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Ecosystm Insights - A new age Technology Research platform to help you access latest market insights,expert opinions and research data
Web3 Evolution: 2025 Trends To Watch

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In my previous Ecosystm Insight, I spoke about Web3’s key initiatives in 2024 that caught my attention. As we navigate through the rapidly evolving tech landscape, I’ve chosen to highlight a few trends in the ecosystem that truly excite me – though this list is far from exhaustive. Interestingly, some of the most hyped trends, like the memecoin frenzy and AI agent platforms, didn’t pique my interest enough to dive deeper into.

Here are 7 trends I’ll be keeping an eye on in 2025.

Click here to download “Web3 Evolution: 2025 Trends To Watch” as a PDF.

1. Stablecoins: The Bridge Between Fintech and Crypto

In 2024, a significant shift occurred in how global fintechs view crypto, driven by stablecoins. What started as a collateral tool for crypto trading has now evolved into a proven solution for cross-border payments and remittances. Companies like Stripe, Revolut, Robinhood, and Nubank are expanding their role as crypto gateways, offering on and off-ramps alongside stablecoin-enabled payments. With global payment revenues projected to reach USD 3.3 trillion by 2031, traditional systems still face challenges like high fees, slow settlement times, and inefficiencies – issues that stablecoin rails are now set to address!

Expect more launches and M&A in this space as every web2 fintech becomes “crypto-ready”!

“Whether intentionally or because of their ability to support third-party apps, every fintech will become a crypto gateway. Fintechs will grow in prevalence and may perhaps rival smaller centralised exchanges in crypto holdings.”PAUL VERADITTAKIT

2. Bringing Real-World Assets to DeFi: Simplifying Complexities

Over 12,500 DeFi pools currently serve around 7 million users, facing challenges such as onboarding, price discovery, liquidity management, and safeguards against arbitrage. It is expected that decentralised secondary marketplaces for trading real-world assets will be launched, aiming to simplify these complexities and potentially attract new users to DeFi.

3. Smart DePINs: The Rise of AI-Driven Coordination

More DePIN projects are expected to integrate AI and agentic computation to automate coordination, optimise demand and supply, and enhance interoperability. AI may also be used for tasks like node selection, choosing light nodes for accessibility and switching to heavier nodes for network reliability and redundancy. Nvidia’s embrace of DePINs like IPFS Filecoin could be a game-changer, with the company recently sharing potential approaches to leveraging decentralised data structures.

4. Web2.5: The Secret to Scaling Web3 Adoption

Tell me it’s crypto without telling me it’s crypto!

Prediction markets hit their stride in 2024, particularly with Polymarket and the elections, where most users didn’t even realise they were using blockchains. This could be the key to scaling web3 – enter web2.5. However, what is more exciting is the rise of the Telegram mini app ecosystem, the Worldcoin app store, and the Solana phone app store. These simple and intuitive web2-like interfaces are slated to bring more new and first-time users to web3 than some of the louder narratives like the AI-driven memecoin frenzy.

5. Proof-of-Humanity: Securing the Digital Self

While Tools for Humanity faced early criticism for scanning irises, the project, which now has over 20 million users, will gain more traction as people recognise the importance of proof-of-humanity. With the rise of AI-generated content and deepfakes, proof-of-humanity is becoming crucial – not just for combating Sybil attacks and frauds. Projects like SpaceID, Sign, and Mocaverse are also developing universal identity systems that enable users to access multi-chain services with a single private key or ID. Verifiable identity and credentialing via blockchains will be one of the most compelling use cases for the technology.

6. NFTs Reimagined: A New Era of Digital Assets

Story Protocol, which raised USD 80 million at a USD 2.25 billion valuation, aims to tokenise the world’s IP, placing originality at the heart of creative exploration and supporting creators. NFTs can be used not only for ID transactions, transfers, ownership, and memberships but also to represent and value assets. We can expect the emergence of many such NFT use cases beyond profile pictures, particularly in loyalty programs, brand memberships, and token-gated experiences. The second coming of NFTs is set for 2025!

