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.
Qualtrics, a leading global Voice of Customer (VoC) provider, held its annual X4 conference in May, at the ICC in Sydney. The event included an exclusive media lunch and focused on Qualtrics’ latest announcements and product enhancements, many of which were first unveiled at its US event in March.
The conference combined insights into the company’s technology roadmap with real-world customer success stories, featuring organisations such as KFC, ServiceNow, David Jones, Hilton, and others.
In a world dominated by AI agents, the opportunity lies in building real human connections. The challenge, however, is to do this at scale. Empowering people with AI agents, rather than replacing them, can improve efficiency while also creating space for more empathetic, human-centred interactions, the vendor argues. The theme of building connections and making every connection count came through loud and clear and was weaved through the product announcements.
Here are my key takeaways from attending the conference.
Culture is Key
It was refreshing to see culture take centre stage at a vendor briefing – a critical pillar for CX success that’s too often overlooked in technology conversations.
While technology is critical to enable a successful CX practice and continuously improve customer experience, building a culture of customer centricity must be the foundation for technology to be successful.
It’s critical to break down internal silos to unify data across the organisation and democratise insights. With customer feedback now coming into the organisation through various channels (surveys, calls, emails, social media, etc.), GenAI enables organisations to create a holistic understanding of experiences across all channels and touchpoints. Likewise, that data needs to be shared with the right internal teams to enable continuous improvement opportunities. For that to happen, organisations need to develop a culture of customer centricity and break out of their silo-centric mindset.
Qualtrics Experience Agents
No surprise, Agentic AI has made it into the world of customer feedback with Brad Anderson, President – Products, User Experience, and Engineering, introducing Qualtrics Experience Agents.
Qualtrics has started to develop AI agents and is slowly embedding this capability into the platform. Think about closing the loop with customers, automating small tasks, and proactively identifying issues before we hear about them.
The Experience Agents can respond to customers during the survey process or can be embedded into the digital experience to address problems in real time. Closing the loop with customers, across surveys and other service requests, can be a timely and resource intense undertaking. Qualtrics’ autonomous agents can close the loop with 100% of customers, automatically responding in real time, building empathy and making your customers feel heard.
It’s still early days for Qualtrics’ Experience Agents and I look forward to seeing tangible outcomes of customer implementations. I’m sure we’ll hear more about this over the coming months!
Surveys Just Got Smarter
Qualtrics introduced “agentified” surveys, a new way to respond to verbatim survey feedback, adjust follow up questions accordingly, and turn surveys into conversations.
This is an evolution of what’s referred to as verbatim probing. They represent a new way of getting actionable feedback from customers through AI enabled and adaptive questioning during a survey.
The new technology enhances the insight quality and aims to build empathy with customers. Verbatim responses become richer in value and Qualtrics reports a slight increase in survey completion rates. The aim is to turn surveys into conversations, leaving customers feeling heard and building stronger connections.
Despite the adoption of unsolicited feedback as a source of customer insights, surveys still represent the foundation for any VoC program, and they’re not going to go away any time soon. Enhancing survey capabilities while adding operational and unsolicited feedback to the mix will be key to establishing a deeper understating of customer experience and identifying improvement opportunities.
Show Me the Money
Qualtrics highlights the importance of linking CX initiatives to business outcomes and results to demonstrate ROI and gain buy-in and continued support from key stakeholders.
When VoC programs were first introduced, the main challenge for most organisations was gathering customer feedback. Once that hurdle was overcome – thanks to technology – the next challenge became converting raw data into meaningful insights, especially with the addition of unstructured data sources.
The focus then shifted from insights to identifying and driving action. Mature organisations are now at the stage of tangibly linking CX results to business outcomes and showcasing ROI. Quantifying business impact is an essential step in enabling CX success, yet it is often neglected.
Most organisations are still working on building robust Insights-to-Action frameworks and translating insights into tangible action; efforts often hindered by limited collaboration and a lack of customer-centric culture. For more mature organisations, the challenge now lies in clearly demonstrating the business outcomes and ROI of their CX programs.
Other Announcements
Qualtrics Assist. Alongside other technology giants, Qualtrics’ ‘Assist’ solution is an easy way to query the data in a natural language style, i.e. asking data questions to find insights. This is particularly important for larger data sets that comprise survey and unsolicited feedback, as it significantly speeds up the insight generation process. Analysis that used to take days or weeks, can now be completed in minutes or seconds.
