Day: July 23, 2026

Singapore’s data professionals are proving to be among the most cautious in the world when it comes to letting artificial intelligence (AI) operate unsupervised.

According to a new global study, 61 per cent of the city-state’s data analysts prefer a human-in-the-loop approach to AI, the highest share recorded across all regions surveyed.

Also Read: AI in Singapore: From generative tools to real-world impact

The finding comes from Alteryx’s “2026 State of the Data Analyst: The Rise of Business Logic” report, which polled 1,400 respondents worldwide, including 175 data analysts and IT leaders in Singapore. It paints a picture of an ecosystem that is deploying AI aggressively, yet remains deeply uneasy about handing it the keys.

Just 1 per cent of Singapore respondents said they were comfortable with AI operating fully autonomously, a third of the already-slim 3 per cent global average. For a market that has positioned itself as Southeast Asia’s AI testbed, courting hyperscaler data centres and government-backed AI programmes, the reluctance is telling. It suggests that enthusiasm for AI adoption at the policy and infrastructure level has not necessarily trickled down into trust at the operational level, where analysts are the ones left cleaning up after the models.

Strategic weight is rising, but so is the workload

The report does not describe a market turning its back on AI. Quite the opposite — 75 per cent of Singapore respondents said AI’s strategic impact on their organisation has grown over the past year, as companies lean further into automation and agent-based systems. And 66 per cent agreed that AI and agent-based systems perform best when managed at the business-unit level rather than by centralised data or IT teams, a preference that echoes a broader shift happening across the region, where domain teams increasingly want ownership over the tools shaping their decisions, rather than waiting on a central function to translate their needs.

That shift, however, is generating friction of its own. Singapore’s analysts are spending significant chunks of their working week doing the unglamorous groundwork AI still cannot do reliably alone: an average of five hours a week preparing and cleaning data, and a further three hours correcting and validating AI-generated outputs. Put together, that is roughly a full working day each week spent making sure the machine’s homework is actually right.

Data quality, not the models, is the real bottleneck

Perhaps the most pointed figure in the report is this: 46 per cent of AI and analytics projects in Singapore that fail to meet their objectives are attributed primarily to data-related issues, rather than problems with the underlying models or tooling. In other words, the technology is rarely the weak link; the data feeding it is.

Also Read: Singapore turns AI scrutiny towards chatbots, personal data, and digital twins

This tracks with a pattern seen repeatedly across Southeast Asia’s broader digitalisation push, where legacy systems, fragmented data ownership across departments, and inconsistent data hygiene practices have quietly undermined more ambitious AI rollouts. Singapore, despite its relatively mature digital infrastructure compared with regional peers, is not immune.

The report also flags governance as a growing pain point sitting alongside the data quality problem. Thirty-seven per cent of respondents cited data quality issues as a leading source of friction when deploying AI, while 38 per cent pointed to data access approvals, the bureaucratic back-and-forth of getting the right people cleared to use the right datasets. Meanwhile, 46 per cent said unclear ownership and accountability for AI-driven decisions is a barrier standing between generating an AI insight and actually being able to act on it.

Taken together, the figures describe an organisational bottleneck as much as a technical one. Companies can buy the AI tools, but if nobody is clearly responsible for the decisions those tools inform, and if data access still requires multiple rounds of sign-off, the promised speed gains from automation start to erode.

“Business logic” as the missing layer

Philip Madgwick, Alteryx’s regional vice-president for Asia, framed the findings around a familiar tension: the gap between deploying AI and actually trusting what it produces. He said many organisations in Singapore are still contending with poor data quality, weak governance and lingering uncertainty over AI-generated outputs, and that what separates companies that pull ahead from those that stall is whether the people closest to the business are the ones defining and managing the logic behind AI’s decisions.

That framing matters for how Southeast Asian companies think about their next phase of AI investment. Much of the region’s AI narrative over the past two years has centred on adoption velocity: how quickly enterprises can bolt generative AI or agentic systems onto existing workflows. Alteryx’s data suggests the more pressing question for 2026 is not how fast organisations can deploy AI, but whether they have built the underlying data foundations and accountability structures to actually trust what it outputs.

