Ecosystm Advisors, Ullrich Loeffler, Sash Mukherjee, Achim Granzen, and Manish Goenka share their take on Salesforce’s announcements, demos, and messaging, highlighting what resonated, what stood out, and what it means for the future. 

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Click here to download “Agentforce World Tour: Highlights from Singapore” as a PDF.

What truly stood out in Salesforce’s messaging? 

ULLRICH LOEFFLER, CEO & Co-Founder 

What stood out at the Salesforce event was their pragmatic, integrated approach to scaling AI. They made it clear AI isn’t plug-and-play, emphasising the complexity and cost involved in what they call ‘self-plumbing’ AI – spanning infrastructure, data management, model development, governance, and application integration. Their answer is a unified platform that lowers costs, accelerates time to market, and reduces risk by removing the need to manage multiple disconnected tools. This seamless environment tackles the real challenge of building and running a layered AI stack. 

Equally notable is their view of Agentic AI as a capability refined through iteration, not a sudden overhaul. By urging businesses to start with the right use cases for faster adoption, less disruption, and tangible impact, they show a realistic grasp of enterprise change. 

Salesforce offers a clear, practical path to AI: simplifying complexity through integration and driving adoption with measured, value-focused steps. 

SASH MUKHERJEE, VP Industry Insights 

What truly stood out at the Salesforce event was their unwavering commitment to Trust. They understand that AI agents are only as reliable as the data they use, and they’ve built their platform to address this head-on. Salesforce emphasises that building trusted AI means more than just powerful models; it requires a secure and well-governed data foundation. They highlighted how their platform, with 25 years of embedded security, ensures data resilience, protects sensitive information during development and testing, and provides robust visibility into how AI interacts with your data. 

A key assurance is their Trust Layer, a unique innovation that safeguards your data when interacting with AI models. This layer automatically masks sensitive data, ensures zero data retention by LLM providers, and detects harmful language. This means organisations can leverage GenAI’s power without compromising sensitive information. 

Ultimately, Salesforce is empowering organisations to confidently deploy AI by making trust non-negotiable, ensuring organisational data is used responsibly and securely to drive real business value. 

How does Salesforce differentiate their approach to Agentic AI? 

ACHIM GRANZEN, Principal Advisor 

Salesforce’s focus on Agentic AI focus stands out for its clarity and depth. The Agentforce platform takes centre stage, demonstrating how clients can now build Agentic AI with little or no code and deploy agents seamlessly across the Salesforce environment. 

But beyond the polished demos and compelling customer stories, the most critical takeaway risked being overlooked: Agentforce is not a standalone capability. It’s tightly integrated with Data Cloud and the broader Salesforce platform. That layered architecture is more than just a technical decision; it’s what ensures every AI agent is governed, auditable, and constrained to what’s been provisioned in Data Cloud. It’s the foundational safeguard that makes Agentic AI viable in the enterprise. 

And that’s the message that needs greater emphasis. As organisations move from experimentation to real-world deployment, trust and control become just as vital as ease of use. Salesforce’s architecture delivers both – and that balance is a key differentiator in the crowded enterprise AI space. 

MANISH GOENKA, Principal Advisor 

Salesforce has moved beyond passive AI assistance to autonomous agents that can take meaningful action within trusted boundaries. Rather than focusing solely on chat-based copilots, Salesforce emphasises intelligent agents embedded into business workflows, capable of executing tasks like claims processing or personalised service without human intervention. 

What sets Salesforce apart is how deeply this vision is integrated into their platform. With Einstein Copilot and Copilot Studio, customers can build their own cross-system agents, not just those limited to Salesforce apps. And by enabling partners to create and monetise agents via AppExchange, Salesforce is building a full-fledged AI ecosystem, positioning themselves as a platform for enterprise AI, not just a CRM. 

Trust is a cornerstone of this approach. Salesforce’s focus on governance, auditability, and ethical AI ensures that Agentic AI is not only powerful, but also secure and accountable – key concerns as agents become more autonomous. 

In a crowded AI space, Salesforce stands out by offering a grounded, scalable vision of Agentic AI, anchored in real use cases, platform extensibility, and responsible innovation. 

Where are Salesforce’s biggest growth opportunities in APAC? 

