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.
Retail transformation is a continuous, dynamic journey of reinvention – driven by agility, experimentation, and the need to keep pace with ever-evolving consumer behaviour. It’s not a fixed destination but an ongoing process of innovation.
At its heart, retail transformation is about putting the customer squarely in control. It’s the strategic overhaul that allows retailers to truly understand individual desires, offering hyper-personalised journeys that blur the lines between online browsing and in-store discovery.
Click here to download “Future Forward: Reimagining Retail” as a PDF.
Enabling Growth with Smarter Sales and Distribution
India’s Tata Consumer Products, aiming to grow their FMCG market share, set out to digitise sales across the vast ‘kirana’-driven retail network.
The company replaced outdated tools with a system that streamlines distributor onboarding, order management, and retail execution – cutting setup times from days to minutes.
A mobile app gives field reps real-time inventory, auto-applied promos, and personalised KPIs, while dashboards give managers live territory insights. Built in seven months, the platform now handles 6M+ transactions monthly, supports 8,000 reps, 12,000 distributors, and 1.6M outlets. Centralised service and rapid feature rollouts keep Tata Consumer fast, responsive, and customer-focused.
Addressing Legacy Limitations
One of New Zealand’s leading grocery retailer, Foodstuffs South Island, faced growing limitations from aging ERP systems and hardware nearing end-of-life.
Instead of reinvesting in outdated tech, FSSI launched Project Petra – a leap to a unified, cloud-based ERP platform.
The shift enabled intelligent replenishment, robotic automation, and a vastly improved user experience. In 18 months, FSSI streamlined roles, rebuilt core apps, and completed a smooth go-live in just three days. The payoff: forecasting and replenishment times cut by up to 50%, faster transactions, seamless price updates, and real-time insights. What began as a tech upgrade became a full transformation – boosting agility, empowering teams, and fuelling future-ready growth.
Streamlining Workflows, Empowering Employees
UCC Group, the Japanese coffee pioneer, is brewing a transformation internally. With 88 locations across 21 countries, UCC faced mounting inefficiencies from outdated legacy systems – slow, complex workflows and clunky portals that frustrated employees and slowed approvals.
UCC replaced their legacy systems with a cloud-first, mobile-first platform.
VPNs were eliminated. Approvals that once took multiple logins now take one tap. A clean, co-designed portal replaced the old interface, putting ease of use first. E-signatures and digitised requests cut paper use by 90% – over 1.5 million forms saved. A new life-event portal launched in just one month, proving speed and simplicity can coexist. Now expanding globally, UCC is unifying ERP, data, and apps into a single, employee-first hub – built for scale, speed, and the future.
Scaling Customer Experience at Speed
Aditya Birla Fashion Retail Limited (ABFRL) faced the challenge of scaling their multi-brand presence without compromising customer experience. As growth surged across stores, online platforms, and marketplaces, their order management system struggled to keep up – putting fulfillment speed, accuracy, and satisfaction at risk.
To solve this, ABFRL implemented a scalable, multi-instance order management solution that streamlined inventory tracking, fulfillment, and refunds.
The result: 99.5% faster inventory sync, zero refund failures, smarter order decisions, and accurate delivery estimates across all channels. This strategic overhaul helped ABFRL maintain service excellence while fuelling sustainable growth – proving that operational agility is key to scaling customer experience at speed.
Solving Reliability & Scalability Challenges
Chicks Lifestyle is a trusted home-grown brand in Hong Kong known for quality innerwear and thermal wear. As they expanded online and geared up for sustainable growth, outdated on-prem systems began to strain under peak-season pressure – causing crashes, long checkout lines, and customer frustration.
To fix this, they migrated their core ERP and POS systems to the cloud in just six weeks with zero data loss.
The result: 99.99% uptime, 30% jump in efficiency, 15% faster checkouts, and 40% lower IT costs. Loyalty data access dropped from minutes to seconds, enabling personalised service at scale. With a stable, scalable tech backbone in place, Chicks Lifestyle is now exploring AI to power their next phase of innovation.
