I’ve spent over a decade placing IT professionals across Vietnam’s tech landscape. I thought I’d seen every version of a talent war. Then healthtech arrived at scale, and it’s different.

The shortage isn’t in general developers. It’s at a very specific intersection: someone who can build robust software and understand why a clinician’s workflow looks the way it does. That profile is rarer than most hiring managers realise.

The market isn’t optional anymore

Vietnam’s digital health market is projected to reach US$815 million in 2026, growing to US$9.5 billion by 2034. But the more important story isn’t market size; it’s regulatory necessity.

Under Decision 749/QĐ-TTg, all healthcare facilities were mandated to implement EHR by 2025–2026. As of early 2026, approximately 1,210 of 1,650 hospitals have announced EMR implementations. The 2025 Decree on Health Data Management raised the bar further, requiring HL7/FHIR compliance, data encryption, audit logging, and digital signatures. The government has committed VND 30,000 billion (~US$1.26 billion) to back it.

This is compliance-driven demand. It doesn’t wait for organisations to be ready.

FDI is moving in lockstep: roughly 160 foreign projects in healthcare and pharmaceuticals with nearly US$1.8 billion in registered capital as of mid-2024. Every new foreign-backed healthtech venture needs the same thing, skilled IT professionals who can operate in a high-compliance, clinically-adjacent environment.

Four layers, four different hiring profiles

The mistake I see most often: treating “healthtech hiring” as one problem.

• Infrastructure (HIS/EHR): The most urgent layer. Compliance-aware engineers with HL7/FHIR fluency. Most non-negotiable demand, least glamorous work. FPT and VNPT dominate domestically, but international vendors are entering fast.
• Clinical AI: AI/ML Engineers with medical imaging experience (DICOM, radiology pipelines). Globally scarce — Vietnam is no exception.
• Telehealth and consumer: Product-minded mobile and backend engineers. Closest to fintech talent in profile, and therefore most contested.
• Healthcare commerce: High-transaction backend, supply chain architecture. BuyMed (US$51.5M Series B) has defined this layer’s standard.

Hiring “a backend developer for our healthtech project” without knowing which layer you’re building for is a mistake that shows up three months into onboarding.

Also Read: Vietnam isn’t just inviting private capital in. It is structurally dependent on it

The real gap

Vietnam produces 50,000–60,000 IT graduates per year across 153+ universities. The pipeline isn’t the problem. Average IT compensation has climbed ~35 per cent year-over-year, with senior developers commanding 50–70 per cent more than two years ago, signs of fierce competition, not scarcity of raw talent.

The problem is the intersection. Engineers with genuine clinical workflow understanding, HL7/FHIR experience, and compliance-aware development instincts are a small fraction of the market. According to the ITviec IT Salary and Recruitment Report 2025–2026, demand for digital transformation talent is surging across all sectors — healthtech is competing for the same pool as fintech, e-commerce, and enterprise SaaS, all at once.

In our pipeline, roles like Healthcare IT PM, medical imaging AI Engineer, or Health Data Security Specialist routinely take two to three months to fill through standard processes. Organisations that haven’t invested in employer branding for healthtech will lose candidates at the offer stage, consistently.

Speed is the strategy

The organisations winning right now have one thing in common: they treat hiring as a product problem, not an HR process.

EHR mandates, funding milestones, and product launches don’t accommodate 90-day recruitment cycles. The practical moves that work:

• Pre-built talent pipelines over job postings, the difference between a seven-day shortlist and a 70-day search is whether the candidate relationship already exists
• Staff augmentation to align headcount with project phases, not lock in fixed overhead at the wrong moment
• EOR structures for Singapore, Japanese, and Australian companies building Vietnam teams — bypassing the three-to six-month entity setup before a single hire is made

One overlooked angle: interoperability engineers. Vietnam’s EHR adoption numbers look strong, but hospitals are still storing data in incompatible formats, a problem the Vietnam Medical Informatics Association has flagged publicly. Engineers with HL7 FHIR integration experience and legacy system API skills are disproportionately valuable right now, before the next regulatory tightening cycle.

Also Read: Vietnam’s biggest PE bet of 2025 was not on tech. It was on what 100M people eat every day

The window is open (for now)

The regulatory mandate is real. The FDI is deployed. The government’s commitment is sustained. What’s uncertain is which organisations build the right teams before the talent market tightens further.

The ones that win won’t necessarily have the largest budgets. They’ll be the ones who understood early: in a compliance-driven, deadline-pressured market, hiring speed is a competitive advantage, and they structured their strategy accordingly.

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I’ve lived across Southeast Asia long enough to know what the heat feels like in five different cities.

Jakarta. Ho Chi Minh City. Singapore. Kuala Lumpur. Manila.

They all feel different. But they’re all on the same clock.

I’m not a climate scientist. I’m not a fund manager with a climate mandate. I’m someone who has spent over a decade moving across this region, watching capital flow, watching ventures rise and fold, watching ecosystems get built and ignored. I write about impact capital every week. But this week I want to write about something more personal.

I don’t have kids. My relationship with the future works differently from most people my age. It runs through two dogs, a meditation practice, and a stubborn, quiet belief that this region doesn’t have to end the way the projections currently suggest.

That’s why I keep watching climate when the room has moved on.

And right now the room has very much moved on.

The AI gravity problem

Every founder pitch I see has an AI angle. Every fund narrative has pivoted to include intelligence, automation, and agents. I get it. The returns are real. The narrative is loud. The FOMO is louder.

