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.
