As one of Asia’s most digitally mature economies, Singapore was an early mover in national digital transformation and is now turning that head start into resilient, innovation-led economic value. Today, the conversation across boardrooms, regulators, and industry circles has evolved: it’s no longer just about adopting technology but about embedding digital as a systemic driver of competitiveness, inclusion, and sustained growth.
Singapore’s approach offers a model for the region, with its commitment to building a holistic digital ecosystem. This goes beyond infrastructure, it includes nurturing digital talent, fostering a vibrant innovation and startup culture, enabling trusted cross-border data flows, and championing public-private collaboration. Crucially, its forward-looking regulatory stance balances support for experimentation with the need to uphold public trust.
Through our conversations with leaders in Singapore and Ecosystm’s broader research, we see a country intentionally architecting its digital future, focused on real-world outcomes, regional relevance, and long-term economic resilience.
Here are five insights that capture the pulse of Singapore’s digital transformation.
Theme 1: Digital Governance as Strategy: Setting the Pace for Innovation & Trust
Singapore’s approach to digital governance goes beyond policy. It’s a deliberate strategy to build trust, accelerate innovation, and maintain economic competitiveness. The guiding principle is clear: technology must be both transformative and trustworthy.
This vision is clearly visible in the public sector, where digital platforms and services are setting the pace for the rest of the economy. Public service apps are designed to be citizen-centric, secure, and efficient, demonstrating how digital delivery can work at scale. The Government Tech Stack allows agencies to rapidly build and integrate services using shared APIs, cloud infrastructure, and secure data layers. Open data initiatives like Data.gov.sg unlock thousands of datasets, while tools such as FormSG and SG Notify make it easy for any organisation to digitise services and engage users in real time.
By leading with well-designed digital infrastructure and standards, the public sector creates blueprints that others can adopt, lowering the barriers to innovation for businesses of all sizes. For SMEs in particular, these tools and frameworks offer a practical foundation to modernise operations and participate more fully in the digital economy.
Singapore is also setting clear rules for responsible tech. IMDA’s Trusted Data Sharing Framework and AI Verify establish standards for secure data use and transparent AI, giving businesses the certainty they need to innovate with confidence. All of this is underpinned by strategic investments in digital infrastructure, including a new generation of sustainable, high-capacity data centres to meet growing regional demand. In Singapore, digital governance isn’t a constraint, it’s a catalyst.
Theme 2: AI in Singapore: From Experimentation to Accountability
Few places have embraced AI’s potential as strongly as Singapore. In 2022 and 2023, fuelled by the National AI Strategy and commercial pressure to deliver results, organisations across industries rushed into pilots in 2022 and 2023. Ecosystm research shows that by 2024, nearly 82% of large enterprises in Singapore were experimenting with AI, with 37% deploying it across multiple departments.
However, that initial wave of excitement soon gave way to realism. Leaders now speak candidly about AI fatigue and the growing demand for measurable returns. The conversation has shifted from “What can we automate?” to “What’s actually worth scaling?” Organisations are scrutinising whether their AI projects deliver tangible value, integrate into daily operations, and meet evolving regulatory expectations.
This maturity is especially visible in Singapore’s banking sector, where the stakes are high and scrutiny is intense. Banks were among the first to embrace AI aggressively and are now leading the shift toward disciplined prioritisation. From actively hunting down use cases, they’ve pivoted to focusing on the select few that deliver real business outcomes. With increasing pressure to ensure transparency, auditability, and alignment with global standards, finance leaders are setting the tone for AI accountability across the economy.
The result: a more grounded, impact-focused AI strategy. While many regional peers are still chasing pilots, Singapore is entering a new phase, defined by fewer but better AI initiatives, built to solve real problems and deliver meaningful ROI.
Theme 3: The Cyber Imperative: Trust, Recovery, and Resilience
Singapore’s digital leadership brings not only opportunities but also increased exposure to cyber threats. In 2024 alone, the country faced 21 million cyberattacks, ranking eighth globally as both a target and a source. High-profile breaches, from vendor compromises affecting thousands of banking customers to earlier incidents like the SingHealth data breach, have exposed vulnerabilities across critical sectors.
These incidents have sparked a fundamental shift in Singapore’s cybersecurity mindset from building impenetrable digital fortresses to embracing digital resilience. The government recognises that breaches are inevitable and prioritises rapid containment and recovery over prevention alone. Regulatory bodies like MAS have tightened incident reporting rules, demanding quicker, more transparent responses from affected organisations.
For enterprises in Singapore, cybersecurity has moved beyond a technical challenge to become a strategic imperative deeply tied to customer trust and business continuity. Leaders are investing heavily in real-time threat detection, incident response, and crisis management capabilities. In a landscape where vulnerabilities are real and constant, cyber resilience is now a critical competitive advantage because in Singapore’s digital economy, trust and operational reliability are non-negotiable.
Theme 4: Beyond Coding: Singapore’s Quest for Hybrid Digital Talent
Singapore’s digital ambitions increasingly depend on its human capital. While consistently ranking high in global talent competitiveness, the city-state faces a projected shortfall of over 1.2 million digitally skilled workers, particularly in fields like cybersecurity, data science, and AI engineering.
