Mental well-being is a big issue across the world. In Singapore, mental well-being costs nearly US$16 billion a year, accounting for about 2.9 per cent of the nation’s GDP, while in Malaysia, a survey conducted by the Ministry of Health found that one in three Malaysian adults aged 16 and above has a mental health condition.

Organisations and businesses aren’t the only ones affected, and caregivers are at risk. A study in Singapore revealed that 72 per cent of mental health care caregivers felt exhausted, and three in four needed temporary separation from the people they cared for.

The pandemic shined the spotlight on mental well-being and helped chip away at the stigma. This has also resulted in tech advancements in the space. Even as tech improves, we believe the human connection should always be the underlying foundation that guides the way. Here are some of the touchpoints in the space.

Mental well-being touchpoints we see so far

Some touchpoints connect individuals to healthcare providers. Telehealth solutions have come a long way and are a prime example of this. Medical records, medications, prescriptions are treatment plans are synced across the board. This has reduced the risk of spreading contagious diseases while cutting down on travel and waiting times, making it especially helpful for people living in remote areas.

Other touchpoints connect individuals with themselves. Across wearables and thousands of wellness apps, we see them cover a few bases. Some apps promote a mindful, active lifestyle, covering meditation, fitness, and yoga. Others track sleep and nutrition and provide assessments and information to help people get through the hard times. These options provide individuals with different gateways that could lead them to seek professional help.

The Village approach to well-being: An untapped touchpoint

We humans are social creatures, and close relationships are integral to our survival. A research article cited that 308,849 individuals across 148 studies revealed humans with relatively strong social relationships increased their likelihood of survival by 50 per cent. The CDC has reported that loneliness and social isolation significantly increase a person’s risk of premature death from all causes, including smoking, obesity, and physical inactivity.

Also Read: ‘HUGgy’ng innovation: Dolbomdream’s tech vest aims to bridge mental healthcare gap

Despite the growth of social media and the many connections people have on the platforms, an international study has shown that people using social media to maintain their relationships feel lonelier. Social media facilitates social contact, but it doesn’t meet the quality of connection of those who are on it for that reason.

We propose the Village approach to well-being. Each individual in the village has close friends and family. A tight-knit group with strong bonds. Everyone in the village is trusted to be open, honest, and proactive in their thoughts, feelings, and interactions with one another. The Village approach to well-being is a manner that possesses the potential to support the individual’s physical and mental well-being.

The touchpoint connecting the individual with their village can be improved. With the Village approach to care as the framework, we can vastly enhance the well-being of the space and the mileage from our well-being solutions.

Though professional help is readily available, and society is more open, factors such as cost, timing, and availability of therapists prevent people from reaching out. There are also concerns regarding having treatment on official records that dissuade people from seeking help, fearing the ramifications it could have on their professional lives.

The Village approach to well-being involves helping individuals identify and curate their villages. The need to share sensitive data points safely and privately is essential to encourage proactiveness and engagement. With the previous touch point, the individual has to either deal with their challenges alone or seek professional help.

The village becomes a sounding board and support group, serving as scaffolding to proactively help individuals with those challenges. This middle ground can identify and resolve issues earlier, which takes a heavy load off caregivers at home and the healthcare system.

Everyone has a different baseline. A video from Norwich Football Club for World Mental Health Day shows how the signs can be hard to spot, even for close ones. With curated villages, the individual can be forthcoming and honest, allowing accurate assessments to gauge their moods and mental states.

The individuals in a village can share how they feel, and others can share their observations of how the individuals feel as well, to see the difference and gain perspectives. Notifications can be sent when there are changes in the individual’s mood so the village can intervene and support.

Also Read: The rise of generative AI in digital mental health solution

The Village approach to care can also be a counterbalance to social media. People on social media tend to share the parts of their lives they want people to see – the wins and the highlights, and this has led to pressures that result in a negative effect on mental health. The village provides a forum to listen intently and engage in real conversations, as well as a safe space to share losses or lowlights, normalising daily challenges and overcoming them.

Ultimately, through the village, the individual will have access to their accountability partners for their well-being and deepen their connections through activities, community events, and multiple touchpoints in the real world.

Tech and the lifelong journey of mental well-being

As tech improves, knowing its place and context for our mental well-being is essential to support and enhance human connection, not replace it.

We should also look at mental well-being tech and the Village beyond a solution and preventive measure. For high-at-risk individuals, the village is a safety net, for individuals who are doing well, the village is a trampoline.

No matter where anyone is at, it should always be worked on. Professional athletes and high-performing individuals know the value of accountability partners, no matter where anyone is in life, mental well-being can and should always be worked on.

We must be wary of bad tech and how much of our bandwidth it can occupy. Ironically, the success marker of good tech for mental well-being in the future could be a solution where anyone can be a user and act as a catalyst for human connection so everyone uses it as little as they need to.

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This article was first published on March 13, 2024

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The Asia‑Pacific (APAC) cybersecurity sector saw a notable slowdown in 2025, with investor appetite shifting from breadth to depth as funds and corporates pick fewer, more mature targets, according to data from Tracxn.

While the regional market has accumulated meaningful capital over the last several years, the 2025 picture is one of moderation: total capital raised in APAC reached US$8.35 billion to date, but annual inflows have decelerated sharply from the peak years.

Also Read: From fraud fighters to zero-trust builders: SEA’s cyber stars

Tracxn’s dataset shows funding peaked in 2021 (about US$1.7 billion that year) and has tapered since. In 2025, the region attracted US$185.2 million, representing a 27.7 per cent year‑on‑year decline from 2024. The number of rounds also contracted to 43, down 27.1 per cent year‑on‑year, suggesting investors applied far stricter filters on deal quality and go‑to‑market proof points.

The picture in APAC diverged from global trends: worldwide cybersecurity funding expanded to US$14.6 billion in 2025, a 41 per cent year‑on‑year uptick, even though the number of global rounds fell to 457 — a 13.5 per cent decline.

In short, the world pumped more capital into fewer, bigger bets; APAC pulled back.

Early‑stage resilience, late‑stage drought

Stage‑level analysis reveals a nuanced internal dynamic. Early‑stage investments (seed and Series A equivalents) accounted for the lion’s share of 2025 activity in APAC, with US$138.8 million deployed, up 15 per cent year‑on‑year. That suggests venture investors retained an appetite for early product‑market experiments in cybersecurity, provided founders could show rapid adoption or defensible data moats.

By contrast, seed rounds as a category declined 34 per cent year‑on‑year to US$25.1 million, and late‑stage funding collapsed to US$21 million, a 78 per cent year‑on‑year drop.

The late‑stage drought is particularly striking: it points to a scarcity of crossover and growth capital available for scaling cybersecurity companies in the region, forcing many to either seek offshore capital or stretch to profitability sooner.

Geography of capital: India dominates

India, Australia, and Singapore held the ground in APAC funding in 2025, with funding heavily concentrated in a handful of markets. India led the region with US$116 million raised, followed by Australia with US$32.1 million and Singapore with US$24 million.