Why it matters for the region

Singapore’s caution here is worth watching precisely because of its outlier status. As a market often seen as further along the AI maturity curve than its Southeast Asian neighbours, its analysts’ reluctance to cede control signals that the “trust gap” between AI capability and AI governance may not simply close with more advanced tooling or bigger budgets.

Also Read: Singapore’s AI tools are ready. Its workforce isn’t

If anything, the report suggests that as agentic systems become more capable and more embedded in day-to-day business decisions, the human-in-the-loop instinct may harden rather than fade, with organisations that invest early in data governance and clear decision ownership best placed to convert AI adoption into genuine business results.

The post Singapore’s data analysts trust AI to work, not to think appeared first on e27.

The post Bitcoin just broke US$66,000: Is this the start of the next bull run or a trap for late investors? appeared first on e27.

For years, corporate innovation in Southeast Asia often meant a hackathon, an accelerator demo day, or a small pilot that rarely survived the next budget cycle. Singapore is trying to push the model into something more durable: corporate venturing as a repeatable business capability, not a branding exercise.

That is the central argument of a new joint publication by global management consultancy Arthur D. Little and the Singapore Economic Development Board (EDB), titled “Singapore As A Global Platform For Corporate Venturing”. The paper examines how Singapore’s mix of multinational corporations, startups, research institutions, investors and public-sector support can help companies build new products, enter adjacent markets and commercialise emerging technologies faster.

Also Read: How corporate innovation in Vietnam is fledgling the B2B startup ecosystem

The timing is not accidental. Across Southeast Asia, large companies are under pressure from faster technology cycles, shifting supply chains, AI adoption and a more selective funding environment. Startups, meanwhile, are finding that access to corporate customers, distribution and technical validation can be as valuable as capital. Corporate venturing sits at that intersection.

In simple terms, corporate venturing refers to the ways established companies work with startups or build new ventures themselves to access innovation beyond their internal research and development teams. This may involve startup partnerships, venture building, co-development, pilots, minority investments or commercial spin-outs. Done well, it gives corporations a way to test new ideas without betting the entire organisation on them.

From innovation theatre to business discipline

The ADL-EDB publication argues that corporate venturing is becoming more important because traditional growth playbooks are no longer enough. Acquisitions can be expensive and slow. Internal R&D can be too insulated from market feedback. Partnerships without ownership or governance can stall after an initial trial.

By contrast, structured corporate venturing allows companies to identify emerging technologies, run pilots in real-world settings, validate market demand and scale successful ideas through established commercial channels.

“Corporate venturing is no longer an organisational initiative, it is a core strategic capability for companies seeking to stay competitive in the rapidly changing innovation landscape,” said Daniel Chow, Principal at Arthur D. Little Singapore.

That distinction matters. Many large organisations in the region have experimented with startup engagement, but fewer have built the internal machinery needed to turn experiments into business outcomes. The study highlights five requirements: access to emerging technologies, faster validation through pilots, clear governance and ownership, ecosystem partnerships that reduce execution risk, and portfolio-based innovation management.

Also Read: Why it maybe the opportune time to consider Corporate Venture Capital

The last point is especially relevant in Southeast Asia, where market fragmentation can make scaling difficult. A product that works in Singapore may need different pricing, regulations, logistics or customer education in Indonesia, Vietnam, Thailand or the Philippines. Corporate partners can help startups navigate these differences, but only if collaboration goes beyond a press release.

Why Singapore is leaning into the model

Singapore’s pitch is that it can serve as a controlled launchpad for corporate-startup collaboration before companies expand regionally. The city-state has long used its position as a headquarters hub to draw multinational corporations, capital and talent. The publication notes that Singapore is ranked as the most popular regional headquarters destination in Asia, giving it an unusual density of decision-makers for a market of its size.