But here’s what’s getting drowned out.

Jakarta is sinking. Literally. Parts of North Jakarta have already dropped several metres, and the projection hasn’t changed. Ho Chi Minh City floods regularly now in ways it didn’t a decade ago. Manila’s coastal communities are being quietly relocated. Bangkok is dealing with saltwater intrusion. Singapore, the most climate-prepared city in the region, is spending billions on sea walls and still isn’t sure it’s enough.

This isn’t future risk. This is the current reality.

And yet. According to Tracxn data tracking SEA climate tech, funding in 2026 so far has recorded only four rounds totalling roughly US$17 million. That’s down nearly 60 per cent from the same period last year. Meanwhile, global AI funding crossed US$100 billion in the first half of 2025 alone.

The attention gap is real. And it’s widening.

Also Read: Funded: I keep a notebook by my bed with one question about SEA climate

The observer’s dilemma

I sit at the edge of the climate ecosystem. Not fully inside it. More like someone with their nose pressed against the glass, taking notes.

What I see from out here is a gap between urgency and attention. The urgency is accelerating. The attention is fragile and easily stolen by whatever narrative is loudest that quarter.

In 2021, it was crypto. In 2023, it was generative AI. In 2025, it was agents. Climate was supposed to have its moment in between. It did, briefly. Then the room moved again.

The venture building in climate didn’t move. They’re still here. Rice decarbonisation tackling one of SEA’s largest methane sources. Seaweed biostimulants are replacing chemical fertilisers across smallholder farms. Biochar carbon removal. Agrifood waste converted to sustainable fuels. Decentralised solar reaching communities the grid forgot. These aren’t concepts. They’re operating companies with revenue, with farmers, with real emissions reductions happening right now.

They just don’t trend.

The handful of funds that stayed committed to this space know this. SEEDS Capital, Entrepreneur First, East Ventures, SGInnovate and 100×100 formerly Wavemaker Impact, which just rebranded after spinning out as an independent fund manager with a fresh US$100 million mandate to build 50 climate companies across SEA and India, have done the unglamorous work of showing up round after round. Between them, they represent what conviction actually looks like in a space that doesn’t reward impatience.

Everyone else mostly came once.

What the future generation inherits

I think about this a lot. Not in a guilt-ridden way. More practical.

The cities I’ve lived in across this region are places people love. Street food at midnight. Communities that take care of each other. Chaos that somehow works. There is a version of 2040 where all of that is still here, adapted, resilient, figuring it out.

And there is another version.

Also Read: Investing in impact: High-growth tech for climate and community

The IPCC estimates that without significant intervention, Southeast Asia faces GDP losses of up to 11 per cent by 2100 due to climate impacts. The Asian Development Bank puts the region’s climate adaptation financing gap at over US$100 billion annually. Indonesia’s JETP commitment alone sits at US$21.6 billion. Vietnam’s at US$15.5 billion. The money being talked about is enormous.

The money actually reaching climate ventures at the early stage is not.

The capital deployed in the next five years will have more influence over which version of 2040 shows up than most people in the venture ecosystem currently acknowledge. That’s not an activist talking. That’s just what the data says when you read it without the AI hype in the background.

The ask isn’t to stop building AI

It’s to hold both.

The founders building climate ventures in SEA right now don’t need sympathy. They need capital that stays. They need fund managers who treat climate the same way they treat AI – as a structural bet on where the world is going, not a checkbox on an LP deck.

900 backers have put money into climate tech in SEA, according to recent data. You can count the ones who kept showing up on one hand.

The next generation doesn’t get to choose the cities they inherit. But the people reading this do get to choose where they put their attention and their capital right now.

I’m watching. Still at the window. But watching closely.

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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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Every project I’ve worked on that went over budget had one thing in common: the people paying for it thought they understood what they were buying. They had a quote, a timeline, and a feature list. What they didn’t have was a real picture of what drives cost in software development. And that gap is expensive. 

I’ve seen a six-figure project balloon because nobody mapped the third-party integrations before kickoff. I’ve seen a “simple” admin panel turn into a three-month ordeal because the access control requirements weren’t defined until week five. That’s exactly what can happen with your project if your cost planning stays surface-level. 

According to Radixweb, a software development company, project costs typically range from US$15,000 to over US$100,000, with the final number shaped by complexity, feature scope, tech stack, and integration requirements. That range exists because software cost isn’t a fixed thing; it’s the sum of hundreds of decisions, many of which get made casually and early. 

Here’s what actually moves the needle. 

The core factors that shape your development budget

Scope: where budgets go to die

Scope isn’t just a list of features. It’s the depth of each feature, the edge cases each one has to handle, and the integrations each one touches. A login screen is a login screen, until it needs MFA, social logins, SSO for enterprise clients, and role-based permissions. Now it’s a two-week job. 

What makes this dangerous isn’t that requirements grow. It’s that they grow quietly. A stakeholder adds something in a meeting. A developer makes an assumption. A “small change” gets absorbed without a conversation about what it costs. By the time anyone notices, the timeline has shifted and the budget is already stressed. 

Before any development begins, force a prioritisation conversation. Not “what do we want” but “what do we actually need at launch.” Every feature pushed to v2 is real money saved, and it’s almost always a feature you thought was essential until you asked the hard question. 

Team structure: You’re not just paying for hours

The sticker price of a developer rate is the least interesting cost question here. What actually matters is how your team is structured and how well it functions. 