MANISH GOENKA 

Salesforce has significant growth opportunities across Asia Pacific, with Singapore playing a pivotal role in its regional strategy. The company’s USD 1 billion investment and the launch of their first overseas AI research hub firmly position Singapore as more than just a sales market. It becomes a core engine for product innovation and a key driver of Salesforce’s long-term AI leadership. 

Across the region, public sector transformation and SME digitisation represent major areas of opportunity. Salesforce’s secure and compliant Government Cloud is well suited to support Smart Nation goals and modernise public digital services. At the same time, governments are actively pushing SME digitisation, creating demand for scalable, modular platforms that can grow from basic CRM solutions to AI-enabled automation. 

Sustainability is also emerging as a strong growth vector. As ESG reporting becomes commonplace in more markets, tools like Net Zero Cloud are well positioned to help businesses meet compliance requirements and improve data transparency. 

Finally, the rapidly expanding ecosystem of certified professionals and ISV partners across Asia Pacific is enabling faster, more localised implementations. This grounds Salesforce’s capabilities in local context, accelerating time to value and delivering business outcomes that are tailored to the region’s diverse needs. 

What does the Informatica acquisition mean for Salesforce’s AI strategy? 

ACHIM GRANZEN 

The planned acquisition of Informatica is a strategically important move that completes Salesforce’s Agentforce narrative. At the World Tour, Agentforce was positioned as the future of enterprise AI, allowing organisations to build and deploy autonomous agents across the Salesforce ecosystem. But some lingering concerns remained around how deeply Data Cloud could handle governance, especially as AI agents begin making decisions and executing tasks without human oversight. 

Informatica answers that question. With proven tools for data quality, lineage, and policy enforcement, Informatica brings a level of governance maturity that complements Salesforce’s ambition. Its integration into Data Cloud strengthens the trust layer that underpins Agentforce and reinforces Salesforce’s positioning as an enterprise-grade AI platform. 

Of course, there are broader implications too. Salesforce will gain access to Informatica’s installed base, potentially opening up cross-sell opportunities. And there are questions to resolve, such as how Informatica will operate as a product line within the larger Salesforce ecosystem. 

But the core value of the deal is clear: by bringing Informatica’s governance expertise into the fold, Salesforce can significantly accelerate its ability to deliver trusted, production-ready AI at scale. From a risk and compliance standpoint, that governance capability may prove to be the most valuable part of the acquisition. 

What will define Salesforce’s next chapter of growth in APAC? 

SASH MUKHERJEE 

Just as Salesforce is driving an integrated enterprise platform from the CRM and customer experience lens, competitors (and partners) are taking a similar platform-centric approach from other functional vantage points – whether it’s HR (like Workday), Finance (like Oracle), or IT (like ServiceNow). In fast-growing, cost-sensitive markets across APAC, competing on price alone won’t be sustainable, especially with strong regional players offering leaner, localised alternatives. 

To win, Salesforce must adopt a nuanced strategy that goes beyond product breadth. This means addressing local economic realities – offering right-sized solutions for businesses at different stages of digital maturity – while consistently reinforcing the long-term value, resilience, and global standards that set Salesforce apart. Their differentiators in data security, compliance, and ecosystem depth must be positioned not as add-ons, but as essential foundations for future-ready growth. 

More flexible entry points – whether modular offerings, usage-based pricing, or vertical-specific bundles – can reduce friction and make the platform more accessible. At the same time, strengthening local partnerships with ISVs, system integrators, and government bodies can help tailor offerings to market-specific needs, ensuring relevance and faster implementation. 

Ultimately, Salesforce’s growth across APAC will depend on their ability to balance global strengths with local agility.   

ULLRICH LOEFFLER

Salesforce is well positioned to lead in AI-driven transformation, but doing so will require evolving their sales approach to match the complexity and expectations of today’s enterprise buyers. With a strong foundation selling to marketing and customer leaders, the company now has an opportunity to deepen engagement with CIOs and CTOs, reframing themselves not just as a CRM provider, but as a full-spectrum enterprise platform. 

Traditional sales reps who excel at pitching features to business users are no longer enough. Selling AI – particularly agentic, autonomous AI – demands sales professionals who can link technical capabilities to strategic outcomes and lead conversations around risk, compliance, and long-term value. 