Also Read: Why Flexxon thinks software-only cybersecurity is no longer enough

The Indian figure underscores the country’s accelerating enterprise digitisation and chronic security needs across finance, fintech, healthcare, and public‑sector projects. Australia’s placement reflects a robust security vendor scene and strong crossover investors, while Singapore continues to punch above its weight as a regional security hub, buoyed by a mature professional services base and government emphasis on cyber resilience.

Segment winners: application security and data protection surge

Segmental winners in 2025 point towards enterprise pain points being monetised through AI and automation. Application security software attracted US$46.1 million (a dramatic 922 per cent increase from US$4.5 million in 2024) driven by startups that combine automated offensive testing, attack surface management and AI‑assisted remediation.

India-based FireCompass, a continuous automated red‑teaming and attack surface management player, and Protectt.ai, focusing on mobile app and AI security, were among the most funded in this category.

Data security platforms also made a leap, securing US$43.1 million — up 130.5 per cent from US$18.7 million in 2024. Startups such as QuintessenceLabs, which brings quantum‑enhanced cybersecurity capabilities, and Pantherun, an Indian innovator with a patented data protection algorithm, drew significant investor interest. The surge in data security reflects enterprises’ strategic shift from perimeter defence to protecting the data and models that power digital services.

Website security software, previously neglected by investors in the region, recorded US$20 million in 2025 — all attributable to Kasada, the Sydney‑based anti‑bot and scraping defence firm, which closed a US$20 million Series C. That single transaction highlights a broader trend: focused, revenue‑generating point solutions with clear ROI on defence spend continue to attract concentrated capital.

Notable names: Kasada, FireCompass, CloudSEK and the long‑tail leaders

On a deal basis, 2025’s largest rounds in APAC were led by Kasada (US$20 million Series C), FireCompass (US$20 million Series B), and CloudSEK (US$19 million Series B). Kasada’s raise is emblematic of demand from large digital platforms for robust bot‑defence as attackers weaponise automation and cheap compute to scale attacks.

Looking at historical funding across the region, Tracxn shows Acronis as the most capitalised cybersecurity company to date, with US$658 million raised. That is followed by Cloudwalk (US$504 million) and Druva (US$475 million) — names that underscore the diverse paths to scale in APAC, from endpoint and backup solutions to AI and imaging companies with wider market ambitions.

What this means for 2026: higher quality, deeper enterprise integration

Tracxn’s outlook suggests 2026 will not be a return to the frothy, high‑round environment of 2019–2021, but rather a transition to healthier, more focused expansion.

Also Read: Singapore’s AI adoption surges, but data complexity raises security risks: Report

Several tailwinds support this: enterprises across APAC continue to digitise, regulatory pressure for cyber resilience is rising (particularly in finance and critical infrastructure), and the attack surface is ballooning with cloud, APIs and AI deployments.

Key areas to watch include:

• Application security and attack surface management: enterprises seek continuous, automated defences as software delivery accelerates.
• Data security platforms and model protection: the proliferation of AI systems creates new vectors that require both cryptographic and behavioural defences.
• Identity‑led architectures: zero‑trust and identity verification will remain top priorities as workforce and customer interactions decentralise.
• AI‑driven threat detection: startups that can reduce mean time to detect and respond by leveraging cross‑organisational telemetry will find receptive buyers.

Investors will gravitate towards companies that demonstrate product maturity, enterprise traction and clear paths to profitability or substantial annual recurring revenue. The late‑stage capital gap may persist unless crossover and growth funds re‑enter the market; absent that, promising founders will increasingly pursue strategic partnerships, M&A exits or US and European capital sources.

Regional implications: a proving ground for global players

For Southeast Asia, the 2025 data carries actionable lessons. Markets such as Singapore, Indonesia, and Malaysia are fertile testing grounds for identity, payments and cloud security products that can scale regionally. The rise of serial founders and the repatriation of talent from global hubs can accelerate build‑outs of enterprise‑grade vendors.

However, to emulate more mature ecosystems, APAC needs a stronger pipeline of later‑stage investors willing to finance scaling security firms across time zones and regulatory regimes.

The 2025 contraction is not a setback so much as a correction. Tracxn’s figures reveal that capital is not fleeing cybersecurity; it is being more deliberately allocated. The companies that win in 2026 will be those that turn the complexity of modern attack surfaces into repeatable, enterprise‑grade services with measurable ROI.

For those firms, APAC remains a vast market of unmet demand if they can prove they can deliver results at scale.

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When we talk about AI in biotechnology, the conversation usually revolves around science: faster drug discovery, protein folding breakthroughs, or personalised medicine. But the real story is bigger than science. It’s about how ethics, governance, economics, politics, and society intersect at this new frontier.

Ethics: Who gets a cure, and who doesn’t?

AI can now generate millions of molecular candidates in weeks. AlphaFold 3 takes us further, predicting how proteins interact with DNA, RNA, and small molecules. That’s revolutionary for discovery. But who benefits? If incentives remain profit-driven, rare diseases and those affecting poorer regions may still be ignored.

There’s also the dual-use dilemma: the same AI that designs a life-saving drug could be misused to generate harmful compounds. Ethics here isn’t just about dataset bias—it’s about responsibility for outcomes. And it’s not abstract. Imagine a parent in a low-income country watching global headlines about “AI-discovered cures” while their own child has no access. The technology promises universality, but the distribution risks being anything but.

Governance: Science sprints, law walks

Regulation is scrambling to keep up. The EU AI Act now classifies biotech applications as “high risk,” demanding strict oversight. In the U.S., the FDA has introduced Predetermined Change Control Plans (PCCPs), allowing developers to declare in advance how AI systems may evolve post-approval.

These are steps forward, but they reveal a deeper challenge: science is sprinting, while law is jogging behind. Worse, regulations are fragmented. The EU enforces precaution; the U.S. experiments with flexibility; China pushes rapid state-backed adoption. Companies may exploit these gaps, moving data and trials to whichever jurisdiction offers the lightest touch. Governance isn’t just slow—it’s uneven.

Also Read: AI, advanced therapeutics, and the geopolitical balancing act in biotech

Economics: The monopoly question

AI should lower the cost of early-stage discovery. In theory, that democratises innovation. In practice, it risks concentrating power. Training and deploying frontier models requires massive compute and proprietary datasets—resources controlled by a handful of tech and pharma giants. Smaller labs risk becoming dependent, licensing access rather than innovating independently.

The economic implications ripple outward. Will insurers reimburse AI-designed treatments differently? Will costs actually fall for patients, or will monopoly pricing persist? Alphabet’s Isomorphic Labs, which has already signed multi-billion-dollar partnerships with major drugmakers, embodies this tension: breakthrough efficiency paired with concentrated control.

Politics: Biotech as geopolitical strategy

COVID-19 taught nations that control over vaccines and treatments is not just a health issue—it’s sovereignty. Governments are now pouring billions into AI-biotech ecosystems. Whoever leads in this space doesn’t just sell cures; they wield geopolitical leverage.

