e27 has been nurturing a supportive ecosystem for entrepreneurs since its inception. Our Contributor Programme offers a platform for sharing unique insights. As part of our ‘Contributor Spotlight’ series, we shine a spotlight on an outstanding contributor and dive into the vastness of their knowledge and expertise.

This episode features Jaslyin Qiyu, Founder and Managing Director of Mad About Marketing Consulting. With over 20 years of B2B and B2C marketing experience across the Asia Pacific, she has led regional teams at global MNCs, including Citibank, EY, JLL, Kantar, Credit Suisse, and State Street.

At Mad About Marketing, Qiyu focuses on brand building, client experience, MarTech, and performance marketing, while championing flexible, fractional talent models that empower women to pursue both career and personal goals. She also serves on advisory and industry boards, including CX Networks, University College Dublin, Trigger-UNDP, and RSVP Singapore.

In the sections below, she reflects on her journey, the lessons he’s learned, and what keeps her going.

How I got here

The turning point for me came when I was advising a startup on their wealth tech platform’s customer journey and value proposition. Their genuine trust in my inputs, coupled with their dedication despite having limited resources, made me realise I could create more impact outside of traditional corporate structures.

That experience crystallised my frustration with the industry’s one-size-fits-all approach and inspired me to start Mad About Marketing Consulting, to democratise access to sophisticated marketing capabilities.

If I had to explain my work to a kid

I help companies show people how cool their products are, kind of like when you see a toy ad on TV that makes you say, “I want that!” I teach them how to tell stories so customers understand why their stuff is fun, useful, or special. It’s like being a matchmaker, helping the right people find the things they’ll really love.

Also Read: Leading through transformation: How CMOs and CEOs must evolve in the AI era

Lessons learned along the way

I used to think I had to feel “completely ready” before saying yes to new opportunities, so I turned down many roles because I thought I wasn’t qualified enough. Over time, I realised that real growth comes from solving real problems for real people, not from collecting more credentials.

My question shifted from “Am I qualified enough?” to “How can I create value while learning?” That change in mindset transformed my approach, from being cautious and over-preparing to stepping forward with confidence and contributing right away.

What more people should notice

There’s a dangerous misconception that generative AI can replace experienced marketing expertise. Too many startups think they can bypass hiring seasoned marketers by using AI tools to generate campaigns, content, and strategies without understanding fundamental customer psychology or market dynamics.

The result is often superficial marketing that lacks depth and misses the mark. The real opportunity lies in combining AI’s capabilities with experienced marketing leadership, where strategy, customer insight, and execution excellence create impact that AI alone cannot deliver.

Why I write

I have always been passionate about writing. In fact my childhood ambition was to be a writer! I also believe knowledge should be free and accessible to all, which is why AI powered research and info crawling has taken off so rapidly.

My writing often starts with real client challenges I’ve encountered and evolves into actionable insights others can use. At its core, my flow comes from a genuine belief that sharing knowledge creates positive ripple effects across the business community.

Also Read: How tech startups can attract Gen Z and millennials seeking flexibility and purpose

My advice for aspiring thought leaders

Here are a few principles I follow whenever I share my thoughts and experiences:

• Start with solving real problems rather than trying to sound impressive. Authenticity resonates more than jargon.
• Always explain complex ideas simply, because clarity demonstrates true understanding better than complexity.
• Share your failures and learning moments alongside successes. Vulnerability creates deeper connections and more valuable insights for your audience.

What drives my curiosity

All things spiritual and the universal realm, including what exists outside of what we can see with the human eye and mind.

Influences that shaped me

Growing up would be my parents and in terms of books, it’s mix of animal books by James Herriot and philosophical ones that prompted me to become naturally curious about the human mind and perception. One of the earliest books I read was Sophie’s World that got me really inspired to learn and read more about philosophy.

Take a look at Qiyu’s articles here for more insights and perspectives on her expertise.

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I was trained as a general practitioner. My early career began in rural clinics, working face-to-face with patients who often arrived late into illness, and sometimes too late for care. Some walked for hours just to reach the nearest facility. Many couldn’t afford the medicine. A few never came at all, out of fear, stigma, or the belief that nothing would change anyway.

Practising in those conditions changed how I understood health systems. The pathways that led people to care or kept them away from it entirely are so much deeper than just treatments or interventions. Distance, cost, shame, and bureaucracy are all systemic and technical obstacles, not human obstacles (and definitely not something your physicians can get rid of with a snap of their fingers). These obstacles existed long before diagnosis or treatment.