Qualtrics Edge. Qualtrics has started to introduce synthetic data to its Research product suit. It’s a niche market at this stage but certainly growing in popularity as utilising synthetic data, panels and personas not only significantly speeds up the research process but also reduces cost. I’m interested to see market uptake for this. While it’s not new per se, organisations still need to overcome the “trust” hurdle to fully embrace synthetic data and research.
Customer Service and VoC: Boundaries Blur Further
While AI agents have dominated contact centre conversations in recent months, Qualtrics is one of the few VoC vendors now introducing Agentic AI with its Experience Agents.
This is particularly relevant for the digital experience space, where a variety of vendors are offering solutions. Qualtrics’ Experience Agents can detect signs of frustration and rage clicking during digital sessions and proactively engage to close the loop in real time.
It will be interesting to see how the growing number of agents from different vendors ultimately work together in a coordinated way to enhance experiences, rather than introduce new points of friction.
The contact centre has long been a goldmine for customer experience data and insights. Today, tapping into conversational data has become an open field for vendors across VoC, contact centre, and conversational intelligence categories. While this brings innovation, it also complicates decision-making for technology buyers. With vendors from different backgrounds offering overlapping capabilities, often to different internal stakeholders, organisations risk ending up with complex, costly tech stacks.
That said, it’s encouraging to see Qualtrics continue to develop and embed GenAI and Agentic AI into its platform. As a leader in the CX space, it’s setting a high bar for the rest of the market.
At the Agentforce World Tour in Singapore, Salesforce presented their vision for Agentic AI – showcasing how they’re helping customers stay ahead of rapid technological change and unlock stronger business outcomes with speed, trust, and agility.
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.
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.
Asia is undergoing a digital renaissance. Central to this transformation is the rise of digital natives: companies born in the digital era, built on cloud infrastructure, powered by data, and guided by customer-centric agility. Unlike traditional businesses retrofitting technology onto old models, Asia’s digital natives have grown up with mobile-first architecture, software-as-a-service models, and a mindset of continuous iteration. They’re not merely disrupting — they are reshaping economies, industries, and even governance.
But we are seeing a new wave of change. The rise of Agentic AI – autonomous, multi-agent systems that handle workflows, decision-making, and collaboration with minimal human input – is set to redefine industries yet again. For digital natives, embracing Agentic AI is no longer optional. Those who adopt it will unlock unprecedented automation, speed, and scale. Those who don’t risk being leapfrogged by competitors operating faster, smarter, and more efficiently.
The path forward demands evolution and collaboration. Digital natives must upgrade their capabilities, while large tech vendors must shift from selling solutions to co-creating them with these agile players. Together, they can accelerate time-to-market and build future-ready ecosystems.
Reimagining Scale and Customer Experience
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.
For marketers, the “golden goal” has always been to deeply understand customers, enabling more effective cross-selling, upselling, and targeted campaigns. The promise of maximising wallet share hinges on this fundamental principle. Imagine having technologies that can analyse customer journeys deeply, uncovering meaningful, real-time insights into customer behaviour and sentiment. This rich, dynamic data could empower marketing teams to move beyond static profiles, gaining immediate visibility into how customers react to campaigns, messages, and interactions across all channels.
Data Fragmentation: The CMO’s Blind Spot
However, achieving this goal has become increasingly difficult. The modern marketing stack, built upon CRM, content marketing platforms, retargeting ad solutions, social listening tools, and countless other applications, often operates in silos. This wide, disconnected array of tools creates a significant challenge: making sense of the fragmented data. Efforts to truly understand customers and identify valuable prospects frequently fall short of desired outcomes. The lack of integration and the sheer volume of disparate data leave marketers struggling to connect the dots and extract actionable insights.
Unified Customer Vision: AI Agents for Intelligent Marketing
The solution lies in leveraging AI agents that operate seamlessly in the background. By implementing AI agents, CMOs can gain a comprehensive understanding of their customers, enabling them to run more effective campaigns, drive greater wallet share, and build stronger, more meaningful customer relationships.
These intelligent agents can bridge the gaps in customer data, sentiment, and campaign perception by accessing and processing information across the entire marketing stack. By learning from metadata, successful and failed campaigns, and a broad range of customer insights – including conversational and digital contact centre data – these agents can provide a unified view of the customer.
What They Bring to the Table
- Unified Data Access. AI agents can traverse siloed marketing applications, extracting and correlating data from various sources.