To sustain their leadership, Salesforce will need to invest in a new generation of sales talent: domain-fluent, consultative, and able to navigate complex, cross-functional buying journeys. 

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.

Tone plays a big role too. Thanks to advances in reinforcement learning and tone conditioning, models like ChatGPT are trained to respond with calm, non-judgmental empathy. The result feels emotionally safe; a rare and valuable quality for those facing anxiety, isolation, or uncertainty. A recent PLOS study found that not only did participants struggle to tell human therapists apart from ChatGPT, they actually rated the AI responses as more validating and empathetic. 

And finally, and perhaps surprisingly, is trust. Unlike wellness apps that push subscriptions or ads, AI chat feels personal and agenda-free. Users feel in control of the interaction – no small thing in a space as vulnerable as mental health. 

None of this suggests AI should replace professional care. Risks like dependency, misinformation, or reinforcing harmful patterns are real. But it does send a powerful signal to enterprise leaders: people now expect digital systems to listen, care, and respond with emotional intelligence. 

That expectation is changing how organisations design experiences – from how a support bot speaks to customers, to how an internal wellness assistant checks in with employees during a tough week. Humanised AI is no longer a niche feature of digital companions. It’s becoming a UX standard; one that signals care, builds trust, and deepens relationships. 

Digital Companionship as a Solution for Support 

Ten years ago, talking to your AI meant asking Siri to set a reminder. Today, it might mean sharing your feelings with a digital companion, seeking advice from a therapy chatbot, or even flirting with a virtual persona! This shift from functional assistant to emotional companion marks more than a technological leap. It reflects a deeper transformation in how people relate to machines. 

One of the earliest examples of this is Replika, launched in 2017, which lets users create personalised chatbot friends or romantic partners. As GenAI advanced, so did Replika’s capabilities, remembering past conversations, adapting tone, even exchanging voice messages. A Nature study found that 90% of Replika users reported high levels of loneliness compared to the general population, but nearly half said the app gave them a genuine sense of social support. 

Replika isn’t alone. In China, Xiaoice (spun off from Microsoft in 2020) has hundreds of millions of users, many of whom chat with it daily for companionship. In elder care, ElliQ, a tabletop robot designed for seniors has shown striking results: a report from New York State’s Office for the Aging cited a 95% drop in loneliness among participants. 

Even more freeform platforms like Character.AI, where users converse with AI personas ranging from historical figures to fictional characters, are seeing explosive growth. People are spending hours in conversation – not to get things done, but to feel seen, inspired, or simply less alone. 

The Technical Leap: What Has Changed Since the LLM Explosion 

The use of LLMs for code editing and content creation is already mainstream in most enterprises but use cases have expanded alongside the capabilities of new models. LLMs now have the capacity to act more human – to carry emotional tone, remember user preferences, and maintain conversational continuity. 

Key advances include: 

• Memory. Persistent context and long-term recall 

• Reinforcement Learning from Human Feedback (RLHF). Empathy and safety by design 

• Sentiment and Emotion Recognition. Reading mood from text, voice, and expression 

• Role Prompting. Personas using brand-aligned tone and behaviour 

• Multimodal Interaction. Combining text, voice, image, gesture, and facial recognition 

• Privacy-Sensitive Design. On-device inference, federated learning, and memory controls 

Enterprise Implications: Emotionally Intelligent AI in Action 

The examples shared might sound fringe or futuristic, but they reveal something real: people are now open to emotional interaction with AI. And that shift is creating ripple effects. If your customer service chatbot feels robotic, it pales in comparison to the AI friend someone chats with on their commute. If your HR wellness bot gives stock responses, it may fall flat next to the AI that helped a user through a panic attack the night before. 

The lesson for enterprises isn’t to mimic friendship or romance, but to recognise the rising bar for emotional resonance. People want to feel understood. Increasingly, they expect that even from machines. 

For enterprises, this opens new opportunities to tap into both emotional intelligence and public comfort with humanised AI. Emerging use cases include: 

• Customer Experience. AI that senses tone, adapts responses, and knows when to escalate 

• Brand Voice. Consistent personality and tone embedded in AI interfaces 

• Employee Wellness. Assistants that support mental health, coaching, and daily check-ins 

• Healthcare & Elder Care. Companions offering emotional and physical support 

• CRM & Strategic Communications. Emotion-aware tools that guide relationship building 

Ethical Design and Guardrails 

Emotional AI brings not just opportunity, but responsibility. As machines become more attuned to human feelings, ethical complexity grows. Enterprises must ensure transparency – users should always know they’re speaking to a machine. Emotional data must be handled with the same care as health data. Empathy should serve the user, not manipulate them. Healthy boundaries and human fallback must be built in, and organisations need to be ready for regulation, especially in sensitive sectors like healthcare, finance, and education. 