A misaligned team (where the client, project manager, and developers are working from different assumptions) generates rework. Rework is expensive not just in hours, but in the momentum it kills. I’ve watched projects where the developers were sharp, and the hourly rate was fair, but the communication structure was so poor that the same features got rebuilt two and three times. 

When you’re evaluating a development partner, ask about their discovery and requirements process before you ask about their rate. A team that charges 20 per cent more but does a proper kickoff, documents requirements, and flags risks early will almost always be cheaper by the end. 

Also Read: The agentic shift: Why AI agents are rewriting the rules of ERP software in Singapore and Malaysia

Technology stack: Two costs, not one

People usually think about the tech stack in terms of build cost: what will it take to develop this? But there’s a second cost that hits you later: the operational cost of running what you built. 

Your infrastructure choices, your database architecture, your reliance on third-party APIs — all of which show up on a monthly bill once you’re live. A product built without scalability in mind might run fine at a few hundred users and require an expensive re-architecture at a few thousand. That’s not a hypothetical. It happens regularly, and it’s almost always preventable with the right conversations upfront. 

Pick a stack that has a healthy developer ecosystem (because you’ll need to hire or replace people eventually), that matches the operational demands of your product, and that your team actually knows well. Novelty is rarely worth the cost premium. 

The hidden costs that quietly break budgets

This is where I see the most financial damage, not in the obvious line items, but in the things nobody budgeted for because nobody mentioned them. 

Maintenance isn’t optional, it’s ongoing

The moment your software ships, the clock starts on its upkeep. Dependencies need updates. Security patches need to be applied. Browsers and operating systems change, and your product has to keep up. A rough but reliable rule: budget 15–20 per cent of your initial development cost every year for maintenance. If that number surprises you, the surprise is worse when it arrives unplanned. 

QA gets cut first and costs the most 

When timelines get tight, testing is usually the first thing squeezed. That decision consistently backfires. A bug caught in development costs a fraction of what it costs in production – in developer time, in user trust, and sometimes in legal exposure. A proper QA process isn’t overhead. It’s the thing that protects everything else you spent. 

Also Read: AI skills now translate into real pay gains for software engineers, NodeFlair finds

Integrations are underestimated almost universally 

Connecting your software to a CRM, payment gateway, ERP, or analytics platform takes longer than anyone expects, tests in ways that are genuinely hard to predict, and creates dependencies you’ll be maintaining forever. The more integrations your product needs, the more you should buffer your timeline and budget — not by 10 per cent, but meaningfully. 

Compliance is a technical cost, not just a legal one 

If your product touches personal data, health records, or financial information, frameworks like GDPR, HIPAA, or PCI DSS require specific technical controls. These aren’t checkboxes but features that need to be designed and built. According to the IBM Cost of a Data Breach Report, organisations that build security in from the start see significantly lower breach costs than those that treat it as a post-launch consideration. Retrofitting compliance after the fact is one of the most expensive things you can do in software. 

The decisions made in week one cost the most

Here’s the thing I wish more clients understood before we started working together: the most expensive part of building software isn’t the development. It’s features built on unclear requirements, architecture chosen for speed instead of longevity, integrations discovered after the fact, and bugs shipped because testing got cut. 

Every major cost overrun I’ve been close to was traceable to something that happened (or didn’t happen!) in the first two weeks. The practical answer is a real discovery phase. Before coding starts, map your requirements in detail, identify your integration points, flag your technical risks, and define what “done” actually means for each feature. It feels like slowing down. It’s actually the fastest path to a product that comes in on budget, because it’s the only way to know what you’re actually building before you’re paying to build it. 

Software development costs are not arbitrary. They are the accumulated result of decisions, some deliberate, many not. Get serious about the decisions, and the costs take care of themselves. 

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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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CME Group recently announced plans to launch Compute Futures, tied to GPU and AI compute capacity. At first glance, this feels counterintuitive. Compute is a technology-driven input where costs consistently decline over time due to hardware improvements, manufacturing scale, and efficiency gains. If the long-run direction is structurally downward, what is there to hedge or price in a futures market?

The key misunderstanding is assuming futures markets exist to express long-term price direction. In reality, they exist to manage short- to medium-term uncertainty, typically within a three- to 24-month horizon, the exact window where real-world capital allocation decisions are made.

This is why even structurally deflationary commodities such as crude oil, natural gas, DRAM, and solar modules still have deep and liquid futures markets. Their long-term cost curves may trend downward, but their short-term prices are driven by highly volatile factors: supply chain disruptions, capacity constraints, inventory cycles, and demand shocks. Market participants are not hedging the fact that something becomes cheaper over decades; they are hedging whether it becomes more expensive or scarce over the next operating cycle.

The same logic applies to compute. For AI labs, hyperscalers, and enterprise users, the relevant risk is not GPU prices in 10 years, but the cost of training runs, inference capacity, and cluster usage in the next quarter or fiscal year. Compute Futures allow these participants to lock in a forward price for compute capacity, converting a variable input cost into a fixed, predictable operating expense.

Also Read: 15 Southeast Asian semiconductor startups moving beyond assembly

This also reflects a structural shift in what compute actually is. Compute is no longer purely a capital good like a CPU or server. It is increasingly a consumable infrastructure service, closer to electricity, airline seats, or hotel rooms. These markets share a critical property: non-storability. An unused GPU-hour cannot be saved for later use, just as an empty hotel room or unsold airline seat has zero value once the time window passes.