- Real-Time Insight Generation. They can analyse customer interactions, including social media sentiment, conversational AI data, and voice bot interactions, to provide dynamic, real-time insights.
- Autonomous Action & Adaptation. Agentic workflows can adapt to campaigns, email blasts, and lead generation activities autonomously, refining strategies and messaging on the fly.
- Content Curation & Optimisation. Content curation agents can tailor content based on real-time customer feedback and preferences.
- Proactive Opportunity Identification. By identifying gaps in customer understanding and campaign performance, AI agents can empower marketers to uncover new opportunities for engagement and growth.
Extending AI Agent Value: Practical Applications for the Modern CMO
Beyond unified data access and autonomous action, AI agents offer a wealth of practical applications that can revolutionise marketing operations. Consider the following scenarios:
- Automating Time-Consuming Tasks. Identify and offload repetitive, manual tasks associated with campaign execution and lead generation to a team of AI agents, freeing up valuable human resources for strategic initiatives.
- Enhancing Sales Pipeline Intelligence. Leverage AI agents to extract insights from sales pipelines and customer feedback, enabling data-driven campaign adjustments and improved sales alignment.
- Real-Time Sentiment Analysis. Deploy multiple AI agents to monitor customer sentiment across conversations and social media platforms, providing immediate feedback on campaign effectiveness and brand perception.
- Strategic Scenario Planning. Use AI agents to formulate and evaluate various marketing spend scenarios across different channels and agencies, optimising resource allocation and maximising ROI.
- Dynamic Campaign Monitoring. Implement AI agents to track campaign performance in real-time, allowing for immediate adjustments and optimisation.
- Event Sentiment Analysis. Employ AI to monitor customer sentiment during live events, providing immediate insights into audience reactions and engagement.
- Unlocking Conversational Intelligence. Extract valuable insights from sales conversations and contact centre interactions, feeding them into future sales strategies and upselling opportunities. This extends beyond relying solely on CRM data, providing a richer, more nuanced understanding of customer interactions.
By implementing these capabilities, CMOs can transform their marketing operations, moving from reactive to proactive, and ultimately driving greater customer engagement and business success.
The “Wow” Factor: Agentic AI and Unified Data
Ultimately, the pursuit of a seamless customer journey and deeper conversational engagement hinges on bridging the persistent departmental disconnect. Despite each team interacting with the same customer, data remains siloed, hindering a holistic understanding and unified approach.
The missing link lies in fostering a dynamic, interconnected data ecosystem where insights from campaigns, social listening, contact centre conversations, chatbot interactions, VoC programs, marketing applications, and CRM flow freely and mutually reinforce each other.
This is where Agentic AI steps in. By empowering AI agents to adapt and act autonomously across these diverse data sources, we create a symphony of customer intelligence. These agents, working in harmony, unlock the potential for real-time, actionable insights, enabling marketers to craft truly exceptional, “wow” moments that resonate deeply with customers. In essence, Agentic AI transforms fragmented data into a unified, powerful force, driving unparalleled customer experiences and forging lasting brand loyalty.
A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.
DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today.
While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost.
The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model.
Real Barriers to GenAI
• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets.
• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific.
• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training.
• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start.
• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices.
GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome.
Customer Success leaders are keenly aware of AI’s burgeoning potential, and our latest research confirms it. AI is no longer a futuristic concept; it’s a present-day reality, already shaping content strategies for 55% of organisations and poised to expand its influence across a multitude of use cases.
Over the past two years, Ecosystm’s research – including surveys and deep dives with business and tech leaders – has consistently pointed to AI as the dominant theme.
Here are some insights for Customer Success Leaders from our research.
Click here to download “AI Stakeholders: The Customer Success Perspective” as a PDF.
AI in Action: Real-World Applications
The data speaks for itself. We’re seeing a significant uptake of AI in automating sales processes (69%), location-based marketing (63%), and delivering personalised product/service recommendations (61%). But beyond the numbers, what does this look like in practice?
In Marketing, AI tailors campaigns in real time based on customer behaviour, ensuring content and offers resonate. For e.g. in the Travel industry, AI analyses customer preferences to create customised itineraries, boosting satisfaction and repeat bookings. In Sales, AI-driven analysis of buying patterns helps teams stay ahead of trends, equipping them with the right products to meet demand. In Customer Experience, AI-powered feedback analysis identifies pain points before they escalate, leading to proactive problem-solving. We have already seen organisations using conversational AI to enable 24/7 customer engagement, instantly resolving issues while reducing team workload and enhancing CX.