But the challenge isn’t purely technical. Organisations now demand talent that bridges technology, business strategy, and regulatory insight. Many digital initiatives stall not from technology limitations, but from a lack of professionals who can translate complex digital concepts into business value and ensure regulatory compliance.
To address this, government initiatives like the TechSkills Accelerator (TeSA) offer training subsidies and career conversion programmes. Meanwhile, leading tech providers including AWS, Microsoft, Google, and IBM, are stepping up, partnering with government and industry to deliver specialised training, certification programmes, and talent pipelines that help close the skills gap.
Still, enterprises grapple with keeping pace amid rapid technological change, balancing reskilling local talent with attracting specialised professionals from abroad. The future of Singapore’s digital economy will be defined as much by people as by technology; and by the partnerships that help bridge this critical gap.
Theme 5: Tracking Impact, Driving Change: Singapore’s Sustainability and Tech Synergy
Sustainability remains a core pillar of Singapore’s digital ambitions, driven by the government’s unwavering focus and supportive green financing options unlike in some markets where momentum has slowed. Anchored by the Singapore Green Plan 2030, the nation aims to double solar energy capacity and reduce landfill waste per capita by 30% by 2030.
Digital technology plays a critical role in this vision. Initiatives like the Green Data Centre Roadmap promote energy-efficient infrastructure and sustainable cooling technologies, balancing growth in the digital economy with carbon footprint management. Singapore is also emerging as a regional hub for carbon services, leveraging digital platforms such as the Carbon Services Platform to track, verify, and trade emissions, fostering credible and transparent carbon markets.
Government-backed green financing schemes, including the Green Bond Grant Scheme and Sustainability-Linked Loans, are accelerating investments in eco-friendly projects, enabling enterprises to fund sustainable innovation while meeting global ESG standards.
Despite these advances, leaders highlight challenges such as the lack of standardised sustainability metrics and rising risks of greenwashing, which complicate scaling green finance and cross-border sustainability reporting. Still, Singapore’s ability to integrate sustainability with digital innovation underscores its ambition to be more than a tech hub. It aims to be a trusted leader in building a responsible, future-ready economy.
From Innovation to Lasting Impact
Singapore stands at a critical inflection point. Already recognised as one of the world’s most advanced digital economies, its greatest test now is execution transforming cutting-edge technology from promise into real, everyday impact. The nation must balance rapid innovation with robust security, while shaping global standards that reflect its unique blend of ambition and pragmatism.
With deep-rooted trust across government, industry, and society, Singapore is uniquely equipped to lead not just in developing technology, but in embedding it responsibly to create lasting value for its people and the wider region. The next chapter will define whether Singapore can move from digital leadership to digital legacy.
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.
Digital transformation in the Philippines has moved from being a goal to an essential part of how organisations operate, compete, and serve their communities. This shift is evident across sectors – from financial services and government to education, healthcare, and commerce – as digital platforms become integral to everyday life.
In recent years, the country has been recognised as a leading improver in the UN E-Government Development Index, reflecting steady advances in digital public service delivery. Yet, progress across all sectots has been uneven, influenced by a mix of geography, regulation, and existing infrastructure. Organisations continue to adapt, responding to fast-paced technological change, rising user expectations, and an increasingly interconnected global digital economy.
Through a series of roundtables with national leaders, Ecosystm examined the realities of digital transformation on the ground. What emerged were valuable insights into what’s working and where challenges and shifts are reshaping the definition of success in this evolving stage of digital maturity.
Theme 1: Strengthening the Foundations for Nationwide Digital Equity
The Philippines is advancing steadily in digitalisation, especially in Metro Manila and major urban centres, though the full benefits have yet to reach all regions evenly. Rural provinces and smaller islands face ongoing challenges with broadband access, latency, and mobile coverage, reflecting the country’s unique geography and historic underinvestment in digital infrastructure.
National programs like the National Broadband Plan and Free Wi-Fi for All have established important foundations. Fibre rollouts by private telecom providers are extending coverage, but last-mile connectivity in geographically isolated and disadvantaged areas (GIDAs) still needs attention. Bridging this gap is key not only for broader inclusion but also to enable widespread adoption of technologies such as cloud computing, AI, and edge solutions.
Achieving nationwide digital transformation requires a focused effort on regional infrastructure as a driver of inclusive growth. This involves co-investment, innovative public-private partnerships, and policies supporting shared towers, data centres, and satellite-backed connectivity. This benefits enterprises and critical citizen services like e-learning, e-health, and digital banking.
Theme 2: From Outsourcing Hub to Innovation Engine – The Next Chapter for Talent
The Philippines has established a strong global presence as a trusted centre for BPO and IT-enabled services, contributing nearly 9% to the national GDP and employing over 1.5 million professionals. In recent years, this foundation has rapidly evolved, with talent increasingly taking on complex roles in knowledge process outsourcing (KPO), AI annotation, fintech support, and cybersecurity operations.
This shift reflects a broader transformation – from a labour-cost-driven outsourcing model to a high-skill, innovation-focused services economy. However, this transition is placing growing demands on the talent pipeline. Skilled cloud engineers, AI developers, and cybersecurity experts remain in short supply, with demand surpassing the current capacity of training and reskilling programs.