Because of this, even if GPU hardware continues a long-term deflationary trajectory, compute rental prices can still exhibit sharp short-term volatility. The constraints are not just chip prices, but system-level bottlenecks: data centre construction cycles (often 18 to 36 months), power grid availability, cooling infrastructure, and uneven deployment of GPU capacity.

On the demand side, volatility is amplified by AI-specific cycles: model breakthroughs, hyperscaler capex waves, startup funding cycles, and sudden surges in inference demand. These factors create mismatches between supply and demand that can push compute prices sharply higher or lower in short periods, independent of hardware cost trends.

Conclusion

Compute Futures are not a bet against long-term price decline. They are a response to short-term price instability in a rapidly scaling AI infrastructure market. As compute becomes a core production input in the AI economy, financial markets are beginning to treat it less like technology hardware and more like a tradable infrastructure commodity with its own risk management and pricing system.

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The moment we are asked to implement while still experimenting, something within starts to strain.

Lately, I have been noticing this across organisations and teams working through AI integration. There is still a lot of curiosity. New tools, new ideas, new ways of working. At the same time, there is a very real shift happening underneath it.

Conversations are moving from what is possible to what is actually delivering value. The energy has not gone away, but the expectations have changed.

This same pattern has shown up in every major shift in technology

If you look back, the internet followed this exact rhythm. In the early days, it was about presence. Build a website. Try things. See what sticks. Over time, that shifted to performance. E-commerce, conversion, measurable outcomes.

Social media followed a similar path. Brands experimented with voice, content, and engagement. There was freedom in not knowing what would work. Then the shift came. Metrics tightened. Budgets followed performance. Creativity gave way to accountability.

SaaS created another version of this cycle. Organisations adopted tools quickly, often faster than they could integrate them. Over time, the conversation changed from access to utilisation. Are we actually using what we are paying for? Are these tools driving efficiency or just adding complexity?

Cloud was no different. The early push was migration. Move everything. Modernise. Then came the next phase. Cost control. Optimisation. Making sure the investment delivered real operational value.

Also Read: The death of the traditional org chart: How AI is reshaping work

AI is following the same pattern, just at a faster pace

There is a period of expansion where experimentation takes the lead. Organisations explore, build, and test what could work. Over time, that expansion gives way to compression, where the focus turns to implementation, execution, and scale.

The goal is no longer discovery, it is impact.

When organisations move on from experimentation before fully implementing, they leave value on the table and dilute the return on their investment. Right now, many organisations are sitting in between those two states, and in professional services, this tension is even more pronounced.

The people who would benefit most from the efficiency AI can create are often the ones with the least amount of time to engage with it. They are delivering, managing clients, and keeping momentum. Their days are already full. Adding new tools and new ways of working on top of that does not create transformation. It creates strain.

There is also a mismatch happening with clients. Expectations for delivery remain high, often unchanged, while internal teams are being asked to rethink how the work gets done. That gap creates pressure that does not always get acknowledged.

At the same time, we are seeing more adoption happening at senior levels of organisations because they have more space to step back and engage with what is new. They have the ability to explore, test, and think more broadly about applications.

That creates a gap between where AI is being explored and where it actually needs to be implemented.

Junior teams, the ones closest to execution, are often operating in a different reality. They are focused on output, timelines, and immediate deliverables. Without space to experiment, the tools never fully integrate into how the work gets done.

This is where organisations begin to stall

Leadership is pushing for results. Teams are trying to keep up with existing demands. The shift from experimentation to implementation gets stuck in the middle.

There is a natural rhythm at play between expansion and compression. Expansion thrives on curiosity and openness. It invites exploration and new thinking. Compression requires focus, clarity, and space to execute. It demands prioritisation and discipline.

Both are necessary. But they cannot be forced to happen at the same time in the same way.

Also Read: The AI layoff trap points straight at Southeast Asia

As leaders, our role is to recognise where we are in that cycle

Not where we want to be or where the market says we should be, but where we actually are in how our teams operate and what they’re being asked to deliver.

Three reflections for leaders navigating this shift:

• Define where value should show up: Not every experiment needs to scale. Be clear on where implementation matters most and focus your energy there. This creates direction in a moment that can easily feel scattered.
• Create space for change to take hold: If teams are fully consumed by delivery, new ways of working will not stick. Capacity is part of the work. This might mean some hard conversations with clients to reset expectations or reallocate effort.
• Support the shift in how work gets done: Tools alone won’t change outcomes. Adoption requires new habits, new expectations, and time to integrate both. Without that, the tools remain separate from the work instead of improving it.

The movement between expansion and compression is constant. It does not stop with one wave of technology, and it does not resolve all at once. Each new cycle brings the same opportunity and the same risk.

Recognising where you are within it and adjusting how you lead accordingly is what allows progress to take hold in a way that lasts.

This article was co-written with TJ Kelly, a senior partner at Penta Group.

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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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People used to ask AI for help with parts of modern life, like making an email sound less annoyed or explaining a spreadsheet formula without forcing anyone to revisit their relationship with mathematics. Lately, the exchange has become more intimate. The same tools built to summarise, draft and optimise are now being invited into moments of doubt, stress and loneliness.

This shift is global, but Singapore gives us a useful early signal. In my work with individuals and organisations in Singapore, I often see how much emotional load people carry while still functioning. Many keep moving through demanding workdays, family responsibilities and social expectations while privately trying to make sense of what they feel.

AI is starting to enter that private space.