Its startup ecosystem has also climbed sharply, rising from 16th globally in 2020 to fourth in 2025, according to the publication. That rise reflects years of public investment in research and innovation, stronger university-industry links, and a deepening pool of founders, venture investors and technical talent.

EDB’s Corporate Venture Launchpad is one example of how the government has tried to institutionalise this activity. The programme supports companies in building new ventures from Singapore, often by pairing corporate assets with entrepreneurial teams and market validation processes.

“This report reflects the growing momentum of corporate venturing across Singapore’s business community, especially in AI-enabled growth sectors such as advanced manufacturing, healthcare, semiconductors, and the digital economy,” said Joseph Tay, Vice President and Head of Innovation Strategy and Partnerships at EDB.

Also Read: AI in Singapore: From generative tools to real-world impact

These sectors are not chosen at random. Advanced manufacturing and semiconductors are tied to Singapore’s role in global supply chains. Healthcare and biomedical sciences build on the country’s research base, hospitals and regulatory credibility. AI and the digital economy cut across nearly every industry, from financial services and logistics to drug discovery and factory automation.

The Southeast Asian relevance

For the wider region, Singapore’s corporate venturing push could have effects beyond its borders. Many Southeast Asian startups use Singapore as a funding, headquarters or enterprise sales base while operating in larger neighbouring markets. If more multinationals and regional conglomerates build structured venturing teams in Singapore, startups could gain better access to paid pilots, technical expertise and cross-border commercial opportunities.

This is particularly important in the current funding climate. After the excesses of 2021, investors have become more disciplined, and founders are under pressure to prove revenue quality, not just user growth. Corporate partnerships can help bridge that gap, but they can also be slow, bureaucratic and difficult to convert into meaningful contracts.

That is why governance matters. A common failure point in corporate-startup collaboration is the absence of a clear business owner. A startup may impress an innovation team but fail to secure support from procurement, legal, compliance or the operating unit that actually owns the problem. The ADL-EDB publication’s emphasis on ownership and commercialisation pathways is a recognition that innovation must eventually survive inside the corporate machine.

Singapore may have advantages here, including strong legal infrastructure, regulatory clarity and proximity to regional headquarters. But it also faces competition from other Asian hubs. Japan and South Korea have large corporate balance sheets and deep technology sectors. India offers scale, software talent and a thriving startup market. China remains a major centre for hardware, manufacturing and AI application, despite geopolitical complexities.

Singapore’s differentiation is less about domestic market size and more about orchestration. It can convene corporates, startups, universities, investors and regulators in a compact ecosystem. The challenge is ensuring that this orchestration produces companies and products that scale beyond Singapore.

What comes next

The publication by ADL and EDB is not a market-moving announcement by itself. It does, however, reflect a broader shift in how Singapore wants to position itself in the next phase of innovation: not merely as a place where startups raise money, but where corporations build new growth engines with startups and research partners.

Also Read: AI meets IP: Why Singapore is the launchpad for AI-driven startups

For founders, that could mean more opportunities to work with enterprise customers earlier. For corporates, it raises the bar: startup collaboration can no longer sit at the edge of the organisation, disconnected from strategy and profit-and-loss responsibility.

The real test will be whether more of these ventures move from pilot to procurement, from experiment to revenue, and from Singapore launchpad to Southeast Asian scale. If they do, corporate venturing may become less of an innovation buzzword and more of a practical route to building the region’s next generation of technology businesses.

The post Singapore’s startup rise puts corporate venturing in sharper focus appeared first on e27.

Balaji Srinivasan has never lacked for big ideas. The former Coinbase CTO and Andreessen Horowitz General Partner has spent years evangelising the “network state”, a borderless, internet-native community that eventually negotiates diplomatic recognition from existing governments.

In late 2024, that theory got a physical address: Network School, a pop-up village of founders, engineers, and crypto enthusiasts housed inside the half-empty towers of Forest City in Johor, Malaysia.

