeting underserved consumers and micro-entrepreneurs with thin financial records through its Bank of Thailand-licensed apps.

Philippines talent gap threatens strategic sector growth: A Monroe Consulting report warns that transformation is outpacing talent readiness across energy, finance, and tech, with above-market salary increases of 7–10% projected for automation, cybersecurity, and AI engineering roles.

Pandai’s low-cost growth playbook earns LSE 100x Impact spot: The Malaysian edutech startup scaled to over one million users with near-zero paid marketing and a 94% monthly retention rate, landing in the LSE initiative that identifies organisations with potential to improve one billion lives.

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INTERVIEWS & FEATURES

Netbank CEO: We want to be the full meal, not just ingredients: CEO Gus Poston explains how owning the banking ledger rather than wrapping legacy infrastructure lets Netbank serve fintechs at startup speed, with FY2025 revenue up 88% driven primarily by QR.Ph payment volume growth.

SEON CEO: AI exposes weak risk operations, not fix them: Tamas Kadar warns that automating before defining decision ownership is the industry’s biggest mistake, with fragmented fraud and AML systems creating faster confusion rather than clarity across Southeast Asian markets.

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INTERNATIONAL

Anthropic eyes US$900B valuation in new funding round: The Claude maker is in talks with investors for a raise that would top OpenAI’s valuation, as its annualised revenue reportedly hit US$30B driven largely by demand for Claude Code, though no term sheet has been signed.

Parallel Web Systems hits US$2B valuation after back-to-back raises: Founded by former Twitter CEO Parag Agrawal, the AI search and research API startup raised a US$100M Series B led by Sequoia just five months after its Series A, bringing total funding to US$230M with over 100,000 developers on the platform.

SoftBank plans US listing of AI robotics unit Roze at US$100B: Japan’s investment giant is preparing to spin off an AI and robotics company focused on data centre construction, potentially targeting a public debut as early as 2026 at a US$100B valuation.

Ant International bets on AI commerce infrastructure at scale: The Alibaba affiliate’s payments network now links 150M merchants with 2B consumer accounts across 220 markets, handling 20M daily transactions, while Alipay’s new tool enables merchants to accept payments made by AI agents.

US tech giants set to outspend China 7:1 on AI infrastructure: American hyperscalers led by Google, Microsoft, Meta, and Amazon are projected to spend over US$700B on AI infrastructure this year, versus an estimated US$105B by Chinese cloud providers constrained by chip export curbs.

Crypto equities plunge as big tech earnings impress markets: Macro pressures from surging oil prices and a hawkish Fed hammered crypto-linked equities (Robinhood fell 14%, Coinbase 6-8%) even as Bitcoin held near US$76,000, exposing how differently macro cycles transmit through digital asset layers.

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CYBERSECURITY

After the breach: How companies respond now defines trust: A Penta report draws on 18 months of stakeholder sentiment data to argue that rapid transparency and visible leadership — not technical containment alone — now determine reputational recovery, with retail carrying the sharpest negative sentiment at minus 77.

SEA’s cyber market races toward US$10B fortress by 2030: With cyberattacks up 85% year-on-year in 2024 and the regional market valued at US$2.8B growing at 28.5% CAGR, Singapore-based Darktrace, Senzing, and CyberArk command 45% market share as detection times collapse from 21 days to 47 minutes.

SEA’s AI-fuelled cyber war: Six defining trends for 2026: As Southeast Asia’s digital economy surpasses US$1T, deepfake phishing attacks spike 150%, quantum-resistant cryptography becomes a regulatory mandate, and ransomware groups hit 40% more Indonesian SMEs, forcing startups and governments into agile defence.

US$1.5B crypto hack exposes exchange security gaps: The largest digital theft ever sent Bitcoin below US$80,000 and swung sentiment from extreme greed to fear overnight, calling for multi-layered technical infrastructure, human-centric protocols, and transparent asset management as the new security standard.

APJ finance faces 3.7B attacks as ransomware victims surge 204%: Akamai’s State of the Internet report found 92.3% of APJ finance sector attacks targeted banks, while zero-day and one-day vulnerabilities drove a 204% jump in ransomware victims, making zero-trust architectures and microsegmentation critical defences.

