In my previous insights, I explained why organisations need to rethink their End-User Computing (EUC) strategies and shared a simple checklist to help them build smarter, more responsible plans tailored to their goals, users, and regions.
As that foundation is laid, it’s critical to put sustainability at the core. From laptops and desktops to peripherals and accessories, the choices made around devices impact not only IT budgets and user productivity but also environmental footprints and regulatory compliance.
Sustainable EUC means selecting devices that align with your company’s climate goals, regulatory mandates, and ethical commitments, while delivering reliability and performance in diverse working environments.
This guide offers a comprehensive sustainability checklist to help IT leaders embed responsible sourcing and lifecycle management into their EUC strategy.
What to Demand from Vendors & Devices
- Specify recognised eco-label tiers (e.g., TCO Gen 9, EPEAT Climate+). Ensures devices meet verified environmental and social standards, reducing overall carbon footprint.
- Request embodied-carbon disclosures (ISO 14067, PAS 2050). To understand full lifecycle emissions to inform refresh cycle decisions.
- Insist on vendor-funded take-back in all deployment regions. Supports responsible recycling and circular economy for end-user devices.
- Audit supply-chain ethics (latest RBA VAP score, Modern Slavery compliance). Certifies devices against verified environmental and social standards, cutting their overall carbon footprint.
- Set minimum firmware support periods and repairability targets. Extends usable device lifespan, lowering total cost of ownership and e-waste.
- Test devices for local climate conditions (humidity, altitude). Guarantees device reliability and energy efficiency in diverse workplaces.
Key Eco-Labels & Certifications for EUC Devices
Not all certifications are created equal. Here are the most relevant for end-user devices, what they mean, and recent updates to watch:
Regional Regulations & Compliance for EUC
EUC devices often span multiple jurisdictions; understanding regional regulations helps avoid compliance risks and future-proofs procurement:
Australia & New Zealand. Minimum Energy Performance Standards (MEPS) for monitors and power supplies; NTCRS take-back requirements; Modern Slavery Act disclosures
Singapore. Resource Sustainability Act (EPR for IT equipment) since 2021; green procurement guidelines for public sector
Japan. Minimum Energy Performance Standards (MEPS) for monitors and power supplies; NTCRS take-back requirements; Modern Slavery Act disclosures
China. China RoHS 2 with new 2024 testing standards for restricted substances
India. E-Waste (Management) Rules 2022 requiring OEMs/importers to collect 80% of products sold; ongoing amendments under legal review
South Korea. Eco-Label expansion to tablets and mini-PCs; EPR scheme in public tenders
Embedding Ethical Sourcing in Your EUC Strategy
Ethics matter beyond environmental impact; responsible sourcing reduces risk and protects brand reputation:
Responsible Business Alliance (RBA) Code of Conduct v8.0. Check for vendor audit results to ensure compliance.
Conflict Minerals / Responsible Minerals Initiative. Especially relevant for supply chains feeding US/EU markets.
Modern Slavery Legislation. Mandate supplier disclosures and risk assessments, especially in Australia and New Zealand.
Public Sector Procurement & EUC Sustainability
Many government buyers set strong sustainability expectations, which can serve as best-practice benchmarks:
Australia (Commonwealth & States). Preference for EPEAT Silver+, NTCRS take-back, and Modern Slavery compliance statements
Singapore GovTech. ENERGY STAR compliance, Resource Sustainability Act adherence, and use of low-halogen plastics
Japan National Procurement. Top Runner energy efficiency, Eco-Mark or equivalent certification
Why Sustainability Matters for End-User Computing
Sustainability in your EUC strategy drives more than just environmental benefits. It:
- Reduces Total Cost of Ownership (TCO) by extending device lifecycles and lowering energy consumption
- Mitigates Supply Chain Risks by ensuring ethical sourcing and regulatory compliance
- Supports Corporate Climate Commitments with transparent carbon accounting and circular economy practices
- Enhances User Satisfaction and Reliability by testing devices for local conditions and durability
By integrating these sustainability criteria into procurement, IT leaders can transform their EUC strategy into a powerful enabler of business value and responsible growth.
Over the past year of moderating AI roundtables, I’ve had a front-row seat to how the conversation has evolved. Early discussions often centred on identifying promising use cases and grappling with the foundational work, particularly around data readiness. More recently, attention has shifted to emerging capabilities like Agentic AI and what they mean for enterprise workflows. The pace of change has been rapid, but one theme has remained consistent throughout: ROI.
What’s changed is the depth and nuance of that conversation. As AI moves from pilot projects to core business functions, the question is no longer just if it delivers value, but how to measure it in a way that captures its true impact. Traditional ROI frameworks, focused on immediate, measurable returns, are proving inadequate when applied to AI initiatives that reshape processes, unlock new capabilities, and require long-term investment.
To navigate this complexity, organisations need a more grounded, forward-looking approach that considers not only direct gains but also enablement, scalability, and strategic relevance. Getting this right is key to both validating today’s investments and setting the stage for meaningful, sustained transformation.
Here is a summary of the key thoughts around AI ROI from multiple conversations across the Asia Pacific region.
1. Redefining ROI Beyond Short-Term Wins
A common mistake when adopting AI is using traditional ROI models that expect quick, obvious wins like cutting costs or boosting revenue right away. But AI works differently. Its real value often shows up slowly, through better decision-making, greater agility, and preparing the organisation to compete long-term.
AI projects need big upfront investments in things like improving data quality, upgrading infrastructure, and managing change. These costs are clear from the start, while the bigger benefits, like smarter predictions, faster processes, and a stronger competitive edge, usually take years to really pay off and aren’t easy to measure the usual way.
Ecosystm research finds that 60% of organisations in Asia Pacific expect to see AI ROI over two to five years, not immediately.
The most successful AI adopters get this and have started changing how they measure ROI. They look beyond just money and track things like explainability (which builds trust and helps with regulations), compliance improvements, how AI helps employees work better, and how it sparks new products or business models. These less obvious benefits are actually key to building strong, AI-ready organisations that can keep innovating and growing over time.
2. Linking AI to High-Impact KPIs: Problem First, Not Tech First
Successful AI initiatives always start with a clearly defined business problem or opportunity; not the technology itself. When a precise pain point is identified upfront, AI shifts from a vague concept to a powerful solution.