Emotional intelligence is no longer just a human skill; it’s becoming a core design principle, and soon, a baseline expectation. 

Those who build emotionally intelligent AI with integrity can earn trust, loyalty, and genuine connection at scale. But success won’t come from speed or memory alone – it will come from how the experience makes people feel. 

5/5 (2)

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.

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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.

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The financial services sector stands at a pivotal moment. Shaped by shifting customer expectations, fintech disruption, and rising demands for security and compliance, the industry is undergoing deep, ongoing transformation. From personalised digital engagement to AI-driven decisions and streamlined operations, BFSI is being fundamentally reshaped.

To thrive in this intelligent, interconnected future, financial organisations must embrace new strategies that turn challenges into opportunities.

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Click here to download “Future Forward: Reimagining Financial Services” as a PDF.

Scaling for Impact

CreditAccess Grameen, a leading microfinance institution in India, struggled to scale its operations to meet the rising demand for microloans. Its manual processes were inefficient, causing delays and hindering its ability to serve an expanding customer base.

To overcome this, CreditAccess Grameen digitised its operations, automating processes to handle over 80,000 loans per day, streamlining loan approvals and improving operational efficiency.

This transformation significantly reduced loan processing times, from seven to ten days down to a more efficient, timely process. It also enhanced customer satisfaction, empowered financial independence, and strengthened CreditAccess Grameen’s position as a leader in financial inclusion, driving economic growth in rural India.

Seamless Operations, Improved Reporting

After merging three separate funds, Aware Super, one of Australia’s largest superannuation funds, faced fragmented operations, inconsistent documentation, and poor visibility into workflows. These inefficiencies hampered the organisation’s ability to optimise operations, ensure compliance, and deliver a seamless member experience.

To overcome this, Aware Super implemented a business process management suite to standardise and automate key processes, providing a unified platform for continuous improvement.

The transformation streamlined operations across all funds, improving reporting accuracy, reducing waste, and boosting procurement efficiency. The creation of a Centre of Excellence fostered a culture of ongoing process improvement and regulatory compliance, elevating Aware Super’s process maturity and solidifying its leadership in the financial services sector.

Empowering Employees and Improving Operations

The Norinchukin Bank, a major financial institution serving Japan’s agriculture, forestry, and fisheries sectors, struggled with outdated, paper-based processes and disconnected systems. Manual approvals and repetitive data entry were hindering operations and frustrating staff.

The digital team implemented a low-code platform that quickly automated approvals, integrated siloed systems, and streamlined processes into a single, efficient workflow.

The results were striking: approval times dropped, development cycles halved, and implementation costs fell by 30% compared to legacy upgrades. Employees gained real-time visibility over requests, cutting errors and speeding decisions. Crucially, the shift sparked a wave of digital adoption, with teams across the bank now embracing automation to drive further efficiency.

Eliminating Handoffs, Elevating Experience

Axis Bank, one of India’s largest private sector banks, struggled with slow, manual corporate onboarding processes, which hindered efficiency and customer satisfaction. The bank sought to streamline this process to keep up with growing demand for faster, digital services.

The bank implemented a robust API management solution, automating document handling and onboarding tasks, enabling a fully digital and seamless corporate client experience.

This transformation reduced corporate onboarding time by over 50%, eliminated manual handoffs, and enabled real-time monitoring of API performance, resulting in faster service delivery. As a result, Axis Bank saw a significant increase in customer satisfaction, a surge in API traffic, and a deeper, more loyal corporate client base.

Taming Latency, Unleashing Bandwidth

WebSpace, renowned for its in-store payment systems, faced challenges as it expanded to wholesalers. The migration to a new architecture required low-latency cloud connectivity, but its legacy network, relying on hardware routers, caused performance slowdowns, complexity, and high costs.