Diverse IT teams are a cybersecurity weapon, not just good HR: With women comprising only 34-40% of Southeast Asia’s tech workforce, organisations with homogenous teams miss region-specific attack patterns; diverse cultural and technical backgrounds improve threat detection, social engineering recognition, and compliance across borders.

Connected cars are becoming prime targets for cybercriminals: From the 2015 Jeep Cherokee remote hack to Tesla’s 2020 keyless entry vulnerability, smart vehicles face growing risks as decentralised security models like DePIN and Soarchain’s blockchain infrastructure emerge as more resilient alternatives to centralised systems.

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SEMICONDUCTOR

Samsung Q1 profit surges 8x to US$38.9B on AI chip demand: Record-breaking memory chip revenue driven by AI server demand and tight supply pushed first-quarter operating profit to 57.2 trillion won,  surpassing Samsung’s entire full-year 2025 earnings, with strong second-half server memory demand expected as hyperscalers expand AI capacity.

TSMC exits Arm Holdings with US$231M share sale: Taiwan’s chip giant has fully divested its Arm stake acquired at IPO for US$51 per share, selling remaining shares at US$207.65 each through TSMC Partners, adding US$174M to retained earnings and completing a profitable exit from the British chip designer.

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AI

Google DeepMind CEO expects AGI arrival by 2030: Demis Hassabis says the 20-year mission to build AGI is precisely on track, urging business leaders to treat the timeline as operational reality, and crediting DeepMind’s early success to pairing deep learning with reinforcement learning ahead of rising GPU power.

SEA’s AI agent opportunity lives in messy workflows: With the region’s digital economy at US$263B GMV, the clearest wins for AI agents are in fraud detection, BPO workflows, and healthcare admin — where operational drag, not a lack of intelligence, is the real bottleneck startups should target first.

How AI agents are quietly rewriting the growth marketing playbook: An always-on AI agent that flagged 18% of monthly ad spend burning with near-zero conversions illustrates how agents amplify human intelligence in marketing — but strategy, cross-functional trade-offs, and accountability still require named human owners, not autonomous loops.

AI agents and the end of the all-in-one employee fantasy: SMEs that have long searched for one person to do everything are finding that AI agents can carry much of the operational load, but the real risk is hollowing out junior talent pipelines before future managers have a chance to build judgment through real work.

Why startup founders shouldn’t trust AI to replace a PR team: A founder’s six-month experiment replacing his PR agency with AI tools produced flawless-looking outreach lists full of outdated contacts and generic thought leadership pieces, revealing that AI accelerates volume but cannot replicate the relational judgment of an experienced communicator.

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THOUGHT LEADERSHIP

ESG tech’s next frontier is defensibility, not measurement: As sustainability disclosures are increasingly held to financial reporting standards, platforms that cannot explain data lineage, who touched a number, when, and why, will fail enterprise assurance tests, splitting the market between workflow tools and auditability-first infrastructure.

APAC founders must treat communication as a leadership skill: In a region where social media backlash goes regional within hours and regulations shift without warning, crisis-ready startups need pre-approved messaging templates, a single source of truth, and media relationships built before they are needed, not after a crisis hits.

The digital transformation lie SMEs have been sold: Most SMEs are drowning in disconnected tools rather than transformed by them, and the hidden tax of bad software — duplicate work, slow invoicing, blind spots — costs real margin daily; the winning platforms will be those that reduce friction before they sell a solution.

Accessibility in business is an ROI driver, not just compliance: With 1 in 4 US adults living with a disability and digital accessibility lawsuits rising annually, businesses that embed inclusive design into websites, documents, and physical spaces improve SEO, reduce legal risk, and expand market reach for all users.

Why exhibition leads fail and how to build a pipeline that converts: Most organisations invest heavily in booth presence but neglect the structured post-event follow-up process where momentum is actually won or lost: clear ownership, contextual data capture, and defined timelines matter more than visitor counts.

When collaboration systems break down in tech-driven workplaces: With 69% of APAC organisations on hybrid models and 6 in 10 employees reporting burnout, leaders must treat collaboration as intentional infrastructure, not a set of perks, using interactive digital tools to restore equity, alignment, and engagement across distributed teams.