Emerging technologies like quantum computing add another layer. Quantum promises to simulate molecules and chemical interactions at scales classical computers can’t touch. Partnerships such as Boehringer Ingelheim with Google Quantum AI are early signals. For countries, this isn’t just about innovation—it’s about future control over health, defense, and economic power.

Social impacts: Access, trust, and equity

Let’s say AI shortens discovery cycles tenfold. Who gets the new drugs first? Wealthy nations with purchasing power? Patients with premium insurance? Without careful policy, AI risks widening global health inequities between the Global North and South.

Trust is another fault line. How comfortable will people be taking a treatment designed largely by machines? In healthcare, perception shapes adoption as much as efficacy. Review articles note that even when accuracy improves, social acceptance cannot be assumed.

There’s also the question of data. AI-driven biotech relies on genomic and clinical datasets. Who owns this data? Are patients fully consenting to its use? And when genomic data flows across borders, what protections follow it? Without clear answers, privacy and trust will become major bottlenecks.

Also Read: Asia-Pacific governments step in as private biotech investors pull back

Workforce and culture: Who gets left behind?

AI’s acceleration raises uncomfortable questions about the people inside the system. If algorithms handle much of early-stage drug design, what happens to thousands of researchers who once did that work manually? There’s a risk of deskilling, where human expertise erodes as machines take over the hardest parts of the pipeline.

Yet there’s also an opportunity. New roles are emerging: data stewards, model auditors, bioethics officers. The biotech workforce could shift from “pipette in hand” to “oversight of AI-wet lab loops.” Whether this is empowering or displacing depends on how institutions prepare now.

The real blueprint

The future of AI in biotechnology isn’t just about algorithms—it’s about design. A resilient blueprint must embed ethics, governance, economics, politics, and social equity from the start.

Because the promise is enormous: faster cures, more resilient health systems, breakthroughs against diseases that have long eluded us. But the risks are just as stark: monopolies, inequity, regulatory arbitrage, public mistrust, and workforce disruption.

For innovators, leaders, and policymakers, the central question is simple but urgent:

Can we design a biotech future where AI serves not only markets, but humanity?

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Each year, millions of lives across Asia are sustained by blood transfusions, yet many countries in the region continue to experience recurring shortages. According to the World Health Organisation, 40 per cent come from high-income countries, despite these countries accounting for 16 per cent of the world’s population. In Asia, where demand outpaces supply, this imbalance is starkly felt.

Why people hold back

As a regular blood donor, I’ve come to appreciate both the urgency of the need and the depth of public hesitation that surrounds blood donation. When I ask friends why they don’t donate, the answers are strikingly similar: “It must be painful” or “I’m worried I won’t qualify.”

The truth is, the procedure itself is far less intimidating than people assume. However, one obstacle remains—screening potential donors for low haemoglobin levels. The WHO estimates that anaemia affects an estimated 571 million women and 269 million young children worldwide, with the highest prevalence in South and Southeast Asia.

In fact, up to 15–20 per cent of potential donors are turned away in some Asian countries due to low haemoglobin counts. Pre-screening usually involves a finger-prick blood sample. While minor, it acts as a psychological barrier for many. For those living with Sensory Processing Sensitivity, which includes about 20 per cent of Singapore’s population—myself among them—that single fingertip prick can be particularly painful even hours after testing.

Also Read: Healthtech in South and Southeast Asia – Seeing beyond the “obvious”

Reimagining screening through technology

The gap is what motivated me to create an app with a non-invasive haemoglobin scanner. The idea was simple: if people could check their haemoglobin levels quickly and painlessly before visiting a donation centre, we could remove one of the main barriers to participation. And by scanning their lips.

According to a retrospective study of the Singapore Blood Transfusion Service, about 14.4 per cent of prospective blood donors were deferred either temporarily or permanently at the pre-donation screening stage. Among the top reasons for deferral were a low haemoglobin count, alongside factors such as recent medication use and recovery from a flu or fever. This means that one in seven Singaporeans presenting to donate blood are turned away.

The first wave of feedback was eye-opening, not just from prospective blood donors but also from individuals who regularly require blood testing for medical reasons, as well as inactive donors who shared that they might be more inclined to donate if pre-screening felt less invasive. And fast. Both groups shared that an easier, pain-free haemoglobin check could be a meaningful help in reducing anxiety, encouraging participation and lessening care costs.

Developing Genesis1 is not just about convenience. It is about using advanced technology – specifically, Artificial Intelligence and Machine Learning – to normalise blood donation and reframe the experience as empowering rather than intimidating. What struck me most was that modern technology could transform the act of giving blood from something intimidating into something empowering.

Beyond donor centres

From a healthtech perspective, the potential extends far beyond donor centres. Anaemia often goes undiagnosed, particularly in communities with limited access to healthcare. A portable, easy-to-operate tool could empower clinics, schools and even self-help groups to screen populations at scale, turning what is currently a reactive diagnosis into a proactive measure.

For governments and Non-Governmental Organisations managing national blood banks and public health campaigns, this kind of solution could optimise donor pools, reduce deferral rates and ultimately help build more resilient healthcare systems.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

Small shifts, big impact

The strength of health innovation in Asia lies in its scale. With large and varied populations, even a slight increase in active donors results in a significant impact. Think about it.

A five per cent increase in active donors translates into thousands of lives saved annually. By reducing barriers to eligibility screening, the application takes a small but significant step towards closing the supply gap, particularly in regions like Thailand and Indonesia, where shortages can mean the difference between life and death.

From fear to solidarity

The broader vision is clear: technology should serve as an enabler, not a gatekeeper. By designing tools that are intuitive, affordable and scalable, we can influence public attitudes towards blood donation and tackle one of the region’s most urgent yet solvable healthcare challenges.

Innovations like Genesis1 can help reshape public health attitudes across Asia. If people start to view health checks as accessible and painless, blood donation will feel less like a test of endurance and more like an act of shared responsibility. That shift, from fear to solidarity, could help cultivate a culture where giving blood is not unusual, but expected.

Blood donation is fundamentally an act of solidarity. By combining that human kindness with thoughtful innovation, we can ensure that fear and inconvenience never stand in the way of saving lives.

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Breaking into any industry as a startup in Asia is no small feat, but healthcare is in a league of its own. Known for its sky-high barriers to entry, strict regulations, and entrenched systems, healthcare is one of the hardest industries to disrupt and build in, especially in a region as diverse and dynamic as Asia.

Yet, for entrepreneurs willing to take the leap, the potential for massive impact and lucrative rewards make healthcare an industry worth exploring. Let’s delve into why healthcare is so tough to crack, the opportunities hiding within these challenges, and why startups should still consider this as a game-changing frontier.