An industrial firm in Asia Pacific reduced production lead time by 40% by applying AI to optimise inspection and scheduling. This result was concrete, measurable, and directly tied to business goals.
This problem-first approach ensures every AI use case links to high-impact KPIs – whether reducing downtime, improving product quality, or boosting customer satisfaction. While this short-to-medium-term focus on results might seem at odds with the long-term ROI perspective, the two are complementary. Early wins secure executive buy-in and funding, giving AI initiatives the runway needed to mature and scale for sustained strategic impact.
Together, these perspectives build a foundation for scalable AI value that balances immediate relevance with future resilience.
3. Tracking ROI Across the Lifecycle
A costly misconception is treating pilot projects as the final success marker. While pilots validate concepts, true ROI only begins once AI is integrated into operations, scaled organisation-wide, and sustained over time.
Ecosystm research reveals that only about 32% of organisations rigorously track AI outcomes with defined success metrics; most rely on ad-hoc or incomplete measures.
To capture real value, ROI must be measured across the full AI lifecycle. This includes infrastructure upgrades needed for scaling, ongoing model maintenance (retraining and tuning), strict data governance to ensure quality and compliance, and operational support to monitor and optimise deployed AI systems.
A lifecycle perspective acknowledges the real value – and hidden costs – emerge beyond pilots, ensuring organisations understand the total cost of ownership and sustained benefits.
4. Strengthening the Foundations: Talent, Data, and Strategy
AI success hinges on strong foundations, not just models. Many projects fail due to gaps in skills, data quality, or strategic focus – directly blocking positive ROI and wasting resources.
Top organisations invest early in three pillars:
- Data Infrastructure. Reliable, scalable data pipelines and quality controls are vital. Poor data leads to delays, errors, higher costs, and compliance risks, hurting ROI.
- Skilled Talent. Cross-functional teams combining technical and domain expertise speed deployment, improve quality, reduce errors, and drive ongoing innovation – boosting ROI.
- Strategic Roadmap. Clear alignment with business goals ensures resources focus on high-impact projects, secures executive support, fosters collaboration, and enables measurable outcomes through KPIs.
Strengthening these fundamentals turns AI investments into consistent growth and competitive advantage.
5. Navigating Tool Complexity: Toward Integrated AI Lifecycle Management
One of the biggest challenges in measuring AI ROI is tool fragmentation. The AI lifecycle spans multiple stages – data preparation, model development, deployment, monitoring, and impact tracking – and organisations often rely on different tools for each. MLOps platforms track model performance, BI tools measure KPIs, and governance tools ensure compliance, but these systems rarely connect seamlessly.
This disconnect creates blind spots. Metrics sit in silos, handoffs across teams become inefficient, and linking model performance to business outcomes over time becomes manual and error prone. As AI becomes more embedded in core operations, the need for integration is becoming clear.
To close this gap, organisations are adopting unified AI lifecycle management platforms. These solutions provide a centralised view of model health, usage, and business impact, enriched with governance and collaboration features. By aligning technical and business metrics, they enable faster iteration, responsible scaling, and clearer ROI across the lifecycle.
Final Thoughts: The Cost of Inaction
Measuring AI ROI isn’t just about proving cost savings; it’s a shift in how organisations think about value. AI delivers long-term gains through better decision-making, improved compliance, more empowered employees, and the capacity to innovate continuously.
Yet too often, the cost of doing nothing is overlooked. Failing to invest in AI leads to slower adaptation, inefficient processes, and lost competitive ground. Traditional ROI models, built for short-term, linear investments, don’t account for the strategic upside of early adoption or the risks of falling behind.
That’s why leading organisations are reframing the ROI conversation. They’re looking beyond isolated productivity metrics to focus on lasting outcomes: scalable governance, adaptable talent, and future-ready business models. In a fast-evolving environment, inaction carries its own cost – one that may not appear in today’s spreadsheet but will shape tomorrow’s performance.
Typically, business leaders rely on forecasts to secure budgets for achieving their goals and objectives. Forecasts take historical trends and project them forward, with added assumptions about what may or may not change in the market or operating environment.
But in today’s volatile economic and political climate, traditional forecasting is increasingly unreliable.
The threat of tariffs, actual tariffs, ongoing and emerging conflicts, political transitions and rising authoritarianism, along with the uncertain impact of AI on employment and productivity, are all undermining not just business and consumer confidence, but also supply chains and manufacturing capacity.
Look at the PC market in Asia Pacific. Shipments have traditionally been relatively straightforward to forecast; but in 2025, projections have swung from a 10% decline to 12% growth, and everything in between! These forecasts continue to shift month by month as market conditions evolve. The same applies to tech and non-tech products and services across many industries. Forecasts are no longer reliable or trustworthy.
So, if we cannot trust forecasts, what can we do to secure budget for our short-, medium- and longer-term initiatives? For many leaders, the answer is “Backcasting”.
When Forecasts Break Down, Backcasting Steps Up
Put simply, backcasting is creating a future vision, and building a plan to make that vision a reality.
For example, imagine you are the Asia Pacific Managing Director of a US-based software company aiming to move from the fifth to the second-largest provider in the region by 2030. To reach this goal, you’ll need to build specific capabilities such as adding distributors; expanding implementation and systems integration partners across ASEAN and India (which means strengthening your partner management team); increasing sales and account managers in tier 2 cities; and developing localised product versions and language support. You might also need to choose a different cloud provider to access certain markets like China and adapt your software to meet local regulations.
Backcasting helps you plan all these steps by starting with your 2030 goal and working backwards to create a clear roadmap to get there.
The benefit of backcasting over forecasting is that it gives your organisation defendable goals, targets and initiatives. It moves the thinking beyond the traditional quarterly targets to a longer-term vision. When global leaders ask you to cut budgets, it provides them with clear insight into how those cuts will affect the organisation’s success in Asia Pacific over the medium to long term. It also helps to understand which resources will help you achieve the longer-term goals and which will not.
Ultimately, backcasting is a better way of helping you defend your budgets from the tactical cuts and short-sighted strategies and sharpens your capability to deliver results in the longer term.