“SaaS is dead!” – this paraphrased comment from Satya Nadella during an interview was taken wildly out of context. It reminded me of those 2014-2017 industry reports predicting that voice commerce would be a USD 500B market by 2025, or that self-driving cars would be everywhere by 2027 – just two years from now. As it turns out, people still prefer ordering groceries themselves rather than relying on smart speakers connected to IoT fridges. And those early chatbot pop-ups? More annoying than intelligent. As for autonomous cars, we might still be better drivers – though that’s starting to shift. But I digress.
Back to SaaS. A global industry with over 30,000 companies, mostly in the US, now finds itself under the shadow of the latest buzz: AI agents (still software, not humanoid robots). These agents – programs built on top of LLMs – take actions within set parameters, showing a degree of autonomy.
But to make AI agents enterprise-ready, we’ll need to rethink access control, ethics, authentication, and compliance. So far, they’ve mostly tackled low-value, repetitive tasks. And despite the hype, we’re still some distance from real, meaningful impact.
Predictions Are Fine – But Best Taken with a Pinch of Salt
Salesforce, the world’s largest SaaS company, has played its part in driving this shift — alongside, of course, Microsoft. Microsoft’s aggressive push into AI, with a massive USD 80 billion CapEx on AI data centres and a flurry of product launches like Copilot chat, may just be the beginning. Microsoft even describes Copilot as the “UI for AI.” Despite its size, Salesforce has moved quickly, rolling out Agentforce, its enterprise AI agent suite. While still early days, Salesforce is leveraging its formidable sales and marketing muscle to push the AI agent narrative — while upselling Agentforce to thousands of existing customers.
For context: Salesforce, the largest player, generates around USD 35 billion in annual revenue. Across the industry, there are roughly 300 SaaS unicorns – but even combined, the entire global SaaS sector brings in only about USD 300B a year. Beyond big names like Salesforce, HubSpot, and Atlassian, the market is dominated by a long tail of smaller, vertical SaaS firms that serve niche sub-industries and specialised use cases.
Today, about 70% of enterprise software is delivered through SaaS. But beyond the top 30 vendors, the landscape is highly fragmented — and arguably primed for disruption by AI agents that can automate and streamline more bespoke, industry-specific workflows.
But the thousands of smaller SaaS firms haven’t all moved as quickly as Salesforce has. Most will likely stick to announcements and incremental upgrades rather than radical deployments – especially as enterprises tread carefully while every vendor suddenly becomes “AI-inside”, the new “Intel-inside.”
AI Washing, Hype, and a Flood of Start-Ups
Since ChatGPT’s historic launch in late 2022, the GenAI AI hype curve hasn’t slowed. In SaaS, the early impact has largely been “AI washing”: companies hastily sprinkling “Generative AI” across their websites, collateral, and social feeds while snapping up .ai domains at premium prices.
Meanwhile, over 3,000 AI-first start-ups have emerged, building wrappers around foundational models to deliver bespoke inferences and niche services. Thanks to ongoing hype, some of these are flush with venture capital – even without revenue. At the same time, traditional SaaS firms face tough investor scrutiny over profitability and free cash flow. The contrast couldn’t be starker.
Yet, both the AI upstarts and the older SaaS players face similar go-to-market challenges. Early product-market fit (PMF) is no guarantee of real traction, especially as most enterprise clients are still experimenting, rather than committing, to AI agents. That’s prompting start-ups to build agentic layers atop inference services to bridge the gap.
The Real Race: Embedding AI with Real Impact
It’s too early to call winners. Whether it’s cloud-first SaaS firms evolving into “AI-inside” platforms, or AI agent start-ups challenging the status quo, success will hinge on more than just AI. It will come down to who can combine proprietary data, compelling use cases, and proven workflow impact.
McKinsey sees AI agents serving two broad patterns: the “factory” model for predictable, routine tasks, and the “artisan” model for augmenting more strategic, executive functions. Another compelling narrative does not make the distinction between the earlier crop of cloud-first and the recent crop of AI-first companies. They see this as a natural progression of the SaaS business model, with VSaaS or “vertical Saas with AI-inside” becoming the broader industry.
I’d argue the original cloud-first SaaS firms might actually be better positioned. Their biggest moat? Existing customer relationships. AI start-ups haven’t yet faced the reality of renewing their first multi-year enterprise contracts. That’s where theory meets enterprise buying behaviour – and where this battle will get interesting.
