e27 has been nurturing a supportive ecosystem for entrepreneurs since its inception. Our Contributor Programme offers a platform for sharing unique insights. As part of our ‘Contributor Spotlight’ series, we shine a spotlight on an outstanding contributor and dive into the vastness of their knowledge and expertise.

This episode features Jaslyin Qiyu, Founder and Managing Director of Mad About Marketing Consulting. With over 20 years of B2B and B2C marketing experience across the Asia Pacific, she has led regional teams at global MNCs, including Citibank, EY, JLL, Kantar, Credit Suisse, and State Street.

At Mad About Marketing, Qiyu focuses on brand building, client experience, MarTech, and performance marketing, while championing flexible, fractional talent models that empower women to pursue both career and personal goals. She also serves on advisory and industry boards, including CX Networks, University College Dublin, Trigger-UNDP, and RSVP Singapore.

In the sections below, she reflects on her journey, the lessons he’s learned, and what keeps her going.

How I got here

The turning point for me came when I was advising a startup on their wealth tech platform’s customer journey and value proposition. Their genuine trust in my inputs, coupled with their dedication despite having limited resources, made me realise I could create more impact outside of traditional corporate structures.

That experience crystallised my frustration with the industry’s one-size-fits-all approach and inspired me to start Mad About Marketing Consulting, to democratise access to sophisticated marketing capabilities.

If I had to explain my work to a kid

I help companies show people how cool their products are, kind of like when you see a toy ad on TV that makes you say, “I want that!” I teach them how to tell stories so customers understand why their stuff is fun, useful, or special. It’s like being a matchmaker, helping the right people find the things they’ll really love.

Also Read: Leading through transformation: How CMOs and CEOs must evolve in the AI era

Lessons learned along the way

I used to think I had to feel “completely ready” before saying yes to new opportunities, so I turned down many roles because I thought I wasn’t qualified enough. Over time, I realised that real growth comes from solving real problems for real people, not from collecting more credentials.

My question shifted from “Am I qualified enough?” to “How can I create value while learning?” That change in mindset transformed my approach, from being cautious and over-preparing to stepping forward with confidence and contributing right away.

What more people should notice

There’s a dangerous misconception that generative AI can replace experienced marketing expertise. Too many startups think they can bypass hiring seasoned marketers by using AI tools to generate campaigns, content, and strategies without understanding fundamental customer psychology or market dynamics.

The result is often superficial marketing that lacks depth and misses the mark. The real opportunity lies in combining AI’s capabilities with experienced marketing leadership, where strategy, customer insight, and execution excellence create impact that AI alone cannot deliver.

Why I write

I have always been passionate about writing. In fact my childhood ambition was to be a writer! I also believe knowledge should be free and accessible to all, which is why AI powered research and info crawling has taken off so rapidly.

My writing often starts with real client challenges I’ve encountered and evolves into actionable insights others can use. At its core, my flow comes from a genuine belief that sharing knowledge creates positive ripple effects across the business community.

Also Read: How tech startups can attract Gen Z and millennials seeking flexibility and purpose

My advice for aspiring thought leaders

Here are a few principles I follow whenever I share my thoughts and experiences:

• Start with solving real problems rather than trying to sound impressive. Authenticity resonates more than jargon.
• Always explain complex ideas simply, because clarity demonstrates true understanding better than complexity.
• Share your failures and learning moments alongside successes. Vulnerability creates deeper connections and more valuable insights for your audience.

What drives my curiosity

All things spiritual and the universal realm, including what exists outside of what we can see with the human eye and mind.

Influences that shaped me

Growing up would be my parents and in terms of books, it’s mix of animal books by James Herriot and philosophical ones that prompted me to become naturally curious about the human mind and perception. One of the earliest books I read was Sophie’s World that got me really inspired to learn and read more about philosophy.

Take a look at Qiyu’s articles here for more insights and perspectives on her expertise.

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I was trained as a general practitioner. My early career began in rural clinics, working face-to-face with patients who often arrived late into illness, and sometimes too late for care. Some walked for hours just to reach the nearest facility. Many couldn’t afford the medicine. A few never came at all, out of fear, stigma, or the belief that nothing would change anyway.

Practising in those conditions changed how I understood health systems. The pathways that led people to care or kept them away from it entirely are so much deeper than just treatments or interventions. Distance, cost, shame, and bureaucracy are all systemic and technical obstacles, not human obstacles (and definitely not something your physicians can get rid of with a snap of their fingers). These obstacles existed long before diagnosis or treatment.

That experience stayed with me. It led me to ask harder questions about how health should work. It also led me into innovation, initially in ways that felt small. I started testing different approaches. I looked into patient vital sign self-check-ins, mobile consults, early screening tools, and how digital workflows could reduce drop-offs in care, even insurance products based on real cost and claim data (within our own beta population). But it didn’t take long to realise that the moment you try something new in healthcare, you run into friction.

That friction isn’t always direct from users (whether it’s physicians, health admins, or patients themselves, but rather it’s policy. Sometimes it’s culture, sometimes it’s just inertia. It becomes difficult to move fast when every step is bound by systems that were designed to minimise risk. That instinct makes sense in a clinical context. But it creates resistance when we’re trying to redesign the system itself.

This point in building user feedback and commencing trials with healthcare facilities or healthcare payors that have a sandbox for innovation is a lifesaver (for an early stage company like mine). We started integrating with other services to strengthen their reach, building mutual channel partnership. We saw how technology, when placed carefully, could expand care without increasing pressure on already overburdened systems. We focused on design that removed barriers for both patients and providers.