7. Web3 Gateways: Wallets Evolve into Comprehensive Platforms

Similar to how browsers serve as gateways to the internet, web3 wallets like Metamask and Phantom are becoming essential entry points to the web3 experience. These wallets will evolve into all-encompassing platforms, integrating dApps and decentralised applications into their feature set. Along with enhanced security, leading wallets will soon offer services such as trading, gaming, minting, and token swapping, all directly within the wallet interface.

Ecosystm Opinion

It’s a reminder of how far we’ve come – and how much further we have to go. Yuval Noah Harari once pointed out that early use cases of the printing press were often conspiracy theories, and early Internet days were filled with chatrooms and adult content. We’re in the early stages of web3, and with each passing day, new use cases emerge.

This space is still unfolding, and it will be fascinating to see where it leads!

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DeepSeek Changes Everything, Yet Nothing at the Same Time 

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A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.

DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today. 

While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost. 

The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model. 

Real Barriers to GenAI 

Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets. 

Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific. 

Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training. 

AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start. 

AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices. 

GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome. 

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AI Infrastructure 2025: Dominance, Disruption, and Collaboration

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Barely weeks into 2025, the Consumer Electronics Show (CES) announced a wave of AI-powered innovations – from Nvidia’s latest RTX 50-series graphics chip with AI-powered rendering to Halliday’s futuristic augmented reality smart glasses. AI has firmly emerged from the “fringe” technology to become the foundation of industry transformation. According to MIT, 95% of businesses are already using AI in some capacity, and more than half are aiming for full-scale integration by 2026.

But as AI adoption increases, the real challenge isn’t just about developing smarter models – it’s about whether the underlying infrastructure can keep up.

The AI-Driven Cloud: Strategic Growth

Cloud providers are at the heart of the AI revolution, but in 2025, it is not just about raw computing power anymore. It’s about smarter, more strategic expansion.

Microsoft is expanding its AI infrastructure footprint beyond traditional tech hubs, investing USD 300M in South Africa to build AI-ready data centres in an emerging market. Similarly, AWS is doubling down on another emerging market with an investment of USD 8B to develop next-generation cloud infrastructure in Maharashtra, India.

This focus on AI is not limited to the top hyperscalers; Oracle, for instance, is seeing rapid cloud growth, with 15% revenue growth expected in 2026 and 20% in 2027. This growth is driven by deep AI integration and investments in semiconductor technology. Oracle is also a key player in OpenAI and SoftBank’s Stargate AI initiative, showcasing its commitment to AI innovation.

Emerging players and disruptors are also making their mark. For instance, CoreWeave, a former crypto mining company, has pivoted to AI cloud services. They recently secured a USD 12B contract with OpenAI to provide computing power for training and running AI models over the next five years.

The signs are clear – the demand for AI is reshaping the cloud industry faster than anyone expected.

Strategic Investments In Data Centres Powering Growth

Enterprises are increasingly investing in AI-optimised data centres, driven by the need to reduce reliance on traditional data centres, lower latency, achieve cost savings, and gain better control over data.

Reliance Industries is set to build the world’s largest AI data centre in Jamnagar, India, with a 3-gigawatt capacity. This ambitious project aims to accelerate AI adoption by reducing inferencing costs and enabling large-scale AI workloads through its ‘Jio Brain’ platform. Similarly, in the US, a group of banks has committed USD 2B to fund a 100-acre AI data centre in Utah, underscoring the financial sector’s confidence in AI’s future and the increasing demand for high-performance computing infrastructure.

These large-scale investments are part of a broader trend – AI is becoming a key driver of economic and industrial transformation. As AI adoption accelerates, the need for advanced data centres capable of handling vast computational workloads is growing. The enterprise sector’s support for AI infrastructure highlights AI’s pivotal role in shaping digital economies and driving long-term growth.