Why healthcare is so challenging to disrupt

Entering the healthcare industry as an entrepreneur is not for the faint-hearted. Unlike sectors that thrive on rapid innovation and quick market entry, healthcare demands a meticulous, patient-centered approach. The industry is tightly regulated, highly capital-intensive, and deeply entrenched in legacy systems. For startups, this means navigating a minefield of challenges before making an impact.

Here are the key barriers that make healthcare one of the most difficult industries to disrupt:

• Regulatory complexity

Healthcare is one of the most tightly controlled sectors globally and Asia’s regulatory landscape is especially intricate. Each country enforces its own rigorous approval processes for new technologies, drugs, or devices. Entrepreneurs must invest significant time and resources into navigating these frameworks, with timelines often stretching into years before a product or service can be commercialised.

• High capital requirements

Building a healthcare startup demands substantial funding. Unlike industries where a lean MVP (Minimum Viable Product) can validate ideas, healthcare innovation requires clinical trials, certifications, and compliance testing—all of which are time-intensive and costly. Startups need to be prepared for a long runway to achieve profitability.

• Fragmented markets

Asia’s diversity is both a blessing and a challenge. While it offers enormous market potential, each country’s healthcare infrastructure, patient demographics, and consumer behaviour differ widely. Entrepreneurs must create hyper-localised solutions while maintaining scalability—a balancing act that’s easier said than done.

• Consumer trust and adoption

In healthcare, trust is paramount. Patients, providers, and regulators are cautious about adopting unproven solutions. Startups must focus on not just innovation but also credibility, ensuring that their offerings meet the highest standards of quality and reliability.

Also Read: Decoding digital preferences: A glimpse into the future of health tech ecosystem in SEA

Why startups should still take the leap

While the barriers are steep, the rewards for healthcare entrepreneurs who succeed are unparalleled. The healthcare sector is ripe for disruption in Asia, with several drivers creating fertile ground for innovation:

• Unmet Needs and Inefficiencies: The region is plagued with challenges such as uneven access to care, long wait times, and underfunded healthcare systems. These inefficiencies represent opportunities for startups to step in and create transformative solutions.
• Digital Transformation: The pandemic accelerated digital adoption in healthcare, opening doors for telemedicine, AI-driven diagnostics, and healthtech platforms. Entrepreneurs can now leverage technology to address age-old challenges more effectively.
• Growing Middle Class: As incomes rise across Asia, there’s an increasing demand for high-quality healthcare services. Startups focusing on affordability, accessibility, and convenience can cater to this expanding demographic.
• Social Impact and Legacy: Few industries offer the chance to create as much tangible, positive change as healthcare. Entrepreneurs venturing into this space have the opportunity to build companies that save lives, improve well-being, and shape the future of medicine.

Key opportunities for startups in healthcare

Despite the significant challenges, the healthcare industry in Asia presents immense opportunities for entrepreneurs who are willing to innovate and persevere. The region’s growing population, rising healthcare demands, and increasing adoption of technology have created fertile ground for startups to address critical gaps in care.

By identifying unmet needs and leveraging cutting-edge technologies, startups can disrupt traditional healthcare models and create transformative solutions. Here are some of the most promising opportunities in the healthcare sector for entrepreneurs:

• Telemedicine and virtual care: Platforms like Halodoc (Indonesia) and Practo (India) have shown that connecting patients and doctors remotely is not only viable but highly scalable. Entrepreneurs can explore niche markets, such as mental health support or specialised consultations, to carve out a unique space.
• AI-powered diagnostics: Artificial intelligence is revolutionising diagnostics by improving speed and accuracy. Startups can focus on creating affordable diagnostic tools tailored to specific markets, like rural areas where access to medical expertise is limited.
• Personalised medicine: With advances in genomics, startups can deliver tailored healthcare solutions, from customised treatment plans to preventive care, allowing patients to receive more effective interventions.
• Preventive healthcare and wellness: Wearable technology, health monitoring apps, and digital platforms promoting preventive care are gaining traction. These solutions appeal to tech-savvy consumers seeking to take charge of their health.

Startup survival tips for healthcare entrepreneurs

To thrive in this challenging yet rewarding space, consider these essential survival strategies:

• Play the long game: Healthcare is a marathon, not a sprint. Be prepared for long lead times and plan your funding runway accordingly. Investors with deep industry knowledge can be invaluable partners.
• Localise and scale thoughtfully: While the ultimate goal may be regional or global scale, begin by deeply understanding and solving the problems of a specific market. Once proven, expand strategically to new geographies.
• Build credibility from day one: Trust is everything in healthcare. Collaborate with established healthcare providers, hire domain experts, and prioritise data security and regulatory compliance to build confidence with all stakeholders.
• Embrace collaboration over competition: Healthcare is not an industry where disruption is synonymous with destroying incumbents. Often, startups succeed by working alongside established players to create synergies.

Also Read: What telemedicine and health tech holds across SEA amidst COVID-19

Why the healthcare industry needs entrepreneurs

The healthcare industry is overdue for innovation, particularly in Asia. It’s a space crying out for fresh ideas, bold thinkers, and courageous doers. While the challenges may seem insurmountable, they also act as a moat, ensuring that only the most committed and visionary entrepreneurs enter.

For those willing to invest their time, effort, and ingenuity, the rewards extend far beyond financial gain. They include the satisfaction of making a meaningful impact, improving lives, and leaving a legacy.

In healthcare, the stakes are high—but so are the rewards. If you’re an entrepreneur ready to take on one of the hardest industries to disrupt, Asia’s healthcare market is waiting for you. Will you answer the call?

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In today’s hyperconnected world, cybersecurity and data governance have become board-level imperatives. A single breach, data leak, or regulatory misstep can inflict not only financial loss but also reputational damage, legal penalties, and erosion of stakeholder trust. Yet, despite escalating threats, many boards in Asia still treat cybersecurity as a technical issue rather than a strategic risk requiring active oversight.

Boards that treat cybersecurity and data governance as strategic responsibilities safeguard enterprise value, build stakeholder confidence, and enable sustainable growth.

The rising stakes of cyber and data risks

Asia is a hotspot for cyber threats due to its large digital economies, rapid adoption of cloud and AI technologies, and cross-border data flows. Boards must consider risks that include:

• Ransomware and cyberattacks: Disrupting operations, supply chains, and customer services.
• Data privacy breaches: Regulatory fines under GDPR, PDPA, or local privacy laws.
• Third-party vendor vulnerabilities: Supply chain attacks exposing sensitive information.
• AI and algorithmic risks: Mismanaged models leading to bias, fraud, or operational errors.
• Reputational exposure: Loss of customer trust can impact market position and valuation.

The frequency, complexity, and financial impact of cyber incidents are growing. According to recent studies, Asian organisations face a 40–50 per cent higher risk of cyberattacks than global averages, making board-level attention essential.