Now, I work more deeply with AI in healthcare. I see the same patterns re-emerging. We talk about scribing, supply chain coordination, clinical decision support, Software as a Medical Device (SaMD), and even risk modelling for population health. Each use case offers clear advantages. Yet the resistance often comes before the discussion starts.

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

People worry about safety, scope, ownership, ethical review, and clinical validity. These concerns matter. But what I’ve observed is that this resistance isn’t stronger than any pushback we’ve seen before (new drugs, supplements, wearables, even robotic surgery; once faced the same level of pushback and some even scrutiny). Every medical innovation in history has gone through it, whether it was antiseptics, laparoscopic surgery, or digital health records. Change is often uncomfortable. But it is never new.

So what’s the real challenge?

Instead of calling it a blocker, I think we need to shift the frame. The misconception is that value is the main driver for innovation. Only after innovating you understand that it actually is about understanding regulation, workforce, education, procurement, reimbursement, and behaviour. Medical innovation becomes normalised when the whole ecosystem is ready to hold it and is aligned across multiple levels of influence (not a single breakthrough overnight).

I’ve seen AI pilots fail, especially because the workflows couldn’t adapt to the real-time day to day operations our healthcare workers face, not because the models. I’ve seen great tools ignored because they didn’t match how clinicians document cases. I’ve seen hospitals decline adoption because IT budgets weren’t structured to handle long-term updates or retraining. These are signals that we need better integration strategy and regulatory pathways (like any other new drug in the market).

Healthcare is complex because it should be. We are dealing with lives. We are dealing with trust. But complexity shouldn’t stop us from building. It should shape how we build.

Also Read: What telemedicine and Health Tech holds across SEA amidst COVID-19

In Southeast Asia, the opportunities are real. We have gaps that technology can help close. The transformation should starts with people who understand the gaps and are willing to build bridges. It starts with small, focused systems that can grow and scale. It starts with conversations that go beyond hype and address what readiness actually looks like. Once we understand that, product building now becomes problem solving deliveries on a deep level.

My path began in rural clinics. I now build for broader systems. The problems have changed shape, but the mission remains the same. Make care more reachable. Make care more trusted. Make care feel possible.

If we want AI in healthcare to succeed, we need to stop waiting for the perfect pilot. We need to understand what adoption truly takes. We need to stop labelling every pause as resistance, and start seeing it as part of a wider transformation journey. Every advancement in medicine required coordination. This one is no different.

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A significant shift is underway in the corporate finance world, as Chief Financial Officers (CFOs) abandon cautious approaches to Artificial Intelligence (AI) in favour of aggressive, strategic investments aimed at long-term revenue growth.

New research from Salesforce reveals that AI, particularly ‘agentic AI‘ — digital labour capable of autonomous task execution — is not merely a tool for cost reduction but a fundamental driver of business transformation. This global trend holds crucial implications for tech startups and established enterprises across Southeast Asia looking to optimise operations and foster innovation.

The paradigm shift in AI strategy

Just five years ago, a striking 70 per cent of global CFOs adhered to a conservative AI strategy, a figure that dropped to 34 per cent two years ago.

Today, that number has plummeted to a mere 4 per cent, indicating a widespread recognition among financial leaders that AI is now a crucial tool for enhancing efficiency, optimising operations, and driving critical long-term growth. In fact, a third of CFOs have now officially adopted an aggressive approach to AI.

Also Read: Forget the rest: ChatGPT alone drives more traffic than 10,500 AI tools combined

This rapid transformation stems from a fundamental rethinking of technology investment Return on Investment (ROI). Robin Washington, President and Chief Operating and Financial Officer at Salesforce, commented, “The introduction of digital labour isn’t just a technical upgrade; it represents a decisive and strategic shift for CFOs. With AI agents, we’re not merely transforming business models; we’re fundamentally reshaping the entire scope of the CFO function. This demands a new mindset as we expand beyond financial stewards to also become architects of agentic enterprise value.”

The rise of agentic AI and redefined ROI

A significant 61 per cent of CFOs report that AI agents are changing how they evaluate ROI, moving beyond traditional metrics to encompass a broader range of business outcomes. On average, CFOs are now dedicating a substantial 25 per cent of their current total AI budget to AI agents. This commitment underlines a belief among 61 per cent of CFOs that AI agents and digital labour are, and will continue to be, critical for competing in the current economic environment.

Furthermore, 64 per cent of CFOs state that AI agents are changing their perspective on how their business spends money, with over a third (35 per cent) acknowledging that AI necessitates a riskier mindset around technology investments.

Beyond cost-cutting: Revenue and strategic value

While traditional technology investments often focused on immediate, measurable results, the perception of AI’s value extends far beyond short-term cost-cutting. Today, CFOs recognise AI’s returns may accrue over the long term through ongoing processes and new business models.

Seventy-four per cent of CFOs believe that AI agents will not only cut costs but also drive revenue. CFOs implementing AI agents anticipate these agents will increase company revenue by almost 20 per cent.

AI agents are uniquely suited to improve long-term business outcomes such as revenue generation, productivity gains, and improved decision-making. Significantly, 72 per cent of CFOs say AI agents will transform their business model, and 55 per cent believe AI agents will take on more strategic work than routine tasks.

New metrics for success

The introduction of AI agents has expanded the top factors CFOs consider when evaluating AI ROI.

These now include:

• Cost savings, risk and compliance improvements, and revenue growth (tied as the number one factor).
• Productivity or efficiency improvements (ranked second).
• Improved decision-making (ranked third).

One CFO survey respondent noted, “Traditional technology investments mainly focus on immediate financial returns that can be easily visible, but AI benefits are a mix of long- and short-term duration. KPIs are focused based on business outcomes.”

