Job hopping has become a popular career strategy among Millennials and Gen Z, who are drawn to the idea of rapid progression and diverse experiences across multiple companies. In contrast, traditional internal mobility programs, long established by Fortune 500 companies, offer structured role rotations, leadership training, and paths to advancement.

With rapid technological changes driving efficiency—and, in turn, more layoffs—many employees are focusing on self-directed career mobility to stay relevant and secure. Below, we’ll explore the pros and cons of each approach and provide practical strategies for both employers and employees.

Job hopping as a path to career mobility

Pros

• Broader Skillset and Learning Opportunities: By switching roles and industries, young professionals can build a more diverse skill set faster than through internal rotations alone. This is especially valuable in dynamic fields like AI, Fintech, and Web3, where staying updated on emerging technologies is essential to career progression. Job hopping enables them to engage with a variety of projects, which promotes rapid adaptability.
• Higher Earning Potential: External moves often come with significant salary increases and better benefits, offering a straightforward way for employees to boost their compensation without waiting for internal promotions. In competitive job markets, financial growth is a strong motivator, especially for younger workers facing high living costs.

Cons

• Lack of organisational depth: Moving frequently can limit an individual’s ability to gain a deep understanding of any single company’s culture, strategy, and operations. Traditional organisations emphasise the value of comprehensive internal knowledge, which is often essential for long-term leadership roles. Job hopping may sacrifice this depth for breadth, which can impact an employee’s trajectory toward senior roles.
• Potential for instability: While job hopping can be advantageous in the short term, it can signal a lack of commitment to potential future employers. In economic downturns, job hoppers may be at a disadvantage compared to employees with longer tenures, as they are sometimes perceived as less reliable. This instability can be a drawback for those seeking greater career resilience.

Also Read: Cultural intelligence (CQ): The key to unlocking success in global workspaces

Advantages of traditional internal mobility programs

Pros

• Structured growth and organisational knowledge: Internal mobility programs, particularly those tailored for High Potential (HIPO) employees, offer a clear pathway for progression within the company. These programs emphasise cross-functional rotations, leadership development, and mentorship, helping employees build deep organisational knowledge and long-term relationships that enhance their career within the company.
• Long-term stability and loyalty: These programs foster loyalty and offer stability, aligning employees’ career goals with the company’s strategic direction. Employees who grow within an organisation are often more invested in its success, which can lead to a more secure career path. This stability is especially appealing to those who prioritise long-term career growth over rapid role changes.

Cons

• Slower advancement and limited flexibility: Internal programs can sometimes lack the agility young professionals seek, as they tend to operate within established promotion cycles and budgets. This can lead to a slower pace of career progression compared to job hopping. Additionally, these programs may limit exposure to new skills and areas of expertise outside the employee’s immediate department or function.
• Vulnerability to technological disruptions: As companies implement AI and automation, roles are becoming more streamlined, often resulting in layoffs. Even loyal employees in internal mobility programs may face job insecurity, as companies increasingly prioritise efficiency. This reality pushes some employees to prioritise self-driven career mobility, including job hopping, to mitigate the risk of redundancy.

Strategies for employers: Retaining key talent

• Create flexible, project-based mobility options: By offering short-term project roles across departments, companies can provide diverse learning opportunities without requiring a complete role change. Project-based work allows employees to experience new areas of the business, addressing the desire for variety while retaining talent within the organisation.
• Invest in continuous skill development: Implement programs that emphasise both technical and soft skills training, encouraging employees to upskill in areas aligned with company goals. Companies can provide training on emerging technologies, leadership, and project management, which can help employees feel valued and foster a culture of learning.
• Develop clear and accelerated career pathways: Introduce merit-based fast-track programs for high performers that provide recognition, bonuses, and leadership roles as they demonstrate potential. Employees will be more likely to remain engaged and committed when they see tangible growth opportunities within the company.
• Enhance communication on career progression: Ensure that managers hold regular one-on-one discussions with employees about their career goals and available opportunities. Transparency about internal mobility options and promotion criteria can help employees feel empowered to take charge of their growth without needing to look elsewhere.

Also Read: 5 lucrative strategies Gen Z investors use to empower themselves financially

Strategies for employees: Achieving career goals within large organisations

• Seek out cross-functional projects and assignments: Request stretch assignments or temporary roles on cross-functional teams to broaden your skill set without changing departments. Engaging in these projects can provide valuable exposure to other parts of the business and build connections that support future growth.
• Focus on skills, not just titles: Prioritise developing skills that align with industry trends and the company’s goals. Stay informed about key technologies and initiatives in your field and pursue relevant training. Skills-based growth helps you stay adaptable and positions you for advancement, whether or not it’s tied to a specific title.
• Proactively manage your career path: Communicate your career aspirations to your manager and seek mentors who can guide you in navigating internal opportunities. Express interest in lateral moves or learning new skills, demonstrating that you’re invested in growth within the company.
• Take advantage of company resources: Many large organisations offer learning resources such as online courses, workshops, and conference sponsorships. Maximise these opportunities to keep your skills relevant and demonstrate commitment to ongoing development. This approach ensures that you are continually progressing, even without external moves.

Building a balanced approach to career mobility

While job hopping offers rapid financial growth and skill diversification, traditional internal programs provide stability, long-term growth, and a deep understanding of organisational dynamics.

For companies, the challenge is to make internal mobility programs more responsive to the needs of a younger workforce, offering flexibility, variety, and timely progression. For employees, a focus on skill development, proactive career management, and engagement in cross-functional opportunities can enhance career growth within a single organisation.

In today’s fast-evolving job market, a balanced approach benefits both employees and employers, supporting agility, loyalty, and the continuous development of tomorrow’s leaders.

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In the ever-evolving landscape of technological advancements, Asia stands at the forefront, pioneering new and transformative technologies. One of the most significant areas of innovation is genetics AI—a fusion of artificial intelligence and genetics research.

This groundbreaking convergence is revolutionising the continent’s healthcare, agriculture, and bioinformatics. From personalised medicine to sustainable agriculture, genetics AI is shaping a future where technology and biology work hand in hand to solve some of humanity’s most pressing challenges.

