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Adrian’s Arena: Navigating the Complexities of AI Copyright Across Asia
Discover how Asia is tackling AI and copyright challenges with innovative laws, landmark cases, and a focus on balancing creativity and innovation.
Published
3 months agoon

TL;DR:
- Asia is at the forefront of AI copyright regulation, with diverse legal frameworks tailored to foster innovation while safeguarding intellectual property.
- Singapore’s 2021 Copyright Act and China’s 2023 landmark rulings highlight progressive approaches to AI-generated content.
- Key challenges include defining authorship, using copyrighted data for AI training, and balancing creator rights with AI development.
- Asia’s leadership is shaping global standards, offering valuable insights for navigating copyright in the AI era.
How Asia’s diverse legal landscapes are addressing the challenges of AI-generated creativity and copyright protection
Artificial intelligence (AI) is revolutionising the way content is created, consumed, and protected. From generating music to writing articles and producing digital art, AI has become a key player in industries reliant on creativity and intellectual property. As this technology advances, it brings with it significant questions about copyright—particularly in Asia, where diverse legal frameworks, cultural practices, and technological innovation intersect.
In this article, we’ll explore how Asia is addressing copyright in the AI era, examining the legal landscapes of key nations, highlighting challenges, and forecasting the region’s influence on global standards.
The Intersection of AI and Copyright
AI’s ability to produce content has sparked a debate: Can works created by machines truly be copyrighted? If so, who owns the rights? Traditional notions of authorship hinge on human creativity, but AI blurs those lines by operating as both a tool and an independent creator.
This has led to critical questions about copyrightability, the use of copyrighted works for training AI, and the responsibilities of human oversight. The answers are far from uniform, especially in Asia, where the legal and cultural contexts vary widely.

Current Legal Landscapes in Asia
Singapore: Leading the Way
Singapore has emerged as a leader in adapting its copyright laws for AI. The 2021 Singapore Copyright Act introduced a defence for copyright infringement related to machine learning, making it the first Southeast Asian country to do so. This amendment allows businesses to conduct computational analysis using copyrighted material, fostering an AI-friendly environment while maintaining safeguards against misuse. By providing a safe harbour for companies engaging in AI development, Singapore aims to attract global investments in the sector. However, purely AI-generated works remain unprotected, as human authorship is still a requirement for copyright protection.
China: Landmark Rulings
China has taken bold steps to address AI and copyright. In November 2023, the Beijing Internet Court ruled in favour of granting copyright protection to an AI-generated image, provided there was substantial human involvement in its creation. The court emphasised the importance of “intellectual inputs” and “personal expressions,” recognising that the prompts and aesthetic judgments of a human user are key to establishing originality. While this case-by-case approach reflects caution, it also sets a precedent for recognising AI-generated works under certain conditions.
Japan: A Balancing Act
Japan has adopted a permissive stance regarding the use of copyrighted materials for AI. The revised Copyright Act of 2019 allows for the ingestion of copyrighted works in AI training without requiring permission, provided it serves technological development. This flexibility has spurred AI innovation, but it has also raised concerns among content creators. Recent discussions suggest Japan may impose stricter protections for copyright holders while maintaining its innovation-friendly policies.
South Korea: Prioritising Human Creativity
South Korea has taken a cautious approach, requiring evidence of human thought and emotion to grant copyright for AI-generated works. This policy underscores the importance of preserving human creativity while navigating the ethical and legal implications of AI.
India: Co-Authorship Approach
India is unique in its recognition of co-authorship for AI-generated works. Rather than introducing new laws, the country relies on its existing intellectual property framework, which it considers sufficient to address these challenges. This pragmatic approach allows for flexibility while protecting human contributions.
Other Asian Nations
- Taiwan: Requires consent or licensing for using copyrighted materials in AI training, considering such activities as “reproduction” under copyright law.
- Hong Kong: Exploring exceptions to copyright infringement for AI training, similar to Singapore and Japan.
