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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Image credit: Theodore Ng, Analyst at Integra Partners

This article was first published on September 13, 2022

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