The genesis of genetics AI

Genetics, the study of genes and heredity, has been revolutionised by advancements in AI. AI’s ability to analyse vast amounts of data with high precision has enabled researchers to uncover complex genetic patterns and correlations that were previously elusive. This synergy is particularly potent in fields like personalised medicine, agricultural biotechnology, and evolutionary biology.

The integration of AI in genetics is not a recent phenomenon. However, its rapid development and application in Asia have marked a new era. Countries like China, Japan, South Korea, and Singapore have heavily invested in AI research and development, recognising its potential to unlock new genetics insights.

In genetics, AI algorithms can process vast amounts of data far more efficiently than traditional methods. This capability is crucial, considering the complexity of genetics information. Genomics, the study of an organism’s complete set of DNA, involves analysing millions of base pairs, identifying mutations, and understanding gene functions. AI’s ability to handle and interpret this data is accelerating discoveries and applications in genetics.

The rise of genetics AI in Asia

Asia’s rapid technological advancements and substantial investments in scientific research have created a fertile ground for innovations in genetics and AI. Here are some leading countries in the charge, each contributing uniquely to this emerging field.

China: A powerhouse of genetics research

China’s significant investments in genetics and AI, exemplified by companies like BGI Group, have propelled it to the forefront of global biotechnology. With extensive biobanks and AI-driven analytics, China is advancing the understanding of genetics diseases and developing targeted therapies. AI’s role in machine learning and data processing enhances genetics research, leading to precision medicine where treatments are tailored to individual genetics profiles.

Japan: Innovating at the intersection of robotics and genetics

Japan leverages its expertise in robotics and AI to enhance genetics research. AI accelerates gene editing processes and optimises the differentiation of stem cells for regenerative medicine. This approach has significant implications for treating spinal cord injuries and degenerative diseases. AI algorithms predicting CRISPR outcomes exemplify Japan’s innovative integration of technology and genetics.

Also Read: Is generative AI the game-changer for productivity?

South Korea: Bridging genomics and AI

South Korea’s advanced digital infrastructure and AI research drive genetics AI innovations. The country’s extensive health data repositories enable AI to uncover genetics disease insights and develop new diagnostics and therapies. South Korea also leads in AI-driven drug discovery, using genetics data to identify drug targets and accelerate the development process.

Singapore: A hub for biomedical innovation

Singapore’s strategic investments in biomedical research position it as a key player in genetics AI. Initiatives like the National Precision Medicine Program utilise AI to analyse genetics data and identify disease biomarkers. Collaborative efforts between academia, industry, and government drive innovative solutions in cancer genomics, infectious diseases, and aging, ensuring rapid application of scientific discoveries to clinical practice.

India: Advancing agricultural biotechnology

India is utilising genetics AI to revolutionise agriculture. AI-driven gentics research develops high-yield, climate-resilient crop varieties, enhancing food security. This approach addresses challenges posed by climate change and population growth, ensuring sustainable agricultural practices and improved crop yields.

Taiwan: Leading in precision medicine

Taiwan’s focus on precision medicine integrates AI with genetics research to develop personalised treatments. AI analyses genetics data to predict disease risk and guide preventive measures. Taiwan’s healthcare initiatives aim to provide tailored therapies based on individual genetics profiles, improving patient outcomes and reducing healthcare costs.

Applications of genetics AI in Healthcare

The applications of genetics AI in healthcare are vast and transformative. From early disease detection to personalised treatment plans, AI-driven genetics research is revolutionising medicine.

Early disease detection

AI algorithms can analyse genetics data to predict the risk of hereditary diseases. By identifying genetics markers associated with conditions like cancer, diabetes, and cardiovascular diseases, genetics AI enables early detection and intervention. This proactive approach can significantly improve patient outcomes and reduce healthcare costs.

Personalised medicine

One of the most promising applications of genetics AI is personalised medicine. By analysing an individual’s genetics profile, AI can recommend tailored treatment plans that are more effective and have fewer side effects. This approach is particularly beneficial for patients with complex conditions like cancer, where traditional treatments may not be effective.

Drug development

Genetics AI is also transforming drug development. AI-driven analysis of genetics data can identify potential drug targets and predict how patients will respond to new treatments. This accelerates the drug development process and increases the likelihood of success in clinical trials.

Also Read: Cybersecurity in the AI age: How startups can stay ahead

Challenges and ethical considerations

While the potential of genetics AI is immense, several challenges and ethical considerations must be addressed to ensure its responsible and equitable use.

Data privacy and security

The collection and analysis of genetics data raise significant privacy and security concerns. Ensuring that genetics information is stored securely and used ethically is paramount. Governments and organisations must establish robust data protection frameworks to safeguard individuals’ genetics data.

Ethical implications

The use of fenetics AI also raises ethical questions related to genetics discrimination and informed consent. It is crucial to develop guidelines that prevent the misuse of genetics information and ensure that individuals are fully informed about how their data will be used.

Accessibility and equity

Ensuring equitable access to the benefits of genetics AI is another challenge. There is a risk that advanced genetics treatments may only be accessible to wealthy individuals or countries, exacerbating existing health disparities. Efforts must be made to make these technologies affordable and accessible to all.

Future prospects: A new era of innovation

The future of genetics AI in Asia looks promising, with ongoing research and development poised to unlock even greater potential. As technology continues to evolve, so too will the applications of genetics AI. Collaborative efforts between countries, institutions, and private companies are crucial for advancing this field and ensuring that its benefits are realised across the continent.

In healthcare, the continued integration of AI and genetics will lead to more personalised and effective treatments. Advances in genomics will enable early detection and prevention of diseases, improving healthcare outcomes for millions of people.

In agriculture, the development of AI-driven genetics technologies will enhance food security and sustainability. By creating crops that are more resilient and nutritious, Asia can address the challenges of climate change and ensure a stable food supply for its growing population.

In bioinformatics, the fusion of AI and genetics will lead to groundbreaking discoveries in biology and medicine. By analysing genetics data on an unprecedented scale, researchers will uncover new insights into human biology, leading to the development of innovative therapies and diagnostics.

 Conclusion

Asia’s pioneering efforts in genetics AI are shaping the future of technology, healthcare, and genetics research. The region’s advancements in precision medicine, genetics editing, and genomic research are setting new benchmarks for the global scientific community. By leveraging AI to unlock the potential of genetics data, Asian countries are driving innovations that promise to transform healthcare and improve lives.