AI Hardware Reimagined: Beyond the GPU

While cloud providers are racing to scale up, semiconductor companies are rethinking AI hardware from the ground up – and they are adapting fast.

Nvidia is no longer just focused on cloud GPUs – it is now working directly with enterprises to deploy H200-powered private AI clusters. AMD’s MI300X chips are being integrated into financial services for high-frequency trading and fraud detection, offering a more energy-efficient alternative to traditional AI hardware.

Another major trend is chiplet architectures, where AI models run across multiple smaller chips instead of a single, power-hungry processor. Meta’s latest AI accelerator and Google’s custom TPU designs are early adopters of this modular approach, making AI computing more scalable and cost-effective.

The AI hardware race is no longer just about bigger chips – it’s about smarter, more efficient designs that optimise performance while keeping energy costs in check.

Collaborative AI: Sharing The Infrastructure Burden

As AI infrastructure investments increase, so do costs. Training and deploying LLMs requires billions in high-performance chips, cloud storage, and data centres. To manage these costs, companies are increasingly teaming up to share infrastructure and expertise.

SoftBank and OpenAI formed a joint venture in Japan to accelerate AI adoption across enterprises. Meanwhile, Telstra and Accenture are partnering on a global scale to pool their AI infrastructure resources, ensuring businesses have access to scalable AI solutions.

In financial services, Palantir and TWG Global have joined forces to deploy AI models for risk assessment, fraud detection, and customer automation – leveraging shared infrastructure to reduce costs and increase efficiency.

And with tech giants spending over USD 315 billion on AI infrastructure this year alone – plus OpenAI’s USD 500 billion commitment – the need for collaboration will only grow.

These joint ventures are more than just cost-sharing arrangements; they are strategic plays to accelerate AI adoption while managing the massive infrastructure bill.

The AI Infrastructure Power Shift

The AI infrastructure race in 2025 isn’t just about bigger investments or faster chips – it’s about reshaping the tech landscape. Leaders aren’t just building AI infrastructure; they’re determining who controls AI’s future. Cloud providers are shaping where and how AI is deployed, while semiconductor companies focus on energy efficiency and sustainability. Joint ventures highlight that AI is too big for any single player.

But rapid growth comes with challenges: Will smaller enterprises be locked out? Can regulations keep pace? As investments concentrate among a few, how will competition and innovation evolve?

One thing is clear: Those who control AI infrastructure today will shape tomorrow’s AI-driven economy.

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Web3 Evolution: From Speculation to Real-World Applications

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2024 was a pivotal year for cryptocurrency, driven by substantial institutional adoption. The approval and launch of spot Bitcoin and Ethereum ETFs marked a turning point, solidifying digital assets as institutional-grade. Bitcoin has evolved into a macro asset, and the ecosystem’s outlook remains robust, with signs of regulatory clarity in the US and increasing broad adoption. High-quality research from firms like VanEck, Messari, Pantera, Galaxy, and a16Z, has further strengthened my conviction.  

As a “normie in web3,” my perspective comes from connecting the dots through research, not from early airdrops or token swaps. While the speculative frenzy, rug pulls, and scams at the “casino” end are off-putting, the real potential on the “computer” side of blockchains is thrilling. Events like TOKEN2049 in Dubai and Singapore highlight the ecosystem’s energy, with hundreds of side events now central to the experience.

As the web3 ecosystem evolves, new blockchains, roll-ups, and protocols vie for attention. With 60 million unique wallets in the on-chain economy, adoption is set to expand beyond this base. DeFi transaction volumes have surpassed USD 200B/month, yet the ecosystem remains in its early stages, with only 10 million users.

Despite current fragmentation, the future looks promising. Themes like tokenising real-world assets, decentralised public infrastructure, stablecoins for instant payments, and the convergence of AI and blockchain could reshape finance, identity, infrastructure, and computing. Web3 holds transformative potential, even if not in marketing terms like “unstable” coins or “unreal world assets.”