Want to Know More?
You can access a detailed report on backcasting: what it is, how it differs from traditional forecasting, and how it can be applied within your organisation. The report includes examples of companies using backcasting to shape strategic initiatives and support innovation, as well as a scenario outlining how an Asia Pacific tech vendor might use the approach to meet growing regional demands.
We have also helped clients start their backcasting journeys through targeted workshops, internal presentations, training programs and helping them set the backcasting strategy and processes in place. These services can support organisations at a strategic level, by aligning long-term plans with overarching goals; or at a team level, by helping functions like sales and marketing meet specific performance expectations.
We welcome your feedback – feel free to contact me or Alea Fairchild. If backcasting could support your organisation’s growth or budget planning, we’d be happy to connect via call or in person to discuss specific needs.
Here’s how we can help:
- Workshops. In-person or virtual workshops designed to build backcasting capabilities, such as setting long-term goals, creating roadmaps, and shifting focus from short-term tactics to strategic outcomes.
- Training (Internal Presentations & Webinars). Sessions to introduce teams to backcasting, explaining what it is, how it can be used, and why it supports more effective mid- to long-term planning.
- Client-Facing Presentations. Presentations tailored for clients and customers to show how backcasting can support their planning and investment decisions, potentially strengthening alignment with available solutions.
- Podcasts & Videos. Co-created audio or video content with leadership to explore how backcasting fits into current workflows, where the value lies, and how teams can tailor their efforts to organisational priorities.
Consider the sheer volume of information flowing through today’s financial systems: every QR payment, e-KYC onboarding, credit card swipe, and cross-border transfer captures a data point. With digital banking and Open Banking, financial institutions are sitting on a goldmine of insights. But this isn’t just about data collection; it’s about converting that data into strategic advantage in a fast-moving, customer-driven landscape.
With digital banks gaining traction and regulators around the world pushing bold reforms, the industry is entering a new phase of financial innovation powered by data and accelerated by AI.
Ecosystm gathered insights and identified key challenges from senior banking leaders during a series of roundtables we moderated across Asia Pacific. The conversations revealed a clear picture of where momentum is building – and where obstacles continue to slow progress. From these discussions, several key themes emerged that highlight both opportunities and ongoing barriers in the Banking sector.
1. AI is Leading to End-to-End Transformation
Banks are moving beyond generic digital offerings to deliver hyper-personalised, data-driven experiences that build loyalty and drive engagement. AI is driving this shift by helping institutions anticipate customer needs through real-time analysis of behavioural, transactional, and demographic data. From pre-approved credit offers and contextual investment nudges to app interfaces that adapt to individual financial habits, personalisation is becoming a core strategy, not just a feature. This is a huge departure from reactive service models, positioning data as a long-term strategic asset.
But the impact of AI isn’t limited to customer-facing experiences. It’s also driving innovation deep within the banking stack, from fraud detection and SME loan processing to intelligent chatbots that scale customer support. On the infrastructure side, banks are investing in agile, AI-ready platforms to support automation, model training, and advanced analytics at scale. These shifts are redefining how banks operate, make decisions, and deliver value. Institutions that integrate AI across both front-end journeys and back-end processes are setting a new benchmark for agility, efficiency, and competitiveness in a fast-changing financial landscape.
2. Regulatory Shifts are Redrawing the Competitive Landscape
Regulators are moving quickly in Asia Pacific by introducing frameworks for Open Banking, real-time payments, and even AI-specific standards like Singapore’s AI Verify. But the challenge for banks isn’t just keeping up with evolving external mandates. Internally, many are navigating a complicated mix of overlapping policies, built up over years of compliance with local, regional, and global rules. This often slows down innovation and makes it harder to implement AI and automation consistently across the organisation.
As banks double down on AI, it is clear that governance can’t be an afterthought. Many are still dealing with fragmented ownership of AI systems, inconsistent oversight, and unclear rules around things like model fairness and explainability. The more progressive ones are starting to fix this by setting up centralised governance frameworks, investing in risk-based controls, and putting processes in place to monitor things like bias and model drift from day one. They are not just trying to stay compliant; they are preparing for what’s coming next. In this landscape, the ability to manage regulatory complexity with speed and clarity, both internally and externally, is quickly becoming a competitive edge.
3. Success Depends on Strategy, Not Just Tech
While enthusiasm for AI is high, sustainable success hinges on a clear, aligned strategy that connects technology to business outcomes. Many banks struggle with fragmented initiatives because they lack a unified roadmap that prioritises high-impact use cases. Without clear goals, AI projects often fail to deliver meaningful value, becoming isolated pilots with limited scalability.
To avoid this, banks need to develop robust return-on-investment (ROI) models tailored to their context — measuring benefits like faster credit decisioning, reduced fraud losses, or increased cross-selling effectiveness. These models must consider not only the upfront costs of infrastructure and talent, but also ongoing expenses such as model retraining, governance, and integration with existing systems.
Ethical AI governance is another essential pillar. With growing regulatory scrutiny and public concern about opaque “black box” models, banks must embed transparency, fairness, and accountability into their AI frameworks from the outset. This goes beyond compliance; strong governance builds trust and is key to responsible, long-term use of AI in sensitive, high-stakes financial environments.
4. Legacy Challenges Still Hold Banks Back
Despite strong momentum, many banks face foundational barriers that hinder effective AI deployment. Chief among these is data fragmentation. Core customer, transaction, compliance, and risk data are often scattered across legacy systems and third-party platforms, making it difficult to access the integrated, high-quality data that AI models require.
This limits the development of comprehensive solutions and makes AI implementations slower, costlier, and less effective. Instead of waiting for full system replacements, banks need to invest in integration layers and modern data platforms that unify data sources and make them AI-ready. These platforms can connect siloed systems – such as CRM, payments, and core banking – to deliver a consolidated view, which is crucial for accurate credit scoring, personalised offers, and effective risk management.
Banks must also address talent gaps. The shortage of in-house AI expertise means many institutions rely on external consultants, which increases costs and reduces knowledge transfer. Without building internal capabilities and adjusting existing processes to accommodate AI, even sophisticated models may end up underused or misapplied.