As the integration of AI in genetics continues to evolve, Asia’s leadership and commitment to ethical practices will play a crucial role in realising the full potential of this transformative technology. The future of genetics AI in Asia is bright, with ongoing advancements poised to revolutionise our understanding of genetics and usher in a new era of personalised medicine and genetics innovation.

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This article was first published on August 5, 2024

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Healthtech startups come in many forms. You have Electronic Health Record (EHR) platforms, at-home test kits and AI image analysis tools, to name a few. Spend enough time speaking with healthtech founders, though, and you will soon realise that no matter the sub-sector, most of them are playing towards the same endgame; to accumulate sufficient and sufficiently high-quality data to be of interest to major stakeholders of the healthcare ecosystem.

Data is the new oil, they say, and in the world of tech, drilling has been fueled by the twin forces of Venture Capital (VC) and the growing abundance of connected devices.

But how similar are oil and data, really? And what can their similarities and differences teach us, especially in the emerging healthtech sector in Southeast Asia, where valuations are rising but exits remain somewhat unproven?

Different machines, different strategies, different data

As when drilling for oil, the equipment itself is of paramount importance. Different acquisition methods will predispose startups to accumulate certain types of data.

Startups selling consumer-grade DNA tests, for example, might gather huge amounts of direct, first-party genetic data in a short period of time. But such data will also likely be episodic (from a single point in time), which is less appealing and useful to insurance and pharma companies compared to longitudinal data (from the same patient over a period of time).

Besides the data from analysing test kits, medical history is usually collected as part of the process. However, the information is usually self-reported by consumers through online surveys and, therefore, patchy and less reliable.

This is why some companies are starting to offer complementary services, like genetic counselling, that enable them to build longer-term, repeated patient interactions and acquire data from that same patient over time.

On the flip side, startups focusing on EHRs, especially in emerging markets, will likely struggle with their initial go-to-market. Driving EHR adoption can be challenging as it requires convincing entire clinics and/or hospitals to overhaul legacy systems and implement software to manage financial, clinical, and administrative operations.

Also Read: Traveloka ex-CMO’s healthtech startup Diri Care closes US$4.3M seed round

The raw data acquired, however, will likely be longitudinal and more reliable as they are collected from clinical tests done by the same patients rather than primarily self-reported. While the initial onboarding can be challenging, there are potential mitigants like easy onboarding for care settings trying to adopt an EHR for the first time and partnership agreements with data exclusivity terms.

Everyone has the same end goal of data aggregation, but there are different means of getting there. In the end, though, it all comes down to three attributes: breadth, depth, and exclusivity. As in, the breadth of the data set when it comes to population size and demographic diversity, the depth of each patient’s healthcare profile, and exclusivity in terms of access and ownership to more unique data.

The rig operators and rig operability

The second consideration is the human element. Who operates the rig has a huge impact on whether the machine is used to its full potential. We think about usability in two ways.

First, user experience encourages usage among trained medical staff. In theory, workflow software and diagnostic support algorithms can save physicians a lot of time through automation.

In reality, however, automation is not as useful if the number of conditions that can be identified and diagnosed by the algorithm is limited. For example, take an AI tool that helps diagnose lung cancer. Radiologists still have to spend the same amount of time examining each scan or X-ray to check for possible conditions that the AI can’t identify.

In the end, adopting these diagnostic tools can be challenging if the new technology doesn’t add much to the existing workflow of medical professionals.

Second, technology enables us to tap into lower-skilled resources. AI guidance is especially helpful in ultrasound, where operator skills can impact results. Unlike MRIs or X-rays, ultrasounds are taken using a wand held by an operator, who decides the angle and depth from which the recording is taken.

With AI-powered workflow software that can tell you whether the device is placed correctly and guide you step-by-step, even untrained staff that are unfamiliar with taking echos can use the machine. Such software can also produce high-quality and therapeutic-area-specific data, though access to and exclusivity to quality data at scale depends greatly on partnerships with medical institutions and providers.

These features are highly valuable, especially in rural areas in Southeast Asia cities that have limited access to specialised expertise and equipment. For healthtechs operating in this area, they would need to look at partnership agreements that allow them to continue to commercialise their algorithm, which was built based on borrowed data during the partnership.

The data refinery: From raw to useful

Data preparation is a key next step to ensure the final product can be useful to the acquirer. In this case, we’re talking about the big players in the healthcare ecosystem: large medtechs, clinical research organisations, pharmaceutical companies and insurers. Instead of raw data, they want their data sets cleaned, curated, and structured, ready to answer the questions they want to ask of it.

But how much are they willing to pay for that data? That depends as the potential use case for the data influences its premium in price. Exits have been few and far between, but some examples we’ve found include general EHR/claims data ranging from US$15 to US$50 per record and genomic data ranging from US$2,900 per record for general data to US$26,000 for oncology-focused data.

Also Read: How mental health startup Intellect’s founder catalysed his personal battle with anxiety

These examples are a good starting point for us to understand how and where premiums accrue across different data types. At first glance, we can see how genomic data is a hotter commodity than EHR data. Still, oncology-focused data sets are more in demand than less curated general data.

When data is not oil

Unlike crude which gets processed and separated, data becomes more valuable when amalgamated and layered on top of each other. Another point we should make is around the reusability of data and how it affects the price.

Simply put, reusability is largely determined by ownership rights and exclusivity. Who gets to mine the data? Who gets access to the mined data?

Although data wells are pretty much inexhaustible, different rigs mining from the same well over and over again commoditise the data extracted, resulting in lower prices.

At the other end of the spectrum, we can see that precision health companies that own and guard the gates to the genomic data that they harvest enjoy a frothy price premium. Ultimately, it’s about controlling the access to high-demand supply.

Putting it all together

Now, back to the overarching question, we discussed at the start: how does everything we’ve discussed translate to exits for healthtechs in Southeast Asia? While there’s no straightforward answer, we can start to piece together some rules of thumb on how we can think about it.

In order to reach the endgame of accumulating sufficient, and sufficiently high-quality data, healthtechs that accumulate data across the three buckets of breadth, depth, and exclusivity are surely heading in the right direction. Ultimately, however, we think that the key to healthtech exits will come down to breadth even as depth and exclusivity are table stakes.