To fully unlock its potential, the country needs to future-proof its talent ecosystem. This includes expanding technical education, strengthening collaboration between academia and industry, scaling national upskilling initiatives, and creating incentives that encourage tech professionals to build their careers locally. With targeted investment, the digital workforce can become a powerful competitive advantage on the global stage.
Theme 3: Government Digitalisation Is Accelerating – But Interoperability Remains a Challenge
The Philippines has made major progress in digitising government services – from online business registrations via Business Name Registration System (BNRS) to digital ID rollout through PhilSys (Philippine Identification System), and integrated platforms like eGov PH Super App. The pandemic accelerated adoption of e-payment systems, telemedicine, and virtual public services, driving faster digital transformation across agencies.
Despite this progress, interoperability challenges remain a key hurdle. Many government agencies still rely on siloed legacy systems that limit seamless data exchange. This fragmentation affects real-time decision-making, slows service delivery, and creates a fragmented experience for citizens and enterprises navigating multiple platforms.
Going forward, the priority is system-wide integration. Building a truly citizen-centric digital government requires interoperable data architectures, strong privacy-by-design frameworks for cross-agency collaboration, and scalable API-driven platforms that enable secure, real-time connections between national and local government systems. A connected digital state not only boosts efficiency but also strengthens public trust and paves the way for more adaptive, responsive services.
Theme 4: Cyber Resilience Is No Longer Optional – It’s Strategic
As digital transformation accelerates, the Philippines has become one of Southeast Asia’s most targeted countries for cyberattacks – particularly in sectors like financial services, critical infrastructure, and government. High-profile breaches at agencies such as PhilHealth, the Philippine Statistics Authority, and COMELEC have brought cybersecurity to the forefront of national priorities.
Regulatory steps such as the Cybercrime Prevention Act and the establishment of the Department of Information and Communications Technology (DICT) Cybersecurity Bureau have laid important groundwork. Yet, enterprise readiness remains uneven. Many organisations still rely on outdated defences, limited threat visibility, and ad hoc response plans that are outpaced by today’s threats. More importantly, many still look at cyber purely from a compliance angle.
As AI, IoT, and cloud-based platforms scale, so too does the attack surface. Cyber resilience now demands more than compliance – it requires dynamic risk management, skills development, intelligence sharing, and coordinated action across sectors. The shift from reactive to adaptive security is becoming a defining capability for both public and private institutions.
Theme 5: Financial Access at the Grassroots: The Digital Shift
One of the Philippines’ most notable digital transformation successes has been in fintech and digital financial services. Platforms like GCash, Maya, and the government’s Paleng-QR PH program have significantly expanded access to cashless payments, savings, and credit – especially among unbanked and underbanked communities.
By 2024, nearly 80% of Filipinos were using mobile financial apps – a striking milestone that reflects not only growing digital adoption but also evolving cultural and economic behaviours. From sari-sari stores to market vendors, digital wallets are reshaping everyday commerce and opening new avenues for financial empowerment at the grassroots level.
Still, digital inclusion is not automatic. Maintaining this momentum will require continued investment in digital literacy – particularly for older adults, rural communities, and lower-income groups – as well as stronger measures for cybersecurity, consumer protection, and interoperable ID and payment systems. Done right, digital finance can serve as the foundation for a more inclusive and resilient economy.
A Moment to Rethink What Progress Looks Like
As digital systems take root across the Philippines’ economy and institutions, the focus is shifting from speed to staying power. The next phase will depend on the country’s ability to translate broad adoption into long-term value – through strategies that are inclusive, resilient, and built to scale.
The Voice of Customer (VoC) market continues to evolve, often driven by technological advancements as well as M&As. Medallia, Qualtrics, and InMoment are the three major VoC players in the enterprise segment globally and locally across ANZ.
Until recently, InMoment was the smaller contender among the three, but that changed with their acquisition by PG Forsta in May 2025. The move marks a significant shift that will alter the competitive dynamics globally and in the region.
By combining PG Forsta’s strengths in market research, structured feedback, and regulated industries with InMoment’s expertise in AI, unstructured data analysis, and its strong presence in ANZ, the newly formed entity is poised to challenge the dominance of larger rivals.
The Key Players
Medallia
Medallia is a mature player in the VoC market, with a focus on larger, more complex and ideally global programs. With ANZ, and particularly New Zealand dominated by SMB’s, the vendor is not quite as prominent locally as they are at a global level.
Medallia underwent a major executive shakeup earlier this year, bringing in a wave of leadership talent from Qualtrics, and former Clarabridge. This leadership reset brings not only strategic focus but also a significant transfer of domain expertise and IP, positioning Medallia to compete more aggressively, especially against Qualtrics.
The changes include Mark Bishof as Chairman and CEO (formerly Chief Business Officer at Qualtrics and CEO of Clarabridge), Sid Banerjee as Chief Strategy Officer (Founder of Clarabridge), and several other senior leaders.
While the vendor has been relatively quiet in the ANZ market in recent years, with renewed leadership and an internal reset, Medallia re-focuses its efforts on local customer engagement across ANZ and aims to be more active and visible in the market. For ANZ enterprises, this could bring more choice, and a potentially stronger Medallia presence in upcoming VoC initiatives.