The Decentralisation Paradox of Web3

Decentralisation may have been a core tenet of web3 at the onset but is also seen as a constraint to scaling or improving user experience in certain instances. I always saw decentralisation as a progressive spectrum and not a binary. It is, however, a difficult north star to maintain, as scaling becomes an actual human coordination challenge.

In Blockchains. We have seen this phenomenon manifest with the Ethereum ecosystem in particular. Of the fifty-plus roll-ups listed on L2 Beat, only Arbitrum and OP Mainnet have progressed beyond Stage 0, with many still not posting fraud proofs to L1. Some high-performance L1s and L2s have deprioritised decentralisation in favour of scaling and UX. Whether this trade-off leads to greater vulnerability or stronger product-market fit remains to be seen – most users care more about performance than underlying technology. In 2025, we’ll likely witness the quiet demise of as many blockchains as new ones emerge.

In Finance. On the institutional side, some aspects of high-value transactions in traditional finance or TradFi, such as custody, need trusted intermediaries to minimise counterparty risk. For web3 to scale beyond the 60-million-odd wallets that participate in the on-chain economy today, we need protocols that marry blockchains’ efficiency, composability, and programmability with the trusted identity and verifiability of the regulated financial systems. While “CeDeFi” or Centralised Decentralised Finance might sound ironical to most in the crypto native world, I expect much more convergence with institutions launching tokenisation projects on public blockchains, including Ethereum and Solana. I like underway pilots, such as one by Chainlink with SWIFT, facilitating off-chain cash settlements for tokenised funds. Some of these projects will find strong traction and scale coupled with regulatory blessings in certain progressive jurisdictions in 2025.

In Infrastructure. While decentralised compute clusters for post-training and inference from the likes of io.net can lower the cost of computing for start-ups, scaling decentralised AI LLMs to make them competitive against LLMs from centralised entities like OpenAI is a nearly impossible order. New metas such as decentralised science or DeSci are exciting because they open the possibility of fast-tracking fundamental research and drug discovery.

Looking Back at 2024: What I Found Exciting

ETFs. BlackRock’s IBIT ETF became the fastest to reach USD 3 billion in AUM within 30 days and scaled to USD 40 billion in 200 days. The institutional landscape now goes beyond traditional ETFs, with major financial institutions expanding digital asset capabilities across custody, market access, and retail integration. These include institutional-grade custody from Standard Chartered and Nomura, market access from Goldman Sachs, and retail integration from fintechs such as Revolut.

Stablecoins. Stablecoin usage beyond trading has continued to grow at a healthy clip, emerging as a real killer use case in payments. Transaction volumes rose from USD 10T to USD 20T in a year, and yes, that is a trillion with a “t”! The current market capitalisation of stablecoins is approximately USD 201.5 billion, slated to triple in 2025, with Tether’s USDT at over 67% market share. We might see new fiat-backed stablecoins being launched this year, such as Ethena’s yield-bearing stablecoin, but I don’t expect USDT’s dominance to change.

RWAs. Even though stablecoins represent 97% of real-world assets on-chain and the dollar value of all other types of assets is still insignificant, the potential market for asset tokenisation is still a staggering USD 1.4T, and with regulatory clarity, even if RWAs on-chain were to quadruple, the resulting USD 50B will be a sliver of the overall opportunity. We can expect more projects in asset classes such as private credit – rwa.xyz is a great dashboard to watch this space.

DePIN. Decentralised public infrastructure across wireless, energy, compute, sensors, identity, and logistics reached a USD 50B market cap and USD 500M in ARR. Key developments include the emergence of AI as a major driver of DePIN adoption, the maturation of supply-side growth playbooks, and the shift in focus toward demand-side monetisation. More than 13 million devices globally contribute to DePINs daily, demonstrating successful supply-side scaling. Notable projects include:

  • Helium Mobile: Adding 100k+ subscribers and diversifying revenue streams.
  • AI Integration: Bittensor leading decentralised AI with successful subnets.
  • Energy DePINs: Glow and Daylight addressing challenges in distributed energy systems.
  • Identity Verification: World (formerly Worldcoin) achieving 20 million verified identities.