The Playbook for SaaS Winners in the Age of AI Agents
The SaaS companies that will thrive over the next few years will, in my view, focus on these key elements:
- Leverage Early Clients as a Moat. Invest in the success of your first enterprise clients, ensuring they extract real, sustainable value before chasing new logos. Build enough trust, and you could co-create AI agents trained on their proprietary data, enhancing your core product in the process. Snowflake, with its broad enterprise footprint, has a head start here, but start-ups like Collectivei and Beam are targeting similar use cases, while platforms like Letta help companies deploy their own agents.
- Codify the Use Case. Build products that go deep – not broad. Focus on specific use cases or verticals that a horizontal SaaS company is unlikely to prioritise. Eventually, most enterprise users will care less about which foundation model powers your tool and more about the outcomes.
- Operate with a GTM-First Mindset. Many SaaS firms struggle with margins because of high sales and marketing costs, often wavering between sales-led and product-led growth without a clear go-to-market (GTM) plan. AI start-ups, too, are learning that pure product-led growth doesn’t scale in crowded markets and often pivot to sales-led motions too late. Companies like Chargeflow show why a GTM-first approach is key to building real traction and a growth flywheel.
- Rethink Bundling. Bundling has long been a SaaS pricing play – slicing features into tiers. AI-first start-ups are poised to disrupt this. The shift will be towards outcome-based pricing rather than packaging features. Winners will iterate constantly, tuning bundles to different user cohorts and business goals.
- Charge for Success, Not Seats. AI’s biggest impact may be on pricing. Traditional seat-based models will give way to success or outcome-based pricing, with minimal or no set-up fees. Professional services for customisation will still have value, especially where products align deeply with client workflows and outcomes.
- Prioritise Renewal Over Acquisition. Many AI-first start-ups focus on acquiring logos but underestimate that enterprises are still experimenting – switching costs are low, and loyalty is thin. Building for retention, renewal, and upselling will separate winners from the rest. Focus on churn early.
The Next Chapter in Enterprise Automation
Automation has always been a continuum. Remember when cloud vs. on-prem dominated enterprise debates? Or when RPA was expected to replace most workflows as we knew them? The reality was more measured, and we’re seeing a similar pattern with AI today. Enterprises will first focus on making AI co-pilots work safely, reliably, and effectively before they’re ready to hand over the keys to AI agents running workflows on autopilot. This shift won’t happen overnight.
We’re already seeing early winners capable of negotiating this shift, on both sides: established SaaS giants adapting and AI-native start-ups rising. But make no mistake, this will be a long, hard-fought race. Sustained value capture will demand more than just better tech; it will require a fundamental shift in mindset, go-to-market strategies, and sales motions.
Don’t be surprised if the acronym flips along the way – with Software-as-a-Service giving way to Service-as-Software, as AI agents begin to run entire business processes end to end.
Through it all, one principle will remain timeless: an obsession with customer success – whether the agent is human or machine.
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.
The Asia Pacific region is rapidly emerging as a global economic powerhouse, with AI playing a key role in driving this growth. The AI market in the region is projected to reach USD 244B by 2025, and organisations must adapt and scale AI effectively to thrive. The question is no longer whether to adopt AI, but how to do so responsibly and effectively for long-term success.
The APAC AI Outlook 2025 highlights how Asia Pacific enterprises are moving beyond experimentation to maximise the impact of their AI investments.
Here are 5 key trends that will impact the AI landscape in 2025.
Click here to download “The Future of AI-Powered Business: 5 Trends to Watch” as a PDF.
1. Strategic AI Deployment
AI is no longer a buzzword, but Asia Pacific’s transformation engine. It’s reshaping industries and fuelling growth. Initially, high costs and complex ROI pushed leaders toward quick wins. Now, the game has changed. As AI adoption matures, the focus is shifting from short-term gains to long-term, innovation-driven strategies.
GenAI is is at the heart of this shift, moving beyond the periphery to power core business functions and deliver competitive advantage.
Organisations are rethinking AI investments, looking beyond pure financials to consider the impact on jobs, governance, and data readiness. The AI journey is about balancing ambition with practicality.
2. Optimising AI: Tailored Open-Source Models
Smaller, open-source, and specialised AI models will gain momentum as organisations seek efficiency, flexibility, and sustainability in their AI strategies.
Unlike LLMs, which require high computational power, smaller, task-specific models offer comparable performance while being more resource-efficient. This makes them ideal for organisations working with proprietary data or limited computational resources.