5. Collaboration and Capability Building are Key Enablers
AI transformation isn’t just a technology project – it’s an organisation-wide shift that requires new capabilities, ways of working, and strategic partnerships. Success depends on more than just hiring data scientists. Relationship managers, credit officers, compliance teams, and frontline staff all need to be trained to understand and act on AI-driven insights. Processes such as loan approvals, fraud escalations, and customer engagement must be redesigned to integrate AI outputs seamlessly.
To drive continuous innovation, banks should establish internal Centres of Excellence for AI. These hubs can lead experimentation with high-value use cases like predictive credit scoring or real-time fraud detection, while ensuring that learnings are shared across business units. They also help avoid duplication and promote strategic alignment.
Partnerships with fintechs, technology providers, and academic institutions play a vital role as well. These collaborations offer access to cutting-edge tools, niche expertise, and locally relevant AI models that reflect the regulatory, cultural, and linguistic contexts banks operate in. In a fast-moving and increasingly competitive space, this combination of internal capability building and external collaboration gives banks the agility and foresight to lead.
Indonesia’s vast, diverse population and scattered islands create a unique landscape for AI adoption. Across sectors – from healthcare to logistics and banking to public services – leaders view AI not just as a tool for efficiency but as a means to expand reach, build resilience, and elevate citizen experience. With AI expected to add up to 12% of Indonesia’s GDP by 2030, it’s poised to be a core engine of growth.
Yet, ambition isn’t enough. While AI interest is high, execution is patchy. Many organisations remain stuck in isolated pilots or siloed experiments. Those scaling quickly face familiar hurdles: fragmented infrastructure, talent gaps, integration issues, and a lack of unified strategy and governance.
Ecosystm gathered insights and identified key challenges from senior tech leaders during a series of roundtables we moderated in Jakarta. The conversations revealed a clear picture of where momentum is building – and where obstacles continue to slow progress. From these discussions, several key themes emerged that highlight both opportunities and ongoing barriers in the country’s digital journey.
Theme 1. Digital Natives are Accelerating Innovation; But Need Scalable Guardrails
Indonesia’s digital-first companies – especially in fintech, logistics tech, and media streaming – are rapidly building on AI and cloud-native foundations. Players like GoTo, Dana, Jenius, and Vidio are raising the bar not only in customer experience but also in scaling technology across a mobile-first nation. Their use of AI for customer support, real-time fraud detection, biometric eKYC, and smart content delivery highlights the agility of digital-native models. This innovation is particularly concentrated in Jakarta and Bandung, where vibrant startup ecosystems and rich talent pools drive fast iteration.
Yet this momentum brings new risks. Deepfake attacks during onboarding, unsecured APIs, and content piracy pose real threats. Without the layered controls and regulatory frameworks typical of banks or telecom providers, many startups are navigating high-stakes digital terrain without a safety net.
As these companies become pillars of Indonesia’s digital economy, a new kind of guardrail is essential; flexible enough to support rapid growth, yet robust enough to mitigate systemic risk.
A sector-wide governance playbook, grounded in local realities and aligned with global standards, could provide the balance needed to scale both quickly and securely.
Theme 2. Scaling AI in Indonesia: Why Infrastructure Investment Matters
Indonesia’s ambition for AI is high, and while digital infrastructure still faces challenges, significant opportunities lie ahead. Although telecom investment has slowed and state funding tightened, growing momentum from global cloud players is beginning to reshape the landscape. AWS’s commitment to building cloud zones and edge locations beyond Java is a major step forward.
For AI to scale effectively across Indonesia’s diverse archipelago, the next wave of progress will depend on stronger investment incentives for data centres, cloud interconnects, and edge computing.
A proactive government role – through updated telecom regulations, streamlined permitting, and public-private partnerships – can unlock this potential.
Infrastructure isn’t just the backbone of digital growth; it’s a powerful lever for inclusion, enabling remote health services, quality education, and SME empowerment across even the most distant regions.
Theme 3. Cyber Resilience Gains Momentum; But Needs to Be More Holistic
Indonesian organisations are facing an evolving wave of cyber threats – from sophisticated ransomware to DDoS attacks targeting critical services. This expanding threat landscape has elevated cyber resilience from a technical concern to a strategic imperative embraced by CISOs, boards, and risk committees alike. While many organisations invest heavily in security tools, the challenge remains in moving beyond fragmented solutions toward a truly resilient operating model that emphasises integration, simulation, and rapid response.
The shift from simply being “secure” to becoming genuinely “resilient” is gaining momentum. Resilience – captured by the Bahasa Indonesia term “ulet” – is now recognised as the ability not just to defend, but to endure disruption and bounce back stronger. Regulatory steps like OJK’s cyber stress testing and continuity planning requirements are encouraging organisations to go beyond mere compliance.
Organisations will now need to operationalise resilience by embedding it into culture through cross-functional drills, transparent crisis playbooks, and agile response practices – so when attacks strike, business impact is minimised and trust remains intact.
For many firms, especially in finance and logistics, this mindset and operational shift will be crucial to sustaining growth and confidence in a rapidly evolving digital landscape.
Theme 4. Organisations Need a Roadmap for Legacy System Transformation
Legacy systems continue to slow modernisation efforts in traditional sectors such as banking, insurance, and logistics by creating both technical and organisational hurdles that limit innovation and scalability. These outdated IT environments are deeply woven into daily operations, making integration complex, increasing downtime risks, and frustrating cross-functional teams striving to deliver digital value swiftly. The challenge goes beyond technology – there’s often a disconnect between new digital initiatives and existing workflows, which leads to bottlenecks and slows progress.
Recognising these challenges, many organisations are now investing in middleware solutions, automation, and phased modernisation plans that focus on upgrading key components gradually. This approach helps bridge the gap between legacy infrastructure and new digital capabilities, reducing the risk of enterprise-wide disruption while enabling continuous innovation.
The crucial next step is to develop and commit to a clear, incremental roadmap that balances risk with progress – ensuring legacy systems evolve in step with digital ambitions and unlock the full potential of transformation.
Theme 5. AI Journey Must Be Rooted in Local Talent and Use Cases
Ecosystm research reveals that only 13% of Indonesian organisations have experimented with AI, with most yet to integrate it into their core strategies.