Additionally, AI is viewed as a valuable way to ensure ROI through better financial control, with one CFO stating, “AI provides real-time budget tracking, which improves forecasting accuracy and helps protect ROI from overspending through better financial control”. For CFOs, redefining ROI demands a mindset shift from valuing short-term to long-term success.

Concerns and the path forward

Despite the aggressive adoption, CFOs still face significant concerns regarding their AI strategy. The two primary worries are security or privacy threats (66 per cent) and the long time to ROI (56 per cent). Concerns also include the ethical risks associated with AI, which could affect reputational cost and ROI, and the ongoing investment required for retraining, monitoring, and improving AI models, making ROI more fluid compared to fixed-function tools.

Also Read: AI for the real world: SEA’s cost-efficient playbook is winning investors over

This global shift underscores the growing imperative for businesses, including those in the vibrant Southeast Asian tech startup ecosystem, to strategically integrate AI into their core operations. As financial leaders redefine value beyond immediate returns, embracing agentic AI is becoming a critical competitive differentiator for long-term growth and innovation in the digital labour era.

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Despite the rapid advancement of artificial intelligence (AI) in customer experience (CX), consumers are far from ready to abandon human interaction.

A new Verizon report highlights a strong preference for the human touch, presenting a clear mandate for brands to adopt a hybrid approach to CX rather than seeking fully automated solutions.

Humans still reign supreme in customer satisfaction

The report’s findings are unequivocal: 88 per cent of consumers are satisfied with online interactions involving human agents, compared to only 60 per cent for interactions driven solely by AI. This significant gap underscores a fundamental truth: while AI can handle routine queries efficiently, complex or emotionally charged situations necessitate human empathy and problem-solving.

Also Read: Verizon report: Businesses hail AI in CX, but customers still prefer humans

Consumers are “broadly relaxed” about AI for tasks like purchase transactions and product inquiries, but “fewer are comfortable when AI handles their complaints”.

The most prominent frustration consumers cite in automated interactions, by a large margin, is the inability to speak or chat with a live agent when needed, affecting 47 per cent of respondents. Brands concur, noting a similar proportion of customer complaints regarding this lack of human access. Stacy Sherman highlights that even when human agents are eventually involved, “information about you/the customer is lost (must be repeated) at different stages of the interaction,” causing further “friction with customers”.

A hybrid future for CX investments

Acknowledging this preference, companies are not solely betting on AI. The report indicates that a substantial 44 per cent of brands intend to split their future CX investments roughly equally between AI-driven and human-driven improvements. This signals a recognition that a balanced approach, integrating the strengths of both AI and human agents, is the most effective path forward. Only 29 per cent foresee CX operations being mostly or fully AI-driven.

“Some challenges require more than just solutions—they require the empathy and care that only people can provide,” states Morlon Bell-Izzard from Exelon.

Upskilling the human workforce

For this hybrid model to succeed, customer-facing staff require specific upskilling to work effectively alongside AI. Executives are prioritising training in three key areas:

• Handling customer complaints about chatbots.
• Understanding AI prompts during interactions.
• Handling complaints about data privacy issues.

The report also stresses the importance of addressing the “emotional and psychological barriers” employees might have about AI, ensuring transparency about how AI will enhance, not replace, their roles. As Abhii Parakh, Head of Customer Experience at Prudential Financial, observed, employees initially sceptical of AI became excited “once they saw how beneficial AI is for them”. Companies can use AI itself as a “powerful simulation tool” for training, allowing employees to practice interactions and build confidence.

Also Read: AI personalisation isn’t working; more consumers say it hurts CX than improves it

In essence, while AI will continue to automate and optimise parts of the CX journey, the human element remains irreplaceable for delivering truly empathetic and comprehensive customer service, making a seamless human-AI collaboration paramount.

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Customer expectations now grow faster than most operating models can adapt. Cisco’s 2025 Global Contact‑Centre Survey predicts that by 2028, 68 per cent of customer‑service interactions will be handled by agentic AI systems. Meanwhile, a 2024 Dixa poll shows 96 per cent of consumers still consider empathy critical to brand relationships.

Technology must scale, but humanity cannot be engineered out. The solution is not another point bot; it is a platform-first architecture that orchestrates data, workflows, autonomous agents and human experts inside a single, governed fabric.

The disconnect: Why AI isn’t delivering on the hype

Boards approve nine‑digit AI budgets, yet many projects remain stalled in pilot purgatory. Forrester’s 2024 Digital Business Strategy Survey reports that only 56 per cent of leaders have an enterprise‑wide view of technology. McKinsey’s 2025 research finds 95 per cent of AI initiatives never scale beyond pilots. Fragmented systems starve models of context‑rich data, while governance is treated as an afterthought.

Consider a telecom provider that launched separate chatbots for billing, network faults and promotions. Each bot answered its narrow script, but none shared a common data layer. Customers bounced between channels, escalation volumes spiked, and Net Promoter Score barely moved. Scattered tools, even “smart” ones, cannot substitute for a unifying platform.

What makes agentic AI different?

Traditional chatbots follow decision trees; agentic AI is goal‑driven, contextual and action‑oriented. Picture an AI agent that detects a billing anomaly, issues the refund, updates the CRM, emails an apology and alerts a human only if the amount crosses a threshold. Autonomy at that level creates three non‑negotiables:

• Context hunger: Curated, lineage‑tracked data streams
• Governance demand: Transparent audit trails and policy controls
• Interoperability: The freedom to swap models without re‑wiring applications

Only a platform layer that abstracts data, policy and workflow can meet all three at production scale.