Achieving regional breadth is likely the most challenging to accomplish out of the trifecta and, therefore, will be the biggest differentiator among healthtechs, especially in Southeast Asia, where there’s great cultural, infrastructural, and political diversity.

Whoever manages to build an oil rig that taps on the many wells across the region will stand a much better chance of getting the attention of these global healthcare giants.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic

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This article was first published on September 13, 2022

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Artificial Intelligence (AI), heralded as a silver bullet solution, has rapidly permeated diverse sectors and the blockchain realm is no exception. Given the Asia-Pacific region’s concurrent leadership in AI development, it comes as no surprise that APAC will become a hotbed for innovative AI-blockchain hybrid solutions.

Despite widespread blockchain adoption in APAC, the level of engagement differs significantly. Vietnam stands out, particularly in fintech — with almost 90 per cent of Vietnamese individuals involved in decentralised finance. However, expertise in one technology does not translate to the mastery of another.

Blockchain enthusiasts may struggle with AI, and AI experts could find blockchain complex. In fact, despite Vietnam’s strong blockchain adoption, only 27 per cent of its organisations are fully prepared to deploy AI.

The varying stages of blockchain and AI development across APAC countries present a unique landscape for collaboration. These synergistic technologies are poised to drive transformative advancements, fostering greater efficiency, security, and transparency across various industries — from finance to supply chain management. As of now, the combined market for these technologies is projected to exceed US$703 million by 2025.

The cloud plays a pivotal role in this evolution, enabling businesses to develop decentralised, resilient blockchain networks capable of scaling to meet the demands of an expanding user base. Yet, challenges around scalability, regulatory compliance, and sustainability must be addressed to fully realise the potential AI-blockchain innovations.

AI’s transformative influence on blockchain

The decentralised nature of blockchain aligns seamlessly with AI’s growing need for autonomy. AI applications – especially those in collaborative industries like logistics, finance and manufacturing – benefit from distributed systems that enable real-time, secure data sharing across multiple entities.

Blockchain’s decentralised architecture enables seamless, secure data sharing between organisations without relying on a central authority, reducing the risk of single points of failure and ensuring AI systems remain resilient in multi-stakeholder environments.

The financial industry, which handles vast quantities of data, is a prime example of how AI and blockchain can be effectively combined. For instance, AI algorithms can detect fraud and money laundering on blockchain data, while blockchain ensures data security.

For startups, this integration presents an opportunity to compete with larger firms on a more level playing field. As startups leverage blockchain to enhance supply chains, they have begun to recognise the limitations of centralised systems. Although these systems initially offered cost efficiency and better visibility, challenges such as decision-making bottlenecks and reduced flexibility were also faced.

The result? Many are now seeking decentralised solutions that can provide greater adaptability and responsiveness. Blockchain’s decentralised, transparent structure empowers startups to streamline their supply chain management, ensuring improved quality control, traceability, and transparency through smart contracts.

Also Read: Strategies for effectively integrating AI into your organisation

With that, businesses are moving towards a future characterised by automation, transparency, and decentralised decision-making, reshaping traditional paradigms and fostering a more collaborative and innovative environment.

Potential pitfalls with AI-Blockchain integration

Despite the excitement around AI within the blockchain ecosystem, the path forward is riddled with challenges that must be addressed.

One of the most significant barriers to AI-blockchain integration is scalability. Blockchain networks, designed with security and decentralisation as their core features, are not optimised for high transaction throughput. When AI is introduced, particularly resource-intensive machine learning models and real-time data processing, the demands placed on the network increase exponentially. Blockchains are not optimised for frequent data writes and reads, a necessity for AI model training, updates, and optimisation.

Moreover, storing large datasets required by AI systems on blockchain can be inefficient and costly. These can lead to slower processing speeds, higher latency, and escalated operational costs, particularly in cloud-based environments where services are billed based on resource consumption.

Furthermore, with the regulatory landscape surrounding AI and blockchain still evolving, organisations struggle to ensure compliance with data protection laws and industry standards. This uncertainty can lead to reluctance in adopting these technologies, hindering innovation and slowing implementation. This is especially acute for startups operating in multiple countries and managing operations across different regions.

Sustainability concerns also loom large over the future of both AI and blockchain technologies, largely due to the substantial energy and data centre resources needed for their operation. In fact, by 2026, the combined energy usage  of AI and blockchain-powered cryptocurrencies could double to over 1,000 terawatt-hours (Twh), roughly equivalent to the annual electricity consumption of Japan. Addressing these sustainability issues is critical, not only to mitigate environmental impact but also to ensure the long-term viability of both technologies.

Building a strong foundation

Cloud-based blockchain solutions merge the decentralised, transparent, and immutable qualities of blockchain with the scalability and accessibility of cloud computing.

Also Read: Data driven healing: The potential of analytics and AI in advancing mental health

The availability of hybrid and multi-cloud options play a critical role in developing blockchain infrastructure, enabling interoperability and decentralisation. They also address data residency concerns for organisations operating across multiple jurisdictions by storing sensitive data within designated geographic regions, minimising the risk of data breaches and regulatory non-compliance.

This must be complemented with high-performance, purpose-built, dedicated servers like bare-metal servers. Unlike virtualised or shared cloud environments, bare-metal servers provide the raw power and memory capacity to ensure optimal performance — crucial for blockchain’s heavy transaction loads, large-scale data storage, and complex consensus mechanisms. For instance, bare-metal servers can be tailored to optimise Graphics Processing Units (GPUs) for AI algorithms used in conjunction with blockchain for predictive analytics and fraud detection.

Given the data-heavy nature of blockchain networks, cost transparency will be vital for the long-term sustainability of blockchain-based cloud services. Businesses which prioritise maximising uptime and cost-efficiency need more transparent pricing models to manage costs effectively. By adopting affordable and fair cloud services, startups can effectively maintain their exchange infrastructure and focus on core business activities.

Lastly, businesses should also prioritise selecting cloud vendors that integrate sustainability into their operations. Choosing cloud vendors with green data centres which balance power efficiency and scalability while reducing environmental impact will be essential.