While Indonesia’s AI maturity remains uneven, there is a broad recognition of AI’s potential as a powerful equaliser – enhancing public service delivery across 17,000 islands, democratising diagnostics in rural healthcare, and improving disaster prediction for flood-prone Jakarta.
The government’s 2045 vision emphasises inclusive growth and differentiated human capital, but achieving these goals requires more than just infrastructure investment. Building local talent pipelines is critical. Initiatives like IBM’s AI Academy in Batam, which has trained over 2,000 AI practitioners, are promising early steps. However, scaling this impact means embedding AI education into national curricula, funding interdisciplinary research, and supporting SMEs with practical adoption toolkits.
The opportunity is clear: GenAI can act as an multiplier, empowering even resource-constrained sectors to enhance reach, personalisation, and citizen engagement.
To truly unlock AI’s potential, Indonesia must move beyond imported templates and focus on developing grounded, context-aware AI solutions tailored to its unique landscape.
From Innovation to Impact
Indonesia’s tech journey is at a pivotal inflection point – where ambition must transform into alignment, and isolated pilots must scale into robust platforms. Success will depend not only on technology itself but on purpose-driven strategy, resilient infrastructure, cultural readiness, and shared accountability across industries. The future won’t be shaped by standalone innovations, but by coordinated efforts that convert experimentation into lasting, systemic impact.
This year’s theme at the ETHDenver – one of crypto’s OG annual gatherings, was “Year of the Regenerates.” This captures the core tension in Web3: the casino vs. the computer. On one side, the pump-and-dumps, meme coin frenzies, and hyper-financialisation. On the other, the cypherpunk ideals of decentralisation, open infrastructure, and a freer, fairer web.
It’s a timely moment for reflection. Crypto prices are tanking alongside global markets, Bitcoin is down, and headline scandals – like the USD1.3B hack of ByBit and millions lost by retail investors to the meme coin mania – paint a bleak picture.
But the full story isn’t just chaos and collapse. There’s real momentum beneath the noise – and a dose of optimism is exactly what the space needs right now.
ByBit: Green Shoots Amidst the Biggest Hack
The crypto market has seen renewed bearish sentiment, intensified by the USD1.5 billion ByBit hack on February 21, 2025 – the largest crypto heist to date, reportedly carried out by North Korea’s Lazarus Group. Notably, the attack’s impact was limited thanks to Copper’s Clearloop custody infrastructure, which protected user funds through its bankruptcy-remote design.
Yet despite the headline-grabbing loss, several market watchers have pointed to unexpectedly bullish signals emerging from the aftermath.
- Reduced Leverage and Market Stability. A potential silver lining is the decline in leverage across the market. With meme coin fatigue setting in, investors may be shifting toward more sustainable strategies. This could pave the way for long-term capital, especially as independent advisors begin recommending crypto and ETF products.
- Liquidity Injection from Loss Coverage. ByBit CEO Ben Zhou confirmed the company is covering 80% of the stolen funds through bridge loans. Some view this as bullish, arguing that while the stolen ETH remains on-chain, ByBit’s repurchases inject fresh liquidity into the market.
- No Bank Run and Trust in Exchanges. The lack of a bank run after the hack signals strong trust in ByBit’s solvency and response. Despite being one of the biggest heists in crypto, ByBit’s handling has been steady – prices have held, and users haven’t rushed to withdraw. That, in itself, is a positive sign. CEO Ben Zhou echoed this confidence, stating: “ByBit is solvent even if the loss isn’t recovered. All client assets are 1:1 backed.”
- Unexpected Positive Spin: Hacks as Catalysts. A contrarian view suggests that hacks, despite their damage, can drive platform evolution. This hack, for instance, could be seen as bullish – profit was extracted from value extractors, pushing ByBit to strengthen, become more anti-fragile, and reset stale positions and liquidity. The takeaway: crises can spark necessary resets and infrastructure upgrades – an unexpected upside in an otherwise negative event.
While some views may be unconventional, they underscore a maturing market better equipped to handle challenges, offering optimism for long-term recovery and growth beyond the current value and liquidity fluctuations.
Institutional Adoption Peaking Despite Bearish Sentiment
The tokenisation of real-world assets (RWAs) and the growing institutional adoption of digital assets are gaining momentum, even amid broader bearish sentiment in the crypto market. Driven by technological innovation, clearer regulations, and tangible benefits like enhanced liquidity, cost efficiency, and streamlined operations, these trends continue to evolve. Here’s an overview of the latest developments:
- Tokenisation of Real-World Assets. Despite bearish sentiment, the RWA tokenisation market is set for rapid growth. Analysts like Clearpool’s Ozean predict tokenised RWAs could hit a USD 50 billion market cap by 2025, driven by TradFi moving on-chain. Other forecasts from Standard Chartered (USD 30 trillion by 2034) and Boston Consulting Group (USD 16 trillion by 2030) highlight long-term potential, even if short-term conditions are volatile.
- Expansion of Asset Classes. Tokenisation is expanding beyond U.S. Treasuries and stablecoins to include real estate, private credit, commodities, carbon credits, and intellectual property. Real estate tokenisation, for example, is unlocking liquidity in traditionally illiquid markets, with platforms showing savings in home equity lines of credit (HELOCs) and collateralised loans. The total value locked in tokenised assets surpassed USD 176 billion in 2024, a 32% increase, with non-stablecoin assets growing 53%.
- Stablecoins as the “Killer App”. Stablecoins, pegged to assets like the U.S. dollar or treasuries, are becoming a safe haven in crypto. Their stability during market downturns has boosted their reputation as a “killer app” for blockchain, shifting focus from speculative tokens to practical, low-volatility tools. With a market cap surpassing USD 200 billion in 2025, Tether (USDT) and USD Coin (USDC) lead the way. New entrants like PayPal’s PYUSD (launched 2023) and treasury-backed stablecoins (e.g., Ondo Finance) are making waves. The “PayFi” race is on, with stablecoins integrating yield-bearing features linked to tokenised treasuries.
- Technological Advancements. Blockchain platforms are evolving, with AI driving RWA tokenisation and decentralised public infrastructure (DePIN). AI tools are enhancing risk assessment, compliance, and trading, making tokenised assets more attractive to institutions. Multi-chain technologies are improving interoperability and scalability, overcoming past limitations.