- Philippines: The Intellectual Property Office of the Philippines (IPOPHL) is working on drafting guidelines for AI-generated artwork, currently, copyrightable works in the Philippines require a “natural person” as the creator.
- Indonesia: Indonesian Copyright Law is currently silent on the protection of AI-generated works, making the country’s position uncertain. Yet the Directorate General of Intellectual Property (DGIP) in Indonesia has clarified that copyrightable works require a “human touch,” which purely AI-generated works cannot meet.
- Vietnam: The current Intellectual Property Law in Vietnam does not specifically address AI-generated content that infringes on IP rights. Only human individuals or organisations can hold copyright under Vietnamese law; entities like computers, robots, and AI are not considered copyright holders
- ASEAN Initiatives: In March 2024, ASEAN released a non-binding Guide to AI Governance and Ethics, encouraging member states to harmonise approaches to AI regulation and intellectual property.

Key Challenges
Copyrightability of AI-Generated Works
The question of whether AI-generated works qualify for copyright protection is at the heart of the debate. Countries like China and Singapore require significant human involvement, while Japan allows for more permissive use in technological development. This divergence highlights the challenge of creating unified standards in a fragmented regulatory environment.
Training Data and Infringement Risks
The use of copyrighted materials for training AI models has raised legal concerns across Asia. While some nations, like Japan, allow this under specific conditions, others are still grappling with how to balance innovation with the rights of content creators.
Balancing Innovation and Protection
Governments face the challenge of fostering AI innovation while safeguarding intellectual property. Striking this balance is critical for ensuring both technological progress and the protection of creators.
Asia’s Role in Shaping Global Standards
Influencing International Frameworks
Singapore and Japan’s AI-friendly copyright laws provide valuable case studies for other regions. By addressing copyright concerns proactively, these nations are influencing global debates on AI governance. China’s landmark rulings on AI-generated works further contribute to shaping international norms.
Driving AI Innovation
The permissive copyright environments in countries like Singapore and Japan are attracting AI investments and fostering regional innovation. Initiatives like ASEAN’s guide encourage harmonisation, which could create a more cohesive regulatory landscape.
Challenging Traditional Concepts
As countries like China redefine the relationship between human creativity and machine output, traditional notions of authorship and originality are being reexamined. These developments could have far-reaching implications for global intellectual property laws.

Future Outlook
Asia’s diverse approaches to AI and copyright will likely continue to evolve as technology advances. Emerging challenges, such as voice cloning and AI-generated art, will test the limits of current laws and inspire new solutions. By taking the lead in addressing these issues, Asian countries are not only shaping their own futures but also influencing global standards.
For businesses and creators, staying informed about these developments is essential. As the legal landscape becomes increasingly complex, adaptability and awareness will be critical to thriving in this dynamic environment.
Conclusion
Asia is at the forefront of the global conversation on AI and copyright, demonstrating leadership through diverse legal frameworks and innovative policies. By balancing the rights of creators with the need for technological advancement, the region is setting a precedent for how the world can navigate the complexities of AI-driven creativity.
As this journey unfolds, Asia’s experience will provide valuable insights for shaping a fair and innovative global framework for copyright in the AI era.
Join the Conversation:
What do you think? Should AI-generated works be granted the same copyright protections as human-created content, or does this risk undermining the value of human creativity? What’s your take on how Asia is handling this balance? Leave your thoughts in the comments section below.
Share your thoughts and experiences with AI technologies, and don’t forget to subscribe for updates on AI and AGI developments here. Let’s build a community of tech enthusiasts and stay ahead of the curve together!
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Author
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Adrian is an AI, marketing, and technology strategist based in Asia, with over 25 years of experience in the region. Originally from the UK, he has worked with some of the world’s largest tech companies and successfully built and sold several tech businesses. Currently, Adrian leads commercial strategy and negotiations at one of ASEAN’s largest AI companies. Driven by a passion to empower startups and small businesses, he dedicates his spare time to helping them boost performance and efficiency by embracing AI tools. His expertise spans growth and strategy, sales and marketing, go-to-market strategy, AI integration, startup mentoring, and investments. View all posts
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Can PwC’s new Agent OS Really Make AI Workflows 10x Faster?