As AI-blockchain technologies gain traction in APAC, it will be crucial for blockchain businesses to implement robust strategies to scale their networks and deliver reliable, secure services. Fortifying transparent partnerships between blockchain businesses and cloud service providers will be instrumental in propelling the region towards innovation, transparency and trust.

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In an era where Artificial Intelligence (AI) permeates through every facet of our lives, its impact on the creative sectors has become a focal point of both enthusiasm and debate. As a fervent advocate for the metaverse, I find myself deeply entrenched in the fascinating process of creating virtual universes with AI that cater to the imaginative whims of our clients.

The dialogue surrounding AI’s role in creativity and artistic production is vibrant and multifaceted. Within this context, my journey as a startup co-founder aimed at democratising metaverse creation has been both challenging and enlightening.

Our platform leverages design templates to facilitate the seamless deployment of metaverse environments, aiming to inspire a significant increase in their unique creations. However, the preference among numerous enterprises and brands for custom-tailored metaverse designs that reflect their distinct visions and needs has been a consistent trend.

Navigating challenges with AI innovations

Our ambition to scale and expand rapidly is met with several challenges, primarily centred around the visualisation and design process of metaverse concepts:

• Visualisation hurdles: The initial obstacle is the clients’ inability to fully grasp the envisioned metaverse design through mood boards alone.
• Time-intensive modelling: This leads our skilled 3D modellers to undertake the time-consuming task of creating preliminary 3D drafts for each proposal.
• Iterative design process: Finalising a metaverse project often requires extensive collaboration with the client, involving multiple rounds of design revisions.

The incorporation of AI in our design process, particularly through the use of tools like Dall-E, Midjourney, and Stable Diffusion, has revolutionised the speed at which we can produce metaverse design proposals. This technological advancement has not only doubled our proposal generation speed but has also empowered our sales team to use AI to create visual designs, significantly enhancing our sales conversion rates.

Also Read: Rewriting the creation process of ad creatives using generative AI

The question arises: does the advent of AI spell the demise of human creativity in design? Far from it. AI acts not as a replacement but as a catalyst that amplifies the creative process. The initial spark of creativity continues to originate from the human mind, with AI serving as a tool that brings these ideas to fruition more vividly and rapidly.

This synergy underscores the enduring value of human imagination, guiding AI towards generating outcomes that resonate with depth and meaning.

Expanding the creative horizon

Creativity manifests in myriad forms, not solely through the invention of novel ideas but also through the reinterpretation and expansion of existing concepts. AI democratises the creative process, offering a foundation upon which designers can build, refine, and innovate.

Initial designs generated by AI, though not flawless, serve as springboards for further creative exploration, enabling designers to infuse additional layers of sophistication and personalisation into their work.

The integration of AI into creative workflows represents a pivotal moment in the evolution of the creative industries. It heralds a new era where technology and creativity converge, opening up a realm of possibilities for innovation and expression. The key to harnessing the full potential of this convergence lies in embracing AI not as a threat but as an invaluable ally in the creative journey.

The interplay between AI and creativity in the development of the metaverse is a testament to the symbiotic relationship between technology and human imagination. As we navigate this exciting frontier, it becomes clear that AI serves not to eclipse creativity but to enrich it, offering new avenues for exploration and expression.

The future of creativity in the metaverse and beyond is not just about adapting to the wave of AI but riding it to unlock new dimensions of imaginative possibility. In this journey, the fusion of AI and human creativity heralds a promising horizon for the creative industry, one brimming with opportunities for growth, innovation, and unparalleled artistic expression.

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This article was first published on April 4, 2024

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As organisations navigate their digital transformation, protecting data becomes increasingly vital as it’s the lifeblood of their business operations. However, in an era of escalating cyber threats, safeguarding is no longer just about security — it’s about ensuring data availability and integrity in the face of disasters.

This is where artificial intelligence (AI) is revolutionising modern backup and disaster recovery (DR) strategies.

But does the rise of AI signal the end of traditional backup and disaster recovery methods?

Limitations of traditional backup and recovery

Historically, disaster recovery strategies revolved around physical backup sites or periodic data backups to tapes or external drives. While these methods were once effective, they are increasingly inadequate in today’s cloud-based environment. They struggle to address modern cyber threats like malware, ransomware, and other malicious attacks, which can compromise data long before it’s even backed up.

In its first Cybersecurity Health Report, the Cyber Security Agency of Singapore (CSA) revealed that more than eight in ten organisations experienced a cybersecurity incident in a year, with nearly half encountering it several times.

Consider this scenario: many organisations follow strict daily backup protocols. Yet, when disaster strikes, they discover their backups are compromised because malware had infiltrated their systems long before the threat was detected. Malware can lie dormant for weeks or even months, infecting backup files unnoticed, only to resurface when compromised data is restored during recovery.

This underscores a crucial evolution in backup strategies. Regularly backing up data is no longer enough; we need to ensure that the backed-up is secure, clean, and recoverable. This is where AI becomes indispensable.

AI’s role in modern backup and disaster recovery

AI’s strength lies in its ability to analyse vast amounts of data in real-time, detect anomalies, and respond to potential threats. In the context of backup and disaster recovery, AI-driven solutions can perform real-time malware scans before any data is backed up. This allows AI to detect and isolate threats, ensuring that backups remain secure and reliable for future recovery.

Also Read: Coded in your DNA: How Singapore can help avert a global data storage crisis

Moreover, AI-powered disaster recovery integrates Managed Detection and Response (MDR), where AI continuously monitors systems for potential threats and takes pre-emptive action before those threats escalate. As ransomware attacks become more frequent and severe, such proactive measures are invaluable.

In the past, organisations invested heavily in physical disaster recovery sites that were often left unused until disaster struck. Today, cloud-based data recovery powered AI offers a more agile, cost-effective, and scalable solution.

The shift from reactive to proactive disaster recovery

One of the key advantages AI brings to disaster recovery is the shift from reactive to proactive strategies. Traditionally, organisations would only test their disaster recovery plans sporadically, often after a disaster had already occurred. With AI, continuous monitoring becomes a reality, allowing organisations to identify vulnerabilities before they escalate into larger issues.