WebSpace adopted a cloud-based routing solution, replacing physical routers with a virtual, automated system for multicloud connectivity, enabling on-demand configuration changes from a central control point.

With the new solution, WebSpace achieved faster cloud connectivity, reducing latency and increasing bandwidth. The modern, agile network reduced management costs and complexity, while usage-based billing ensured that WebSpace only paid for the resources it used, supporting its strategic expansion and enhancing overall efficiency.

Asia’s digital natives have already proven that scale and localisation are not mutually exclusive. Grab, for instance, evolved from a ride-hailing app into Southeast Asia’s super-app by integrating services that reflect local habits, from hawker food delivery in Singapore to motorcycle taxis in Indonesia. 

Rather than building physical infrastructure, they leverage platforms and cloud-native tools to reach millions at low marginal cost. AI has been their growth engine, powering hyper-personalisation and real-time responsiveness. Shopee dynamically tailors product recommendations, pricing, and even language options to create user experiences that feel “just for me.” 

But with Agentic AI, the bar is rising again. The next leap isn’t just personalisation; it’s orchestration. Autonomous systems will manage entire customer journeys, dynamically adjusting pricing, inventory, and support across markets in real-time. Digital natives that embrace this will set new standards for customer responsiveness and operational scale.  

To navigate this leap, co-creating AI solutions with tech partners will be crucial. Joint innovation will enable digital natives to move faster, build proprietary capabilities, and deliver richer customer experiences at scale. 

Reshaping Work, Operations, and Organisational Models 

Digital natives have long redefined how work gets done, breaking down silos and blending technology with business agility. But Agentic AI accelerates this transformation. Where AI once automated repetitive tasks, it now autonomously manages workflows across sales, legal, HR, and operations. 

Tokopedia, for example, uses AI to triage customer queries, detect fraud, and optimise marketplace operations, freeing employees to focus on strategic work. This shift is reshaping productivity itself: traditional KPIs like team size or hours worked are giving way to outcome-driven metrics like resolution speed and value delivered. 

With leaner but more impactful teams, digital natives are well-positioned to thrive. But success hinges on evolving workforce models. Upskilling employees to collaborate with AI is no longer optional. Data and AI literacy must be embedded across roles, transforming even non-technical teams into AI-augmented contributors. 

This is where partnerships with big tech providers can unlock value. By co-developing workforce models, training frameworks, and governance structures, digital natives and vendors can accelerate AI adoption while keeping humans at the centre. 

Unlocking New Ecosystems Through Data and Collaboration 

Asia’s digital natives understand that data is more than an asset: it’s a strategic lever for building defensible moats and unlocking new ecosystems. Razorpay processes billions in payment data to assess SME creditworthiness, while LINE integrates messaging, payments, and content to deliver deeply personalised services. 

What’s emerging is a shift from vendor-client dynamics to co-innovation partnerships. Flipkart’s collaboration with tech providers to deploy GenAI across customer support, logistics, and e-commerce personalisation is a prime example. By co-developing proprietary AI solutions – from multi-modal search to real-time inventory forecasting – Flipkart is turning its data ecosystem into a competitive advantage. 

Agentic AI will only deepen this trend. As autonomous systems handle tasks once outsourced, firms are repatriating operations, creating resilient, data-governed ecosystems closer to consumers. This shift challenges traditional outsourcing models and aligns with Asia’s growing emphasis on data sovereignty and sovereign AI capabilities. 

How Agentic AI Will Challenge Digital Natives 

Even for Asia’s most agile players, Agentic AI presents new hurdles: 

• Loss of Advantage. Without Agentic AI, digital natives risk falling behind as competitors unlock unprecedented automation and optimisation. What was once their competitive edge – speed and agility – could erode rapidly. 

• Adaptation Costs. Transitioning to Agentic AI demands serious investment – in infrastructure, talent, and change management. Scaling autonomous systems is complex and resource-intensive. 

• Talent Shift. Agentic AI will redefine traditional roles, enhancing employee contributions but also requiring massive upskilling and workflow redesigns. HR, sales, and operations teams must evolve or risk obsolescence. 

Navigating these challenges will require digital natives to evolve not just technologically, but organisationally and culturally – and to seek partnerships that accelerate this transformation. 