Also Read: Agentic AI, urban mobility & smart tourism: 2025’s travel investment hotspots

Human + AI: Better together

Automation excels at speed and pattern recognition; humans excel at judgment, negotiation and relationship‑building. The goal is augmentation, not substitution. A global med‑tech firm recently introduced an agentic‑AI layer that triages tickets and surfaces knowledge‑base articles.

Human agents now focus on complex clinical queries, pushing first‑contact resolution and CSAT to record highs. When machines handle the routine, people deliver empathy exactly what 96 percent of customers want.

Platforms: Architecture for orchestrated intelligence

In Forrester’s 2024 survey, 70 per cent of digital leaders said technology and business executives now collaborate closely on change initiatives a shift directly linked to unified platform strategies. Such platforms provide:

• Composable services: API‑driven micro‑components that let teams plug in new AI models within days, not quarters
• Unified data fabric: Clean, trusted streams feeding both agents and analysts
• Governance by design: Access controls and audit logging embedded where functionality lives

This is strategic infrastructure, not middleware. A robust platform turns isolated bots into a coordinated workforce that learns, adapts and stays auditable.

APAC spotlight: DBS Bank

A 2024 Harvard Business School case study details how Singapore‑based DBS Bank built an internal data‑and‑model hub plus a PURE (Purposeful, Unsurprising, Respectful, Explainable) responsible‑AI framework. With that foundation, DBS scaled 300‑plus AI use cases across lending, fraud and service, boosting self‑service adoption and helping lower false‑positive fraud alerts. The case exemplifies platform‑first thinking in one of EdgeVerve’s key regions.

Also Read: 88% of consumers favour human agents; AI alone fails to deliver CX satisfaction

Scaling through strategic orchestration

A platform mind‑set reframes AI from a stand‑alone tool to a capability woven through every business process. High‑performing organisations:

• Swap models without disruption: Treating language or vision models as hot‑swappable modules under existing guardrails.
• Propagate success: Templating connectors so a winning use case in one region can be cloned elsewhere with minimal recoding.
• Monitor holistically: Combining experimentation metrics and production KPIs in a single observability stack.
• Automate compliance: Making centrally defined policies inherit automatically to every new workflow.

One Asia‑Pacific conglomerate recently merged a dozen AI pilots onto a single platform. Release cycles for new virtual‑assistant features shrank from months to weeks, and CSAT climbed double digits proving orchestration, not model count, drives value.

CXO playbook: three principles for the next wave

• Think platform‑first: Invest in data fabric, API gateways and governance layers before chasing the next generative model.
• Design for empathy + autonomy: Map journeys where human touch is irreplaceable and bake those checkpoints into orchestration.
• Embed governance early: Treat explainability, lineage and compliance as design inputs, not retrospective audits.

The road to harmony

Enterprises that orchestrate human and agentic intelligence on a strong platform spine will transform customer experience from reactive support to proactive value creation. Cisco’s survey notes 81 per cent of CX leaders believe vendors that master agentic AI will carve out enduring competitive advantage.

The stakes and the opportunities couldn’t be clearer. From hype to harmony, the future belongs to those who blend scalable technology with human empathy into one coherent, governed platform

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While AI is increasingly becoming a common practice in organisations, the ability of organisations to develop better growth prospects and more efficient strategies and customised consumer experiences also emerges clearly. AI brings unseen possibilities, such as automating decisions and delivering distinctively targeted advertising campaigns.

However, this technological advancement has not come easy without a set of problems. For instance, AI has alarmed privacy issues, the main reason being that systems demand personal data as inputs, and this acts as a violation of customer trust between firms and customers. Apart from that, the possibility of involuntary biases in the algorithms themselves, whereby an AI system unwittingly designs bias into the algorithm, resulting in “unfair” results, could go a long way in straining brand image and consumer trust.

Regardless, there is no doubt that AI has profoundly transformed the rapidly growing marketing technology (martech) industry, especially in the face of these aforementioned ethical issues. While AI optimises automation, increases customer satisfaction and improves analytical capabilities, organisations have to find the right balance between innovation and accountability. In this article, we will explore what these issues are and how they can be effectively addressed.

Essential ethical considerations to address

As AI continues to penetrate different industries, it has never been more urgent to raise awareness and ensure that AI is properly implemented. According to Forbes, more than 51 per cent of company leaders believe that AI transparency and ethics are critical to their operations, and 41 per cent of top executives have halted the deployment of AI technologies due to a potential ethical concern.

Transparency in the context of AI means that the functioning of artificial intelligence should be perceptible and understandable, while the process of making decisions should conform to ethical norms and general human values. One can find an example of transparency in the case of many companies employing AI to understand the behaviour of customers, targeting their advertisements and the overall marketing management.

To support the increase in transparency, some organisations have started giving customers more information about the usage of their data. AI transparency is also important where the risks are especially high that the consequences of AI decisions will impact lives or have large social outcomes, such as in healthcare and finance.

Another ethical consideration that has to be discussed is the issue of bias and discrimination in AI. AI comes with many advantages but is not without its controversies, particularly on issues of bias and discrimination. This is due to the fact that most AI models are trained from large datasets that could mirror some of the bias in the society hence the biased results.