These trends indicate significant advancements in the web3 ecosystem, and the continued evolution of blockchain technologies and their applications in finance, infrastructure, and beyond holds immense promise for 2025 and beyond.

In my next Ecosystm Insights, I’ll present the trends in 2025 that I am excited about. Watch this space!

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AI Agent Management: Insights from RPA Best Practices

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The promise of AI agents – intelligent programs or systems that autonomously perform tasks on behalf of people or systems – is enormous. These systems will augment and replace human workers, offering intelligence far beyond the simple RPA (Robotic Process Automation) bots that have become commonplace in recent years.

RPA and AI Agents both automate tasks but differ in scope, flexibility, and intelligence:

RPA Vs. AI Agent: A Snapshot on the basis of Scope, Flexibility, Intelligence, Integration, and Adaptability.

7 Lessons for AI Agents: Insights from RPA Deployments

However, in many ways, RPA and AI agents are similar – they both address similar challenges, albeit with different levels of automation and complexity. RPA adoption has shown that uncontrolled deployment leads to chaos, requiring a balance of governance, standardisation, and ongoing monitoring. The same principles apply to AI agent management, but with greater complexity due to AI’s dynamic and learning-based nature.

By learning from RPA’s mistakes, organisations can ensure AI agents deliver sustainable value, remain secure, and operate efficiently within a governed and well-managed environment.

#1 Controlling Sprawl with Centralised Governance

A key lesson from RPA adoption is that many organisations deployed RPA bots without a clear strategy, resulting in uncontrolled sprawl, duplicate bots, and fragmented automation efforts. This lack of oversight led to the rise of shadow IT practices, where business units created their own bots without proper IT involvement, further complicating the automation landscape and reducing overall effectiveness.

Application to AI Agents:

  • Establish centralised governance early, ensuring alignment between IT and business units.
  • Implement AI agent registries to track deployments, functions, and ownership.
  • Enforce consistent policies for AI deployment, access, and version control.

#2 Standardising Development and Deployment

Bot development varied across teams, with different toolsets being used by different departments. This often led to poorly documented scripts, inconsistent programming standards, and difficulties in maintaining bots. Additionally, rework and inefficiencies arose as teams developed redundant bots, further complicating the automation process and reducing overall effectiveness.

Application to AI Agents:

  • Standardise frameworks for AI agent development (e.g., predefined APIs, templates, and design patterns).
  • Use shared models and foundational capabilities instead of building AI agents from scratch for each use case.
  • Implement code repositories and CI/CD pipelines for AI agents to ensure consistency and controlled updates.

#3 Balancing Citizen Development with IT Control

Business users, or citizen developers, created RPA bots without adhering to IT best practices, resulting in security risks, inefficiencies, and technical debt. As a result, IT teams faced challenges in tracking and supporting business-driven automation efforts, leading to a lack of oversight and increased complexity in maintaining these bots.

Application to AI Agents:

  • Empower business users to build and customise AI agents but within controlled environments (e.g., low-code/no-code platforms with governance layers).
  • Implement AI sandboxes where experimentation is allowed but requires approval before production deployment.
  • Establish clear roles and responsibilities between IT, AI governance teams, and business users.

#4 Proactive Monitoring and Maintenance

Organisations often underestimated the effort required to maintain RPA bots, resulting in failures when process changes, system updates, or API modifications occurred. As a result, bots frequently stopped working without warning, disrupting business processes and leading to unanticipated downtime and inefficiencies. This lack of ongoing maintenance and adaptation to evolving systems contributed to significant operational disruptions.