Challenges and Opportunities: Navigating the AI Landscape
However, the path to AI adoption isn’t without its hurdles. Customer Success leaders face significant challenges, including the lack of an organisation-wide AI strategy, data complexity and access issues, and the cost of implementation.
Despite these challenges, the focus on AI to enhance Customer Success is evident, with nearly 40% of AI initiatives geared towards this goal. This requires a more active role for these leaders in shaping AI strategies and roadmaps.
Our research reveals that there lies a critical gap: Customer Success leaders have limited involvement in AI initiatives. Only 19% are involved in identifying and prioritising use cases, and a mere 10% have input into data ownership and governance. This lack of participation is a missed opportunity.
The 2025 Vision: AI-Driven Customer Success
Looking ahead, Customer Success leaders expect AI to deliver significant benefits, including improved customer experience (56%), increased productivity (50%), and enhanced innovation (44%). These expectations underscore AI’s pivotal role in shaping the future of customer success.
To fully harness AI’s potential and advancements like Agentic AI, leaders must take a more active role. This means driving a clear AI strategy, tackling data challenges, and working closely with IT and data science teams to ensure AI solutions address real customer pain points and business gaps.
The Customer Experience (CX) space is set to witness significant advancements in 2025, particularly with the rise of Agentic AI.
Unlike GenAI, which despite enormous promise, has struggled to deliver scalable solutions, Agentic AI offers dynamic, scalable improvements for brands.
With AI agents and an expanding digital AI workforce, front and back-office automation is becoming more independent.
These AI-driven systems will enable precise information retrieval, intelligent, human-like conversations, autonomous decision-making, and seamless customer interactions without constant intervention from CX teams.
Click here to download “An Agentic AI Perspective” as a PDF.
The Challenges of Traditional Conversational AI
Traditional Conversational AI has faced persistent challenges that have hindered its widespread adoption. Many solutions lack contextual awareness, limiting their ability to engage proactively. Siloed back-end data often restricts these systems from making autonomous decisions, while predefined conversational boundaries prevent seamless, natural interactions.
Despite advancements, organisations deploying Conversational AI continue to encounter significant issues:
- Customers frequently need to rephrase or repeat themselves due to misunderstood intent.
- Incorrect options frustrate users, pushing them to call contact centres.
- Many interactions only partially resolve issues, leaving 40-50% of problems unsolved.
These limitations have slowed adoption, particularly in the Asia Pacific region, where enterprises remain cautious, opting for pilots and tests over large-scale deployments.
Adding to the complexity is the challenge of handling local languages like Thai, Bahasa, Chinese, and Indian languages, as well as nuanced regional English dialects, which AI often struggles to interpret accurately.
Agentic AI: A Transformational Solution
Agentic AI is poised to revolutionise Conversational AI by addressing these longstanding challenges. Unlike traditional systems, Agentic AI offers the ability to retrieve precise information, engage in intelligent, human-like conversations, and make autonomous decisions based on vast amounts of customer metadata.
Agentic AI empowers enterprises to create conversational flows that are not only seamless but also adaptive to context and behaviour.
It enables CX systems to overcome language barriers, handle unstructured data dynamically, and deliver faster, more personalised responses. By doing so, Agentic AI enhances customer satisfaction, drives efficiency, and unlocks the potential for proactive, intelligent engagement at scale.
Success Stories and Adoption Trends
Simpler use cases like balance checks, order confirmations, and structured dialogues have garnered positive feedback. Improvements have been achieved through better conversational design and integrating diverse data into unified repositories.
Agent Assist solutions have seen strong adoption in 2024. New developments in AI agents as a digital workforce are unlocking remarkable outcomes. These agents can analyse unstructured CX data, enabling faster, context-rich conversations.
In 2025, AI agents with agentic capabilities will make independent decisions, learn from context, solve complex problems, and adapt dynamically based on customer interactions.
Preparing For What’s Ahead
CX solution buyers and decision-makers must prepare for the transformative potential of Agentic AI.
- Evaluate vendor offerings. Ask vendors about their Agentic AI solutions and assess their capabilities in delivering desired outcomes.
- Look for end-to-end platforms. Ensure platforms provide tools to design, build, test, deploy, and scale AI agents, workflows, and GenAI applications.