That experience stayed with me. It led me to ask harder questions about how health should work. It also led me into innovation, initially in ways that felt small. I started testing different approaches. I looked into patient vital sign self-check-ins, mobile consults, early screening tools, and how digital workflows could reduce drop-offs in care, even insurance products based on real cost and claim data (within our own beta population). But it didn’t take long to realise that the moment you try something new in healthcare, you run into friction.

That friction isn’t always direct from users (whether it’s physicians, health admins, or patients themselves, but rather it’s policy. Sometimes it’s culture, sometimes it’s just inertia. It becomes difficult to move fast when every step is bound by systems that were designed to minimise risk. That instinct makes sense in a clinical context. But it creates resistance when we’re trying to redesign the system itself.

This point in building user feedback and commencing trials with healthcare facilities or healthcare payors that have a sandbox for innovation is a lifesaver (for an early stage company like mine). We started integrating with other services to strengthen their reach, building mutual channel partnership. We saw how technology, when placed carefully, could expand care without increasing pressure on already overburdened systems. We focused on design that removed barriers for both patients and providers.

Now, I work more deeply with AI in healthcare. I see the same patterns re-emerging. We talk about scribing, supply chain coordination, clinical decision support, Software as a Medical Device (SaMD), and even risk modelling for population health. Each use case offers clear advantages. Yet the resistance often comes before the discussion starts.

Also Read: Decoding digital preferences: A glimpse into the future of health tech ecosystem in SEA

People worry about safety, scope, ownership, ethical review, and clinical validity. These concerns matter. But what I’ve observed is that this resistance isn’t stronger than any pushback we’ve seen before (new drugs, supplements, wearables, even robotic surgery; once faced the same level of pushback and some even scrutiny). Every medical innovation in history has gone through it, whether it was antiseptics, laparoscopic surgery, or digital health records. Change is often uncomfortable. But it is never new.

So what’s the real challenge?

Instead of calling it a blocker, I think we need to shift the frame. The misconception is that value is the main driver for innovation. Only after innovating you understand that it actually is about understanding regulation, workforce, education, procurement, reimbursement, and behaviour. Medical innovation becomes normalised when the whole ecosystem is ready to hold it and is aligned across multiple levels of influence (not a single breakthrough overnight).

I’ve seen AI pilots fail, especially because the workflows couldn’t adapt to the real-time day to day operations our healthcare workers face, not because the models. I’ve seen great tools ignored because they didn’t match how clinicians document cases. I’ve seen hospitals decline adoption because IT budgets weren’t structured to handle long-term updates or retraining. These are signals that we need better integration strategy and regulatory pathways (like any other new drug in the market).

Healthcare is complex because it should be. We are dealing with lives. We are dealing with trust. But complexity shouldn’t stop us from building. It should shape how we build.

Also Read: What telemedicine and Health Tech holds across SEA amidst COVID-19

In Southeast Asia, the opportunities are real. We have gaps that technology can help close. The transformation should starts with people who understand the gaps and are willing to build bridges. It starts with small, focused systems that can grow and scale. It starts with conversations that go beyond hype and address what readiness actually looks like. Once we understand that, product building now becomes problem solving deliveries on a deep level.

My path began in rural clinics. I now build for broader systems. The problems have changed shape, but the mission remains the same. Make care more reachable. Make care more trusted. Make care feel possible.

If we want AI in healthcare to succeed, we need to stop waiting for the perfect pilot. We need to understand what adoption truly takes. We need to stop labelling every pause as resistance, and start seeing it as part of a wider transformation journey. Every advancement in medicine required coordination. This one is no different.

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A significant shift is underway in the corporate finance world, as Chief Financial Officers (CFOs) abandon cautious approaches to Artificial Intelligence (AI) in favour of aggressive, strategic investments aimed at long-term revenue growth.

New research from Salesforce reveals that AI, particularly ‘agentic AI‘ — digital labour capable of autonomous task execution — is not merely a tool for cost reduction but a fundamental driver of business transformation. This global trend holds crucial implications for tech startups and established enterprises across Southeast Asia looking to optimise operations and foster innovation.

The paradigm shift in AI strategy

Just five years ago, a striking 70 per cent of global CFOs adhered to a conservative AI strategy, a figure that dropped to 34 per cent two years ago.