Qualtrics
Qualtrics, another major player in the VoC market, is known for its product strength, platform developments and AI capabilities. They are the most active in terms of platform development and shared several major AI announcements earlier this year, including agentic AI capabilities.
Qualtrics also formally introduced the term Experience Management (XM) as a discipline and new software category in 2017 with the launch of the Qualtrics XM Platform and continues to dominate the conversation on XM.
Qualtrics and InMoment have dominated the local ANZ market, in the SMB to enterprise sector. Qualtrics enjoyed winning customer deals that included market research and panel requirements, since InMoment lacked that capability in-house. And that’s where PG Forsta comes in to change the dynamic.
InMoment
InMoment is a VoC technology provider known for their Experience Improvement (XI) competencies. Their strengths include conversational intelligence, reputation management, and predictive analytics. InMoment has grown by acquisition, most notably the acquisition of MaritzCX, Lexalytics, and ReviewTracker, adding online reviews and deep analytics for unstructured data to their platform.
While smaller in size than their main competitors, InMoment has a strong local presence in ANZ and thrives on their strong customer relationships.
PG Forsta Acquires InMoment: A Strategic Move
The VoC landscape, especially in ANZ, is bound to shift significantly following PG Forsta’s acquisition of InMoment. While mergers and acquisitions are common in tech, this move signals a deliberate attempt by PG Forsta and InMoment to expand their market footprint and compete more aggressively with established VoC rivals.
While PG Forsta and InMoment were both established players in the VoC market, their merger is notable not just for its scale, but for its strategic intent: to combine complementary strengths in research, analytics, and AI innovation to provide a more robust, cross-industry VoC platform.
PG Forsta, formed through Press Ganey’s 2022 acquisition of Forsta (itself a merger of Confirmit and FocusVision), offers a Human Experience (HX) platform that integrates customer, employee, and market feedback, with a strong foundation in healthcare. PG Forsta brings deep expertise in structured feedback, large-scale analytics, and regulated industries such as healthcare. InMoment, meanwhile, offers advanced capabilities in AI, machine learning, and unstructured data analysis. By combining these distinct strengths, the merged entity creates a more versatile and comprehensive solution. PG Forsta enhances their AI and omnichannel offering, while InMoment gains access to a broader, compliance-focused client base and robust market research capabilities.
The new entity will serve clients globally with a team of more than 3,000 employees. Unlike its main competitors, InMoment maintains a dedicated presence in New Zealand, bringing deep local market expertise and strong relationships. InMoment’s established footprint in ANZ further enhances PG Forsta’s local presence, providing valuable on-the-ground support that is increasingly important to organizations.
Both companies cite cultural alignment as a key factor in building a stronger, united organization, a critical foundation for any successful acquisition.
AI: The Battlefront for the Future of CX
A crucial aspect of this acquisition is its emphasis on AI. VoC platforms are evolving beyond traditional feedback collection, with growing pressure to gather data from both solicited and unsolicited sources and deliver actionable insights and recommendations. As AI capabilities become more embedded in operations, platforms are increasingly judged by their ability to go beyond static dashboards, to unify data, analyse unstructured data, and generate richer insights and proactive recommendations.
InMoment, along with some competitors, has invested in leveraging contact centre data to extract insights from unsolicited and unstructured sources through conversation intelligence. While initially used for customer insights, this technology is now expanding to serve contact centre teams and broader, organisation-wide intelligence use cases, breaking out of departmental silos.
As the market continues to prioritise outcome-driven CX, AI will be a central differentiator among leading platforms, and InMoment brings those capabilities into the PG Forsta deal.
Looking Ahead
While it’s too early to call the long-term outcome, this acquisition marks a significant shift in the VoC landscape, particularly in ANZ.
With a bold goal “to be THE VoC company in the market”, the bar is set high to deliver. The success of this acquisition will depend on execution. Seamless integration across systems, cultures, and product lines won’t happen overnight. But if they get it right, this merger could reshape the competitive landscape, raising the stakes for Medallia, Qualtrics, and others.
For CX leaders across ANZ, this brings more choice, more innovation, and better capabilities to drive deeper customer insights and business impact.
We’re entering a new cycle of PC device growth, driven by the end-of-life of Windows 10 and natural enterprise upgrade cycles, brought into alignment by the COVID-era device boom. In Asia Pacific, PC shipments are expected to grow by 4-8% in 2025. The wide range reflects uncertainty linked to the US tariff regime, which could impact device pricing and availability in the region as manufacturers adjust to shifting demand globally.
To AI or Not to AI?
“AI PCs” (or Copilot PCs) are set to become a growing segment, but real AI benefits from these devices are still some way off. Microsoft’s announcement to embed Agentic AI capabilities into the OS marks the first step toward moving AI processing from the cloud to the desktop. However, for most organisations, these capabilities remain 12-24 months away.
This creates a strategic question: should organisations invest now in NPU-enabled devices that may not deliver immediate returns? Given typical refresh cycles of 3-5 years, it’s worth considering whether local AI processing could become relevant during that time. The safer bet is to invest in Copilot or AI PCs now, as the AI market is evolving rapidly; and the chances of NPUs becoming useful sooner rather than later are high.