Why eSIM is becoming as important as your passport: As global business travel spending grows toward US$1.57T in 2025, eSIM adoption is accelerating because connectivity must now be ready at the moment of arrival, not sorted out after landing, with travel eSIM users forecast to grow from 40M in 2024 to 215M by 2028.

The post Ecosystem Roundup: GoTo turns profitable, but the story has changed appeared first on e27.

The first wave of ESG tech won attention by making sustainability data easier to collect, organise, and present. That was enough when most firms were still trying to get a report out the door, stand up a dashboard, or show that they were at least taking measurement seriously. That phase is ending. There is a clear weight on sustainability-related disclosures being comparable, verifiable, timely, and understandable, while also requiring connected information between sustainability disclosures and financial reporting.

That changes the commercial logic of the software category. The enterprise buyer is no longer asking only whether a platform can calculate a number. Increasingly, the buyer needs to know whether that number can survive challenges from finance, internal audit, external assurance, regulators, and the board. This is the integrity gap in ESG tech. Many platforms can produce metrics. Far fewer can explain, with discipline, where each input came from, what happened to it in transit, which assumptions touched it, and why the final output should still be trusted. That is not a reporting nicety. It is becoming a buying criterion.

The market has moved from measurement to defensibility

The most important shift is not technical. It is institutional.

Sustainability information is no longer treated as a side narrative. It is treated as part of general-purpose financial reporting and explicitly says the information should be provided in a way that helps users understand connections across governance, strategy, risk management, metrics, targets, and the related financial statements. It also defines verifiability in practical terms, saying information is more useful when it can be corroborated directly or through the inputs used to derive it. That is a higher bar than modern ESG software was originally built for.

Why lineage matters more than teams realise

Data lineage sounds technical, but its real value is managerial. Data provenance in terms equivalent to a chain of custody, covering the generation, transmission, and storage of information in a way that can trace origin. That is exactly the language enterprise climate tech should now be borrowing. For a large company, especially in sectors like oil and gas, utilities, manufacturing, and heavy industry, a reported climate metric rarely comes from one neat system. It is assembled across operational data, finance systems, procurement records, supplier inputs, certificates, spreadsheets, manual adjustments, and judgment calls. If that chain is unclear, the number may still be useful for internal direction, but it becomes harder to defend as enterprise-grade reporting.

Also Read: ESG as strategic value: Why Asian boards must move beyond disclosure

This is not abstract. The core reporting principles of many protocols say transparency depends on a clear audit trail, references to methodologies and data sources, and information being recorded and analysed in a way that allows internal reviewers and external verifiers to attest to credibility. It also states that companies are responsible for the existence, quality, and retention of documentation so as to create an audit trail of how the inventory was compiled. It goes further and says an audit trail or similar mechanism is needed to show that no other entity is claiming the same environmental attributes. In other words, lineage is not an administrative garnish. It sits inside the claim itself.

The uncomfortable part for vendors is that dashboards are no longer enough

Many climate tech products were shaped by the priorities of an earlier market. They were built to speed up collection, improve completion rates, standardise templates, and give sustainability teams a cleaner operating interface. None of that is trivial, but it is no longer where strategic differentiation lives.

The next enterprise question is harsher. Can the platform preserve a defendable history of the number, not just its latest version? Can it show which emission factor was used at the time, who overrode a value, which source file was replaced, when a boundary changed, and whether prior periods were restated consistently? The emphasis is on connected information, consistent data and assumptions, and verifiability means the product has to support coherence across narrative, metrics, and financial context. A tool that calculates well but forgets its own reasoning is going to look weaker with every reporting cycle.

Auditability is becoming a commercial feature, not a compliance burden

This is the part many founders and product leaders still underplay. Auditability is often framed as a back-office requirement that slows the user down. In enterprise climate tech, it is becoming part of the product’s commercial value.

The reason is simple. Buyers are now under pressure to prove consistency between sustainability reporting and financial reporting, and assurance helps ensure that connectivity and consistency. That means a platform with weak controls around lineage, documentation, and historical traceability does not just create future compliance pain. It creates present-day buying friction. The product may look strong in a pilot, yet fail the moment the CFO, controller, assurance provider, or procurement team asks how the evidence trail is preserved.