A recent Singapore-based study offers a useful glimpse of how this is already happening. In 2026, researchers explored how foreign domestic workers in Singapore used a large language model chatbot while managing caregiving burden. The findings were cautious, but revealing. Participants described the chatbot as emotionally validating, psychologically safe, linguistically accessible, and useful for reassurance and companionship.

For a startup audience, this should raise more than a social welfare eyebrow.

The study points to a wider behaviour change that founders, product teams and employers need to understand. People are beginning to use general-purpose AI tools for emotional processing, especially when human support feels too slow, expensive, risky, or socially complicated. The user may begin by asking for help with a message. Within a few minutes, they may be asking whether their reaction makes sense, how to handle a difficult conversation, or why they feel so depleted.

That is where product design crosses into psychology and the appeal is clear. An AI can respond in plain language, adapt to imperfect phrasing, and give people a feeling of being heard without the social exposure that often comes with disclosure. For people under pressure, that can be powerful.

Also Read: How centralised exchanges swapped crypto ethos for Wall Street fees: Why this will fail

A 2026 cross-cultural study of more than 4,600 participants across seven countries found that people are already using large language models as always-available, non-judgmental confidants for emotional support. The prompts collected in that study showed people seeking help for loneliness, stress, relationship conflict and mental health struggles. This is no longer a fringe use case for companion apps. It is becoming part of ordinary interaction with general-purpose AI.

That shift has real commercial relevance.

If people are using AI tools to manage emotional load, then workplace software, productivity platforms, coaching apps, HR tools and digital health products are already operating closer to mental health territory than many companies may realise. A product designed to help someone draft a message can quickly become a place where they disclose fear, resentment, shame or distress. A tool designed to improve productivity can become the place where an employee admits they are no longer coping.

This creates opportunity, but also responsibility.

Emotionally responsive AI can reduce friction. It can help people name what they are experiencing, organise their thoughts and access support earlier. In a place like Singapore, where people may be managing long hours, family responsibilities, cultural expectations and pressure to remain composed, a low-barrier tool can feel useful. For employers and founders, that usefulness is exactly why the ethical design questions cannot be left until later.

Singapore gives this global shift a sharper local frame. In April 2026, NTU Singapore and NHG Health announced ASPIRE, Singapore’s first work-study training pathway for clinical psychology. The announcement pointed to a clear pressure point: demand for mental health support is rising, while the human workforce takes time to build. That is the gap AI is already moving into.

There is also a trust issue here.

People disclose differently when they believe no human is listening. They may share sensitive details with AI because the interaction feels contained, even when the data environment is more complex than it appears. For companies building emotionally fluent products, privacy cannot sit buried in compliance language. It has to be visible in the user experience. People need to understand what they are sharing, where it goes, how it may be used, and what the tool can do when distress escalates.

Also Read: The death of the traditional org chart: How AI is reshaping work

The most important lesson for startups is that emotional support may appear inside products that were never designed for mental health. A person may stumble into it while drafting a resignation email, preparing for a performance review, translating a difficult message, or trying to make sense of workplace tension. The product team may think they are building a writing assistant. The user may experience it as the first place they can say what they are really feeling.

That is where the next stage of AI design needs more psychological literacy.

Emotionally responsive tools should help people reflect, clarify and access support earlier. They should also make their limits clear. When a user starts disclosing distress, the product needs thoughtful guardrails: clear privacy language, careful emotional tone, referral pathways, escalation options and design choices that encourage agency rather than dependence.

Singapore’s 2026 research gives us an early signal of where this is heading. The study focused on foreign domestic workers using an AI chatbot for caregiving burden, but the lesson reaches further than that setting. People are turning to AI because it is immediate, private and easier to approach than many human systems of support.

For founders and organisations, the takeaway is simple: once a product becomes emotionally useful, it carries emotional responsibility.

AI is no longer only answering prompts. It is becoming part of how people process pressure, uncertainty and loneliness. The companies that understand this early will design tools that earn trust, protect users and know when to guide people back towards human support.

That is the next frontier of AI emotional support. The question is no longer whether people will bring their distress into the interface. They already are. The real design challenge is what the interface does with it.

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MAKATI CITY, Philippines — Sprout Solutions, the Philippines’ leading homegrown HR and payroll technology company, will hold the State of HR Summit 2026 on July 8, 2026, at Dusit Thani Manila, bringing together HR leaders, business executives, and people managers to discuss how organisations can accelerate performance with AI while remaining people-first.

Presented in partnership with workplace consultancy BS Works, this year’s summit carries the theme Leading a People-First AI Transformation and will feature the exclusive launch of the State of HR 2026 Report, based on findings from a nationwide survey of 3,516 Filipino professionals.

As AI becomes increasingly embedded in business operations, organisations face a new challenge: ensuring technological transformation strengthens employee trust, engagement, and readiness rather than undermining them. The summit will focus on how leaders can navigate this shift by aligning people, processes, and technology.

The discussion is grounded in findings from Sprout and BS Works’ nationwide AI and Your Evolving Workplace survey, conducted in May 2026 through Sprout’s first AI-powered survey application.

Early results reveal that 95 per cent of Filipino professionals are familiar with AI, while 89 per cent welcome AI-related changes in the workplace. However, organisational readiness appears to lag behind employee enthusiasm. Only 35 per cent of respondents report that their employers currently provide AI-related upskilling opportunities.

From AI adoption to AI leadership

The summit will open with remarks from Patrick Gentry, CEO and Co-Founder of Sprout Solutions. Cliff Eala, Managing Partner of BS Works, will deliver the keynote presentation and formally launch the State of HR 2026 Report, sharing insights on how employees are adapting to AI-driven workplace change.