Also Read: Six practical tips on how to become a digital nomad

Nine months later, Malaysian authorities tightened the valve considerably. Immigration officials began scrutinising the visa status of participants, several of whom were reportedly operating on tourist or social visit passes rather than legitimate work or student permits. Attendees describe increased checks, warnings, and mounting uncertainty over whether the programme can legally continue as advertised.

For a region that has spent a decade courting digital nomads and “future of work” narratives, the crackdown poses an uncomfortable question: was this inevitable, or did Malaysia fumble an opportunity?

A school that wasn’t quite a school

Part of the problem is definitional. Network School was never registered as an accredited institution, nor did it fit Malaysia’s existing frameworks for co-working visas, MM2H, or the DE Rantau digital nomad pass. It was designed to be something new — part co-living space, part ideological experiment, part unofficial curriculum on governance and technology.

That ambiguity is the entire point of a network state: operate in the gaps between existing legal categories until enough legitimacy accrues to demand its own. But immigration officers do not deal in theory. They deal in passport stamps and one blunt question: is this person working, studying, or just visiting? When hundreds cycle through a facility for months at a stretch, ambiguity stops being clever. It becomes a compliance risk.

Seen this way, the crackdown isn’t hostility to innovation. It is a government enforcing the line between visitor and resident, a line every country, Singapore included, guards jealously.

The bigger tension: Ideology meets sovereignty

Still, something here deserved better handling. Forest City, the ghost-town mega-development built by China’s Country Garden, has been desperate for any sign of life since its US$100-billion vision collapsed under oversupply and bad geopolitical timing. Whatever one thinks of its ideology, Network School brought paying occupants and foreign attention into towers that had sat empty for years. A calibrated response — clear guidance, a bespoke visa category, a formal MOU — could have captured the upside while closing the compliance gap.

Instead, ambiguity was left to fester until it became an enforcement story instead of a policy one. That is a familiar Southeast Asian pattern: digital nomad experiments get welcomed in speeches, while the bureaucratic plumbing to support them lags years behind the marketing.

There is a sharper ideological discomfort too. Network State philosophy, at its most provocative, imagines communities that eventually seek sovereignty or special jurisdiction,  a proposition no nation-state, least of all one still nursing a foreign-funded property collapse on its own soil, should wave through without scrutiny. Malaysia is right to ask who governs Johor’s newest experiment, and under whose laws.

A middle path still exists

None of this means the door should close for good. Johor’s ambitions, anchored by the Johor-Singapore Special Economic Zone, depend on attracting exactly the kind of mobile, high-value talent Network School claims to cluster. The lesson isn’t that experimental communities are dangerous. It’s that experiments need a legal home to grow into, not just a rented tower and a manifesto.

Also Read: SPUN raises US$1.8M to fix SEA’s broken visa infrastructure

For founders and investors watching from Singapore, Jakarta, or Bengaluru, this episode is a reminder that Southeast Asia’s openness has limits, defined by sovereignty, labour law, and public accountability. The most interesting network states of the future won’t be the ones that dodge these questions. They’ll be the ones that answer them well enough to earn a seat at the table. Johor still has time to write that invitation. It would be a shame if bureaucratic inertia, not genuine disagreement, was the reason it never got sent.

The post Southeast Asia loves digital nomads until the paperwork gets complicated appeared first on e27.

For years, startup success was often measured by visible growth.

A bigger office. More departments. More employees.

As founders, many of us dreamed of building the next great tech company, and somewhere along the way, headcount became a proxy for success. Every new hire felt like validation that the business was moving in the right direction.

I used to think that way too.

When I built my first SaaS company, People’s Inc., my focus was on growing the team. We hired across sales, marketing, design and operations because that was what successful companies were supposed to do. But as the company expanded, I realised I was spending less time building the business and more time managing it.

The lesson wasn’t that hiring was wrong. It was that I had been optimising for the wrong metric.

Today, I believe AI is forcing founders to rethink one of entrepreneurship’s oldest assumptions: bigger companies are not necessarily better companies.

The businesses that thrive in the next decade may not be the ones with the largest teams. They may simply be the ones with the greatest leverage.