Is the Desktop Being Left Behind?
PC market growth is concentrated in the laptop segment, drawing most manufacturers and chip providers to focus their innovation there. AI and Copilot PCs have yet to meaningfully enter the desktop space, where manufacturers remain largely focused on gaming.
This creates a gap for enterprises and SMEs. AI capabilities available on laptops may not be mirrored on desktops. Recent conversations with infrastructure and End-User Computing (EUC) managers suggest a shift in Asia Pacific toward laptops or cloud/ virtual desktop infrastructure (VDI) devices, including thin clients and desktops. If this trend continues, organisations will need to re-evaluate employee experience and ensure applications are designed to match the capabilities of each device type and user persona.
Fundamental EUC Drivers are Changing
As EUC and infrastructure teams revisit their strategies, several foundational drivers are undergoing significant change:
- Remote work is no longer a default. Once considered the norm for information workers, remote work is now being reconsidered. With some organisations mandating full-time office returns, device strategies must adapt to a more hybrid and unpredictable working model.
- Employee Experience is losing budget priority. During the pandemic, keeping employees productive and engaged was critical. But with rising cost pressures, growing automation through GenAI and Agentic AI, and changing labour dynamics, EX is no longer a top enterprise priority and budgets reflect that shift.
- Cloud-based EUC solutions are now enterprise-ready. Since 2022, cloud adoption in EUC has accelerated. Solutions like Microsoft 365, Google Workspace, AWS WorkSpaces, and VMware Horizon Cloud now offer mature capabilities. Unified Endpoint Management (UEM) is increasingly cloud-managed, enabling more scalable and agile IT operations.
- Zero-trust is moving security closer to the user. EUC security is evolving from perimeter-based models to identity-centric, continuous verification approaches. Investments in EDR, AI-driven threat analytics, MFA, biometric authentication, and proactive threat hunting are now standard, driven by the shift to zero trust.
- Device diversity is increasing. Standardised device fleets are giving way to more diverse options – touchscreen laptops, foldables, and a broader mix of PC brands. Enterprise offerings are expanding beyond traditional tiers to meet varied needs across user personas.
- Metrics are shifting from technical to outcome-based. Traditional KPIs like uptime and cost are giving way to metrics tied to business value – employee productivity, experience, collaboration, cyber resilience, and adaptability. EUC success is now measured in terms of outcomes, not just infrastructure performance.
Build a Modern and Future-Ready EUC Strategy
Organisations must reassess their plans to align with changing business needs, user expectations, and operational realities. Modern EUC strategies must account for a broad set of considerations.
Key factors to consider:
Strategic Business Alignment
- Business Outcomes. EUC strategies must align with core business goals such as boosting productivity, enhancing employee experience, improving customer outcomes, and driving competitive advantage. Consider how device choices enable new work models, such as remote/hybrid setups, gig workforce enablement, and cross-border collaboration.
- Digital Transformation Fit. Ensure EUC refresh cycles are integrated with broader digital transformation efforts – cloud migration, AI adoption, automation, and innovation. Devices should be future-ready, capable of supporting the AI and automation needs of 2026 and beyond. While some workloads may shift to the cloud, others like GenAI-powered video and image creation, may demand stronger local processing across the broader workforce, not just specialist teams.
Technology Considerations
- Device Selection. Move beyond the old “one device per persona” approach. Build a flexible device ecosystem that supports a range of employee types, from frontline workers to power users, while allowing for broader device choices based on real usage patterns. Evaluate form factors like desktops, laptops, tablets, smartphones, and thin/zero clients. With the rise of Desktop-as-a-Service (DaaS), thin clients are becoming more viable, offering cost savings and better security without compromising user experience.
- Flexibility of Choice. High-end features – lightweight design, long battery life, sleek aesthetics – are no longer limited to exec devices. I am currently writing this on a loan device – a Lenovo ThinkPad X1 Carbon Gen 13 Aura Edition – a freakishly light, powerful and slim device with LONG battery life – a device typically targeted towards the top tier of business leaders. But today, many of the features of this device run through the entire Lenovo laptop ecosystem – the “Aura” tag appears in many of the device SKUs and ranges. Hopefully the days of senior management getting the great looking devices and everyone else getting ugly bricks are behind us!
- Operating Systems and Compatibility. Ensure compatibility with current and planned business applications, cloud services, and collaboration tools. Consider ease of management and integration into existing IT ecosystems (such as Windows, macOS, Android, Chrome OS, Linux environments).
- Cloud Integration. Evaluate cloud-readiness and seamless integration capabilities with popular productivity suites (Microsoft 365, Google Workspace), hybrid cloud, and SaaS applications. Leverage VDI, DaaS or application virtualisation solutions to reduce hardware dependency and streamline maintenance.
User Experience
- Employee Productivity and Engagement. Even as EX slips down the priority list – and the budget – EUC leaders must still champion intuitive, user-friendly devices to boost productivity and reduce training and support demands. Seamless collaboration is critical across physical, remote, and hybrid teams. In-office collaboration is back in focus, but its value depends on digitising outcomes: laptops, smartphones, and tablets must enable AI-driven transcription, task assignment, and follow-up tracking from physical or hybrid meetings.