Also Read: Code, power, and chaos: The geopolitics of cybersecurity

Boards must shift from compliance to strategic oversight

Traditional approaches — approving IT budgets or receiving quarterly reports – are no longer sufficient. Boards must integrate cybersecurity and data governance into enterprise risk and strategy discussions:

• Strategic risk lens: Treat cyber and data risks as core to enterprise risk management, not merely IT risk. Consider potential operational, regulatory, financial, and reputational impacts.
• Continuous monitoring and reporting: Boards should receive real-time dashboards on threat levels, incident response readiness, and regulatory compliance. Lagging metrics are insufficient in a rapidly evolving threat landscape.
• Scenario planning and stress tests: Boards should engage management in simulations of cyberattacks, data leaks, or AI system failures. These exercises reveal weaknesses and prepare leadership for high-stakes incidents.

Key questions boards should ask

To fulfil their oversight responsibilities, boards should challenge executives with strategic questions:

• How are we securing critical infrastructure and sensitive data across the organisation?
• What are the key third-party or supply chain vulnerabilities?
• How frequently do we conduct penetration tests, audits, and incident simulations?
• What is our incident response plan, and how quickly can it be executed?
• Are cybersecurity and data governance KPIs embedded into executive performance evaluations?

These questions elevate cybersecurity from a technical discussion to a board-level governance concern.

Integrating cybersecurity into culture and talent strategy

Effective oversight requires more than policies; it requires embedding cyber awareness into organisational culture:

• Executive accountability: CEOs and CIOs must be responsible for implementation, with boards reviewing outcomes.
• Employee awareness: Continuous training reduces risk from human error and phishing attacks.
• Talent capability: Boards should assess whether the organisation has sufficient cybersecurity expertise at all levels.
• Cross-functional integration: Cyber and data governance should be connected with risk, compliance, and business strategy functions.

Culture is the often-overlooked defence layer — it is as important as technology.

Also Read: How cybersecurity crises are redefining corporate accountability

Board capabilities and education

Aspiring independent directors must demonstrate:

• Cyber literacy to understand key threats, mitigation strategies, and emerging technologies.
• Awareness of regulatory trends, including cross-border data flows and privacy compliance.
• Capability to challenge management assumptions while remaining constructive.
• Understanding of AI, cloud, and digital platforms as both opportunities and vulnerabilities.

Boards should periodically engage external advisors, conduct briefings, and participate in tabletop exercises to maintain readiness.

Conclusion: Cybersecurity and data governance as strategic imperatives

Cybersecurity and data governance are no longer IT issues — they are enterprise-wide, strategic imperatives. Boards that integrate these considerations into strategy, risk management, and culture:

• Protect enterprise value from financial and reputational loss
• Strengthen investor and stakeholder confidence
• Enable responsible digital transformation
• Ensure organisational resilience in an increasingly connected world

For Asian boards, the mandate is clear: cyber and data governance are now board responsibilities, not optional technical topics. Boards that lead here create both security and competitive advantage.

This article was first published on The Boardroom Edge.

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As AI’s energy consumption surges, concerns over its environmental impact grow. However, AI also offers solutions — optimising data centre cooling, managing smart grids, and reducing industrial energy waste. This article explores how AI-driven efficiency can help counterbalance its own power demands, creating a path toward more sustainable energy use.

AI-driven efficiency: Mitigating the carbon toll

While AI’s energy consumption is undeniably large, AI technologies also offer powerful tools to cut energy waste and emissions across many industries. From cooling data centres to optimising factory lines and smart grids, AI-driven efficiency gains can act as a counterweight to AI’s own power use. In essence, there is an opportunity for a positive feedback loop: using AI to save energy even as we use energy to run AI.

Some notable examples of AI-enabled efficiency breakthroughs:

• Data centre cooling optimisation: Google’s DeepMind cut data centre cooling energy by 40 per cent by predicting server loads and adjusting cooling in real time.
• Next-gen cooling technologies: Advanced cooling solutions, such as direct-to-chip liquid cooling, have been shown to reduce server energy use by ~30 per cent , with liquid cooling now used in up to 45 per cent of new European facilities.
• AI-managed micro-grids: In regions like Ohio and Texas, experimental micro-grids leverage AI to balance renewable energy with data centre power draw , cutting renewable curtailment by about 22 per cent.
• Industrial and building energy management: AI applications have helped Toyota reduce energy consumption by 29 per cent on certain manufacturing processes and enabled commercial buildings (such as 45 Broadway in Manhattan) to achieve nearly 16 per cent HVAC energy savings through intelligent controls.
• Building energy management: In commercial buildings, AI has shown impressive results in cutting power usage without sacrificing comfort. A notable case is 45 Broadway in Manhattan, where implementing an AI HVAC optimisation system led to a 15.8 per cent reduction in HVAC energy use. AI algorithms learned the building’s patterns and adjusted heating/cooling more intelligently. Similarly, AI-based controls for lighting and appliances can yield up to 30 per cent energy savings in buildings. Multiply these gains across millions of buildings and homes, and the potential energy savings are enormous.

These examples illustrate a hopeful counterpoint to AI’s energy appetite: the energy savings AI enables in other areas could, in theory, offset a significant portion of the energy AI consumes. Smarter grids, smarter buildings, smarter transportation (AI-optimised logistics, etc.) all contribute to lower overall demand.

A Shell analysis suggests AI applications could halve the carbon intensity of global energy by 2050 through such measures — coordinating renewables, improving efficiency, and innovating in materials (for example, using AI-driven design to create wind turbine blades that generate 40 per cent more power.

However, a critical question remains: Can AI’s energy-saving contributions catch up with its own growing consumption? This is the crux of the AI-energy paradox.

The AI-energy paradox: Do savings and consumption converge?

Right now, the net impact of AI on global energy is still an increase in demand. AI’s usage is growing so rapidly that efficiency gains, as valuable as they are, haven’t yet kept pace.

For instance, even as Google’s AI cut 40 per cent of cooling energy, the expansion of Google’s AI computing meant total energy use still rose. The near-term trend is divergence — AI driving more power use overall, despite localised savings.

Current figures bear this out. The US Department of Energy found that data centres (thanks largely to AI growth) consumed about 4.4 per cent of US electricity in 2023, and are on track to reach between 6.7 per cent and 12 per cent by 2028.

In other words, efficiency improvements are not projected to stop a doubling (or more) of data centres energy draw in the next five years.

A recent Electric Power Research Institute analysis likewise forecasts US data centres could hit nine per cent of national electricity use by 2030, up from ~four per cent today. Clearly, in the short run, AI’s footprint is outpacing the savings it enables elsewhere.

Also Read: A step-by-step guide to protecting your time and energy: The art of pre-qualification

Over the longer term, there is a possibility (not a guarantee) that the curves could converge. As AI matures, there’s intense research focus on efficiency: more efficient algorithms, specialised AI chips that deliver more performance per watt, better cooling, and so on. If each new generation of AI hardware is significantly more efficient, the growth in AI’s energy use could level off.

For example, tech firms are now prioritising energy efficiency over pure performance gains — a shift from the early “move fast” approach. Future AI models might be designed to be smaller or use smart techniques (like model sparsity or on-demand activation) that save energy.