For example, AI-integrated MDR solutions can constantly monitor systems for suspicious activity, flagging and containing risks before they can spread. This real-time capability is crucial as cyberattacks become more sophisticated.

In the event of an attack, AI also accelerates response times. Instead of waiting for a disaster to occur and then scrambling to restore data, AI can automate the responses, significantly reducing downtime and minimising business disruptions.

Ethical considerations and the human factor

As AI becomes more pervasive in disaster recovery, new challenges arise, particularly around ethics and governance. The race between cybercriminals and defenders is intensifying, as bad actors also adopt AI to bolster their attacks. To stay ahead, businesses must implement clear ethical guidelines for AI usage in their disaster recovery strategies, ensuring transparency, accountability, and minimising risks, especially concerning sensitive customer data.

However, AI is only as effective as the humans managing it. Regular staff training on cyber threats and data protection remains crucial. Employees are integral to the AI learning model — the more they understand about risks and how to mitigate them, the stronger the organisation’s security posture becomes.

Changing mindset: Treat AI as your partner

One of the ongoing challenges in adopting AI for backup and disaster recovery is that many business leaders still view AI as a complex and expensive tool rather than a critical partner in safeguarding their organisation’s data. To foster AI adoption, this mindset needs to change.

Also Read: Bursting the big data bubble: Why we don’t need more data scientists

AI should be seen as a trusted partner — one that continuously learns and adapts to the organisation’s needs, much like an employee. For AI to reach its full potential, organisations must implement governance frameworks and ethical structures that ensure AI acts in the company’s best interest.

As data volume surges, the need for sophisticated data protection and recovery methods will continue to grow. Experts predict that by 2050, AI will be fully integrated into most business operations, from customer service to public services and beyond.

Many have likened cybersecurity to a game of cat and mouse, where cybercriminals constantly evolve, and solution providers and enforcement agencies are perpetually trying to catchup. AI has the potential to level the playing field. Regardless of any organisation’s reluctance or ignorance over AI, cybercriminals are already utilising it to launch more complex and effective attacks. As a way forward, the consensus among cybersecurity experts is to deploy AI-enabled defences.

With AI, organisations can not only ensure their data is backed up but that it remains secure and recoverable in the face of evolving cyber threats and disasters. However, adapting AI requires a proactive approach: implementing ethical frameworks, training staff, and embracing AI as the critical business partner it is.

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As COVID-19 continues to impact most parts of Asia Pacific, digitising the food system has never been more important. Food safety, hygiene and storage management had already been fundamental to the success of food supply chain systems prior to the pandemic.

With the recent surge of COVID-19 cases in Southeast Asia, consumers have become even more concerned with the source, quality, and safety of their food, leading to an increased need for food safety and accountability.

Consumers across the region have been rethinking their eating habits after the pandemic and shifting away from an ‘on-the-go lifestyle’ to more of a ‘safe in-home consumption’ trend.

The Zebra Technologies’ Food Safety Supply Chain Vision Study found that more than half of the consumers (51 per cent) cite the fear of food borne illness and disease as the reason to learn more about where their food comes from, especially with Singapore researchers recently reporting that the COVID-19 virus can survive in frozen meat for up to three weeks.

As a result, food manufacturers are confronted with the issues of food supply chain transparency. At the same time, they need to meet food safety standards, avoid recalls, maintain compliance, and earn customer trust and loyalty.

Food supply chains will need to bear increased pressure to deliver quality and safe food from the farm, to the factory and finally to the consumer’s table. Concurrently, countries like Singapore, an emerging food tech hub of Asia, must quickly address these issues.

Due to continually increasing consumer demands, food safety will need to be taken more seriously, with increased collaboration between the food industry, regulators, and tech companies to create a safer, more traceable food system.

In Singapore, the government recently launched the Food Manufacturing Industry Digital Plan to help food manufacturers use digital tools to ensure food safety and traceability, amongst other objectives.

Many of these changes will be led by technology-enabled solutions that can garner additional trust and ease business operations by tracing each food item throughout the supply chain.

Also Read: Fixing food waste problem means less hungry people and a great economy

Ultimately, this increased traceability will reduce recalls and food waste, protect consumers by preventing lapses in food safety, and speed up crackdowns on contaminated food. This will provide consumers with the peace of mind in knowing where their food was manufactured, prepared and handled, as a greater number turn toward preparing food at home.

Prioritising consumer care and trust

Only two in 10 consumers surveyed in the Zebra study place complete trust in the industry to ensure food and beverage safety. This severe trust gap stems from various factors, with restaurant kitchen staff hygiene and the fear of food borne illnesses or allergies listed as top food safety concerns for consumers.

Additionally, 60 per cent of surveyed consumers would never return to a restaurant if they contracted a food borne illness from eating its food.

Tracking and traceability also protect brands from damage to their reputations following a food safety incident. Preventing food borne illnesses and product spoilage is a constant challenge in the industry, as one mistake in supply chain management can lead to dangers on store shelves and in restaurants.

With COVID-19, heightened consumer concerns are likely to permanently increase the demand for information and transparency regarding food safety.

Therefore, industry decision-makers can look to technology solutions to ease the strain of curb side and e-commerce deliveries, and at every touchpoint, by improving traceability, safeguarding food items, and mitigating food supply disruptions.

Enhancing traceability in the supply chain

The Zebra’s study also found that 69 per cent of industry decision-makers trust food enterprises’ ability to manage traceability and transparency, with only 35 per cent of consumers stating their assurance.

To address this discrepancy, industry decision-makers can help collect comprehensive data and make that information available to consumers.

Moreover, the study indicates that nine out of 10 decision-makers believe safety and traceability-focused technology can give them a competitive advantage.

Areas that could benefit from devices and technologies include: compliance with food safety and quality guidelines; ensuring proper food handling, transportation and storage; tracking product perishability; intake of raw materials and ingredients; and faster and more efficient lot recall.

Technologies such as RFID tags, rugged handheld mobile computers with scanners, and thermal printers, can track items quickly throughout the supply chain and help increase food product traceability.

Also Read: The spotlight on foodtech: Why we believe that what we put on our plate will determine the future

The implementation of these solutions is projected to rise, with 93 per cent of industry decision-makers surveyed stating that they are planning to increase investment in food monitoring tools within the next year.  It is apparent that companies are recognising the benefits of including these technologies in their operations.