- Personalisation and Mobility. Where practical, offer device personalisation through flexible BYOD or CYOD models. Even in industries or geographies where this isn’t feasible, small touches like device colour or accessories, can improve engagement. UEM tools are essential to enforce security while enabling flexibility.
- Performance and Reliability. Choose devices that deliver the right performance for the task, especially for users handling video, design, or AI workloads. Prioritise long battery life and reliable connectivity, including Wi-Fi 6/7 and 5G where available. While 5G laptops are still rare across many Asia Pacific markets, that’s likely to change as networks expand and manufacturers respond to demand.
- Localised Strategy. Given the distributed nature of many organisations in the region, support and warranty strategies should reflect local realities. Tiered service agreements may provide better value than one-size-fits-all premium coverage that’s difficult to deliver consistently.
Security and Compliance
- Cybersecurity Posture. EUC teams typically work hand-in-hand with their cyber teams in the development of a secure EUC strategy and the deployment of the preferred devices. Cybersecurity teams will likely provide specific guidance and require compliance with local and regional regulations and laws. They will likely require that EUC teams prioritise integrated security capabilities (such as zero-trust architectures, endpoint detection and response – EDR solutions, biometrics, hardware-based security features like TPM). Consider deploying AI-driven endpoint threat detection and response tools for proactive threat mitigation.
- Data Privacy and Regulatory Compliance. Assess devices and management systems to ensure adherence to local regulatory frameworks (such as Australia’s Privacy Act, Singapore’s PDPA, or the Philippines’ Data Privacy Act). Deploy robust policies and platforms for data encryption, remote wiping, and identity and access management (IAM).
Management, Sustainability and Operational Efficiency
- Unified Endpoint Management (UEM). Centralise device management through UEM platforms to streamline provisioning, policy enforcement, patching, updates, and troubleshooting. Boost efficiency further with automation and self-service tools to lower IT overhead and support costs.
- Asset Lifecycle Management (ALM). While many organisations have made progress in optimising ALM – from procurement to retirement – gaps remain, especially in geographies outside core operations. Use device analytics to monitor health, utilisation, and performance, enabling smarter refresh cycles and reduced downtime.
- Sustainable IT and CSR Alignment. Choose vendors with strong sustainability credentials such as energy-efficient devices, ethical manufacturing, and robust recycling programs. Apply circular economy principles to extend device lifespan, reduce e-waste, and lower your carbon footprint. Align EUC strategies with broader CSR and ESG goals, using device refresh cycles as opportunities to advance sustainability targets and reinforce your organisation’s values.
Cost and Investment Planning
- Total Cost of Ownership (TCO). Evaluate TCO holistically, factoring in purchase price, operations, software licensing, security, support, warranties, and end-of-life costs. TCO frameworks are widely available, but if you need help tailoring one to your business, feel free to reach out. Balance CapEx and OpEx across different deployment models – owned vs leased, cloud-managed vs on-premises.
- Budgeting & Financial Modelling. Clearly define ROI and benefit realisation timelines to support internal approvals. Explore vendor financing or consumption-based models to enhance flexibility. These often align with sustainability goals, with many vendors offering equipment recycling and resale programs that reduce overall costs and support circular IT practices.
Vendor and Partner Selection
- Vendor Support & Regional Coverage. Select vendors with strong regional support across Asia Pacific to ensure consistent service delivery across diverse markets. Many organisations rely on distributors and resellers for their extended reach into remote geographies. Others prefer working directly with manufacturers. While this can reduce procurement costs, it may increase servicing complexity and response times. Assess vendors not just on cost, but on local presence, partner network strength, and critically, their supply chain resilience.
- Innovation & Ecosystem Alignment. Partner with vendors whose roadmaps align with future technology priorities – AI, IoT, edge computing – and who continue to invest in advancing EUC capabilities. Long-term innovation alignment is just as important as short-term performance.
Building a modern, future-ready EUC strategy isn’t just about devices – it’s about aligning people, technology, security, sustainability, and business outcomes in a way that’s cost-effective and forward-looking. But we know investment planning can be tricky. At Ecosystm, we’ve helped organisations build ROI models that make a strong case for EUC investments. If you’d like guidance, feel free to reach out – we’re here to help you get it right.
Digital transformation in Malaysia has entered a new phase: less about bold roadmaps, more about fixing what’s broken. With the digital economy expected to reach 25.5% of GDP by 2025, the challenge now is turning strategy into results. Leaders aren’t chasing the next big thing – they are focused on integration bottlenecks, talent gaps, and showing real ROI.
The technology isn’t the problem; it’s making it stick. AI, cloud, and data platforms only deliver value when backed by the right systems, skills, and governance. From aviation to agriculture, organisations are being forced to rethink how they work, how they hire, and how they measure success.
Through a series of interviews and roundtable conversations with Malaysian business and tech leaders, Ecosystm heard firsthand what’s driving – and holding back – digital progress. These weren’t polished success stories, but honest reflections on what it really takes to move forward. The five themes below highlight where Malaysia’s transformation is gaining ground, where it’s getting stuck, and what’s needed to close the gap between ambition and execution.
