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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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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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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This article was first published on August 19, 2024

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I grew up in a remote village where farming was the main work. Family came first. Fields came first. Three generations often worked side by side. Life moved with the seasons. We trusted our elders and community wisdom, prayed for rain and good harvests, and when we felt sick, we used both home remedies and clinical medicine when we could get it.

Over the last 30 years, technology has changed things. First, there was one shared phone at the shop, then basic mobiles, and now smartphones are in everyone’s hands. Today, many things are easier. Farmers check crop prices on a phone instead of waiting for market day. Money transfers arrive in minutes. Short weather messages help us pick a planting day. A video call can save a long bus ride to see a doctor. Children can review lessons on the same phone their parents use to pay for fertiliser.

But new problems also showed up. False prices and rumours spread fast on chat groups. Scammers send links, and some people lose their savings. Apps add small fees that eat into a farmer’s profit. Signals drop during storms, so advice can arrive late. Some elderly people find the apps hard to use. Online classes help, but kids can get pulled into games and short videos. Easy loans on phones help at harvest time, but if prices fall, families can sink into debt. And not every app speaks our language or works well offline.

So, the test is simple: did the tools lower the “survival tax”? In the key areas of farming, health, and education, the answer is complex and demands a closer look at what success truly means.

Driving social impact with tech in SEA

As the introduction makes clear, technology in Southeast Asia is not a simple story of progress. The real measure of its impact is whether it truly reduces the “survival tax”, the daily burden of time, cash, and stress for the people it’s meant to serve. This is a story of nuance, where the same tool that provides a lifeline can also create new vulnerabilities if not designed with empathy and rigour.

Farming: More income this season, not someday

Farmers care about four things: yield, price, cost, and risk. A good tool should improve at least two of these in one season.

• Proven practice that pays: In Vietnam’s Mekong Delta, a rice method called mechanised direct seeding helped farmers use about half the usual seed, cut fertiliser by about one-fifth, grow about five per cent more, and earn around US$200 more per hectare. That is money in hand, not just a nice story.
• Data and better deals: In Thailand, the startup Ricult reports that farmers have raised yields by up to 50 per cent by using better weather and price information, and by getting fairer deals. They also shortened payment times, so farmers get paid in about 48 hours instead of waiting two to three months. Faster pay means fewer costly loans.
• Why trust matters: Not every startup helps. In Indonesia, police detained the founder of a big fish-farming startup in August 2025 during a fraud probe. This is a reminder: farmer data and claims should be checkable and open. Trust is part of the tool. Even with a good app, small transaction fees can accumulate, eating away at a farmer’s thin profit margins. And when a storm knocks out the signal, that embedded climate advice arrives too late.

Also Read: Homegrown solutions for a hungry future: Why Southeast Asia must localise agritech by 2050

Design principles for agri-tech

• Offline-first, local language UX: The technology should be accessible even with patchy connectivity.
• Price transparency + guaranteed off-take: Farmers need to know they can sell their products at a fair price.
• Embedded climate advice: Tools should provide actionable advice on planting times and input optimisation.
• Outcome metrics: The impact should be measurable in terms farmers can feel on a weekly basis, such as feed saved or cash in hand.

Health: Care that arrives earlier and costs less

In many parts of Southeast Asia, the first place people go for help is a phone and the nearest pharmacy. Tech should strengthen that path.

• Lower cost, quicker help: In Indonesia, a study using national telehealth data found a telemedicine visit (with meds) costs about one-third of an in-person visit. When care is cheaper and closer, families don’t wait as long to seek help.
• Platforms at scale: Halodoc serves over 20 million monthly users with doctor chats, medicine delivery, and lab services. This shows that when the service is simple and reliable, people use it at big scale.
• Specialists without a trip to the city: Indonesia has about one skin doctor for every 100,000 people, mostly in big cities. Tele-dermatology has handled hundreds of thousands of cases, saving long and costly travel.
• Pharmacies as the front door: The SwipeRx platform links about 50,000 pharmacies across seven countries. They report reaching ~145 million patients, training 30,000+ pharmacists, and giving 8,000+ pharmacies access to loans so medicines stay in stock. When the local pharmacy is stronger, care gets better for everyone.

Design principles for digital health

• Equip community health workers: Treat community pharmacies, midwives, and Community Health Workers (CHWs) as the “digital front doors” to the healthcare system.
• Zero-rate essential health apps + device financing: Offer affordable access to digital tools, potentially with device financing for low-income users.
• Measure and publish metrics: Track and transparently share key metrics like time-to-care, adherence to treatment plans, and avoidable hospital admissions.