Also Read: Why investors and customers are betting on ESG-aligned startups

That is why I suspect the climate tech category is about to split into two groups. One group will remain workflow-heavy and presentation-strong. The other will start behaving more like financial infrastructure, with native traceability, stronger version control, clearer source attribution, and a deeper understanding of what enterprise assurance actually demands. The second group is where the long-term value will sit. That is an inference, but it is a grounded one given where reporting standards and assurance expectations are heading.

What the next winners will build

The winning products will not simply promise better data quality. They will make integrity visible.

That means preserving source-level provenance rather than flattening everything into a final table. It means maintaining version history for methods, factors, boundaries, and mappings. It means showing not just the result, but the route to the result. It means keeping enough context so that an internal reviewer, auditor, or senior executive can understand why a number changed and whether the change was operational, methodological, or clerical. These are not exotic product choices. They are the practical extension of what the protocols already ask for in audit trails and documentation, and what is asked for in verifiability and connected information.

There is also a strategic opening here for firms that understand complex industrial reporting. Oil and gas is a good example. Many of the most material climate numbers in this sector are assembled across assets, partners, market instruments, engineering assumptions, and operational systems. That makes the integrity challenge harder, but it also means the value of strong lineage is more visible. In sectors where climate reporting is closely tied to capital allocation, operational performance, and external scrutiny, the software that best preserves evidence will start to look more useful than the software that merely looks modern. That is where differentiation becomes real.

Final thought

The climate tech market is maturing out of its measurement phase. The next contest will be about defensibility.

In the years ahead, the most valuable platforms will not be the ones that produce the smoothest ESG narrative. They will be the ones that can help an enterprise answer a much tougher question with confidence: where did this number come from, what changed it, and can we prove it. Once that becomes the standard buying conversation, data lineage and auditability stop being technical extras. They become the product.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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For years, content marketing was something many founders knew they should be doing but rarely prioritised early in their company journey. Not because writing was difficult. But because publishing consistently required a workflow: topic selection, search validation, editing, formatting, distribution planning and platform adaptation.

All of this added up to something that looked less like a single task and more like a small team function, often requiring at least a marketing manager and supporting execution capacity.

AI agents are changing that timeline.

They are not simply helping entrepreneurs write faster. They are making it possible to start publishing earlier, before dedicated marketing teams exist.

That shift is already changing how startups build visibility.

Content marketing used to depend on coordination

A single article rarely exists in isolation.

Even a straightforward piece often involves:

• validating whether the topic matters
• structuring the argument clearly
• aligning tone with audience expectations
• preparing metadata
• formatting inside a CMS
• adapting captions across platforms
• deciding when and where to distribute it

None of these steps individually are complex.

Also Read: Malaysia’s Qarbotech takes top honour at SusHi Tech 2026 global pitch contest

Together, they create friction, especially for founders without a digital marketing background. Even my tech-savvy husband asked what a focus keyword or meta description was when he started planning content for his own website. That moment reminded me how invisible the mechanics of content marketing are until someone has to run them personally.

AI agents reduce that friction across the workflow, not just by drafting articles. As such, consistent publishing is now possible earlier in a company’s lifecycle than before.

Agents change execution capacity, not strategy responsibility

Many teams still treat AI as a writing shortcut. But agents are more useful as workflow collaborators.

Instead of asking whether AI can write a post, founders can now test whether a post is worth writing at all.

Agents can help:

• compare positioning angles
• explore topic clusters instead of isolated ideas
• adapt content across multiple platforms
• maintain tone consistency
• shorten experimentation cycles

This shifts content marketing from production-heavy work toward decision-heavy work.

Also Read: When collaboration systems break down in tech-driven workplaces and how to fix them

Earlier publishing changes startup visibility dynamics

In Southeast Asia especially, many startups operate without dedicated marketing teams in their early stages.

At the same time, online visibility shapes:

• investor discovery
• hiring credibility
• partnership opportunities
• category positioning

Previously, consistent publishing usually followed scale. Now, it can happen earlier.