Today, that number has plummeted to a mere 4 per cent, indicating a widespread recognition among financial leaders that AI is now a crucial tool for enhancing efficiency, optimising operations, and driving critical long-term growth. In fact, a third of CFOs have now officially adopted an aggressive approach to AI.

Also Read: Forget the rest: ChatGPT alone drives more traffic than 10,500 AI tools combined

This rapid transformation stems from a fundamental rethinking of technology investment Return on Investment (ROI). Robin Washington, President and Chief Operating and Financial Officer at Salesforce, commented, “The introduction of digital labour isn’t just a technical upgrade; it represents a decisive and strategic shift for CFOs. With AI agents, we’re not merely transforming business models; we’re fundamentally reshaping the entire scope of the CFO function. This demands a new mindset as we expand beyond financial stewards to also become architects of agentic enterprise value.”

The rise of agentic AI and redefined ROI

A significant 61 per cent of CFOs report that AI agents are changing how they evaluate ROI, moving beyond traditional metrics to encompass a broader range of business outcomes. On average, CFOs are now dedicating a substantial 25 per cent of their current total AI budget to AI agents. This commitment underlines a belief among 61 per cent of CFOs that AI agents and digital labour are, and will continue to be, critical for competing in the current economic environment.

Furthermore, 64 per cent of CFOs state that AI agents are changing their perspective on how their business spends money, with over a third (35 per cent) acknowledging that AI necessitates a riskier mindset around technology investments.

Beyond cost-cutting: Revenue and strategic value

While traditional technology investments often focused on immediate, measurable results, the perception of AI’s value extends far beyond short-term cost-cutting. Today, CFOs recognise AI’s returns may accrue over the long term through ongoing processes and new business models.

Seventy-four per cent of CFOs believe that AI agents will not only cut costs but also drive revenue. CFOs implementing AI agents anticipate these agents will increase company revenue by almost 20 per cent.

AI agents are uniquely suited to improve long-term business outcomes such as revenue generation, productivity gains, and improved decision-making. Significantly, 72 per cent of CFOs say AI agents will transform their business model, and 55 per cent believe AI agents will take on more strategic work than routine tasks.

New metrics for success

The introduction of AI agents has expanded the top factors CFOs consider when evaluating AI ROI.

These now include:

• Cost savings, risk and compliance improvements, and revenue growth (tied as the number one factor).
• Productivity or efficiency improvements (ranked second).
• Improved decision-making (ranked third).

One CFO survey respondent noted, “Traditional technology investments mainly focus on immediate financial returns that can be easily visible, but AI benefits are a mix of long- and short-term duration. KPIs are focused based on business outcomes.”

Additionally, AI is viewed as a valuable way to ensure ROI through better financial control, with one CFO stating, “AI provides real-time budget tracking, which improves forecasting accuracy and helps protect ROI from overspending through better financial control”. For CFOs, redefining ROI demands a mindset shift from valuing short-term to long-term success.

Concerns and the path forward

Despite the aggressive adoption, CFOs still face significant concerns regarding their AI strategy. The two primary worries are security or privacy threats (66 per cent) and the long time to ROI (56 per cent). Concerns also include the ethical risks associated with AI, which could affect reputational cost and ROI, and the ongoing investment required for retraining, monitoring, and improving AI models, making ROI more fluid compared to fixed-function tools.

Also Read: AI for the real world: SEA’s cost-efficient playbook is winning investors over

This global shift underscores the growing imperative for businesses, including those in the vibrant Southeast Asian tech startup ecosystem, to strategically integrate AI into their core operations. As financial leaders redefine value beyond immediate returns, embracing agentic AI is becoming a critical competitive differentiator for long-term growth and innovation in the digital labour era.

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Despite the rapid advancement of artificial intelligence (AI) in customer experience (CX), consumers are far from ready to abandon human interaction.

A new Verizon report highlights a strong preference for the human touch, presenting a clear mandate for brands to adopt a hybrid approach to CX rather than seeking fully automated solutions.

Humans still reign supreme in customer satisfaction

The report’s findings are unequivocal: 88 per cent of consumers are satisfied with online interactions involving human agents, compared to only 60 per cent for interactions driven solely by AI. This significant gap underscores a fundamental truth: while AI can handle routine queries efficiently, complex or emotionally charged situations necessitate human empathy and problem-solving.

Also Read: Verizon report: Businesses hail AI in CX, but customers still prefer humans

Consumers are “broadly relaxed” about AI for tasks like purchase transactions and product inquiries, but “fewer are comfortable when AI handles their complaints”.