Policymakers are also starting to push for convergence. The EU’s proposed AI Act will require large AI models to demonstrate 15 per cent energy efficiency improvements over previous generations — effectively slowing deployment of ultra-large models until they are more efficient (one reason rumours suggest GPT-5 might be delayed until such standards can be met). Governments may introduce carbon taxes or energy caps that make it economically unattractive to run wasteful AI systems, forcing innovation towards frugality.

So, will spending and savings converge? Optimistically, yes — but likely not until late this decade or beyond.

In a scenario where AI’s growth moderates and efficiency tech accelerates, we could see AI’s net impact plateau or even turn net-negative on emissions (especially if AI helps integrate huge amounts of renewables, as Shell’s scenario imagines.

But for the next 5-10 years, business leaders should plan for a world where AI means higher energy consumption and carbon output, and manage that reality accordingly.

The implication for corporates is twofold:

• Invest aggressively in AI-driven efficiency projects within your own operations (to capture savings that can offset your AI usage).
• Anticipate energy costs and capacity needs rising with AI, and incorporate that into everything from site selection (do your data centre/cloud regions have spare power capacity?) to vendor selection (choose partners with greener energy and efficient infrastructure).

In short, don’t assume the problem will solve itself. Proactive action is needed to bend the curve.

Accelerating the renewable transition to power AI

If AI is to spark a green energy revolution instead of exacerbating the crisis, a massive scale-up of clean energy is required. Renewables (solar, wind, hydro) need to grow in tandem with AI compute demand, and AI can be a catalyst to accelerate that growth. But it won’t happen automatically; it requires strategic investments and innovation.

On the plus side, AI is already helping get more out of renewables. We saw how AI can optimise wind and solar output (e.g. smarter inverters yielding 18 per cent more solar farm efficiency. AI can forecast weather and adjust operations to maximise renewable energy capture and reduce downtime.

For instance, autonomous AI-driven networks of electric vehicle (EV) chargers can collectively act as a 450 GWh battery for the grid, smoothing out renewable fluctuations by intelligently timing charging. AI is also being applied to breakthrough research — like using quantum computing and AI to design advanced materials for solar panels or wind turbines, potentially boosting their efficiency dramatically.

However, even optimistic efficiency gains won’t fully bridge the gap. The scale of new clean power needed is enormous.

A McKinsey study estimates that in Europe alone, an additional US$250-300 billion in grid infrastructure upgrades will be required by 2030 to handle 150 TWh of new AI-related electricity demand and connect enough renewables to supply it.

This includes new transmission lines, grid storage, and smarter distribution — essentially building a bigger, smarter grid to feed AI. Without such investment, renewable deployment could lag and AI would end up being powered by whatever is available (often coal or gas).

To put numbers on it: The world added about 300 GW of renewable capacity in 2022. If AI demand is rising by hundreds of TWh, we likely need to add hundreds more GW of renewables per year on top of current plans just to keep AI from increasing fossil fuel use.

Policymakers are starting to respond — the US Inflation Reduction Act, Europe’s Green Deal, China’s massive renewables build-out — all boost clean energy, which indirectly supports AI’s growth sustainably. But targeted actions may be needed, such as incentives for energy-intensive tech firms to directly finance renewable projects (as Microsoft is doing).

Also Read: Why the future of space and energy storage might be growing in a Thai hemp farm

One promising idea is direct clean power procurement for AI infrastructure. Instead of buying offsets or generic renewable credits, companies can invest in additional renewable generation that is tied to their data centres. Google has been a leader here, aiming for “24/7 carbon-free” energy by sourcing clean power in every hour and region that its servers operate. Other firms are now looking at similar models, which could drive significant new solar/wind development.

In summary, AI can accelerate the renewable transition — by necessity and by capability. It provides a strong business motive (big tech needs clean power, so they’ll fund it) and new tools (AI to optimise renewable performance). But it also raises the stakes: if renewables don’t scale fast enough, AI will end up entrenching fossil fuel use at exactly the wrong time for the climate.

For corporate leaders, this means aligning AI strategy with energy strategy. Embrace AI projects that further sustainability (smart grid, energy optimisation) and be cautious of AI expansions that outpace your access to green power. Seek partnerships in the energy sector — for example, co-develop a solar farm or wind park that can power your AI workloads. Those who proactively secure clean energy for AI will not only mitigate environmental impact but also hedge against future carbon regulations or fossil price volatility.

Geopolitical and economic crossroads

AI’s energy demands are now a factor on the geopolitical chessboard. Nations are racing to support their tech industries with reliable power (often in competition with climate goals), and energy dependencies are influencing tech policies. Three major theatres highlight this dynamic: the US-China tech competition, Europe’s regulatory balancing act, and emerging markets vying for data centre investments.

The US-China tech war’s energy dimension

China and the United States are both pouring billions into AI, and with that comes a hunger for energy. China has launched an “East Data, West Computing” initiative, investing an estimated US$75 billion to build huge data centre hubs in its inland provinces. Why inland? Because electricity is cheaper there — for example, coal-rich Inner Mongolia offers industrial power rates around US$0.03 per kWh, among the lowest in the world.

By situating AI data centre next to coal plants in the interior, China can fuel its AI growth at low cost (albeit with high emissions). This strategy effectively leverages China’s vast coal infrastructure to gain an edge in computing capacity.

Meanwhile, the US is responding with investments to support AI hotbeds at home. The Department of Energy recently announced US$2 billion for grid upgrades focused on “AI corridors” like Northern Virginia and Ohio. This includes improving transmission and reliability to ensure these regions (where many US cloud data centres cluster) can handle the increased load without blackouts or slowdowns. It’s essentially an infrastructure subsidy to keep US AI development on track and independent of energy bottlenecks.

There’s also a security aspect: both nations view leadership in AI as strategic, so ensuring the energy security of AI facilities is crucial. This could lead to more efforts like backup gas peaker plants for key data centres, or even dedicated small nuclear reactors, to immunise critical AI infrastructure from grid disruptions or fuel supply risks. In a hypothetical future standoff, a country that cannot power its AI systems reliably would be at a serious disadvantage.

Europe’s cautious approach

Europe, in contrast, is trying to chart a path that prioritises sustainability — but at the risk of dampening its AI momentum. The EU’s proposed regulations (like the AI Act) not only address ethics but also efficiency. As noted, the AI Act could effectively delay deployment of power-hungry models (e.g., next-gen GPT) until efficiency targets are met.

Also Read: How we generated 100+ leads on zero budget

Additionally, some European countries have taken hard stances on data centre growth due to energy concerns. Ireland’s moratorium on new Dublin-area data centres, for instance, was driven by fears that the national grid couldn’t meet both climate targets and a surge in data centre demand. That moratorium led companies to shift investments to places like Poland and Norway where power is more available.

The consequence is that Europe risks falling behind in AI infrastructure. While US and China race ahead with massive builds (regardless of carbon cost), Europe’s combination of slower cloud growth and higher energy prices could make it less attractive for AI development.