That creates a different starting point for how entrepreneurs shape their narrative. Instead of waiting until a marketing function exists, companies can begin building presence while they are still defining their category.

Why this matters for how founders build early teams

There is another shift happening quietly alongside this. Many early-stage founders still prioritise hiring sales before marketing.

The logic is understandable. Revenue feels urgent. Pipeline feels measurable. Sales conversations feel closer to outcomes.

But without positioning, messaging and content, sales teams often end up building their own materials as they go. That slows conversations instead of accelerating them.

Also Read: When AI agents start deciding, what happens to human judgment?

I saw this directly in a previous startup I worked in, where the sales team visibly relaxed once someone finally owned messaging and content. They immediately shared with me a long list of materials they needed in order to sell the company’s products and services with credibility.

Traditionally, the alternative was hiring a marketing manager earlier than founders felt comfortable doing. AI agents are changing that trade-off.

Founders can now support early sales activity with structured messaging, lightweight editorial presence and consistent narrative positioning even before a full marketing function exists. This creates a more balanced setup: sales teams still focus on conversations, while founders establish the materials and context those conversations depend on.

At the same time, it raises expectations for founders and early operators, including sales teams. When agents can support parts of the marketing workflow, people can no longer rely entirely on role boundaries. Early teams increasingly need to work across positioning, messaging and execution layers rather than waiting for a dedicated function to exist.

And despite the technical framing around AI, prompt engineering still behaves more like an editorial craft than a precise science.

Agents can accelerate output. They cannot decide what your company should stand for.

That part remains human.

AI agents make editorial testing cheaper

One of the less obvious effects of this shift is how it changes experimentation.

Founders can now explore multiple editorial directions before committing time to full production.

Also Read: Crypto plunges, big tech earnings are strong. So why are markets nervous?

For example, when evaluating whether an announcement or industry development deserves coverage, it is possible to quickly test whether it works better as:

• a search-driven article
• a founder-perspective reflection
• a newsletter insight
• a platform-specific short-form post

Previously, testing those options required drafting each version separately or consulting a PR agency.

Now the decision can happen earlier in the process. That reduces the cost of experimentation, and lower experimentation cost usually leads to better positioning decisions.

Agents expose weak content strategy faster

There is understandable concern that AI-assisted publishing will increase generic content online. That risk exists. But weak positioning did not begin with AI.

Agents simply make it easier to replicate surface-level strategies across the market. If content depends entirely on summarising trends or repeating competitor narratives, the advantage disappears quickly once everyone has access to similar workflows.

What remains difficult to replicate is interpretation.

Editorial judgement still determines:

• whether a topic matters
• how it should be framed
• who it is for
• why it deserves attention now

Agents assist execution. They do not replace positioning.

Also Read: When collaboration systems break down in tech-driven workplaces and how to fix them

Founder perspective becomes more valuable, not less

Another common concern is that AI-assisted workflows reduce authenticity. In practice, the opposite may be happening.

As production becomes easier, differentiation shifts toward perspective. Readers increasingly respond to lived experience, practical lessons, framing decisions and clearly explained trade-offs.

Agents are strong at structure. They are weaker at conviction.

That makes founder-led thinking more visible inside content strategies rather than less relevant.

The real risk is outsourcing judgement

There is a genuine risk in agent-supported workflows. It is not automation replacing marketers.

It is founders allowing agents to decide what should be published.

Agents should reduce execution effort. They should not replace editorial ownership.

If positioning starts sounding interchangeable across companies using the same tools, the issue is usually strategy rather than technology.

The founders who benefit most from agents are the ones who remain intentional about what their content represents.

Content marketing is becoming earlier and more strategic

AI agents are already changing:

• who can publish consistently
• when founders can begin shaping visibility
• how quickly ideas can be tested
• what a marketing team needs to look like

But they are also raising expectations.

If everyone can publish faster, the advantage shifts toward people who know what is worth saying in the first place.

And in the interest of transparency, and perhaps slightly proving the point… yes, this article was written with the help of an AI agent too.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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The first wave of large language models won attention by replicating personal work. They wrote emails, summarised documents, generated images and videos, drafted presentations, and produced usable code. That alone was enough to reshape how many people think about productivity.