Also Read: Blockchain and AI copyright: A revolution in digital rights management

Bias in AI can stem from various sources such as: 

• Bias in training data: If the training data contains inherent biases, the AI system will likely reproduce these biases in its decision-making processes. For instance, in a study, scientists tasked AI with developing a facial recognition system designed to classify individuals into three categories based on their characteristics: doctors, criminals, and homemakers. However, the AI demonstrated bias in its decision-making, frequently labelling women as homemakers, Black men as criminals, and Latino men as janitors, and selecting women of all ethnicities less often as doctors.
• Algorithmic bias: Beyond the data, poorly designed algorithms can amplify existing biases or create new ones. In 2018, Amazon’s AI recruitment algorithm was designed to assess candidates based on their fit for different roles. However, due to the underrepresentation of women in technical positions, the system developed a bias, favouring male applicants as it learned that men were historically preferred for these roles.
• Cognitive bias: Personal experiences and perspectives may lead developers to prioritise certain data over others, potentially skewing the AI’s outputs. For example, favouring data from a particular demographic or geographic region might result in an AI system that does not accurately reflect a global or diverse population. 

Strategies for mitigating bias and promoting fairness in AI

In 2024, Malaysia presented a PDP Bill that outlines significant changes in the Personal Data Protection Act (PDPA), including the definition of the terms, added responsibilities for data controllers, and increasing fines for non-compliance. The government regards these changes as great progress in enhancing data protection in the country and as a part of the continuous shift toward stricter privacy rules. This presents a good chance for companies to enhance their protection of data and bring them to par with global standards.

To start, there are various measures that companies can take to make the process ethical and responsible. One of the key strategies is to prioritise transparency, where businesses must provide clear insights into how AI algorithms operate. For instance, developing an explainable AI (XAI) plays a vital role in this process, as it offers techniques to help users understand and trust the decisions made by AI. By incorporating simplified visuals or user-friendly software interfaces, employees can grasp the underlying processes without relying on AI systems blindly. 

In addition to transparency, maintaining robust data security is critical. Research shows that 44 per cent of security decision-makers say their companies incorporate security and privacy measures from the outset when developing services, products, or applications.

Moreover, 87 per cent of consumers state that they won’t engage in business with a company if they have concerns about its security practices. This underscores the importance of continuous data monitoring, with dedicated personnel responsible for safeguarding information and preventing leaks. 

Also Read: How AI and automation can shape the future of farms

Companies should also ensure that their AI solutions comply with industry regulations and legal standards, as organisations that prioritise ethical AI are more likely to gain consumer confidence and create reliable AI systems. Furthermore, creating the role of human supervision as an AI control factor–where the AI makes suggestions that are then passed on to human experts to make the final decision is another positive since it assures that the systems are running fairly and effectively.

Implementing all these measures is critical in developing AI systems that are not only ethical but also efficient. At OpenMinds, we believe that we have a responsibility to lead by example, and we understand the importance of integrating ethical considerations into any AI development process.

Conclusion

In conclusion, reducing bias and encouraging fairness in the AI system is not only a technical issue but also an ethical issue. The strategies outlined are essential steps towards building trustworthy and ethical AI systems. We believe that these ethical considerations are especially important. As we continue to innovate in the martech industry, we aim to contribute to a future where AI benefits everyone, regardless of their background and identity.

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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: Canva Pro.

This article was first published on August 19, 2024.

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Asia’s 450 million smallholder farmers produce 80 per cent of the food consumed in the region and face increasing pressure to deliver sufficient, nutritious foods to a predicted regional population of five billion by 2050..

But climate-induced erratic weather patterns are threatening their ability to keep up with the demand. In India, the groundwater supply that is essential for agriculture has been steadily declining for years.

Coupled with erratic monsoon seasons and droughts, hundreds of millions of people’s livelihoods and food security are under. Southeast Asian farmers are also particularly vulnerable to changing rainfall patterns and warming temperatures due to the region’s location in the tropics.

Using a combination of crop protection products and digital tools such as artificial intelligence (AI), we can empower farmers to overcome the challenges of climate change and contribute to global food security.

Why technology matters in tackling climate change

Since the Green Revolution of the 1960s, developments in plant science have led to crop protection products that allowed farmers to improve the resilience of their crops against pests, diseases and continue growing amidst difficult conditions such as drought or flooding caused by climate change. Compared with 1960, the world now produces 150 per cent of more food on only 13 per cent more land.

Without crop protection products, 40 per cent of global rice and maize harvests would be lost every year and losses for fruits and vegetables could be as high as 50-90 per cent.

Also Read: COVID-19, the environment, and the tech ecosystem: what opportunity is available out there for us?

In addition, the agriculture industry is increasingly developing AI to help farmers yield healthier crops, control pests, monitor soil and growing conditions, organise data for farmers, help with workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.

Climate change is an ever-growing threat that is leading to severe floods, harsh droughts and heatwaves, violent storms. For farming to be truly sustainable, we need regenerative agricultural practices that prioritises the enhancement of soil health and proper management of water and fertiliser usage.

Also Read: E Green Global bags US$9.2M from Korean social impact VC fund to expand its agritech facility

Continued investments into AI and plant science can accelerate the development of new and better crop protection products that meets our farmers’ needs for sustainable solutions.

While the public sector has traditionally been the driving force behind agricultural R&D, the growth has been slowed recently in many countries due to fiscally constrained policies. The private sector has increasingly filled these gaps.

Today, private investment into agricultural innovation has led to new technologies and production techniques that significantly boost productivity.

How to support agritech developments

The acceleration of breakthrough agri-tech solutions is one of the key cornerstones of Syngenta’s sustainability objectives– the Good Growth Plan, which aims to help farmers adapt to and mitigate the impact of climate change through our investment of US$2 billion that will drive the development of agri-tech and improve our crop protection products.

As part of this investment, Syngenta Crop Protection acquired Valagro, a global biologicals firm, in 2020. They are the leaders in providing innovative and effective solutions for plant nutrition, and care and their solutions complemented our range of bio stimulants and bio controls. Together, we are working towards meeting our goal of developing effective yet environmentally conscious products.