PwC’s Agent OS seamlessly connects and orchestrates AI agents into scalable enterprise workflows, promising 10x faster AI deployment and real-world productivity gains.
Published
3 minutes agoon
April 2, 2025By
AIinAsia
TL;DR – What You Need to Know in 30 Seconds
- PwC’s Agent OS orchestrates diverse AI agents into unified, scalable workflows, promising deployment up to 10x faster.
- Real-world cases already show efficiency boosts: 40% faster supply chains, 70% reduction in compliance tasks, and 30% quicker marketing launches.
- Designed for complex enterprise environments, it’s cloud-agnostic, multilingual, and actively deployed in leading global businesses, including PwC itself.
AI Agents are everywhere—but can they talk to each other?
PwC has unveiled its ambitious “Agent OS,” aiming to streamline AI orchestration at enterprise scale—promising workflows built and deployed 10 times faster. But is this platform truly the missing link enterprises need for their AI strategy?
Let’s dig in.
Enterprise AI sounds fantastic until you realise it often means managing a tangled web of different tools, platforms, and “intelligent” agents, each stubbornly refusing to play nice with each other. Companies regularly find themselves stuck between AI experiments and true enterprise-scale AI adoption because—ironically—these clever AI agents simply can’t collaborate.
Enter PwC’s new Agent OS, positioned as a kind of universal translator and orchestration conductor rolled into one. Imagine a central nervous system for enterprise AI, capable of seamlessly linking different agents and platforms into coherent workflows—no matter where the agents were developed or what tech stack they’re built on.
But is it all hype, or can PwC’s Agent OS genuinely unlock seamless, scalable enterprise AI?
What exactly is PwC’s Agent OS?
PwC’s Agent OS acts as a unified command centre, orchestrating a multitude of AI agents across popular enterprise platforms, including Anthropic, AWS, GitHub, Google Cloud, Microsoft Azure, OpenAI, Oracle, Salesforce, SAP, and Workday, to name just a few. It connects, coordinates, and scales AI agents—whether they’re custom-built, developed via third-party SDKs, or fine-tuned with proprietary data.
Think of it as the ultimate workflow builder, letting users—from AI specialists to your average non-tech-savvy manager—design sophisticated AI processes using intuitive drag-and-drop tools, natural language interfaces, and visual data-flow management.
Better yet, it’s cloud-agnostic, deploying effortlessly across AWS, Google Cloud, Microsoft Azure, Oracle Cloud Infrastructure, Salesforce, and even on-premises solutions.
Real-World Impact (not just theory)
Sceptical about fancy AI promises? Let’s look at some concrete use-cases PwC already claims are working in practice:
- Supply Chain: Imagine reducing your manufacturing firm’s supply chain delays by up to 40% through seamless integration of forecasting, procurement, and real-time logistics tracking agents from SAP, Oracle, and AWS, topped with PwC’s custom disruption detection agents.
- Marketing Operations: What if your retail marketing campaigns could launch twice as fast, with 30% higher conversion rates by orchestrating agents from OpenAI, Google Cloud, Salesforce, and Workday—all talking together in harmony?
- Compliance Automation: Picture a multinational bank automating regulatory workflows, drastically reducing manual reviews by 70%, thanks to agents seamlessly interpreting and aligning evolving regulatory policies via Anthropic and Microsoft Azure.
Who’s Already Benefiting?
PwC’s Agent OS isn’t just theoretical—real companies are already seeing transformative results:
- A tech company revamped its customer contact centre, reducing average call times by nearly 25%, slashing call transfers by 60%, and boosting customer satisfaction.
- A global hospitality firm automated brand standards management, achieving up to 94% reduction in manual review times.