- Notable Projects and Milestones.
- BlackRock’s BUIDL Fund. Launched in March 2024, this tokenised fund became the largest of its kind, managing USD 657 million in assets by January 2025. BlackRock is also investing in tokenisation firms and exploring stablecoins, signalling a strategic shift.
- Clearpool’s Ozean. This protocol processed over USD 650 million in loans in Q4 2024, with a 51% rise in total value locked, reflecting growing traction.
- T-RIZE Group. In December 2023, the firm tokenised a USD 300 million residential project in Canada, showcasing real estate tokenisation at an institutional level.
- JPMorgan. Using its Onyx platform for blockchain-based settlements, tokenisation is now seen as a “killer app” for efficiency.
- Goldman Sachs. Its Digital Asset Platform is tokenising bonds, and repo transactions with Broadridge and J.P. Morgan total trillions monthly.
- Deutsche Bank. Joined Singapore’s Project Guardian in May 2024 to tokenise assets, reflecting institutional global interest.
- Regulatory Progress as a Catalyst. While regulatory uncertainty remains, 2025 shows promise. The potential appointment of crypto-friendly figures under a Trump administration could accelerate clarity in the U.S. Meanwhile, the Financial Action Task Force (FATF) is developing standards for tokenised RWAs, fostering cross-border adoption. Countries like Switzerland, Singapore, and Japan are already testing tokenised financial products, creating a more favourable regulatory environment.
- Institutional Sentiment and Investment Surveys. Institutional confidence is high. A BNY Mellon survey found 97% of institutional investors believe tokenisation will revolutionise asset management. EY-Parthenon research shows two-thirds of institutions are already invested in digital assets, with larger asset managers (AUM > USD 500 billion) launching tokenised funds. The Tokenised Asset Coalition found 86% of Fortune 500 executives recognise tokenisation’s benefits, with 35% actively pursuing projects.
- Bridging TradFi and DeFi. RWAs are bridging traditional and decentralised finance. Stablecoins tied to tokenised assets (e.g., treasuries) mitigate volatility, attracting cautious institutional players. Partnerships like Ripple and Archax aim to bring hundreds of millions in tokenised RWAs to the XRP Ledger, highlighting the convergence of TradFi and DeFi.
Resilience Amid Bearish Sentiments
Despite bearish market conditions driven by crypto volatility and macro pressures like inflation, institutional adoption is gaining momentum. Tokenisation offers tangible benefits – fractional ownership, 24/7 trading, and faster settlements – that solve inefficiencies in traditional systems. These advantages hold steady, regardless of market sentiment. For example, tokenised repos minimise operational errors and unlock intraday liquidity, while tokenised yields, such as treasuries, now outpace DeFi lending rates, drawing capital even in a “crypto winter.”
Regulatory fragmentation and security risks like hacking and smart contract vulnerabilities still pose challenges, while mainstream adoption, though accelerating, trails behind pilot successes.
Yet, the fundamentals remain resilient. With upcoming upgrades like Solana’s Firedancer client and Ethereum’s Pectra, blockchain infrastructure will advance. The focus for web3 builders will shift back to innovation, not token price charts. The path from meme coins to real utility may be long, but with the talent and creativity within the ecosystem, it’s far from impossible.
Home to over 60% of the global population, the Asia Pacific region is at the forefront of digital transformation – and at a turning point. The Asian Development Bank forecasts a USD 1.7T GDP boost by 2030, but only if regulation keeps pace with innovation. In 2025, that alignment is taking shape: regulators across the region are actively crafting policies and platforms to scale innovation safely and steer it toward public good. Their focus spans global AI rules, oversight of critical tech in BFSI, sustainable finance, green fintech, and frameworks for digital assets.
Here’s a look at some of the regulatory influences on the region’s BFSI organisations.
Click here to download “Greener, Smarter, Safer: BFSI’s Regulatory Agenda” as a PDF.
The Ripple Effect of Global AI Regulation on APAC Finance
The EU’s AI Act – alongside efforts by other countries such as Brazil and the UK – signals a global shift toward responsible AI. With mandates for transparency, accountability, and human oversight, the Act sets a new bar that resonates across APAC, especially in high-stakes areas like credit scoring and fraud detection.
For financial institutions in the region, ensuring auditable AI systems and maintaining high data quality will be key to compliance. But the burden of strict rules, heavy fines, and complex risk assessments may slow innovation – particularly for smaller fintechs. Global firms with a footprint in the EU also face the challenge of navigating divergent regulatory regimes, adding complexity and cost.
APAC financial institutions must strike a careful balance: safeguarding consumers while keeping innovation alive within a tightening regulatory landscape.
Stepping Up Oversight: Regulating Tech’s Role
Effective January 1, 2025, the UK has granted the Financial Conduct Authority (FCA) and Bank of England oversight of critical tech firms serving the banking sector. This underscores growing global recognition of the systemic importance of these providers.
This regulatory expansion has likely implications for major players such as AWS, Google, and Microsoft. The goal: strengthen financial stability by mitigating cyber risks and service disruptions.
As APAC regulators watch closely, a key question emerges: will similar oversight frameworks be introduced to protect the region’s increasingly interconnected financial ecosystem?
With heavy reliance on a few core tech providers, APAC must carefully assess systemic risks and the need for regulatory safeguards in shaping its digital finance future.
Catalysing Sustainable Finance Through Regional Collaboration
APAC policymakers are translating climate ambitions into tangible action, exemplified by the collaborative FAST-P initiative between Australia and Singapore, spearheaded by the Monetary Authority of Singapore (MAS).
Australia’s USD 50 million commitment to fintech-enabled clean energy and infrastructure projects across Southeast Asia demonstrates a powerful public-private partnership driving decarbonisation through blended finance models.
This regional collaboration highlights a proactive approach to leveraging financial innovation for sustainability, setting a potential benchmark for other APAC nations.
Fostering Green Fintech Innovation Across APAC Markets
The proactive stance on sustainable finance extends to initiatives promoting green fintech startups.