We also recently announced a collaboration with Hong Kong-based Insilico Medicine, an AI and deep learning company to accelerate the invention and development of new, more effective crop protection solutions that protect crops from diseases, weeds, and pests protecting ecosystems.

By bringing new solutions to APAC farmers faster and more efficiently through innovation, Syngenta can help them meet the ongoing challenges they face to enhance productivity and meet the demand for affordable, quality food.

Also Read: 7 Asian startups putting the spotlight on agriculture

The COVID-19 crisis has further intensified the challenges in agriculture, and technology will be a more important solution than ever before.

Beyond developments in crop protection products, I believe we’ll continue seeing a focus on technology to enhance precision agriculture, data-driven farming and the development of tools that will meet consumers’ demand for higher-quality foods grown with lesser residue products.

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Editor’s note: e27 aims to foster thought leadership by publishing contributions from the community. Become a thought leader in the community and share your opinions or ideas and earn a byline by submitting a post.

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This article was first published on March 15, 2021

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AI adoption is growing, with 85 per cent of financial institutions integrating AI tools to enhance speed, efficiency, and data analysis. However, these systems are no longer exclusive to large institutions or seasoned traders. By making investing safer and more approachable, AI is helping to democratise access to wealth-building opportunities for a broader audience across various levels of expertise.

Understanding the barriers and risks in trading

Trading in financial markets presents significant challenges due to market volatility and the necessity for rapid decision-making. For example, high-frequency trading (HFT), which involves executing a large number of orders at extremely high speeds, accounts for approximately 50 per cent of trading volume in the US stock market. This high-speed environment requires traders to process vast amounts of information in real-time, making it difficult for inexperienced individuals to compete effectively.

The rapid nature of HFT can lead to emotionally charged decision-making, increasing the risk of massive financial losses. This underscores the importance of maintaining emotional discipline and having access to real-time data and advanced trading tools to navigate the complexities of high-frequency trading successfully.

How AI is changing the landscape of trading

Unlike human traders, AI systems process vast amounts of data in real-time. This allows tech-driven solutions to react instantly to market fluctuations, which is a critical advantage in HFT. The ability to provide predictive analysis and trading signals with high accuracy helps investors make informed decisions while avoiding impulsive, emotion-driven choices. Larry Fink, CEO of BlackRock, has highlighted AI’s potential to boost productivity and transform margins across sectors, stating, “I believe that AI has a huge potential to increase productivity, increase knowledge base and transform margins across sectors.”

The predictive capabilities of AI are evident in platforms like StockSmart, which offers AI-powered trading signals with an accuracy rate exceeding 90 per cent, empowering traders to capitalise on emerging market opportunities. Moreover, AI continuously learns and adapts from data, identifying patterns and trading opportunities that human eyes might miss.

AI-driven platforms transforming investment and trading

StashAway

StashAway, a Singapore-based fintech startup, offers AI-driven portfolio management tailored to individual risk profiles and financial goals. The platform dynamically adjusts asset allocations in response to market conditions, which ensures optimal performance. StashAway’s user-friendly interface provides pre-built portfolios and comprehensive risk assessments, making it accessible to both novice and experienced investors. StashAway most recently raised US$25 million in a Series D funding round led by Sequoia Capital India, bringing total funding to around US$75.3 million.

Endowus

Also Singapore-based, Endowus offers an AI-powered investment platform that provides personalised portfolio management services. Utilising advanced algorithms, Endowus tailors investment strategies to individual risk profiles and financial goals, ensuring optimal asset allocation and automatic rebalancing. The platform’s user-friendly interface and comprehensive risk assessments make it accessible to both novice and experienced users. In August 2023, Endowus secured US$35 million in a funding round backed by Prosus Ventures and EDBI, bringing its total funding to US$95 million.

Also Read: The future is here: Seizing the first-mover advantage in AI entrepreneurship

Metafide

Designed for retail and institutional investors, Metafide integrates AI-driven predictive models with the insights of professional traders from around the world. Metafide employs recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to analyse real-time data and predict price movements. Metafide also includes gamified features that allow traders to contribute real-time inputs on predicting short-term market movements, resulting in a platform that combines AI’s precision with human market understanding. Over 500,000 games have been played by more than 100,000 players on FIDE AI and RANGE FIDE(R) games powered by Mantle network.

The company has secured early-stage venture capital funding from investors such as Blockchain Founders Fund, Comma3 Ventures, and Cogitent. Co-Founder and CEO Frank Speiser, who previously co-founded SocialFlow, brings his expertise in combining AI and professional trading sentiment to Metafide. Speiser highlights the company’s unique approach: “Metafide focuses on incentivising community-driven insights, user-friendly experience, and trust through transparency.

The other critically important differentiator is that we own the entire value chain—we don’t send off market suggestions, but rather we execute the trades on behalf of our hedge fund partners. From the audience through to the completed trade, we have the ability and responsibility to modify or improve any part of the system.”

AI’s impact on the future of investing and trading

AI has the potential to make trading accessible to a broader range of individuals, including those who may not have a strong financial background. Hybrid models offer a new approach to risk management by allowing systems to adapt to both market conditions and investor behaviour. AI-driven platforms also provide educational resources that help users learn about market dynamics in a low-risk environment, an invaluable feature for beginners looking to avoid costly early mistakes.

As AI technology advances, it is poised to reshape the investment landscape further. With tools that offer risk assessments, real-time predictions, and educational support, AI allows investing to those who might otherwise be excluded, democratising access to financial growth and empowerment.