Application to AI Agents:

  • Implement continuous monitoring and logging for AI agent activities and outputs.
  • Develop automated retraining and feedback loops for AI models to prevent performance degradation.
  • Create AI observability dashboards to track usage, drift, errors, and security incidents.

#5 Security, Compliance, and Ethical Considerations

Insufficient security measures led to data leaks and access control issues, with bots operating under overly permissive settings. Also, a lack of proactive compliance planning resulted in serious regulatory concerns, particularly within industries subject to stringent oversight, highlighting the critical need for integrating security and compliance considerations from the outset of automation deployments.

Application to AI Agents:

  • Enforce role-based access control (RBAC) and least privilege access to ensure secure and controlled usage.
  • Integrate explainability and auditability features to comply with regulations like GDPR and emerging AI legislation.
  • Develop an AI ethics framework to address bias, ensure decision-making transparency, and uphold accountability.

#6 Cost Management and ROI Measurement

Initial excitement led to unchecked RPA investments, but many organisations struggled to measure the ROI of bots. As a result, some RPA bots became cost centres, with high maintenance costs outweighing the benefits they initially provided. This lack of clear ROI often hindered organisations from realising the full potential of their automation efforts.

Application to AI Agents:

  • Define success metrics for AI agents upfront, tracking impact on productivity, cost savings, and user experience.
  • Use AI workload optimisation tools to manage computing costs and avoid overconsumption of resources.
  • Regularly review AI agents’ utility and retire underperforming ones to avoid AI bloat.

#7 Human Oversight and Hybrid Workflows

The assumption that bots could fully replace humans led to failures in situations where exceptions, judgment, or complex decision-making were necessary. Bots struggled to handle scenarios that required nuanced thinking or flexibility, often leading to errors or inefficiencies. The most successful implementations, however, blended human and bot collaboration, leveraging the strengths of both to optimise processes and ensure that tasks were handled effectively and accurately.

Application to AI Agents:

  • Integrate AI agents into human-in-the-loop (HITL) systems, allowing humans to provide oversight and validate critical decisions.
  • Establish AI escalation paths for situations where agents encounter ambiguity or ethical concerns.
  • Design AI agents to augment human capabilities, rather than fully replace roles.

The lessons learned from RPA’s journey provide valuable insights for navigating the complexities of AI agent deployment. By addressing governance, standardisation, and ethical considerations, organisations

can shift from reactive problem-solving to a more strategic approach, ensuring AI tools deliver value while operating within a responsible, secure, and efficient framework.

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Cyber Lessons from the Frontlines

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2025 is already shaping up to be a battleground for cybersecurity. With global cybercrime costs projected to reach USD 10.5T, by year’s end, the stakes have never been higher. Cybercriminals are getting smarter, using AI-driven tactics and large-scale exploits to target critical sectors. From government breaches to hospital data leaks and a surge in phishing scams, recent attacks highlight the growing financial and operational toll of cyber threats.

As cyber threats intensify, the demand for stronger defences, top-tier cybersecurity talent, and global collaboration has never been more urgent.

Here’s a look at the recent cyber developments that are shaping 2025.

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Click here to download “Cyber Lessons from the Frontlines” as a PDF.

Major Security Breaches: A Costly Wake-Up Call

Cyberattacks are becoming more targeted, disruptive, and costly – impacting governments and organisations worldwide.

In Singapore, mobile wallet fraud is surging, with phishing tactics causing USD 8.9K in losses – 80% linked to Apple Pay. In the UK, security flaws in government IT systems have exposed sensitive data and infrastructure. South Africa’s government-run weather service (SAWS) was also forced offline, disrupting a critical resource for airlines, farmers, and emergency responders. Across the Atlantic, a data breach at a Georgia hospital compromised 120,000 patient records, while BayMark Health Services, the largest addiction treatment provider in the US, alerted patients to a similar breach.

What steps are governments, tech providers, and enterprises taking to protect themselves, critical infrastructure, and individuals?