Hong Kong’s upcoming Green Fintech Map and Thailand’s expanded ESG Product Platform are prime examples. By spotlighting sustainability-focused digital tools and enhancing data infrastructure and disclosure standards, these regulators aim to build investor confidence in ESG-driven fintech offerings.
This trend underscores a clear regional strategy: APAC regulators are not merely encouraging green innovation but actively cultivating ecosystems that facilitate its growth and scalability across diverse markets.
Charting the Regulatory Course for Digital Asset Growth in APAC
APAC regulators are gaining momentum in building forward-looking frameworks for the digital asset landscape. Japan’s proposal to classify crypto assets as financial products, Hong Kong’s expanded permissions for virtual asset activities, and South Korea’s gradual reintroduction of corporate crypto trading all point to a proactive regulatory shift.
Australia’s new crypto rules, including measures against debanking, and India’s clarified registration requirements for key players further reflect a region moving from cautious observation to decisive action.
Regulators are actively shaping a secure, scalable digital asset ecosystem – striking a balance between innovation, strong compliance, and consumer protection.
Ecosystm Opinion
APAC regulators are sending a clear message: innovation and oversight go hand in hand. As the region embraces a digital-first future, governments are moving beyond rule-setting to design frameworks that actively shape the balance between innovation, markets, institutions, and society.
This isn’t just about following global norms; it’s a bold step toward defining new standards that reflect APAC’s unique ambitions and the realities of digital finance.
AI has become a battleground for geopolitical competition, national resilience, and societal transformation. The stakes are no longer theoretical, and the window for action is closing fast.
In March, the U.S. escalated its efforts to shape the global technology landscape by expanding export controls on advanced AI and semiconductor technologies. Over 80 entities – more than 50 in China – were added to the export blacklist, aiming to regulate access to critical technologies. The move seeks to limit the development of high-performance computing, quantum technologies, and AI in certain regions, citing national security concerns.
As these export controls tighten, reports have surfaced of restricted chips entering China through unofficial channels, including e-commerce platforms. U.S. authorities are working to close these gaps by sanctioning new entities attempting to circumvent the restrictions. The Department of Commerce’s Bureau of Industry and Security (BIS) is also pushing for stricter Know Your Customer (KYC) regulations for cloud service providers to limit unauthorised access to GPU resources across the Asia Pacific region.
Geopolitics & the Pursuit of AI Dominance
Bipartisan consensus has emerged in Washington around the idea that leading in artificial general intelligence (AGI) is a national security imperative. If AI is destined to shape the future balance of power, the U.S. government believes it cannot afford to fall behind. This mindset has accelerated an arms-race dynamic reminiscent of the Thucydides Trap, where the fear of being overtaken compels both sides to push ahead, even if alignment and safety mechanisms are not yet in place.
China has built extensive domestic surveillance infrastructure and has access to large volumes of data that would be difficult to collect under the regulatory frameworks of many other countries. Meanwhile, major U.S. social media platforms can refine their AI models using behavioural data from a broad global user base. AI is poised to enhance governments’ ability to monitor compliance and enforce laws that were written before the digital age – laws that previously assumed enforcement would be limited by practical constraints. This raises important questions about how civil liberties may evolve when technological limitations are no longer a barrier to enforcement.
The Digital Battlefield
Cybersecurity Threat. AI is both a shield and a sword in cybersecurity. We are entering an era of algorithm-versus-algorithm warfare, where AI’s speed and adaptability will dictate who stays secure and who gets compromised. Nations are prioritising AI for cyber defence to stay ahead of state actors using AI for attacks. For example, the DARPA AI Cyber Challenge is funding tools that use AI to identify and patch vulnerabilities in real-time – essential for defending against state-sponsored threats.
Yet, a key vulnerability exists within AI labs themselves. Many of these organisations, though responsible for cutting-edge models, operate more like startups than defence institutions. This results in informal knowledge sharing, inconsistent security standards, and minimal government oversight. Despite their strategic importance, these labs lack the same protections and regulations as traditional military research facilities.
High-Risk Domains and the Proliferation of Harm. AI’s impact on high-risk domains like biotechnology and autonomous systems is raising alarms. Advanced AI tools could lower the barriers for small groups or even individuals to misuse biological data. As Anthropic CEO Dario Amodei warns, “AI will vastly increase the number of people who can cause catastrophic harm.”
This urgency for oversight mirrors past technological revolutions. The rise of nuclear technology prompted global treaties and safety protocols, and the expansion of railroads drove innovations like block signalling and standardised gauges. With AI’s rapid progression, similar safety measures must be adopted quickly.
Meanwhile, AI-driven autonomous systems are growing in military applications. Drones equipped with AI for real-time navigation and target identification are increasingly deployed in conflict zones, especially where traditional systems like GPS are compromised. While these technologies promise faster, more precise operations, they also raise critical ethical questions about decision-making, accountability, and latency.
The 2024 National Security Memorandum on AI laid down initial guidelines for responsible AI use in defence. However, significant challenges remain around enforcement, transparency, and international cooperation.
AI for Intelligence and Satellite Analysis. AI also holds significant potential for national intelligence. Governments collect massive volumes of satellite imagery daily – far more than human analysts can process alone. AI models trained on geospatial data can greatly enhance the ability to detect movement, monitor infrastructure, and improve border security. Companies like ICEYE and Satellogic are advancing their computer vision capabilities to increase image processing efficiency and scale. As AI systems improve at identifying patterns and anomalies, each satellite image becomes increasingly valuable. This could drive a new era of digital intelligence, where AI capabilities become as critical as the satellites themselves.
Policy, Power, and AI Sovereignty
Around the world, governments are waking up to the importance of AI sovereignty – ensuring that critical capabilities, infrastructure, and expertise remain within national borders. In Europe, France has backed Mistral AI as a homegrown alternative to US tech giants, part of a wider ambition to reduce dependency and assert digital independence. In China, DeepSeek has gained attention for developing competitive LLMs using relatively modest compute resources, highlighting the country’s determination to lead without relying on foreign technologies.
These moves reflect a growing recognition that in the AI age, sovereignty doesn’t just mean political control – it also means control over compute, data, and talent.