Beyond cost and performance, these models are more energy-efficient, addressing growing concerns about AI’s environmental impact.
3. Centralised Tools for Responsible Innovation
Navigating the increasingly complex AI landscape demands unified management and governance. Organisations will prioritise centralised frameworks to tame the chaos of diverse AI solutions, ensuring compliance (think EU AI Act) while boosting transparency and security.
Automated AI lifecycle management tools will streamline oversight, providing real-time tracking of model performance, usage, and issues like drift.
By using flexible developer toolkits and vendor-agnostic strategies, organisations can accelerate innovation while maintaining adaptability, as the technology evolves.
4. Supercharging Workflows With Agentic AI
Organisations will embrace Agentic AI to automate complex workflows and drive business value. Traditional automation tools struggle with real-world dynamism, but AI-powered agents offer a flexible solution. They empower autonomous task execution, intelligent decision-making, and adaptability to changing circumstances.
These agents, often using GenAI, understand complex instructions and learn from experience. They collaborate with humans, boosting efficiency, and adapt to disruptions, unlike rigid traditional automation.
Agentic workflows are key to redefining work, enabling agility and innovation.
5. From Productivity to People
The focus of AI conversations will shift from simply boosting productivity to using AI for human-centric innovation that transforms both employee roles and customer experiences.
For employees, AI will handle routine tasks, enabling them to focus on creativity and innovation. Education and training will be crucial for a smooth transition to AI-powered workflows.
For customers, AI is evolving to offer more empathetic, personalised interactions by understanding individual emotions, motivations, and preferences. Organisations are recognising the need for transparent, explainable AI to build trust, tailor solutions, and deepen engagement.
Hit or miss AI experiments have leaders demanding results. In this breakneck AI landscape, strategy and realism are your survival tools. A pragmatic approach? High-impact, achievable goals. Know your capabilities, prioritise manageable projects, and stay flexible. The AI winners will be those who champion human-AI collaboration, bake in ethics, and never stop researching.
AI has broken free from the IT department. It’s no longer a futuristic concept but a present-day reality transforming every facet of business. Departments across the enterprise are now empowered to harness AI directly, fuelling innovation and efficiency without waiting for IT’s stamp of approval. The result? A more agile, data-driven organisation where AI unlocks value and drives competitive advantage.
Ecosystm’s research over the past two years, including surveys and in-depth conversations with business and technology leaders, confirms this trend: AI is the dominant theme. And while the potential is clear, the journey is just beginning.
Here are key AI insights for HR Leaders from our research.
Click here to download “AI Stakeholders: The HR Perspective” as a PDF.
HR: Leading the Charge (or Should Be)
Our research reveals a fascinating dynamic in HR. While 54% of HR leaders currently use AI for recruitment (scanning resumes, etc.), their vision extends far beyond. A striking majority plan to expand AI’s reach into crucial areas: 74% for workforce planning, 68% for talent development and training, and 62% for streamlining employee onboarding.
The impact is tangible, with organisations already seeing significant benefits. GenAI has streamlined presentation creation for bank employees, allowing them to focus on content rather than formatting and improving efficiency. Integrating GenAI into knowledge bases has simplified access to internal information, making it quicker and easier for employees to find answers. AI-driven recruitment screening is accelerating hiring in the insurance sector by analysing resumes and applications to identify top candidates efficiently. Meanwhile, AI-powered workforce management systems are transforming field worker management by optimising job assignments, enabling real-time tracking, and ensuring quick responses to changes.
The Roadblocks and the Opportunity
Despite this promising outlook, HR leaders face significant hurdles. Limited exploration of use cases, the absence of a unified organisational AI strategy, and ethical concerns are among the key barriers to wider AI deployments.
Perhaps most concerning is the limited role HR plays in shaping AI strategy. While 57% of tech and business leaders cite increased productivity as the main driver for AI investments, HR’s influence is surprisingly weak. Only 20% of HR leaders define AI use cases, manage implementation, or are involved in governance and ownership. A mere 8% primarily manage AI solutions.
This disconnect represents a massive opportunity.
2025 and Beyond: A Call to Action for HR
Despite these challenges, our research indicates HR leaders are prioritising AI for 2025. Increased productivity is the top expected outcome, while three in ten will focus on identifying better HR use cases as part of a broader data-centric approach.