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The intersection of blockchain technology and artificial intelligence (AI) is an emerging frontier that holds great promise for addressing one of the most pressing issues in the digital age: copyright enforcement. With the proliferation of AI-generated content, the need for robust mechanisms to protect intellectual property rights has never been more critical.

This article explores how blockchain technology can provide innovative solutions to AI copyright challenges, offering a personal perspective on its potential supported by statistics and research.

The rise of AI-generated content

Artificial Intelligence has revolutionised content creation, bringing forth a new era where machines can produce music, art, literature, and more. AI algorithms, such as OpenAI’s GPT series, have demonstrated the ability to generate human-like text, while programs like DeepArt and DALL-E create visual art that rivals human artists. According to a report by MarketsandMarkets, the AI market size is expected to grow from US$150.2 billion in 2023 to US$1345.2 billion in 2030, reflecting the rapid adoption of AI technologies across various industries.

However, this surge in AI-generated content has raised significant questions about copyright ownership and enforcement. Traditional copyright laws, designed for human creators, struggle to address the complexities introduced by AI. Who owns the copyright to a piece of music composed by an AI? How can creators prove ownership and control the distribution of their work? These questions highlight the need for a new framework that can manage the unique challenges posed by AI-generated content.

Blockchain: A decentralised solution

Blockchain technology, with its decentralised and immutable nature, offers a promising solution to the challenges of AI copyright. At its core, blockchain is a distributed ledger that records transactions in a secure and transparent manner. Each block in the chain contains a timestamp and a link to the previous block, making it virtually tamper-proof. This inherent security and transparency make blockchain an ideal platform for managing digital rights.

One of the key advantages of blockchain is its ability to establish provenance and ownership. By recording the creation and subsequent transactions of digital content on a blockchain, creators can prove the originality and ownership of their work. This is particularly valuable for AI-generated content, where the line between human and machine authorship can be blurred.

Smart contracts and automated rights management

Smart contracts, self-executing contracts with the terms of the agreement directly written into code, are another powerful feature of blockchain technology that can revolutionise copyright management. These contracts can automatically enforce copyright terms, ensuring that creators are compensated for the use of their work.

Also Read: How blockchain can enhance sustainability in fashion

For instance, an AI-generated piece of music could be embedded with a smart contract that specifies the terms of its use. Whenever the music is played, the smart contract can automatically collect royalties and distribute them to the rightful owner. This not only simplifies the process of rights management but also ensures that creators receive fair compensation without the need for intermediaries.

A notable example of blockchain-based rights management is the platform Audius, a decentralised music streaming service that uses blockchain to ensure artists retain control over their music and receive fair compensation. As of May 2024, the platform has has between 5 million and 6 million monthly active users, demonstrating the potential of blockchain to disrupt traditional industries and provide new opportunities for creators.

Challenges and considerations

While blockchain technology offers significant potential for AI copyright management, it is not without challenges. One of the primary concerns is the scalability of blockchain networks. As the volume of AI-generated content grows, the blockchain must be able to handle a large number of transactions efficiently. Current blockchain networks, such as Bitcoin and Ethereum, have faced scalability issues, leading to high transaction fees and slower processing times.

However, ongoing research and development in blockchain technology are addressing these issues. Layer 2 solutions, such as the Lightning Network for Bitcoin and Ethereum’s Optimistic Rollups, aim to increase transaction throughput and reduce costs. Moreover, newer blockchain platforms like Solana and Mantle are designed with scalability in mind, offering faster and more efficient networks.

Another consideration is the legal recognition of blockchain records. While blockchain provides a secure and transparent way to record ownership and transactions, the legal system must recognise these records for them to be effective in enforcing copyright. This requires updating existing copyright laws to accommodate blockchain technology and ensure its compatibility with legal standards.

The future of blockchain and AI copyright

Despite these challenges, the future of blockchain and AI copyright management looks promising. As both technologies continue to evolve, they are likely to become increasingly integrated, providing a robust framework for protecting digital rights in the age of AI.

Also Read: 5 dimensions of responsible AI: Enhancing societal needs with blockchain

One potential development is the creation of decentralised autonomous organisations (DAOs) for content creators. These organisations, governed by smart contracts, could provide a collective platform for creators to manage their rights, distribute their work, and receive fair compensation.

For example, an AI-generated artwork could be minted as a non-fungible token (NFT) on a blockchain, with the DAO managing its sale and distribution. The creator would retain ownership and receive royalties from secondary sales, ensuring ongoing compensation for their work.

Conclusion: A personal perspective

As an observer of technological trends, I am optimistic about the potential of blockchain to address the challenges of AI copyright. The decentralised and transparent nature of blockchain provides a robust framework for managing digital rights, ensuring that creators receive fair compensation and retain control over their work. While there are challenges to overcome, the ongoing development of blockchain technology and its integration with AI offer a promising path forward.

The rise of AI-generated content presents a unique opportunity to rethink traditional copyright laws and embrace new technologies that can better serve the needs of creators in the digital age. Blockchain, with its ability to establish provenance, enforce smart contracts, and provide a decentralised platform for rights management, is well-positioned to play a central role in this transformation.

As we move forward, it is essential for policymakers, creators, and technologists to collaborate and develop a legal framework that recognises the potential of blockchain and supports its adoption for AI copyright management. By doing so, we can create a more equitable and efficient system that benefits both creators and consumers, ensuring that the digital economy continues to thrive in the age of AI.

In conclusion, the integration of blockchain and AI represents a significant step forward in the evolution of digital rights management. By leveraging the strengths of both technologies, we can create a future where creators are empowered, intellectual property is protected, and innovation is encouraged. The journey may be challenging, but the potential rewards are immense, making it a worthwhile endeavour for all stakeholders involved.