In the US, the public sector is working to balance oversight with fostering innovation. Unlike the internet, the space program, or the Manhattan Project, the AI revolution was primarily initiated by the private sector, with limited state involvement. This has left the public sector in a reactive position, struggling to keep up. Government processes are inherently slow, with legislation, interagency reviews, and procurement cycles often lagging rapid technological developments. While major AI breakthroughs can happen within months, regulatory responses may take years.
To address this gap, efforts have been made to establish institutions like the AI Safety Institute and requiring labs to share their internal safety evaluations. However, since then, there has been a movement to reduce the regulatory burden on the AI sector, emphasising the importance of supporting innovation over excessive caution.
A key challenge is the need to build both policy frameworks and physical infrastructure in tandem. Advanced AI models require significant computational resources, and by extension, large amounts of energy. As countries like the US and China compete to be at the forefront of AI innovation, ensuring a reliable energy supply for AI infrastructure becomes crucial.
If data centres cannot scale quickly or if clean energy becomes too expensive, there is a risk that AI infrastructure could migrate to countries with fewer regulations and lower energy costs. Some nations are already offering incentives to attract these capabilities, raising concerns about the long-term security of critical systems. Governments will need to carefully balance sovereignty over AI infrastructure with the development of sufficient domestic electricity generation capacity, all while meeting sustainability goals. Without strong partnerships and more flexible policy mechanisms, countries may risk ceding both innovation and governance to private actors.
What Lies Ahead
AI is no longer an emerging trend – it is a cornerstone of national power. It will shape not only who leads in innovation but also who sets the rules of global engagement: in cyber conflict, intelligence gathering, economic dominance, and military deterrence. The challenge governments face is twofold. First, to maintain strategic advantage, they must ensure that AI development – across private labs, defence systems, and public infrastructure – remains both competitive and secure. Second, they must achieve this while safeguarding democratic values and civil liberties, which are often the first to erode under unchecked surveillance and automation.
This isn’t just about faster processors or smarter algorithms. It’s about determining who defines the future – how decisions are made, who has oversight, and what values are embedded in the systems that will govern our lives.
AI has already had a significant impact on the tech industry, rapidly evolving software development, data analysis, and automation. However, its potential extends into all industries – from the precision of agriculture to the intricacies of life sciences research, and the enhanced customer experiences across multiple sectors.
While we have seen the widespread adoption of AI-powered productivity tools, 2025 promises a bigger transformation. Organisations across industries will shift focus from mere innovation to quantifiable value. In sectors where AI has already shown early success, businesses will aim to scale these applications to directly impact their revenue and profitability. In others, it will accelerate research, leading to groundbreaking discoveries and innovations in the years to come. Regardless of the specific industry, one thing is certain: AI will be a driving force, reshaping business models and competitive landscapes.
Ecosystm analysts Alan Hesketh, Clay Miller, Peter Carr, Sash Mukherjee, and Steve Shipley present the top trends shaping key industries in 2025.
Click here to download ‘AI’s Impact on Industry in 2025’ as a PDF
1. GenAI Virtual Agents Will Reshape Public Sector Efficiency
Operating within highly structured, compliance-driven environments, public sector organisations are well-positioned to benefit from GenAI Agents.
These agents excel when powered LLMs tailored to sector-specific needs, informed by documented legislation, regulations, and policies. The result will be significant improvements in how governments manage rising service demands and enhance citizen interactions. From automating routine enquiries to supporting complex administrative processes, GenAI Virtual Agents will enable public sector to streamline operations without compromising compliance. Crucially, these innovations will also address jurisdictional labour and regulatory requirements, ensuring ethical and legal adherence. As GenAI technology matures, it will reshape public service delivery by combining scalability, precision, and responsiveness.
2. Healthcare Will Lead in Innovation; Lag in Adoption
In 2025, healthcare will undergo transformative innovations driven by advancements in AI, remote medicine, and biotechnology. Innovations will include personalised healthcare driven by real-time data for tailored wellness plans and preventive care, predictive AI tackling global challenges like aging populations and pandemics, virtual healthcare tools like VR therapy and chatbots enhancing accessibility, and breakthroughs in nanomedicine, digital therapeutics, and next-generation genomic sequencing.
Startups and innovators will often lead the way, driven by a desire to make an impact.
However, governments will lack the will to embrace these technologies. After significant spending on crisis management, healthcare ministries will likely hesitate to commit to fresh large-scale investments.
3. Agentic AI Will Move from Bank Credit Recommendation to Approval
Through 2024, we have seen a significant upturn in Agentic AI making credit approval recommendations, providing human credit managers with the ability to approve more loans more quickly. Yet, it was still the mantra that ‘AI recommends—humans approve.’ That will change in 2025.
AI will ‘approve’ much more and much larger credit requests.
The impact will be multi-faceted: banks will greatly enhance client access to credit, offering 24/7 availability and reducing the credit approval and origination cycle to mere seconds. This will drive increased consumer lending for high-value purchases, such as major appliances, electronics, and household goods.
4. AI-Powered Demand Forecasting Will Transform Retail
There will be a significant shift away from math-based tools to predictive AI using an organisation’s own data. This technology will empower businesses to analyse massive datasets, including sales history, market trends, and social media, to generate highly accurate demand predictions. Adding external influencing factors such as weather and events will be simplified.
The forecasts will enable companies to optimise inventory levels, minimise stockouts and overstock situations, reduce waste, and increase profitability. Early adopters are already leveraging AI to anticipate fashion trends and adjust production accordingly.
No more worrying about capturing “Demand Influencing Factors” – it will all be derived from the organisation’s data.
5. AI-Powered Custom-Tailored Insurance Will Be the New Norm
Insurers will harness real-time customer data, including behavioural patterns, lifestyle choices, and life stage indicators, to create dynamic policies that adapt to individual needs. Machine learning will process vast datasets to refine risk predictions and deliver highly personalised coverage. This will produce insurance products with unparalleled relevance and flexibility, closely aligning with each policyholder’s changing circumstances. Consumers will enjoy transparent pricing and tailored options that reflect their unique risk profiles, often resulting in cost savings. At the same time, insurers will benefit from enhanced risk assessment, reduced fraud, and increased customer satisfaction and loyalty.
This evolution will redefine the customer-insurer relationship, making insurance a more dynamic and responsive service that adjusts to life’s changes in real-time.