Theme 1. Ecosystem Collaboration Is Driving Malaysia’s Digital Momentum
Malaysia’s digital transformation is being shaped not by individual breakthroughs, but by coordinated momentum across government, industry, and technology providers. This ecosystem-first approach is turning national ambitions into tangible outcomes. Flagship initiatives like JENDELA, Digital Nasional Berhad’s 5G rollout, and cross-agency digital infrastructure programs are laying the groundwork for smarter public services, connected industries, and inclusive digital access.
The Ministry of Digital (MyDigital) is taking a central role in aligning AI, 5G, and cybersecurity efforts under one roof – helping speed up policy execution and improve coordination between regulators and the private sector. Major tech players like Microsoft, Google, Nvidia, and AWS are responding with expanded investments in local cloud regions, chip design collaborations, and foundational AI services designed for Malaysian deployment environments.
What’s emerging is not just a policy roadmap, but a digitally integrated economy – where infrastructure rollouts, vendor innovation, and government leadership are advancing together. As Malaysia targets to create 500,000 new jobs and reach over 80% 5G population coverage, the strength of these partnerships will be critical in ensuring national strategies translate into sector-level execution.
Theme 2. Laying the Groundwork for Malaysia’s AI Economy
With over 90% of online content projected to be AI-generated by 2025, Malaysia faces growing urgency to ensure that the systems powering AI development are secure, interoperable, and locally relevant. This is about more than data sovereignty – it’s about building the infrastructure to support scalable, trusted, and sector-wide AI adoption.
The National AI Office (NAIO), under MyDigital, is leading efforts to align infrastructure with national priorities across healthcare, manufacturing, agriculture, and public services. Initiatives include supporting domestic data centres, enabling cross-sector cloud access, and establishing governance frameworks for responsible AI use.
The priority is no longer just adopting AI tools, but enabling Malaysia to develop, fine-tune, and deploy them on infrastructure that reflects local needs. Control over this ecosystem will shape how AI delivers value — from national security to inclusive fintech. To support this, Budget 2025 allocates USD 11.7 million for AI education and USD 4.2 million for the National AI Framework. Programs like AI Sandboxes, alongside emerging public-private partnerships, are helping bridge gaps in talent and tooling.
Together, these efforts are laying the foundation for an AI economy that is scalable, trusted, and anchored in Malaysia’s long-term digital ambitions.
Theme 3. Malaysia’s Enterprise AI Landscape: Still in Its Early Stages
Malaysian enterprises are actively exploring AI to drive competitiveness, but widespread, production-grade adoption remains limited. While leading banks are leveraging AI for fraud detection and digital onboarding, and manufacturers are exploring predictive maintenance and automation, many companies face barriers in scaling beyond pilots. Core challenges include siloed data systems, unclear return on investment, and limited in-house AI talent. Even when tools are available, businesses often lack the capacity to integrate them meaningfully into workflows.
Cost is another concern. AI implementation, especially when reliant on third-party platforms or cloud infrastructure, can be prohibitively expensive for mid-sized firms. Without a clear link to bottom-line improvement, AI investments are frequently deprioritised. There’s also lingering uncertainty around governance and compliance, which can further slow enterprise momentum.
For AI to scale across Malaysia, enterprise strategies must align with operational realities – offering cost-effective, localised solutions that deliver measurable value and inspire long-term confidence in digital transformation.
Theme 4. Building on Regulation to Achieve True Cyber Resilience
Malaysia is ramping up its cybersecurity strategy with a stronger regulatory backbone and ecosystem-wide initiatives. The upcoming Cyber Security Bill introduces mandatory breach notifications, sector-specific controls, and licensing for Managed Security Operations Centres (SOCs). Agencies like NACSA are driving protections across 11 critical sectors, while the Cybersecurity Centre of Excellence (CCoE) in Cyberjaya is scaling SOC analyst training in partnership with international players. These efforts are complemented by Malaysia’s leadership role in IMPACT, the UN’s cybersecurity hub, and participation in ASEAN-wide resilience initiatives.
Despite this progress, enterprise readiness remains inconsistent. Malaysian businesses faced an average of 74,000 cyberattacks per day in 2023, yet many still rely on outdated playbooks and fragmented systems. Cybersecurity is often viewed through a compliance lens – meeting audit requirements rather than preparing for real-time recovery. Investments are still skewed toward perimeter defences, while response protocols, cross-team coordination, and real-time observability are underdeveloped.
True resilience requires a shift in mindset: cybersecurity must be treated as a board-level business function. It must be operationalised through simulations, automated response frameworks, and enterprise-wide drills. In a threat landscape that is both persistent and sophisticated, Malaysia must evolve from regulatory compliance to strategic continuity – where recovery speed, not just prevention, becomes the defining metric of cyber maturity.
Theme 5. Malaysia’s Digital Transformation Is Being Led by Industry, Not Policy
While national strategies like the New Industrial Master Plan 2030 set out broad ambitions, real AI-led transformation in Malaysia is taking shape from the ground up, driven by industrial leaders tackling operational challenges with data. Manufacturing and Energy firms, which together contribute over 30% of Malaysia’s GDP, are ahead of the curve. Leaders are using AI for predictive maintenance, digital twins, logistics optimisation, and emissions tracking, often outpacing regulatory requirements.