Predictive analytics powered by the visibility provided by these technologies will also allow decision-makers to improve their strategies, optimise transportation efficiency, and tighten loopholes in tracking and traceability.

Future-proof the supply chain with robust digital solutions

Improving food safety is now more challenging more than ever due to the increasing demand and rise of consumers’ expectations. Globalisation also brings new challenges to food supply chain optimisation.

As international trade grows, particularly in a post-pandemic world, so does the necessity for consistent data, reporting and transparency throughout the supply chain.

With traceability and transparency, the future of food safety and food supply seems bright. Comprehensive information and transparency will help eliminate supply chain blind spots.

Companies in Southeast Asia that can demonstrate robust and effective traceability and transparency capabilities will increase business efficiency, protect their consumers and businesses, and ultimately improve customer confidence and loyalty, giving them an edge over their competitors in this rapidly evolving market.

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Editor’s note: e27 aims to foster thought leadership by publishing contributions from the community. This season we are seeking op-eds, analysis and articles on food tech and sustainability. Share your opinion and earn a byline by submitting a post.

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This article was first published on June 28, 2021

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For most e-commerce companies, the path to profitability will hardly be a straight line. It is a journey that will twist and turn within the entire organisation and always work in tandem with the constantly shifting consumer demands. 

A fast and cost-efficient fulfilment process is the mainstay of every e-commerce company. However, recent consumer expectations like free and expedited shipping often have entrepreneurs barely coping with logistics costs.

Therefore, lowering logistics expenses can be a potential saving site for e-commerce companies in India and worldwide.

According to a report by Ti, the global e-commerce logistics market is expected to grow at 8.6 per cent CAGR between 2020 – 2025. Globally, e-commerce logistics costs make up anywhere between a massive 15 to 30 per cent of total costs and about 20 per cent of product prices depending on the size of the business, industry, national economy, and other factors. 

Last-mile delivery operations are the most significant contributor to global e-commerce logistics costs, making up as much as 53 per cent of total e-commerce logistics costs.

It is closely followed by fulfilment services (warehousing and inventory management) at 47 per cent. Ti’s report shows Amazon’s logistics costs for order fulfilment have risen from 17.8% in 2011 to 30.1% in 2020. 

The cost of e-commerce logistics in India

A report by APAC shows shipment costs were about US$1.40 per package in the past. But as volumes increased and technology improved, this figure declined about 25 per cent, just over a dollar.

Last-mile delivery costs can be broken down into three main categories—first, shipping, which covers the cost of any human resources involved in delivering your package.

Second, operating hubs, where packages are stored before being delivered or picked up by customers themselves (think of drop off points). And third, incentives are offered to drivers who take these last few steps for their clients.

The cost of operating a warehouse or sort centre, whether by the company’s own or third-party logistics firm, is about 10 rupees per shipment. This brings the total for your business’ shipments at INR 75  excluding warehousing fees which can add another 20 -25 per cent.

India’s post-COVID economy is steadily improving with the rise of the e-commerce and warehouse market. Some logistics startups have even acquired deals worth US$425 million. The e-commerce warehousing industry has also seen a boom (expected to achieve a CAGR of 12.6 per cent in the next five years).

It has started investing in Automatic Guided Vehicle (AGVs), Automatic Storage and Retrieval (ASRs), and robotic arms. Such technological advancements will help lower e-commerce delivery costs by 40 per cent.

Also Read: E-commerce logistics is at a tipping point in India as Delhivery raises over US$100M from Carlye, Tiger Global

Some factors contributing to India’s high e-commerce logistics costs are lack of multimodal and intermodal transportation systems, heavy reliance on road transport, fragmented storage infrastructure, bad condition of roads, and most importantly, slow technological adoption.

To improve matters, the government has allotted 18 per cent of the US$1.4 trillion capital expenditure of the National Infrastructure Pipeline till 2025 to develop roads.

Tech to the rescue

Here are seven areas in the supply chain where technology is driving the maximum impact:

• Warehouse automation and data analytics can help lower freight costs the need for human resources and allow you to monitor logistics operations closely. Harnessing the power of blockchain technology provides businesses with the much-needed data to create a clear year-on-year comparison of expenditure, profits, and losses. WMS can improve shipping speeds by making order pickups much faster. 
• Inventory management systems can update businesses on real-time inventory status. This will significantly lower customers’ problem of ordering “out of stock” items because the inventory is not updated in real-time. 
• Real-time tracking: Ecommerce giants like 1mg, Nykaa, and others rely on live order tracking updates to keep customers happy about their post-purchase experience. They can also send automated order status updates to customers via SMS, WhatsApp and emails or redirect them to a fully branded tracking page where customers can enter their tracking IDs to find out the status of their order. The white-labelled tracking page also provides online retailers with great cross-selling and upselling opportunities.
• Returns: E-commerce is notorious for product returns. Tech-enabled reverse logistics or returns management saves businesses hundreds of dollars by reducing churn and increasing Customer Lifetime Value (CLV). It can potentially lower the returns processing costs by 25 per cent while boosting net profit by 2-5 per cent. Some logistics platforms also provide a self-serve returns portal where customers place return requests and enter feedback.

Also Read: Why e-commerce startups will revolutionise the supply chain in Southeast Asia

• Robotics is now becoming one of the key differentiators in e-commerce shipping. It uses drones, robots, automated forklifts, scanners, etc., to reduce time spent on every order processing stage. Some of India’s most prominent e-commerce players, like Flipkart, DTDC, Myntra, etc., use robots to manage orders at large warehouses and distribution centres.
• Automating processes like cargo or freight audits, tracking carrier performance, appropriate carrier allocation, etc., can serve as significant risk mitigators by eliminating inaccuracies, streamlining operations, and preventing delays or shipping exceptions. Technology can prove especially helpful in load planning, driver selection, and route optimisation. It can help strategise delivery routes, order prioritisation, and load planning for optimum fuel and fleet usage. Such technology proves exceptionally useful in countries of the Middle East that lack a National Address system.
  