- A healthcare giant applied AI agents to oncology workflows, streamlining clinical document processing to unlock actionable insights 50% faster, while simultaneously reducing administrative burdens by 30%.
And PwC themselves aren’t sitting idle: They’ve deployed over 250 internal AI agents, turbocharging productivity across tax, assurance, and advisory divisions—proving they’re ready to eat their own AI cooking.
Why PwC’s Agent OS Matters to Asia
In Asia, where enterprises are rapidly adopting AI to stay competitive (especially in dynamic markets like Singapore, India, and Indonesia), PwC’s Agent OS could offer a real edge. Asian enterprises grappling with complex multilingual data streams and diverse regional platforms may find a solution in the adaptive, multilingual capabilities of this system.
But it’s not just about tech. It’s about helping Asia’s leading enterprises quickly build, adapt, and scale AI-driven workflows to compete globally—accelerating innovation at a pace that keeps them ahead.
Could PwC’s Agent OS finally mean enterprises spend less time wrestling with AI tech—and more time reaping its benefits?
We’d love your take. Let us know in the comments below.
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Forget the panic: AI Isn’t Here to Replace Us—It’s Here to Elevate Our Roles
Learn why managing AI agents—not fearing them—is key to thriving in the workforce of tomorrow. Discover how to become an effective AI manager today.
Published
7 days agoon
March 26, 2025By
AIinAsia
TL;DR – What You Need to Know in 30 Seconds About the Rise of the AI Manager
- AI creates new leadership roles, not job losses.
- Successful AI managers combine tech knowledge with clear communication.
- AI boosts productivity, creating more jobs and opportunities.
- Invest early in AI literacy and critical thinking to thrive.
Professionals who master the art of managing AI agents are set to define the next era of work.
AI is everywhere right now—and so are fears about job displacement. But take a deep breath; there’s good news! Rather than making human skills obsolete, artificial intelligence is actually paving the way for a new, exciting role: the AI manager.
As AI agents evolve into reliable digital teammates capable of handling complex tasks, the spotlight shifts onto the people who manage them. In fact, the most successful professionals of the future won’t just understand how AI works—they’ll know exactly how to lead, direct, and collaborate effectively with their digital colleagues.
AI as High-Performing Team Members
Today’s AI isn’t just impressive—it’s genuinely useful. In the past few years alone, we’ve witnessed remarkable leaps in capabilities, especially with generative AI. These digital teammates are now expertly handling everything from financial analysis and legal research to content creation and data-driven decision-making.
The next big thing is ‘agentic AI’—digital agents that don’t just assist humans but actively work alongside them with a level of independence. Think about it: consistent, reliable, and tireless digital employees who never need a coffee break. Of course, that might make some of us nervous—who wouldn’t worry about a colleague who can work 24/7 at lightning speed?
But here’s the key: even the best talent needs effective management. AI might be powerful, but it still needs direction, oversight, and human judgement. The professionals who thrive won’t be replaced by AI—they’ll manage teams of digital talent to deliver results greater than anything achievable alone.
What Does It Mean to Manage AI?
Being an AI manager doesn’t mean abandoning traditional leadership skills; it means expanding them. Great managers have always needed two core competencies:
- People management: motivating, inspiring, and guiding human teams. While AI lacks emotions, clear communication and setting precise expectations are still vital.
- Technical management: structuring workflows, delegating tasks strategically, and ensuring alignment towards organisational goals.
Both skill sets are critical when managing AI. A manager of digital agents must understand the nuances of the technology—its strengths, weaknesses, and quirks—while also working effectively with their human counterparts. Just as a great sales manager might stumble managing engineers without understanding their workflows, managing AI requires hands-on technical knowledge combined with clear strategic vision.
Ultimately, being disconnected from practical realities won’t cut it. Leaders in an AI-driven environment must be equally comfortable engaging with technology as they are with strategy and collaboration.
Re-examining the Job Displacement Myth
Fears around AI’s impact often overlook one important economic principle: Jevons paradox. Simply put, when efficiency improves, overall demand frequently increases too. Yes, AI might automate tasks currently performed by humans—but that same efficiency boost can open doors we can’t yet imagine.