Growth creates complexity, not just capacity

Hiring more people certainly increases what a company can accomplish. It also introduces something less obvious: management overhead.

Every additional hire brings communication, coordination, onboarding, alignment and accountability. Decisions take longer because more people need context. Small misunderstandings can become expensive problems.

One of the hardest lessons I learnt as a founder was that, even if you aren’t personally involved in every conversation, you remain responsible for the outcome.

Also Read: Singapore’s data analysts trust AI to work, not to think

A message delivered differently than intended. A decision interpreted incorrectly. A relationship that breaks down because of poor communication. As founders, those responsibilities always come back to us.

That isn’t a criticism of teams. It’s simply the reality of leadership. As organisations grow, complexity grows with them.

AI changes what founders should optimise for

The conversation around AI often focuses on replacing jobs or reducing costs. I think that’s the wrong conversation. AI isn’t valuable because it replaces people. It’s valuable because it changes how human time is spent.

When I built Seraphina, my AI chief of staff, I approached it very differently from how I had previously built companies. Instead of asking, “Who should I hire next?”, I started asking, “Does this task actually require a human?” Many tasks don’t.

Scheduling meetings. Managing reminders. Coordinating workflows. Retrieving information. Organising knowledge. Following up on repetitive administrative work. These activities are essential, but they don’t necessarily require uniquely human judgement.

By allowing AI to handle these operational tasks, the people on my team have more capacity to focus on work that creates disproportionate value. Not because AI is cheaper. Because human attention is more valuable.

The future workforce isn’t smaller, it’s more focused

This doesn’t mean businesses won’t need employees. Restaurants still need chefs and service staff. Healthcare still depends on doctors and nurses. Manufacturers still require skilled operators. Every industry will adopt AI differently.

But even in businesses that will always rely heavily on people, the nature of work is changing.

The most valuable employees won’t simply execute processes. They’ll build relationships. They’ll earn trust. They’ll negotiate. They’ll think strategically. They’ll solve problems creatively.

Those are capabilities that become even more valuable when repetitive execution is increasingly handled by intelligent systems.

People still buy from people

Customers may interact with AI assistants, receive AI-generated recommendations or automate parts of their buying journey. But trust, conviction and long-term relationships remain deeply human.

That is where founders should invest their teams.

Also Read: AI-powered business automation: How SMEs are transforming operations in Southeast Asia

Revenue should come before headcount

One mindset shift has shaped how I build businesses today.

I no longer believe founders should hire simply because they’re growing. They should hire because a human creates value that technology cannot.

That changes the order of operations.

For decades, the startup playbook looked something like this:

• Raise funding.
• Hire aggressively.
• Build the organisation.
• Then chase growth.

Increasingly, AI allows founders to reverse that sequence.

• Validate demand.
• Generate revenue.
• Build systems.
• Automate repetitive work.

Then hire intentionally where human expertise creates the greatest impact.

For founders building software, micro-SaaS businesses or digital-first companies, this shift is particularly powerful. A small, focused team equipped with AI can often accomplish what previously required significantly more people.

In my own work building Seraphina, we’ve been able to grow the platform while keeping the organisation deliberately lean. Today, the product generates revenue while remaining focused on systems, automation and thoughtful hiring rather than expanding headcount for its own sake. It’s also a philosophy I’ve discussed with founders, where the biggest transformation is rarely learning another AI tool. It’s learning how to redesign the way a business operates.

The next generation of founders may build differently

For years, entrepreneurs celebrated companies with hundreds or even thousands of employees because that represented scale. In the AI era, scale may look different. It may be measured by how much value each person creates rather than how many people sit on the payroll.

The founders who succeed won’t necessarily be those who build the biggest organisations. They’ll be the ones who build the strongest systems. The ones who use AI for execution, people for judgement, and processes to connect everything together.

Ultimately, entrepreneurship has never been about collecting employees. It’s about creating value. AI doesn’t change that goal. It simply gives founders a new way to achieve it.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post The end of headcount as a success metric: How AI is redefining scale appeared first on e27.