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

Education: Beyond access, toward equity and outcomes

• Evidence that simple works: Simple, phone-based tutoring models have shown some of the most compelling results in SEA. In the Philippines, a model using weekly SMS messages and a 20-minute phone call boosted children’s math scores by about 40 per cent. This low-cost approach, which can be delivered with basic mobile phones, is a powerful example of “equity-by-design” because it reaches families who lack access to laptops, tablets, or broadband internet, directly combating the educational survival tax.
• Platforms must prove learning, not just logins: The measure of a system’s true health is whether it serves its most vulnerable members. Large edutechs must lean into this playbook: formative assessment to spot gaps, targeted tutoring vouchers for the bottom quartile, and teacher tools that cut paperwork. The bar is simple: can a student sharing a single phone still make weekly progress? If not, the platform is likely perpetuating, not closing, the Alignment Gap in education.

Design principles for edutech

• Low-bandwidth content + printable modules: Ensure learning materials are accessible with limited connectivity.
• Caregiver nudges via SMS/WhatsApp: Engage parents and guardians with simple text message reminders and tips.
• Public dashboards: Use transparent dashboards to track mastery gains for the most vulnerable learners, not just the overall average.

Also Read: Bold moves: Capitalising on market dips in edutech

Conclusion

The test for technology in Southeast Asia is not about innovation for its own sake, but about its ability to make life more livable for the millions of people who live on the margins. It is about whether it genuinely reduces the survival tax, the time spent walking to a clinic, the money lost to a scam, the stress of a failed harvest.

The most powerful tools are those that are simple, reliable, and designed to empower people, not just to connect them. They must be built for the reality of the village, not just the promise of the city.

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As markets navigate a tense pre-speech calm ahead of Federal Reserve Chair Jerome Powell’s pivotal address at the Jackson Hole Symposium, global financial conditions reflect a complex interplay of economic uncertainty, shifting capital flows, and sector-specific pressures.

Investors worldwide have adopted a notably cautious stance, with major asset classes exhibiting muted but telling movements that collectively paint a picture of heightened vigilance rather than outright panic. This careful positioning stems from the profound influence Powell’s remarks could exert on interest rate trajectories, inflation expectations, and ultimately the global economic narrative for the remainder of 2024 and into 2025.

The Jackson Hole gathering, historically a venue for significant policy signalling, carries exceptional weight this year as central bankers grapple with persistent inflationary pressures alongside growing concerns about economic deceleration. Market participants scrutinise every potential nuance in anticipated communications, knowing that even subtle phrasing adjustments could trigger substantial reallocations across trillions of dollars in global assets.

Recent equity performance reveals a telling pattern of consolidation and slight retreat. Major US indices closed modestly lower yesterday, with the Dow Jones Industrial Average dipping 0.34 per cent, the S&P 500 falling 0.40 per cent, and the tech-heavy Nasdaq Composite declining 0.34 per cent. This synchronised pullback across diverse market segments suggests broad-based caution rather than sector-specific concerns. The movement reflects institutional investors strategically reducing exposure ahead of the Powell speech, a common pre-event pattern observed during periods of anticipated policy clarity.

Historical analysis of Jackson Hole symposia shows that markets typically enter a period of compressed volatility immediately preceding key speeches, followed by significant directional moves once policy intentions become clearer. The current equity retreat aligns with this established behavioral pattern, indicating sophisticated market participants are preparing for potential volatility rather than reacting to immediate adverse developments.

Simultaneously, the fixed-income market tells a complementary story through rising Treasury yields. The 10-year Treasury yield climbed three basis points to 4.328 per cent, while the two-year yield increased more substantially by five basis points to 3.79 per cent. This steepening of the yield curve carries important implications. The greater movement in shorter-dated yields suggests market participants anticipate the Federal Reserve maintaining restrictive policy for an extended period, potentially delaying anticipated rate cuts.

Also Read: Bitcoin’s big moment: Can crypto shine as stocks stumble before Jackson Hole?

The two-year yield’s sensitivity to Fed policy expectations makes its sharper rise particularly noteworthy, signaling that traders are adjusting their pricing to reflect a higher probability of prolonged higher interest rates. This dynamic creates significant pressure on growth-oriented sectors and valuation-sensitive assets, as the opportunity cost of holding non-yielding investments increases. The yield movement also reflects persistent inflation concerns, with recent economic data showing services inflation proving stickier than anticipated despite goods inflation moderating.