Think of the industrial revolution: automation displaced manual labour, but it simultaneously created unprecedented wealth, innovation, and new kinds of employment. Similarly, AI’s efficiency will likely spawn entirely new markets, industries, and roles—like AI agent managers—ensuring that human creativity and insight remain irreplaceable.
How Can We Prepare for This Shift?
Change can be uncomfortable, and the rise of AI is no exception. But the transition doesn’t have to be painful. Here’s how we can adapt:
1. Prioritise Practical Skills in Education
Universities excel at theory but often overlook practical skills that the workplace demands. It’s time to elevate vocational and professional training, the kind traditionally offered by polytechnics or community colleges, to build job-ready skill sets.
2. Embrace AI Literacy in the Workplace
Companies should embed AI literacy into their core training, ensuring everyone—from new hires to senior executives—is comfortable using and collaborating with AI tools. Businesses that invest early in AI literacy will hold a powerful competitive advantage.
3. Take Personal Responsibility for Learning
Individuals, especially those in roles susceptible to automation, need to proactively upgrade their skillsets. This doesn’t mean everyone should become a developer, but learning to confidently use AI, understand digital workflows, and develop critical thinking around tech are essential.
Crucially, becoming AI-literate doesn’t mean blindly trusting technology; it means being savvy enough to challenge it. An effective AI manager must know when to push back against the recommendations of digital teammates, recognising that AI isn’t perfect—it’s only as good as the people who oversee it.
Luckily, resources to build these skills abound: free online courses, corporate training, AI boot camps, and independent learning opportunities are readily available. Your job is to start learning—and keep asking smart questions.
Are YOU ready?
The future belongs to those who adapt, question, and lead the digital workforce. Are you ready to become an AI manager?
You may also like:
- Could AI Bosses Outperform Humans?
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- Learn more: read Navigating the AI revolution: A roadmap for managers and companies at the WEF
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Why are CMOs Still Holding Back on AI Marketing?
The New York Times embraces generative AI for headlines and summaries, sparking staff worries and a looming legal clash over AI’s role in modern journalism.
Published
4 weeks agoon
March 3, 2025By
AIinAsia
TL;DR – What You Need to Know in 30 Seconds
- Reluctant 27%: A significant chunk of CMOs have minimal or zero use of AI marketing, citing costs and ethical concerns.
- High Performers: Businesses that exceed profit goals are widely using generative AI for both creative work and strategy.
- Cautious Optimism: While some see major wins in campaign analytics, others struggle to find benefits in cost reduction and customer service.
- Risk of Lagging: Experts warn that slow adoption could leave traditional marketers scrambling to catch up in a rapidly evolving field.
Why Are CMOs Still Holding Back on Generative AI Marketing?
In a world where even your local bakery is dabbling with AI-driven marketing campaigns, it seems a little baffling that some Chief Marketing Officers (CMOs) are still on the fence about generative artificial intelligence (AI). The hype machine is running full tilt, with countless headlines promising a revolution in how we strategise and create marketing materials. And yet, according to Gartner’s latest research, 27% of CMOs report no or limited adoption of generative AI in their teams. What’s going on, and should these marketing chiefs be worried? Let’s explore.
The Reluctant Third
Let’s start with the eye-catching number that’s got tongues wagging across the marketing landscape: 27% of CMOs either aren’t using generative AI at all or are only dabbling on the periphery. Considering we’re several years into the generative AI hype wave, you’d think that figure would be lower. After all, we hear success stories about AI-generated ad campaigns or chatbots that transform customer service on a nearly daily basis. So why the reluctance?
One big reason often cited is the cost. While there are open-source options, enterprise-level tools (complete with robust support and advanced data security features) don’t come cheap. Then, there’s also the legal and ethical minefield: some executives worry about brand risk or data security concerns. If your marketing AI is scraping questionable sources for content, or if it accidentally pinches trademarked materials, the cost could be more than just monetary—it might damage your brand’s reputation.