In some cases, cloud platforms now process millions of machine data points daily to reduce downtime and lower costs at scale. What sets these firms apart is their focus on well-integrated, usable data. Rather than running isolated pilots, they’re building interoperable systems with shared telemetry, open APIs, and embedded analytics, with a focus on enabling AI that adapts in real time.
Malaysia’s next leap in transformation will hinge on whether the data discipline seen in leading industries can be replicated across less-digitised sectors.
If we consider Agriculture – still contributing 7-8% of GDP and employing nearly 10% of the workforce – we find that it remains digitally fragmented. While drones and IoT devices are collecting NDVI and soil data, much of it remains siloed or underutilised. Without clean data pipelines or national integration standards, AI struggles to move from demonstration to deployment.
A Moment to Redefine Ambition
Malaysia stands at a point where digital ambition must evolve into digital maturity. This means asking harder questions – not about what can be built, but what should be prioritised, sustained, and scaled. As capabilities deepen, the challenge is no longer innovation for its own sake, but ensuring technology serves long-term national resilience, equity, and competitiveness. The decisions made now will shape not just digital progress – but the kind of economy and society Malaysia becomes in the decade ahead.
The energy at ServiceNow’s Knowledge25 matched the company’s ambitious direction! ServiceNow is repositioning itself as more than just an IT service platform – aiming to be the orchestration layer for the modern enterprise. Over the past two days, I’ve seen a clear focus on platform extensibility, AI-driven automation, and a push into new functional territories like CRM and ERP.
Here are my key takeaways from Knowledge25.
Click here to download “ServiceNow Knowledge25: Big Moves, Bold Bets, and What’s Next” as a PDF.
AI Everywhere: Agents and Control Towers
ServiceNow goes all in on AI Agents – and makes it easy to adopt.
Like Google, Salesforce, and AWS, ServiceNow is betting big on agents. But with a key advantage: it’s already the enterprise layer where workflows live. Its AI Agents don’t just automate tasks; they amplify what’s already working, layer in intelligence, and collaborate with other agents across systems. ServiceNow becomes the orchestration hub, just as it already is for processes and change.
ServiceNow’s AI Control Tower is a critical accelerator for AI at scale. It enforces policies, ensures compliance with internal and regulatory standards, and provides the guardrails needed to deploy AI responsibly and confidently.
The bigger move? Removing friction. Most employees don’t know what agents can do – so they don’t ask. ServiceNow solves this with hundreds of prebuilt agents across finance, risk, IT, service, CRM, and more. No guesswork. Just plug and go.
Sitting Above Silos: ServiceNow’s Architectural Advantage
ServiceNow is finally highlighting its architectural edge.
It’s one of the few platforms that can sit above all systems of record – pulling in data as needed, delivering workflows to employees and customers, and pushing updates back into core systems. While most Asia Pacific customers use ServiceNow mainly for IT help desk and service requests, its potential extends much further. Virtually anything done in ERP, CRM, SCM, or HRM systems can be delivered through ServiceNow, often with far greater agility. Workflow changes that once took weeks or months can now happen instantly.
ServiceNow is leaning into this capability more forcefully than ever, positioning itself as the platform that can finally keep pace with constant business change.
Stepping into the Ring: ServiceNow’s CRM & ERP Ambitions
ServiceNow is expanding into CRM and ERP workflows – putting itself in competition with some of the industry’s biggest players.
ServiceNow is boldly targeting CRM as a growth area, despite Salesforce’s dominance, by addressing gaps traditional CRMs miss. Customer workflows extend far beyond sales and service, spanning fulfillment, delivery, supply chain, and compliance. A simple quoting process, for instance, often pulls data from multiple systems. ServiceNow covers the full scope, positioning itself as the platform that orchestrates end-to-end customer workflows from a fundamentally different angle.
Its Core Business Suite – an AI-powered solution that transforms core processes like HR, procurement, finance, and legal – also challenges traditional ERP providers, With AI-driven automation for tasks like case management, it simplifies workflows and streamlines operations across departments.
Closing the Skills Gap: ServiceNow University
To support its vision, ServiceNow is investing heavily in education.
The refreshed ServiceNow University aims to certify 3 million professionals by 2030. This is critical to build both demand (business leaders who ask for ServiceNow) and supply (professionals who can implement and extend the platform).
But the skills shortage is a now problem, not a 2030 problem. ServiceNow must go beyond online learning and push harder on in-person classes, tutorials, and train-the-trainer programs across Asia Pacific. Major cloud providers like AWS broke through when large enterprises started training their entire workforces – not just on usage, but on development. ServiceNow needs similar scale and commitment to hit the mainstream.
Asia Pacific: ServiceNow’s Next Growth Frontier
ServiceNow’s potential is massive – and its opportunities even bigger.
In Asia Pacific, many implementations are partner-led, but most partners are currently focused on the platform’s legacy IT capabilities. To unlock growth, ServiceNow needs to empower its partners to engage beyond IT and connect with business leaders.
Despite broader challenges like shrinking tech budgets, fragmented decision-making, and decentralised tech ownership, ServiceNow has a clear path forward. By upskilling partners, simplifying its narrative, and adapting quickly, it’s well-positioned to continue its growth and surpass the hurdles many other software vendors face.