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Blockchain technology, once a niche concept, has rapidly become a transformative force across various industries. Originally designed as the underlying technology for cryptocurrencies like Bitcoin, blockchain has evolved to offer much more than digital currencies.

Its decentralised and tamper-proof nature has sparked innovation in finance, supply chain management, healthcare, and beyond. In this article, we will explore the fundamentals of blockchain technology, its key features, and its far-reaching implications for the future.

Blockchain technology has emerged as a transformative force in the world of finance, significantly impacting global payment solutions and cross-border remittance. By offering transparency, security, efficiency, and cost-effectiveness, blockchain has revolutionised the way money is transferred across borders and has the potential to reshape the entire financial industry.

In this article, we will explore the profound impact of blockchain on global payment solutions and cross-border remittance.

Understanding blockchain technology

At its core, a blockchain is a distributed ledger that records transactions across multiple computers or nodes. Here are some key components and principles of blockchain technology:

• Decentralisation: Unlike traditional systems that rely on a central authority, a blockchain operates on a network of computers, making it decentralised. This means no single entity has complete control, enhancing security and transparency.
• Immutable ledger: Once data is recorded on a blockchain, it becomes nearly impossible to alter or delete. Each new transaction is linked to the previous one, creating a chain of blocks with a complete history.
• Transparency: Information stored on a blockchain is typically visible to all participants, ensuring transparency and reducing the risk of fraud.
• Security: Blockchain employs advanced cryptographic techniques to secure data and transactions. The distributed nature of the network makes it highly resistant to hacking.
• Smart contracts: Smart contracts are self-executing agreements with predefined rules. They automatically execute when conditions are met, reducing the need for intermediaries.

Applications across industries

• Finance: Blockchain has disrupted the financial sector, offering faster, cheaper, and more secure cross-border payments. Cryptocurrencies like Bitcoin and Ethereum have introduced digital assets and decentralised finance (DeFi) platforms that enable lending, borrowing, and trading without traditional banks.
• Supply chain: In supply chain management, blockchain enhances transparency and traceability. It allows stakeholders to track products from origin to destination, reducing fraud, counterfeits, and inefficiencies.
• Healthcare: Blockchain secures health records and ensures interoperability among healthcare providers. Patients gain control over their data, and researchers can access anonymised information for medical studies.
• Voting systems: Blockchain can be used for secure and transparent electronic voting systems. It can eliminate voter fraud and ensure accurate election results.
• Real estate: Property transactions benefit from blockchain’s transparency and efficiency. It simplifies title transfers, reduces fraud, and lowers transaction costs.
• Energy: Blockchain enables peer-to-peer energy trading and grid management. Prosumers can sell excess energy to neighbours, reducing reliance on centralised utilities.

Also Read: How blockchain can enhance sustainability in fashion

Blockchain and global payment solutions

Blockchain technology has disrupted the world of finance by offering faster, cheaper, and more secure global payment solutions and cross-border remittance services. Its decentralised, transparent, and secure nature has the potential to make financial transactions more accessible to people worldwide while reducing costs and enhancing security.

As blockchain continues to evolve and overcome challenges, it will shape the future of global finance and pave the way for a more interconnected and inclusive world economy.

Blockchain technology has transformed global payment solutions in the following ways:

• Faster transactions: Traditional cross-border payments can take several days to clear. Blockchain enables near-instantaneous settlement, reducing transaction times from days to minutes or even seconds.
• Cost-effective: Traditional financial institutions often charge substantial fees for cross-border transactions. Blockchain payments are typically more cost-effective, with lower fees and competitive exchange rates.
• 24/7 availability: Blockchain operates 24/7, eliminating the constraints of banking hours and enabling continuous global payments.
• Enhanced security: Blockchain’s cryptographic security measures significantly reduce the risk of fraud and unauthorised access to payment data.
• Financial inclusion: Blockchain-based global payment solutions are accessible to individuals and businesses worldwide, including those in underserved or unbanked regions.

Blockchain and cross-border remittance

Blockchain’s impact on cross-border remittance is particularly noteworthy:

• Reduced costs: Traditional remittance services often charge high fees and offer unfavorable exchange rates. Blockchain-based remittance platforms can offer significant cost savings for senders and recipients.
• Speed and efficiency: Blockchain allows for real-time or near-real-time remittance transfers, eliminating the delays associated with traditional banking systems.
• Transparency: Blockchain’s transparency ensures that both senders and recipients can track the status of remittances, providing peace of mind.
• Security and fraud prevention: The immutable nature of blockchain records makes cross-border remittances highly secure and less susceptible to fraud.
• Financial inclusion: Blockchain-powered remittance services open up opportunities for financial inclusion, enabling access to remittances for people who lack access to traditional banking services.

Outlook of blockchain technology for global payments and cross-border remittance

While blockchain offers promising solutions for global payment and cross-border remittance, challenges remain. Scalability, regulatory compliance, and interoperability are among the key issues that need to be addressed. Additionally, user adoption and education are crucial for the widespread acceptance of blockchain-based solutions.

The future of blockchain in global payments and cross-border remittance is bright. Ongoing research and development are focused on addressing current limitations, and collaborations between the blockchain industry and financial institutions are driving innovation. As blockchain technology continues to mature, it will play an increasingly pivotal role in creating a more efficient, accessible, and secure global financial ecosystem.

While blockchain technology holds immense promise, it faces challenges. Scalability, energy consumption (especially in proof-of-work systems), regulatory hurdles, and standardisation are key issues to address. Moreover, blockchain’s mainstream adoption requires user-friendly interfaces and education.

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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 August 19, 2024

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