Throughout the day, executive panels and discussions will explore the growing need to align HR, operations, and compliance functions, and the leadership capabilities required to guide organisations through rapid technological change. 

Among a host of industry leaders joining the summit are Maria Lourdes Ann “L.A.” Cruz, Vice President for People at Lufthansa Technik Philippines; Alvanson So, former HR leader at Canva; Eli Harell, Co-Founder of EmergePH; and Atty. Arlene De Castro, Founder and Managing Partner of De Castro Law Firm and De Castro Consulting. 

Attendees will also receive a preview of Sprout’s product roadmap, highlighting how the company’s people-first AI platform aims to connect payroll, compliance, performance, and workforce data within a unified ecosystem.

The readiness gap behind AI optimism

Additional survey findings highlight the urgency of the conversation. While 84 per cent of respondents report that their organisations already use AI and 83 per cent say AI helps them work more efficiently, nearly 45 per cent feel overwhelmed by the pace of workplace change.

Also Read: AI transformation starts with people, not platforms

“Filipinos have embraced AI faster than many organisations anticipated. The challenge now is helping leaders close the gap between adoption and readiness by building trust, developing skills, and creating workplaces where people can thrive alongside technology,” said Kislay Chandra, Chief Operations Officer, Sprout Solutions.

The event’s sponsors include mWell by Metro Pacific Health Tech as Platinum Sponsor, Hubstaff and MetroMart Philippines as Gold Sponsors, and Hive Health as Silver Sponsor. Supporting the initiative as Strategic Partners are Philippine Society for Talent Development, Mind You, and Plan International Pilipinas, while Rappler, Manila Standard, The Manila Times, Vritimes, TechShake, and Malaya Business Insight serve as Media Partners.

Recognising outstanding organisations through the foresight awards

The State of HR Summit 2026 will also host the inaugural Sprout Foresight Awards, recognising organisations demonstrating workforce health and people-first workplace practices.

Major awardees will be honoured through the Great Employer Awards, alongside other recognitions that celebrate excellence across key areas of organisational health. Through the Foresight Awards, Sprout aims to spotlight companies that are helping shape healthier, more resilient, and future-ready workplaces in the Philippines.

Powered by Foresight, Sprout’s new AI-powered predictive and prescriptive HR analytics solution, the awards reflect Sprout’s commitment to helping organisations better understand their workforce through meaningful data and actionable insights. 

Secure your seat at State of HR 2026

Registration for the State of HR Summit 2026 is now open at sohr.sprout.ph. Individual and table packages are available, and interested participants may visit the website for current rates and event details.

About the survey

The AI and Your Evolving Workplace survey was conducted by Sprout Solutions in partnership with BS Works and fielded nationwide in May 2026 through Sprout’s first AI-powered survey application. The study gathered responses from 3,516 professionals across major Philippine industries, including information and communication, business process outsourcing, finance, retail, healthcare, and manufacturing, representing multiple generations in the workforce.

About Sprout Solutions

Sprout Solutions is the people-first AI platform for payroll, compliance, and work, serving businesses across the Philippines and Southeast Asia. Founded in 2015, Sprout combines AI-powered payroll, HR, recruitment, performance management, employee engagement, and financial wellness solutions into one intelligent platform designed to help organisations operate more efficiently while supporting their people at scale.

Trusted by more than 2,000 companies and 350,000 users, Sprout complements its technology with managed services, implementation expertise, and dedicated customer success teams.

Sprout exists to impact the lives of employees by improving businesses in the communities it serves.

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The e27 team produced this article in partnership with Sprout Solutions Phil., Inc.

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Featured Image Credit: Sprout Solutions Phil., Inc.

The post Sprout Solutions’ State of HR Summit 2026 focused on people-first AI transformation appeared first on e27.

Few ideas in product management have travelled as widely, and done as much quiet damage, as the line that the product manager is the CEO of the product.

It sounds flattering. It sounds decisive. It gives the role a sense of weight and status that many product managers, especially early in their careers, are understandably drawn to. It also gives founders and executives a convenient shorthand. Say it once, and everyone feels they understand what product management is supposed to be.

The trouble is that they usually understand the wrong thing.

The phrase survives because it gives emotional clarity in a role that is often structurally ambiguous. Product managers sit in the middle of competing demands, partial authority, and shifting expectations. Telling them they are the CEO of the product feels empowering. It appears to solve the identity problem. It suggests ownership, leadership, and accountability in one neat line.

But neat lines are often expensive in real organisations.

Product leadership is not executive sovereignty

The first issue is simple. A CEO has formal authority. A product manager usually does not.

That is not a trivial difference. It is the difference.

A CEO can allocate capital, set structure, hire leaders, make final trade-offs across functions, and carry formal accountability for enterprise outcomes. A PM does not sit in that position. A PM works through influence, judgement, framing, trust, and alignment across people who often report elsewhere and carry valid goals of their own.

Pretending those are the same kind of leadership is not empowering. It is misleading.

In fact, one of the core disciplines of strong product management is learning how to lead without the fantasy of unilateral control. That is not a lesser form of leadership. In many ways, it is a more demanding one. It requires a PM to earn movement through clarity and substance rather than title. It requires them to understand technical constraints, business context, customer reality, and organisational incentives well enough to help a group arrive at better decisions together.

That is real leadership. It just is not CEO leadership.