- Focus on orchestration. Choose solutions that integrate seamlessly across channels and applications, ensuring alignment with voice and human collaboration tools.
AI has already had a significant impact on the tech industry, rapidly evolving software development, data analysis, and automation. However, its potential extends into all industries – from the precision of agriculture to the intricacies of life sciences research, and the enhanced customer experiences across multiple sectors.
While we have seen the widespread adoption of AI-powered productivity tools, 2025 promises a bigger transformation. Organisations across industries will shift focus from mere innovation to quantifiable value. In sectors where AI has already shown early success, businesses will aim to scale these applications to directly impact their revenue and profitability. In others, it will accelerate research, leading to groundbreaking discoveries and innovations in the years to come. Regardless of the specific industry, one thing is certain: AI will be a driving force, reshaping business models and competitive landscapes.
Ecosystm analysts Alan Hesketh, Clay Miller, Peter Carr, Sash Mukherjee, and Steve Shipley present the top trends shaping key industries in 2025.
Click here to download ‘AI’s Impact on Industry in 2025’ as a PDF
1. GenAI Virtual Agents Will Reshape Public Sector Efficiency
Operating within highly structured, compliance-driven environments, public sector organisations are well-positioned to benefit from GenAI Agents.
These agents excel when powered LLMs tailored to sector-specific needs, informed by documented legislation, regulations, and policies. The result will be significant improvements in how governments manage rising service demands and enhance citizen interactions. From automating routine enquiries to supporting complex administrative processes, GenAI Virtual Agents will enable public sector to streamline operations without compromising compliance. Crucially, these innovations will also address jurisdictional labour and regulatory requirements, ensuring ethical and legal adherence. As GenAI technology matures, it will reshape public service delivery by combining scalability, precision, and responsiveness.
2. Healthcare Will Lead in Innovation; Lag in Adoption
In 2025, healthcare will undergo transformative innovations driven by advancements in AI, remote medicine, and biotechnology. Innovations will include personalised healthcare driven by real-time data for tailored wellness plans and preventive care, predictive AI tackling global challenges like aging populations and pandemics, virtual healthcare tools like VR therapy and chatbots enhancing accessibility, and breakthroughs in nanomedicine, digital therapeutics, and next-generation genomic sequencing.
Startups and innovators will often lead the way, driven by a desire to make an impact.
However, governments will lack the will to embrace these technologies. After significant spending on crisis management, healthcare ministries will likely hesitate to commit to fresh large-scale investments.
3. Agentic AI Will Move from Bank Credit Recommendation to Approval
Through 2024, we have seen a significant upturn in Agentic AI making credit approval recommendations, providing human credit managers with the ability to approve more loans more quickly. Yet, it was still the mantra that ‘AI recommends—humans approve.’ That will change in 2025.
AI will ‘approve’ much more and much larger credit requests.
The impact will be multi-faceted: banks will greatly enhance client access to credit, offering 24/7 availability and reducing the credit approval and origination cycle to mere seconds. This will drive increased consumer lending for high-value purchases, such as major appliances, electronics, and household goods.
4. AI-Powered Demand Forecasting Will Transform Retail
There will be a significant shift away from math-based tools to predictive AI using an organisation’s own data. This technology will empower businesses to analyse massive datasets, including sales history, market trends, and social media, to generate highly accurate demand predictions. Adding external influencing factors such as weather and events will be simplified.
The forecasts will enable companies to optimise inventory levels, minimise stockouts and overstock situations, reduce waste, and increase profitability. Early adopters are already leveraging AI to anticipate fashion trends and adjust production accordingly.
No more worrying about capturing “Demand Influencing Factors” – it will all be derived from the organisation’s data.
5. AI-Powered Custom-Tailored Insurance Will Be the New Norm
Insurers will harness real-time customer data, including behavioural patterns, lifestyle choices, and life stage indicators, to create dynamic policies that adapt to individual needs. Machine learning will process vast datasets to refine risk predictions and deliver highly personalised coverage. This will produce insurance products with unparalleled relevance and flexibility, closely aligning with each policyholder’s changing circumstances. Consumers will enjoy transparent pricing and tailored options that reflect their unique risk profiles, often resulting in cost savings. At the same time, insurers will benefit from enhanced risk assessment, reduced fraud, and increased customer satisfaction and loyalty.
This evolution will redefine the customer-insurer relationship, making insurance a more dynamic and responsive service that adjusts to life’s changes in real-time.