High Performers Blaze the Trail
If you think generative AI is all hype, you might want to pay attention to the marketing teams who are actually succeeding with it. According to Gartner’s findings, 84% of high performers—businesses that exceed their annual profit growth and marketing goals—are leveraging generative AI for creative development, and 52% are putting it towards strategy development.
These stats matter because they highlight a gap between those who’ve embraced the AI revolution and those who are dragging their feet. High-performing organisations see “creative development” as the perfect playground for generative AI: from drafting copy to brainstorming design ideas, the tech is boosting the volume and diversity of creative work. Strategy development is also getting an AI-powered makeover, with marketers crunching campaign data in record time to find winning formulas.
As Gartner notes, CMOs who ignore the technology “are in a position of greater risk.” It’s not just about keeping up with the Joneses—it’s about leveraging a tool that can genuinely make marketing campaigns more efficient, more targeted, and possibly even more profitable.
Not Everyone Sees the Glitter of AI Marketing
Interestingly, the Gartner research also shows that generative AI’s benefits aren’t universally acknowledged. Over a quarter of CMOs surveyed reported little to no benefit in areas like cost reduction, customer service, and scalability. That’s a bit of a head-scratcher when many of us have been sold the dream that AI would turn marketing teams into lean, mean campaigning machines.
Part of the mismatch might come from inflated expectations. Some CMOs might have imagined generative AI swooping in like a marketing superhero, solving every challenge overnight. As a result, when the reality—training, experimenting, refining—sets in, disappointment can ensue.
Many believe GenAI will transform marketing, but despite the hype, many CMOs feel that their GenAI investments have yet to pay off.
It’s also worth noting that 6% of CMOs have no usage of generative AI at all, whereas 21% have only waded into the shallow end. Yet on the other side of the spectrum, around 15% see extremely broad use among their teams. That discrepancy screams caution from some corners and gung-ho enthusiasm from others.
Disruptors and Doubts
Remember those corporate AI solutions that come with hefty price tags? Well, the pace of AI evolution has accelerated massively, especially in Asia. Enter disruptive companies like China’s DeepSeek, which have introduced more affordable—or at least more flexible—versions of AI. They’ve changed the conversation around pricing, data security, and the potential of open-source models.
But not everyone is convinced. A survey by The Wall Street Journal found 21% of IT leaders aren’t currently using AI agents, with reliability being a major sticking point. While that might sound like a small number, keep in mind that these are the folks who sign off on the tech stack. If they harbour doubts, the marketing team’s AI ambitions could remain tethered to a cautionary anchor.
Where Are the Gains?
Despite the reluctance from some, 47% of those who have embraced generative AI are seeing a large benefit in tasks such as campaign evaluation and reporting. This indicates that when deployed properly, AI can absolutely streamline some parts of the marketing machine. Whether it’s quicker insight generation from data analytics or more accurate audience segmentation for targeted campaigns, the gains can’t be ignored.
So, if you find yourself in that 27% who are holding out, consider this: the competitive edge might be slipping away to those high performers who are pairing human creativity with AI efficiency.
Balancing Caution and Curiosity
Let’s be honest: any new technology comes with risks. Data security, ethical boundaries, and steep pricing are real concerns. The key might lie in adopting a balanced approach: start with smaller, safer implementations—like using AI for ad copy testing or initial design mock-ups—before rolling it out to high-stakes areas.
It’s a bit like learning to swim: you wouldn’t jump off the high dive if you’ve never been in the pool before, but you wouldn’t stand on the edge of the pool forever, either.
The Final Word: Ready to Jump In or Watch from the Sidelines?
So, is generative AI in marketing a passing fad or the future of the industry? The data suggests it’s much more than a flash in the pan. High performers are already capitalising on AI’s creative and strategic potential.
The question is: will the sceptics catch up before they’re left behind entirely?
You may also like:
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