Currency and commodity markets further illustrate this complex risk landscape. The US Dollar Index strengthened 0.41 per cent to 98.62, demonstrating the greenback’s enduring appeal as a safe haven during periods of policy uncertainty. This dollar strength exerts additional pressure on emerging market economies and multinational corporations, creating a secondary layer of global economic constraint. Gold, traditionally a hedge against uncertainty, experienced a slight decline of 0.3 per cent to approximately US$2,400 per ounce.

This counterintuitive movement likely reflects the powerful gravitational pull of rising real yields, which diminishes gold’s appeal as investors compare its non-yielding nature against increasingly attractive Treasury returns. Meanwhile, Brent crude oil gained 1.24 per cent to US$67.67 per barrel, supported by robust US demand indicators and persistent geopolitical tensions surrounding the Ukraine conflict. The energy market’s resilience highlights how physical supply and demand fundamentals continue to exert significant influence alongside financial market dynamics.

The cryptocurrency sector presents a particularly volatile snapshot of current risk sentiment. While the broader narrative suggests caution, the digital asset market experienced pronounced turbulence with significant divergences among major tokens. Cardano’s ADA suffered the most severe decline among larger capitalisation alternatives, plummeting over eight per cent to struggle around US$0.85. This substantial drop reflects specific project-related concerns, including perceived delays in network upgrades and developer activity, compounded by broader market risk aversion.

Ripple’s XRP faced its own challenges, falling below the psychologically important US$3.00 threshold to approximately US$2.90 after a four per cent decline. This breach of a key technical support level raises questions about the token’s near-term trajectory within its current market cycle. Ethereum’s movement toward US$4,200 following a one per cent decline, alongside similar losses for BNB, Dogecoin, and several other prominent tokens, indicates widespread profit-taking and position reduction ahead of the Powell speech.

Notable exceptions included Chainlink’s three per cent gain and modest advances for Solana, Tron, and a few others, suggesting selective strength in specific protocol narratives. The total cryptocurrency market capitalisation experienced a significant contraction, losing approximately US$70 billion to settle around US$2.2 trillion according to verified industry trackers, reflecting the sector’s heightened sensitivity to macroeconomic signals and interest rate expectations.

These market movements collectively underscore several critical dynamics shaping the current financial landscape. First, the persistent disconnect between bond market signaling and equity valuations continues to create tension. While equities have maintained relative resilience despite elevated interest rates, the steepening yield curve suggests bond markets anticipate either prolonged restrictive policy or eventual economic weakness that might force the Fed to cut rates.

Second, the Jackson Hole symposium represents more than a routine policy discussion; it serves as a critical inflection point where the Federal Reserve must balance competing mandates of price stability and maximum employment amid increasingly mixed economic data. Third, the cryptocurrency market’s extreme reaction highlights its evolution from an isolated speculative playground to an asset class increasingly integrated with traditional financial market psychology, particularly regarding interest rate sensitivity.

Looking ahead, Powell’s speech tonight will likely focus on the Fed’s evolving assessment of inflation dynamics, labor market conditions, and the appropriate path for monetary policy normalisation. Market participants will parse every phrase for clues about whether the Fed remains committed to its “higher for longer” stance or is preparing to pivot toward rate cuts. Historical precedent shows that Jackson Hole speeches often lay conceptual groundwork for future policy shifts, even if immediate actions aren’t announced.

Also Read: Jackson Hole looms: Can Powell save markets from a global risk meltdown?

The 2012 symposium featured Bernanke’s introduction of forward guidance, while 2020 saw the formal adoption of average inflation targeting. This year’s speech may address the evolving framework for assessing maximum employment or the balance of risks between inflation and recession. Any indication that the Fed perceives inflation as sufficiently subdued to begin rate cuts could trigger significant market repositioning. At the same time, reinforcement of restrictive policy could extend current pressures across risk assets.

The current market environment demands careful navigation. Investors must balance recognition of persistent inflationary pressures against growing evidence of economic slowing in certain sectors. The bond market’s yield curve dynamics suggest caution about near-term growth prospects, while equity markets continue to reflect corporate earnings resilience. Cryptocurrency markets, having demonstrated their sensitivity to macroeconomic forces, now require analysis through the same fundamental lenses applied to traditional assets.

As we await Powell’s remarks, the financial world holds its breath, understanding that the coming hours could significantly reshape the investment landscape for months to come. This moment encapsulates the delicate balancing act central bankers face in managing complex economies through turbulent times, where communication itself becomes a powerful policy instrument with immediate and far-reaching market consequences.

The true test lies not just in Powell’s words tonight, but in how markets interpret and act upon them in the critical days and weeks that follow, potentially setting the stage for the next major phase in the global economic cycle.

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