The analogy encourages PMs to reach for authority they do not actually hold, instead of helping them master the kind of authority the role genuinely requires.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

The myth creates bad PM behaviour

Once people internalise the CEO idea, their behaviour often shifts in subtle but damaging ways.

Some start treating every decision as if the product should be the final arbiter. They become overly attached to control. They mistake coordination for command. They expect engineering, design, data, and go-to-market teams to line up behind product as though product were the natural centre of gravity in every discussion.

That posture creates tension quickly.

Engineering starts feeling managed rather than partnered with. Design feels invited in after the real thinking is done. Commercial teams learn that the product wants accountability without always carrying enough of the customer and market burden. The PM, meanwhile, often becomes more performative than effective. They begin signalling certainty, weight, and strategic dominance when what the situation actually needs is sharper listening, better synthesis, and more honest trade-offs.

It also creates bad organisational expectations

The damage is not limited to PMs themselves. The phrase also teaches the rest of the company to expect the wrong things from the product.

Executives start assuming PMs can simply make difficult trade-offs happen, even when the underlying functions are misaligned. Founders expect product managers to absorb accountability for outcomes without giving them enough organisational leverage to shape those outcomes properly. Engineers begin to resent the product for behaving like management without carrying equivalent depth in technical delivery. Customer-facing teams assume PMs should absorb every strategic tension because the role has been framed as the owner of the whole thing.

This creates a peculiar trap.

The PM is treated as highly accountable, but not always meaningfully empowered. They are expected to think like a general manager, influence like a founder, decide like an executive, and still somehow remain collaborative, humble, data informed, customer centric, and delivery conscious.

Also Read: People don’t want productivity hacks anymore, they want sustainable ways to live

The phrase flatters the product and diminishes everyone else

There is another problem with the CEO analogy that product people do not always say out loud. It quietly reduces the contribution of other disciplines.

If the PM is the CEO of the product, what exactly does that make the engineering lead, the designer, the data lead, the researcher, or the operational teams who deal with adoption reality every day? Supporting cast. Functional experts. Advisers to the central brain.

That is not how good products are built.

Strong products emerge from serious cross-functional thinking, where each discipline shapes the outcome in material ways. Engineering does not merely execute a product idea. It often determines what is elegant, resilient, scalable, and even strategically possible. Design is not visual packaging around a product direction. It shapes behaviour, trust, comprehension, and flow. Research does not just validate. It often reveals that the original framing was weak. Commercial teams do not only distribute value. They expose whether the product meets reality outside internal narratives.

The role is closer to an integrator than a sovereign

If the CEO analogy is wrong, what is a better frame?

In my view, a PM is far closer to an integrator of decision quality than a sovereign owner of the product.

That may sound less glamorous, but it is actually more accurate and, in mature organisations, more powerful.

A good PM helps the company make better product decisions by integrating customer truth, business judgement, delivery reality, and strategic intent. They create coherence where functions would otherwise optimise locally. They help teams decide what matters, what trade-offs are real, what assumptions are weak, and what evidence should change the course.

That is not a small role. In fact, it is a very consequential one.

Also Read: When execution is free, the brief becomes the product

Why younger PMs are particularly harmed by this myth

The CEO phrase does particular damage early in a PM’s career because it teaches the wrong aspiration.

Instead of learning how to ask better questions, build trust across functions, reason through trade offs, and develop genuine taste in product judgement, many younger PMs end up performing seniority. They overfocus on status, decisiveness, and visible ownership. They try to sound like mini executives before they have learned how to become truly useful in complex product environments.

This leads to a familiar pattern. The PM talks strategy when the team needs clarity. They push for alignment without understanding why disagreement exists. They seek authority before they have built enough credibility. They chase the optics of leadership rather than the substance of it.

The irony is that the best PMs often look less like product CEOs and more like unusually effective interpreters of complexity. They are calm under ambiguity. They know when to push and when to absorb. They improve decision quality without needing to dominate every room. They understand that influence is not diluted by collaboration. It is often made stronger by it.

Those are harder lessons to learn if the profession keeps telling people the goal is to act like a CEO.

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Singapore-based dConstruct Technologies has closed a US$125 million Series A round, giving the city-state’s robotics sector one of its more visible funding wins at a time when deeptech companies across Southeast Asia face tougher scrutiny on commercial traction.

The National Robotics Programme of Singapore announced the financing on Thursday, positioning it as the standout outcome from the inaugural cohort of RoboNexus, its venture-building accelerator. The programme also highlighted two other graduates, LionsBot and Spinoff Robotics, as examples of Singapore robotics companies moving from research and pilot deployments into international markets.

Also Read: Thailand’s Amity Robotics raises US$7M to take physical AI from malls to global markets

The funding round places dConstruct among a relatively small group of regional robotics companies able to raise large institutional cheques. The company did not disclose the investors in the round, the valuation, or the breakdown between primary and secondary capital.

That lack of detail matters. Robotics companies tend to be capital-intensive, with long development cycles, massive hardware costs, integration work, and slower enterprise procurement timelines. Still, the size of the round suggests that investors are willing to back robotics platforms that can show deployment potential beyond narrow proof-of-concept projects.

Building for messy, indoor environments

dConstruct’s core technology is d.ASH, a proprietary suite that integrates 3D scanning and perception technologies to enable autonomous robots to operate in complex, GPS-denied environments.