Some experts warn of a potential “digital drift” where European AI innovation migrates to more energy-abundant shores. On the other hand, Europe’s emphasis on efficiency and green power could pay off in the long run, yielding more sustainable operations that align with global climate imperatives (and avoid future regulatory penalties).

Global energy markets and AI investment

It’s not just the big three (US, China, EU). Around the world, countries are jockeying to attract data centre and AI investments — and energy is the key bargaining chip. For example, countries like Norway, Sweden, and Canada promote their abundant renewable energy (hydropower, wind) and cold climates (natural cooling) as ideal for sustainable AI data centres. Norway has lured several major projects by offering 100 per cent renewable power and low cooling costs, appealing to companies with net-zero commitments.

In Asia, Singapore has imposed a temporary freeze on new data centres due to energy and land constraints, then lifted it in favour of a selective policy favouring the most efficient, green designs. India and Indonesia are pitching themselves as emerging data centre hubs, but they’ll need to rapidly expand grid capacity (and ideally renewables) to deliver on those ambitions.

The energy crisis of 2022 (with spiking fuel prices) was a wake-up call for many: any country that wants to be an AI/cloud hub must ensure cheap, reliable power. This has geopolitical implications: nations rich in clean energy (like Iceland or Quebec with hydro, or Middle Eastern countries with solar + land for data centres) could play a bigger role in the digital economy by hosting energy-intensive AI computation. It’s a new twist on the resource competition of the past — instead of oil or minerals, it’s about attracting “computational industry” with the promise of low-cost electrons.

In summary, leaders need to be aware that AI isn’t happening in a vacuum — it’s intertwined with global energy and policy currents. Decisions about where to site AI operations, which markets to enter, or even which governments to partner with may hinge on energy availability and regulations.

Businesses at the cutting edge of AI should engage in policy discussions: for example, advocating for incentives for clean power or workable regulations that encourage efficiency without stifling innovation.

This is part two of a three-part series exploring AI’s energy impact. Read part one here. 

Part three of this series looks at the emerging solutions — tech and policy — that could put AI on a more sustainable path, and how companies can harness them.

This article was originally published here and co-authored by Xavier Greco, Founder and CEO of ENSSO.

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Singapore is doubling down on its ambitions to become Asia’s undisputed payments capital, as a new industry report paints the city-state as one of the world’s most advanced digital and cross-border payments hubs.

The Singapore FinTech Association (SFA), together with PwC Singapore, has launched “Payments’ State of Play 2026”, a sweeping review of how the island nation’s payments ecosystem has evolved over the past decade, and where it is headed next.

Also Read: Fintech rebound: Singapore bags US$1.04B, outpaces global peers

The report argues that Singapore’s rise has been driven by a rare combination of progressive regulation, strong foundational infrastructure, high consumer demand for seamless digital experiences, and close public-private collaboration. What began as basic payment rails has now matured into one of the most sophisticated payment markets globally.

Digital payments dominance and record funding momentum

One of the most striking findings is Singapore’s scale of digital adoption. More than 98 per cent of adults are banked, while real-time payments and digital wallets increasingly dominate everyday transactions.

Digital wallets alone are projected to process US$66 billion in online and point-of-sale transactions by 2027, underscoring how cashless behaviour has become deeply embedded in the country’s economy.

Investor confidence has also remained resilient. The report notes that the city-state’s payments sector raised over US$319 million in funding in the first nine months of 2025 — surpassing the combined fintech funding totals of Indonesia, Malaysia, the Philippines, Thailand, and Vietnam.

Real-time rails powering the ecosystem

Singapore’s domestic payments infrastructure continues to scale rapidly, led by systems such as PayNow and FAST.

FAST transaction volumes hit 500 million in 2024, representing a 31 per cent year-on-year increase, as real-time transfers become the default for consumers and businesses alike.

Card payments also grew strongly, with total value rising at a compound annual growth rate (CAGR) of 12.9 per cent from 2020 to 2024. E-money value expanded at a CAGR of 7.3 per cent over the same period, despite a slight decline in transaction volume.

E-money growth and the global wallet boom

Singapore’s digital payments market is expected to accelerate further. Total transaction value reached US$39.37 billion in 2023 and is forecast to climb to US$113.65 billion by 2030.

Also Read: Singapore’s SME fintechs face growth hurdles amid restricted API access

E-money transactions are projected to rise steadily to US$4.28 billion by 2028, supported by AI adoption, embedded finance innovation, stronger stablecoin regulation, and expanding cross-border payment networks.

This trajectory mirrors a wider global shift, with mobile wallet transactions forecast to surge to an estimated US$17 trillion by 2029.

Cross-border connectivity as a regional differentiator

Singapore is also positioning itself as a key settlement and connectivity hub for Asia. Initiatives such as Project Nexus, alongside PayNow linkages with Thailand and Malaysia, are strengthening the city-state’s leadership in cross-border real-time payments.

Total remittance volume reached US$8.05 billion in 2022 and is expected to grow to US$13.34 billion by 2032, representing a CAGR of 5.2 per cent.

Stablecoins, digital assets, and Singapore’s FX strength

The report highlights Singapore’s rising influence in digital assets, particularly stablecoins. The city-state now accounts for over 70 per cent of Southeast Asia’s non-USD stablecoin market pegged to the Singapore dollar, supported by the Monetary Authority of Singapore’s globally recognised regulatory framework.

Singapore is also reinforcing its status as a major foreign exchange hub. The country is now the world’s third-largest FX trading centre, with average daily trading volumes climbing to US$1.485 trillion in April 2025 —  a 60 per cent increase from April 2022.

Holly Fang, President of the Singapore FinTech Association, said, “Over the past decade, Singapore has developed one of the most advanced, resilient, and trusted payments ecosystems in the world.”

She added that progressive regulation and industry collaboration have
positioned Singapore as a leader in real-time and cross-border payments, while also confronting fraud and scams head-on.

PwC Singapore Partner Wong Wanyi echoed this view, noting, “Payments are evolving rapidly, led by technology and emerging realities, while also presenting new risks.”

Also Read: Singapore’s regulatory vision is shaping cross-border payments in Asia: Report

She emphasised that sustaining Singapore’s leadership will require strong risk management frameworks and regulatory clarity that encourage innovation while building trust.

The next wave: AI, embedded finance, and consumer protection

Looking ahead, the report identifies several trends shaping the next phase of payments innovation:

• Embedded finance and super apps, integrating lending, investment, and payments into everyday platforms
• AI-powered payments, enhancing fraud detection and optimising processing
• Tokenised deposits and regulated stablecoins, expanding use cases in domestic and cross-border payments
• Greater interoperability, driven by regional initiatives like Project Nexus
• Stronger consumer protection, amid escalating scam risks

Fraud remains a pressing challenge. As of November 2025, scam-related losses in Singapore reached US$620 million, close to the US$812 million recorded across the whole of 2024 — underscoring the urgency for coordinated action across the ecosystem.