Digital Natives: From Disruptors to Co-Creators of Asia’s Future 

Asia’s digital natives are no longer just disruptors; they are architects of the region’s digital economy. But as Agentic AI, data sovereignty, and ecosystem shifts reshape the landscape, they must evolve. 

The future belongs to those who co-create. By partnering with large tech vendors, digital natives can accelerate innovation, scale faster, and solve the region’s biggest challenges, from inclusive finance to smart cities and sustainable mobility. 

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As rapid technological change, rising citizen expectations, and growing demands for efficiency and transparency converge, the public sector stands at a pivotal crossroads. Profound, system-wide transformation is no longer a future ambition – it is an urgent imperative. From streamlining services and optimising resources to strengthening engagement and building more resilient infrastructure, the challenges and opportunities facing governments are immense.

Now is the time to reimagine how innovation, technology, and a citizen-first mindset can come together to reshape the very fabric of the public sector.

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Click here to download “Future Forward: Reimagining the Public Sector” as a PDF.

Smarter City Management with Digital Twin

GovTech Singapore faced challenges in managing data from smart infrastructure as part of its Smart Nation initiative. They needed a more efficient way to visualise and analyse data from sources like smart lamp posts to improve urban management and resource optimisation.

GovTech implemented a digital twin solution that integrates real-time data from environmental sensors and traffic cameras into a dynamic 3D visualisation platform, enhanced by AI-driven analytics for predictive maintenance and resource allocation.

This solution provided valuable operational insights, reduced maintenance costs, and improved responsiveness to urban challenges. It boosted scalability and performance, optimising city planning and advancing the Smart Nation initiative towards more sustainable and efficient urban development.

Digital Leap for Accessible Governance

Scattered across the Indian Ocean, the Maldives faced a major challenge: delivering government services to citizens spread over 1,000 islands without forcing them to travel. The Ministry of Environment, Climate Change and Technology aimed to build a digital economy that could bring essential services directly to every island, while cutting costs and supporting sustainability goals.

The Ministry unified government agencies on a single, secure digital platform, enabling cloud-based collaboration, virtual meetings, and streamlined access to services.

Now, 20,000 public servants collaborate online, reducing travel, paper use, and operational costs. Government services have become faster and more accessible, solar energy projects are managed more efficiently, and citizens can access services – from banking to legal hearings – without leaving their islands. With digitalisation now embedded, the Maldives is laying the groundwork for AI-driven innovation and further advances in sustainable governance.

Enhancing Urban Resilience

With rising flood risks and extreme weather, Melbourne Water needed a faster, smarter way to maintain its 4,000 stormwater grates. Manual inspections were slow, costly, and couldn’t keep up with intensifying storms. Crews struggled to inspect grates across 14,000 sq km, often arriving too late or checking clear grates. Frequent site visits drove up costs, safety risks, and stretched resources, while the city needed to scale inspections without overburdening crews.

Melbourne Water implemented a cloud-based image recognition system with real-time cameras, using AI to detect blockages and trigger work orders only when needed. 

The system is expanding beyond trial sites to cover critical points in the drainage network, with early results showing tens of thousands in annual savings and further efficiency gains. By freeing up crews and reducing manual work, Melbourne Water is better prepared to manage flood risks and support the city’s sustainable growth.

Making the Cloud Shift to Address Bottlenecks

The Philippine national police force overhauled its outdated licensing system, where applicants endured weeks-long waits, repeated office visits, and costly delays. Staff were bogged down by manual checks, while on-prem systems struggled with capacity limits, poor scalability, and frequent outages.

By shifting to a cloud-native platform, processing times dropped from four weeks to 24 hours – a 96% improvement.

The system now handles 5x more applications daily, scaling automatically with demand. Secure cloud storage replaced legacy systems, freeing IT teams to focus on citizen services. A nationwide content network and built-in security keep access fast, reliable, and protected. The force is unifying IT on one cloud platform and rolling out disaster recovery to boost resilience and future-proof operations.

Streamlining Employee Services

The New Zealand Parliamentary Service needed a more efficient way to manage employee services. Previously reliant on 11 separate email inboxes and a third-party IT service desk, the system was slow, disjointed, and cumbersome. Onboarding and offboarding were a particularly time-sensitive challenge, often causing delays and frustrations.

To streamline operations, the service transitioned to a unified platform, consolidating services into a single portal.