• Omnichannel fulfilment: With the help of logistics technology, many e-commerce companies have even ventured into adopting the omnichannel fulfilment route. Brands like Aditya Birla, Zara, H&M, and others provide customers with the BOPIS (Buy Online, Pickup In-Store) model. The omnichannel method allows for better utilisation of resources by turning the physical store into fulfilment centres. Brands can then choose to fulfil orders from the nearest brick-and-mortar store.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic

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This article was first published on February 7, 2022

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Echelon Startup Pitch

The Echelon Philippines 2024 Startup Pitching Competition brought together 15 groundbreaking startups, each presenting innovative solutions to pressing challenges. This event not only highlighted the entrepreneurial spirit but also offered a platform for startups to gain feedback, connect with industry leaders, and explore investment opportunities.

Among the standouts was BuildIt, which aims to streamline construction supply procurement, promising up to 10 per cent in cost savings. Baybayin Hub targeted the digital nomad market with remote work-friendly accommodations, while Polka.PH presented a unique employee engagement platform combining instalment payment options with rewards. Coden AI showcased how artificial intelligence could accelerate app development, and XELEQT A.I. provided real-time insights for optimising field operations.

Also Read: Echelon Philippines 2024: The funding landscape for Filipino startups

Healthcare and logistics also took centre stage. SeeYouDoc offers a robust telemedicine platform already serving 40,000 patients through 127 providers, while MedsGo focuses on enhancing medication delivery. Flying Tigers Express proposed inter-island delivery solutions, and SolX Technologies Inc. introduced energy efficiency tools that have already achieved US$29 million in cost savings.

The competition also highlighted transformative solutions in education, employment, and outsourcing. SkoolTek by EdFolio has streamlined school operations for 38 institutions, generating US$360,000 in revenue, while PasaJob leverages referral networks for job placements. Remotify builds remote teams, capturing a slice of the US$1.6 billion Philippine outsourcing market.

With groundbreaking pitches from these and other startups, Echelon Philippines 2024 reaffirmed its role as a launchpad for disruptive technologies and innovative solutions, promising a brighter future for the Philippine startup ecosystem.

Watch the video above to learn more about these insights and the strategies shaping the future of entrepreneurship.

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The surge in generative AI (GenAI) technology across the globe has sparked a new frontier of innovation, and Southeast Asia is no exception. While the region races to harness the potential of GenAI, the startup ecosystem faces unique challenges and opportunities in securing the necessary funding to fuel growth. 

The ASEAN GenAI Startup Report 2024 provides a comprehensive overview of the current funding landscape, exploring why now is an opportune time for venture capital (VC) engagement and strategic investments.

Current funding landscape in ASEAN

Despite a global downturn in startup funding, GenAI stands out as a beacon of potential within SEA. According to the report, while the overall investment has cooled, specific attention is being paid to the burgeoning field of GenAI due to its disruptive capabilities and broad application potential across industries. The current funding stage for GenAI startups is predominantly early—angel, pre-seed, and seed stages—indicating a nascent field ripe for investment opportunities.

Singapore continues to lead in funding, followed by Indonesia and Vietnam, highlighting a disparity in the distribution of capital that favors more established startup ecosystems. This distribution underscores the need for a more inclusive approach to funding that can fuel innovation across all SEA countries.

Challenges in the ASEAN funding landscape

The funding landscape in SEA is fraught with challenges that stem from both external economic conditions and internal ecosystem dynamics. High interest rates and a cautious investment climate have led to tightened capital flows, particularly for early-stage startups that offer high risk but also high potential returns. 

Additionally, the region faces a scarcity of later-stage funding, which is crucial for scaling startups to full commercialisation and profitability.

Also Read: Navigating the go-to-market challenge: Helping ASEAN GenAI startups succeed

The report highlights a muted exit market as a significant deterrent to investors. Few IPOs and mergers and acquisitions activity make it difficult for investors to see a clear path to profitability, thus hesitating to place big bets on SEA’s GenAI startups.

Opportunities for venture capital and strategic investments

Despite these challenges, the GenAI sector in SEA presents unique opportunities that savvy investors are beginning to recognise. The technology’s transformative potential across various sectors—from healthcare and finance to education and logistics—promises long-term gains that can outweigh the current economic uncertainties.

• Innovative applications: GenAI’s ability to drive innovation in traditional industries presents opportunities for VCs to fund startups that are developing unique solutions tailored to SEA’s diverse market needs.
• Government initiatives: Many SEA governments are launching initiatives to support the digital economy, including grants, tax incentives, and co-funding opportunities. These initiatives can mitigate some of the risks associated with investing in GenAI startups.
• Rising demand for AI solutions: As businesses and consumers increasingly rely on AI-driven solutions, the demand for GenAI applications continues to grow, ensuring a market for new innovations that can scale rapidly with the right funding and guidance.

The case for ASEAN GenAI startups

The case for investing in SEA’s GenAI startups is strong. The region offers a rapidly growing digital market with increasing internet penetration, a youthful population adept at adopting new technologies, and a growing number of skilled professionals in the tech sector. Moreover, SEA’s strategic geographic location serves as a gateway to both the Eastern and Western markets.

However, to truly capitalise on these opportunities, investors need to adopt a nuanced approach that considers the unique characteristics of the SEA’s market. This includes understanding local consumer behaviour, navigating varied regulatory landscapes, and supporting startups in achieving product-market fit in diverse environments.

Investing in SEA’s GenAI startups offers a promising avenue for VCs and strategic investors looking to capitalise on the next wave of technological innovation. While the path is fraught with challenges, the potential rewards are considerable. The region’s unique position as a burgeoning hub for digital innovation, combined with the transformative impact of GenAI technologies, provides a compelling case for increased investment.

For investors, the time to act is now. By engaging with and supporting SEA’s GenAI ecosystem, they cannot only yield significant returns but also play a pivotal role in shaping the future of technology in the region and beyond. As the GenAI landscape evolves, those who invest wisely will likely find themselves at the forefront of a new era of digital and economic growth in Southeast Asia.

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This article is the fifth in a series from the ASEAN GenAI Startup Report 2024. GenAI Fund invests in early-stage GenAI startups across Southeast Asia, focusing on growth strategies and exit opportunities. Stay updated with new articles in this series by subscribing and following us on our channels. For more articles, visit: https://e27.co/category/reports/.

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