But the more consequential shift is now underway. AI is beginning to move beyond individual outputs and into business processes — the kind of work that has required a team, a set of procedures, and institutional knowledge that no single person holds alone. That is a fundamentally different challenge.

A personal workflow usually lives inside one person’s head and one person’s screen. A business process lives across a team. It depends on handoffs, approvals, coordination across systems, data from multiple sources, and rules for dealing with exceptions. What looks like a single task is often a web of hidden coordination.

As a venture capital investor, I have recently met founders working on exactly this problem — building agents for e-commerce operations, family office mid- and back-office work, and insurance distribution workflows. The first is trying to replicate much of the e-commerce COO function, from product selection and creative design to merchandising, operations, and logistics coordination. The second focuses on family office workflows such as capital call management, treasury handling of idle cash, reporting, analytics, and forecasting. The third is tackling insurance distribution, from lead qualification and product comparison to documentation, onboarding, and follow-up.

None of these is tools for isolated tasks. They are attempts to codify work that has always depended on coordinated teams — and that, until recently, was considered too messy and too human to automate.

Also Read: When collaboration systems break down in tech-driven workplaces and how to fix them

In the first phase of generative AI, the benchmark was usually the quality of the output. Did the model produce a convincing sales pitch, a striking design, a useful analysis, or working code? Once AI enters a business process, the measure of success changes. The real question becomes whether the system can move work forward reliably, repeatedly, and within the right constraints.

That is where the challenge becomes more organisational than technical. It is one thing to know your processes well enough to run them. It is another thing to describe those processes with enough clarity, consistency, and structure for software to execute them repeatedly. The gap is not necessarily in understanding. It is codification.

A process may exist in many places at once: in a standard operating procedure (SOP); in a manager’s judgment; in a spreadsheet passed around by email; in a series of unwritten escalation habits; or in the head of an experienced employee. Humans can work across that ambiguity because they improvise, ask around, and handle exceptions informally. AI systems are far less forgiving. They need the process to be legible.

This helps explain why so much enterprise software has historically disappointed employees. Many internal knowledge bases are hard to search, dry to read, and detached from the immediate task. Workflow systems often digitise the container of work rather than the work itself.

A good example is the office automation (OA) system common in many Chinese enterprises and state-owned enterprises. In principle, these systems were designed to digitise approvals, document flows, announcements, and internal coordination. In practice, they often became digital wrappers around slow, manual, and bureaucratic routines. The interface changed, but the burden did not. Employees still had to chase approvals, assemble context, and push work forward by hand. The process looked digital on the surface while remaining stubbornly manual underneath.

Also Read: When AI agents start deciding, what happens to human judgment?

One recent McKinsey report on the future of AI in insurance offers a useful glimpse of what automation actually looks like inside a complex workflow. Rather than describing agentic AI as a single smart chatbot, McKinsey breaks the process into a set of specialised roles: one agent gathers and clarifies information, another profiles risk, another structures pricing and product options, another checks compliance and fairness, another decides whether a case can be approved or escalated, and another learns from feedback over time.

A North American insurer even used agentic processes to uncover “implicit judgments” that experienced underwriters had relied on for years and codify them into new rules and protocols. McKinsey notes that this kind of embedded expertise — once invisible, now formalised — could become a central part of a firm’s intellectual property. In other words, the act of making a process legible enough for AI may itself create something valuable that the organisation never knew it owned.

That is the crux of agentic work. Replicating a business process is not simply a harder version of writing assistance. It requires structure: a defined task, authoritative data, decision rules, guardrails, confidence thresholds, and clear points for human intervention when reality diverges from the ideal flow. These are not merely model problems. They are organisational problems.

AI agents raise the possibility of turning organisational knowledge from reference material into executable behaviour. The real test is not whether a firm has documented its processes, but whether it has described them with enough programmatic rigour that software can carry them out repeatedly. The companies that get this right may find that codifying their workflows is not just an IT project. It is a way of discovering — and preserving — how the business actually works.

That, to me, is the real significance of AI agents. The age of agentic work may not be defined by whether machines can sound human. It may be defined by whether organisations can become legible enough for machines to work inside them.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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