That focus is commercially significant. Many real-world robotics applications inside buildings, tunnels, transport hubs, industrial sites, and underground infrastructure cannot rely on GPS. Robots operating in these settings need localisation, mapping, obstacle avoidance, and data capture systems that can function in cluttered, changing environments.

The company is targeting use cases across the built environment, security, inspection, logistics, and entertainment. Its disclosed customers and partners include Boustead Projects, Singapore’s Defence Science and Technology Agency, JRE Ventures of the East Japan Railway Company Group, SBS Transit, and SoftBank Robotics Singapore.

Chinn Lim, CEO of dConstruct Technologies, said RoboNexus had helped the company access industry partners and deployment opportunities, while the funding milestone reflected confidence in its plan to build “reality capture and robot automation solutions from Singapore to the world”.

The phrase is ambitious, but the underlying market is real. The International Federation of Robotics said global sales of professional service robots grew 30 per cent in 2023, with logistics, cleaning, inspection, and hospitality among key categories. Asia remains the centre of gravity for robotics adoption: the federation’s industrial robot data shows Asia accounted for about 70 per cent of new industrial robot installations globally.

Also Read: German-listed DDB acquires Singapore’s Infinium Robotics in US$24M share deal

Singapore has an additional advantage. Its small labour pool, high labour costs, ageing workforce, and dense urban infrastructure make it a natural testbed for automation. The country also ranks among the world’s most robot-dense economies, with the International Federation of Robotics placing Singapore near the top globally for industrial robot density.

A new robotics base in Punggol

dConstruct is also establishing a 42,000-square-metre global headquarters at Punggol Digital District. The facility, named dC Plus, is expected to be completed by the end of 2026.

The site will include robotics testing environments for wheeled, quadruped, and humanoid platforms, as well as collaborative workspaces and community programmes. The location is notable: Punggol Digital District has been positioned by Singapore as a hub for digital economy firms, applied research, and industry-academia collaboration.

For dConstruct, the headquarters could help address one of the sector’s persistent bottlenecks: moving from controlled demonstrations to repeatable deployment in real-world settings. Robotics companies often struggle not because their systems fail in the lab, but because customers need machines that can operate safely and reliably across unpredictable sites.

That challenge is especially acute in Southeast Asia, where built environments vary widely across markets. A robot that performs well in Singapore’s regulated commercial facilities may face different conditions in Indonesia’s logistics sites, Thailand’s factories, Vietnam’s industrial parks, or the Philippines’ mixed-use developments.

RoboNexus’s broader cohort

The National Robotics Programme also pointed to LionsBot and Spinoff Robotics as evidence that the RoboNexus model can support companies beyond early research commercialisation.

LionsBot, a Singapore-based cleaning robotics company, is now present in more than 40 countries and has overseas subsidiaries in Dallas, Amsterdam, and Chennai. The company says it has deployed more than 6,000 robots globally. Its expansion has been supported by a collaboration with WISAG, a European facility management group, which contributed to the development of LionsBot’s R5 cleaning robot launched in April 2026.

Cleaning robots are among the more mature categories in professional service robotics, but competition is intense. LionsBot faces global and regional rivals including Gaussian Robotics, SoftBank Robotics, Pudu Robotics, and Singapore-based players such as Sesto Robotics and OTSAW in adjacent automation segments. The differentiator is less about whether a robot can clean a floor and more about fleet management, maintenance economics, procurement relationships, and customer support across markets.

Dylan Ng, CEO and co-founder of LionsBot, said the company intends to support newer RoboNexus cohorts by sharing lessons from R&D, product development, commercialisation, and international expansion. That is useful if it moves beyond founder storytelling and into practical guidance on manufacturing, compliance, servicing, and channel partnerships.

Spinoff Robotics, meanwhile, has taken a different route. The company was acquired by Nanoveu Limited, an Australian Securities Exchange-listed technology company, giving it access to public-market capital, commercial platforms, and international growth channels.

The company was established to commercialise tethered aerial robotics technology for cleaning and inspection of hard-to-reach infrastructure. Its applications include urban infrastructure, facilities management, and industrial inspection. The acquisition also allows Spinoff Robotics to expand its licensed Singapore University of Technology and Design platform into non-tethered drone applications.

Also Read: AMC Robotics to build US$3.5M Vietnam factory as SEA automation race heats up

Tan Chee How, co-founder of Spinoff Robotics, said the National Robotics Programme helped the company move from research into commercialisation by opening access to industry partnerships and deployment opportunities.

Why this matters for Southeast Asia

For Southeast Asia, the story is not simply that a Singapore startup has raised a large round. The more important question is whether robotics companies in the region can build commercially durable businesses rather than remain grant-supported engineering projects.

Demand is not the problem. Southeast Asia’s factories, airports, ports, hospitals, malls, construction sites, and transport operators all face labour, safety, and productivity pressures. Governments across the region are also pushing automation through industrial upgrading programmes, including Singapore’s Advanced Manufacturing initiatives, Malaysia’s Industry4WRD policy, and Thailand’s Eastern Economic Corridor strategy.

The harder issue is deployment economics. Robotics companies must prove that their systems reduce labour dependency, improve safety, or generate operational savings large enough to justify upfront costs, integration work, and ongoing maintenance. Customers are no longer impressed by pilot projects alone.

dConstruct’s US$125 million Series A gives it the capital to build for that test. RoboNexus gives Singapore a stronger narrative around robotics venture-building. But the next stage will be less about national ambition and more about commercial evidence: repeat customers, overseas revenue, functioning fleets, and technology that survives outside carefully managed demo environments.

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