This move cut response times from weeks to just two days and simplified access to essential services, improving employee satisfaction and operational efficiency. With real-time performance tracking and AI-driven case management, the Parliamentary Service is now equipped to scale and optimise service delivery as it continues its digital transformation.

Enhancing Weather Prediction

The Japan Meteorological Agency (JMA) faces the challenge of enhancing weather predictions, particularly for typhoons and torrential rains, as climate change increases their frequency and intensity.

JMA upgraded its supercomputing infrastructure to boost memory bandwidth and computational power, enabling higher-resolution forecasts and more accurate predictions for linear precipitation zones.

The result: JMA has improved the resolution of its local weather models, extending the forecast time for local predictions from 10 to 18 hours. The new system has also increased prediction accuracy for linear precipitation zones, raising the probability of forecasting these events 15 hours in advance from 33% to higher levels. Additionally, the new infrastructure has reduced the number of nodes needed for some weather prediction models by up to 80%, optimising computational resources for other forecasting needs.

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AI is not just reshaping how businesses operate — it’s redefining the CFO’s role at the centre of value creation, risk management, and operational leadership.

As stewards of capital, CFOs must cut through the hype and ensure AI investments deliver measurable business returns. As guardians of risk and compliance, they must shield their organisations from new threats — from algorithmic bias to data privacy breaches with heavy financial and reputational costs. And as leaders of their function, CFOs now have a generational opportunity to modernise finance, champion AI adoption, and build teams ready for an AI-powered future.

LEAD WITH RIGOUR. SAFEGUARD WITH VIGILANCE. CHAMPION WITH VISION.

That’s the CFO playbook for AI success.

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Click here to download “AI Stakeholders: The Finance Perspective” as a PDF.

1. Investor & ROI Gatekeeper: Ensuring AI Delivers Value

CFOs must scrutinise AI investments with the same discipline as any major capital allocation.

• Demand Clear Business Cases. Every AI initiative should articulate the problem solved, expected gains (cost, efficiency, accuracy), and specific KPIs.

• Prioritise Tangible ROI. Focus on AI projects that show measurable impact. Start with high-return, lower-risk use cases before scaling.

• Assess Total Cost of Ownership (TCO). Go beyond upfront costs – factor in integration, maintenance, training, and ongoing AI model management.

Only 37% of Asia Pacific organisations invest in FinOps to cut costs, boost efficiency, and strengthen financial governance over tech spend.

2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape

AI brings significant regulatory, compliance, and reputational risks that CFOs must manage – in partnership with peers across the business.

• Champion Data Quality & Governance. Enforce rigorous data standards and collaborate with IT, risk, and business teams to ensure accuracy, integrity, and compliance across the enterprise.

• Ensure Data Accessibility. Break down silos with CIOs and CDOs and invest in shared infrastructure that AI initiatives depend on – from data lakes to robust APIs.

• Address Bias & Safeguard Privacy. Monitor AI models to detect bias, especially in sensitive processes, while ensuring compliance.

• Protect Security & Prevent Breaches. Strengthen defences around financial and personal data to avoid costly security incidents and regulatory penalties.

3. AI Champion & Business Leader: Driving Adoption in Finance

Beyond gatekeeping, CFOs must actively champion AI to transform finance operations and build future-ready teams.

• Identify High-Impact Use Cases. Work with teams to apply AI where it solves real pain points – from automating accounts payable to improving forecasting and fraud detection.

• Build AI Literacy. Help finance teams see AI as an augmentation tool, not a threat. Invest in upskilling while identifying gaps – from data management to AI model oversight.

• Set AI Governance Frameworks. Define accountability, roles, and control mechanisms to ensure responsible AI use across finance.

• Stay Ahead of the Curve. Monitor emerging tech that can streamline finance and bring in expert partners to fast-track AI adoption and results.

CFOs: From Gatekeepers to Growth Drivers

AI is not just a tech shift – it’s a CFO mandate. To lead, CFOs must embrace three roles: Investor, ensuring every AI bet delivers real ROI; Risk Guardian, protecting data integrity and compliance in a world of new risks; and AI Champion, embedding AI into finance teams to boost speed, accuracy, and insight.

This is how finance moves from record-keeping to value creation. With focused leadership and smart collaboration, CFOs can turn AI from buzzword to business impact.