The message is clear: HR needs to step up and claim its seat at the AI table. By proactively defining use cases, championing ethical considerations, and collaborating closely with tech teams, HR can transform itself into a strategic driver of AI adoption, unlocking the full potential of this transformative technology for the entire organisation. The future of HR is intelligent, and it’s time for HR leaders to embrace it.
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.
In 2024, technology vendors have heavily invested in AI Agents, recognising their potential to drive significant value. These tools leverage well-governed, small datasets to integrate seamlessly with applications like Workday, Salesforce, ServiceNow, and Dayforce, enhancing processes and outcomes.
2025 is poised to be the year of AI Agent adoption. Designed to automate specific tasks within existing workflows, AI Agents will transform customer experiences, streamline operations, and boost efficiency. Unlike traditional AI deployments, they offer a gradual, non-disruptive approach, augmenting human capabilities without overhauling processes. As organisations adopt new software versions with embedded AI capabilities, 2025 will mark a pivotal shift in customer experience delivery.
Ecosystm analysts Audrey William, Melanie Disse, and Tim Sheedy present the top 5 trends shaping customer experience in 2025.
Click here to download ‘AI-Powered Customer Experience: Top 5 Trends for 2025’ as a PDF
1. AI Won’t Wow Many Customers in 2025
The data is in – the real focus of AI over the next few years will be on productivity and cost savings.
Senior management and boards of directors want to achieve more with less – so even when AI is being used to serve customers, it will be focused on reducing back-end and human costs.
There will be exceptions, such as the adoption of AI agents in contact centres. However, AI agents must match or exceed human performance to see broad adoption.
However, the primary focus in contact centres will be on reducing Average Handling Time (AHT), increasing call volume per agent, accelerating agent onboarding, and automating customer follow-ups.
2. Organisations Will Start Treating CX as a Team Sport
As CX programs mature, 2025 will highlight the need to break down not only data and technology siloes but also organisational and cultural barriers to achieve AI-powered CX and business success.
AI and GenAI have unlocked new sources of customer data, prompting leaders to reorganise and adopt a mindset shift about CX. This involves redefining CX as a collective effort, engaging the entire organisation in the journey.
Technologies and KPIs must be aligned to drive customer AND business needs, not purely driving success in siloed areas.
3. The First “AGI Agents” Will Emerge
AI Agents are set to explode in 2025, but even more disruptive developments in AI are on the horizon.
As conversational computing gains traction, fuelled by advances in GenAI and progress toward AGI, “Complex AI Agents” will emerge.
These “AGI Agents” will mimic certain human-like capabilities, though not fully replicating human cognition, earning their “Agent” designation.
The first use cases will likely be in software development, where these agents will act as intelligent platforms capable of transforming a described digital process or service into reality. They may include design, inbuilt testing, quality assurance, and the ability to learn from existing IP (e.g., “create an app with the same capabilities as X”).
4. Intelligent AI Bots Will Enhance Contact Centre Efficiency
The often-overlooked aspect of CX is the “operational side”, where Operations Managers face significant challenges in maintaining a real-time pulse on contact centre activities.
For most organisations, this remains a highly manual and reactive process. Intelligent workflow bots can revolutionise this by acting as gatekeepers, instantly identifying issues and triggering real-time corrective actions. These bots can even halt processes causing customer dissatisfaction, ensuring problems are addressed proactively.
Operational inefficiencies, such as back-office delays, unanswered emails, and slow issue containment, create constant headaches. Integrating bots into contact centre operations will significantly reduce time wasted on these inefficiencies, enhancing both employee and customer experiences.
5. Employee Experience Will Catch Up to CX Maturity
Employee experience (EX) has traditionally lagged behind CX in focus and technology investment. However, AI-powered technologies are now enabling organisations to apply CX use cases to EX efforts, using advanced data analysis, summaries, and recommendations.
AI and GenAI tools will enhance understanding of employee satisfaction and engagement while predicting churn and retention drivers.
HR teams and leaders will leverage these tools to optimise performance management and improve hiring and retention outcomes.
Additionally, organisations will begin to connect EX with financial performance, identifying key drivers of engagement and linking them to business success. This shift will position EX as a strategic priority, integral to achieving organisational goals.
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.