Protecting Critical Infrastructure: The Digital Backbone

As global connectivity expands, securing critical infrastructure is paramount to sustaining growth, stability, and public trust.

Undersea cables, which carry much of the world’s internet traffic, are a major focus. While tech giants like Amazon, Meta, and Google are expanding these networks to boost global data speed and reliability, the need for protection is just as urgent – prompting the EU to invest nearly a billion dollars in securing them against emerging threats.

Governments and tech providers alike are stepping up. The European Commission has introduced a cybersecurity blueprint to strengthen crisis coordination, rapid response, and information sharing. Meanwhile, Microsoft is investing USD 700M in Poland’s cloud and AI infrastructure, working with the Polish National Defense to enhance cybersecurity through AI-driven strategies.

Quantifying Cyber Risk: Standardised Threat Assessment

As cyber threats grow more sophisticated, so must our ability to detect, measure, and respond to them.

A major shift in cybersecurity is underway – one that prioritises standardised threat assessment and coordinated defense.

The UK is leading the charge with a new cyber monitoring centre that will introduce a “Richter Scale” for cyberattacks, ranking threats much like earthquake magnitudes. Emerging countries are also joining in; Vietnam is strengthening its cyber defences with a new intelligence-sharing platform designed to improve coordination between the government and private sector.

By quantifying cyber risks and enhancing intelligence-sharing, these efforts are shaping global cybersecurity norms, improving response times, and building a more resilient digital ecosystem.

Beyond Defence: Proactive Measures to Combat AI-Driven Cybercrime

Cyber threats evolve faster than defences can keep up – a single click on a malicious email can lead to a breach in just 72 minutes.

With AI making cyberattacks more sophisticated, governments are taking an active role in cyber law enforcement.

Indonesia set up a cyber patrol to monitor and regulate harmful online content while also working to create a safer digital space for children. Thailand, Cambodia, and Laos are cooperating to curb cross-border scams through intelligence sharing and joint enforcement efforts.

Building Trust Online: Digital Identity Solutions

Governments are moving beyond enforcement to strengthen security with digital identity frameworks.

The EU is leading this shift with large-scale pilots for digital identity wallets, designed to offer citizens a secure, seamless way to verify credentials for services, transactions, and age-restricted content. By 2026, each EU member state will issue its own wallet, built on unified technical standards to ensure cross-border interoperability and stronger cybersecurity.

Digital identity wallets mark a major shift in data security, giving citizens greater control over their information while strengthening online trust. By securing identity verification, governments are reducing fraud and identity theft, creating a safer digital landscape.

Closing the Gap: Global Cyber Education Push

Cybersecurity education is no longer just for IT teams – it’s essential at every level, from executives to employees, to build long-term resilience.

Again, governments and tech giants alike are stepping up to bridge the skills gap and enhance cyber awareness.

Singapore is leading by example with a cyber-resilience training program for board directors, ensuring corporate leaders understand cyber risk management. AWS is investing USD 6.35M to support cybersecurity education in the UK, and Microsoft is expanding its global training efforts. The company has partnered with Kazakhstan to strengthen public sector cybersecurity and has committed to training one million South Africans in AI and cybersecurity by 2026.

"We're blocking over 7,000 password attacks per second, and yet the threats keep evolving. This is why it is important to work with the biggest experts in cybersecurity and share knowledge to help governments and organisations stay ahead." - Sergey Leschenko, MICROSOFT CIS DIRECTOR

The Path Forward: A Collective Responsibility

The cybersecurity landscape underscores a crucial truth: resilience can’t be built in isolation. Governments, businesses, and individuals must move past reactive measures and adopt a collective, intelligence-driven approach. As threats grow more sophisticated, so must our commitment to collaboration, vigilance, and proactive defence.

In an increasingly interconnected world, securing the digital landscape is not just necessary – it’s a shared responsibility.

The Resilient Enterprise
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