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Strategic collaborations between research institutions and startups are reshaping the innovation landscape, unlocking new opportunities for growth and delivering meaningful societal impact. These partnerships allow scientific and academic entities to access commercialisation channels and adopt more agile development approaches, while startups benefit from resources and industry expertise needed to scale their innovations effectively.

Many early-stage startups look for the first business partners among corporate players. Yet, challenges remain—according to a Boston Consulting Group survey, 45 per cent of corporations and 55 per cent of startups express dissatisfaction with their partnership experiences, highlighting a gap that science organisations are uniquely positioned to bridge—connecting groundbreaking research with viable business models.

To appreciate the scale of innovation in Southeast Asia, consider this: the region is home to 63 unicorns—companies valued at US$1 billion or more—with over 124,450 startups in total based there as of May 2025.

Around the world, innovation ecosystems are expanding rapidly, with millions of new startups launching annually across regions in North America, Europe, and Asia. Despite this growth, the disconnect between startups and research organisations remains a common obstacle, and the tangible benefits to businesses remain modest. All stakeholders within the innovation ecosystem stand to gain by strengthening these partnerships to better fulfil their promise for society and the economy.

Below are three key benefits to explore.

Enhancing research and development (R&D)

For startups looking to strengthen their R&D efforts by partnering with scientific institutions, there are three key areas to focus on: aligning innovation goals at the project level, establishing clear and open communication channels, and setting precise collaboration expectations within agreements.

Getting everyone aligned on innovation goals at the project level is absolutely crucial. In my experience mentoring startups, many partnerships start with broad, high-level objectives but don’t drill down into specific outcomes for each project. The most successful collaborations are those that sync goals not just strategically, but also at the day-to-day operational level. Using digital tools and collaborative platforms can make this much easier, helping teams coordinate in real time and maintain shared visibility.

Also Read: New research report: The nexus between elite university education and startup funding

Effective communication forms the backbone of any successful partnership, yet transparency often falls short. Issues such as siloed information systems and conflicting priorities can quickly lead to misaligned expectations and wasted resources.

To prevent this, partners should prioritise full visibility into project progress, ensuring that everyone involved has access to accurate, detailed updates—whether by project phase, team, or milestone. Centralising collaboration workflows and clearly understanding associated costs further build trust and accountability.

Equally important is tailoring incentives specifically to joint efforts. Too frequently, research institutions and startups focus on broad research milestones instead of concrete, shared deliverables. This misalignment can cause partners to pursue individual goals rather than common objectives, resulting in resource imbalances where some areas are overstretched while others remain underutilised. Clear, outcome-focused incentives help maintain commitment to the partnership’s overall success.

The Natural Resources Institute Finland (Luke) offers an example of a European research organisation focused on sustainable development through renewable natural resources. Luke conducts extensive research and development across forestry and bioeconomy, supporting both national and international projects.

It provides access to advanced research infrastructures such as greenhouses, research fields, and laboratories, enabling high-quality experimental work. Luke also coordinates the European research infrastructure AnaEE (Analysis and Experimentation on Ecosystems), fostering collaboration and knowledge sharing across countries. Through its involvement in numerous partnerships, Luke plays a key role in turning scientific insights into practical solutions that promote sustainability and well-being.

Fast-tracking commercialisation

Accelerating commercialisation is often the missing piece when startups and research institutions join forces. While both sides excel at innovation, the actual process of getting new ideas to market can get lost in the shuffle. By working together more closely—sharing resources, knowledge, and a unified vision—the journey from discovery to product becomes more efficient and streamlined. This collaboration helps prevent common setbacks such as conflicting priorities, wasted efforts, and delays that can hinder promising technologies.

A concrete example of such effective collaboration is Turion Labs, which recently opened in Singapore as the region’s first comprehensive biotech innovation platform. This joint venture, supported by Korea’s S&S LAB and Indonesia’s Future Lestari, offers modular lab spaces, contract research services, and regulatory assistance within a unified framework.

Turion Labs aims to connect promising scientific research with practical paths to commercialisation. It supports startups and biomedical companies by providing access to advanced laboratory facilities alongside Korean research expertise and Southeast Asian markets. This initiative reflects the growing trend in Southeast Asia to develop collaborative innovation centers that bring together research and industry to help advance biotech development in the region.

Also Read: Nagoya University: Asia’s extensive network of innovation, research, and education

What makes these partnerships work is flexibility. The most successful collaborations aren’t rigid—they adapt to the needs of each project and each team. Startups and research institutions that prioritise both innovation and business efficiency find ways to share risk and align goals, while keeping lines of communication open. This approach is especially important as startups play an ever-larger role in commercialising high-impact innovations.

Uniting diverse talents

Navigating partnerships between science organisations and startups isn’t just about having the latest tech at your fingertips—it’s about bringing together the right people and perspectives. Technology can certainly make collaboration easier, but it’s not a cure-all. The real magic happens when the deep technical know-how of researchers meets the entrepreneurial drive of startup founders, creating space for meaningful innovation.

Still, even with all the collaboration tools available today, many partnerships fall short of their potential. Two issues tend to crop up again and again. First, organisations often jump into new systems without rethinking how they actually work together—like installing state-of-the-art software but sticking to old, inefficient habits. Second, when project goals aren’t clear and data isn’t aligned, teams can end up working at cross purposes, slowing down the move from idea to market.

Good management can make all the difference here. The most effective collaborations bring together cross-functional teams—researchers, entrepreneurs, and other key players—who regularly check in on progress and keep everyone focused on shared milestones. Setting clear, measurable targets keeps things on track and helps spot issues early.

Compelling examples of collaboration between research labs and startups can be seen at the University of Eastern Finland, where joint efforts have led to innovative photonics applications for consumer electronics.

Similarly, the National University of Singapore has partnered with startups through a dedicated program focused on flexible electronics and hybrid systems, driving the development of advanced consumer electronics technologies. These partnerships highlight how academic institutions and startups are working together to push the boundaries of innovation in the consumer electronics sector.

Also Read: Bridging the digital divide: Addressing Malaysia’s skills gap

By combining academic expertise with startup agility, these collaborations have rapidly advanced from lab prototypes to market-ready products.

Starting point

One effective way to kick off collaborations between startups and research institutions is by gaining a thorough, project-level understanding of the partnership landscape. Once that foundation is in place, partners can use a collaboration health map to spot inefficiencies and opportunities at various stages—whether it’s during prototype testing or preparing for market launch.

This kind of tool helps leaders identify the root causes behind common challenges such as misaligned goals or wasted resources. With those insights, they can roll out targeted actions that address the real problems, rather than treating surface symptoms.  Moreover, this approach helps ensure that improvements are sustainable and don’t fade over time.

By adopting these strategies, startups and science organisations can work more smoothly together and unlock greater value for everyone involved. Of course, the exact approach will vary depending on each partnership’s goals and setup. But no matter the details, taking a proactive stance on managing collaboration can lead to smarter decisions and stronger, more rewarding partnerships.

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