The most prominent frustration consumers cite in automated interactions, by a large margin, is the inability to speak or chat with a live agent when needed, affecting 47 per cent of respondents. Brands concur, noting a similar proportion of customer complaints regarding this lack of human access. Stacy Sherman highlights that even when human agents are eventually involved, “information about you/the customer is lost (must be repeated) at different stages of the interaction,” causing further “friction with customers”.

A hybrid future for CX investments

Acknowledging this preference, companies are not solely betting on AI. The report indicates that a substantial 44 per cent of brands intend to split their future CX investments roughly equally between AI-driven and human-driven improvements. This signals a recognition that a balanced approach, integrating the strengths of both AI and human agents, is the most effective path forward. Only 29 per cent foresee CX operations being mostly or fully AI-driven.

“Some challenges require more than just solutions—they require the empathy and care that only people can provide,” states Morlon Bell-Izzard from Exelon.

Upskilling the human workforce

For this hybrid model to succeed, customer-facing staff require specific upskilling to work effectively alongside AI. Executives are prioritising training in three key areas:

• Handling customer complaints about chatbots.
• Understanding AI prompts during interactions.
• Handling complaints about data privacy issues.

The report also stresses the importance of addressing the “emotional and psychological barriers” employees might have about AI, ensuring transparency about how AI will enhance, not replace, their roles. As Abhii Parakh, Head of Customer Experience at Prudential Financial, observed, employees initially sceptical of AI became excited “once they saw how beneficial AI is for them”. Companies can use AI itself as a “powerful simulation tool” for training, allowing employees to practice interactions and build confidence.

Also Read: AI personalisation isn’t working; more consumers say it hurts CX than improves it

In essence, while AI will continue to automate and optimise parts of the CX journey, the human element remains irreplaceable for delivering truly empathetic and comprehensive customer service, making a seamless human-AI collaboration paramount.

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Customer expectations now grow faster than most operating models can adapt. Cisco’s 2025 Global Contact‑Centre Survey predicts that by 2028, 68 per cent of customer‑service interactions will be handled by agentic AI systems. Meanwhile, a 2024 Dixa poll shows 96 per cent of consumers still consider empathy critical to brand relationships.

Technology must scale, but humanity cannot be engineered out. The solution is not another point bot; it is a platform-first architecture that orchestrates data, workflows, autonomous agents and human experts inside a single, governed fabric.

The disconnect: Why AI isn’t delivering on the hype

Boards approve nine‑digit AI budgets, yet many projects remain stalled in pilot purgatory. Forrester’s 2024 Digital Business Strategy Survey reports that only 56 per cent of leaders have an enterprise‑wide view of technology. McKinsey’s 2025 research finds 95 per cent of AI initiatives never scale beyond pilots. Fragmented systems starve models of context‑rich data, while governance is treated as an afterthought.

Consider a telecom provider that launched separate chatbots for billing, network faults and promotions. Each bot answered its narrow script, but none shared a common data layer. Customers bounced between channels, escalation volumes spiked, and Net Promoter Score barely moved. Scattered tools, even “smart” ones, cannot substitute for a unifying platform.

What makes agentic AI different?

Traditional chatbots follow decision trees; agentic AI is goal‑driven, contextual and action‑oriented. Picture an AI agent that detects a billing anomaly, issues the refund, updates the CRM, emails an apology and alerts a human only if the amount crosses a threshold. Autonomy at that level creates three non‑negotiables:

• Context hunger: Curated, lineage‑tracked data streams
• Governance demand: Transparent audit trails and policy controls
• Interoperability: The freedom to swap models without re‑wiring applications

Only a platform layer that abstracts data, policy and workflow can meet all three at production scale.

Also Read: Agentic AI, urban mobility & smart tourism: 2025’s travel investment hotspots

Human + AI: Better together

Automation excels at speed and pattern recognition; humans excel at judgment, negotiation and relationship‑building. The goal is augmentation, not substitution. A global med‑tech firm recently introduced an agentic‑AI layer that triages tickets and surfaces knowledge‑base articles.

Human agents now focus on complex clinical queries, pushing first‑contact resolution and CSAT to record highs. When machines handle the routine, people deliver empathy exactly what 96 percent of customers want.

Platforms: Architecture for orchestrated intelligence

In Forrester’s 2024 survey, 70 per cent of digital leaders said technology and business executives now collaborate closely on change initiatives a shift directly linked to unified platform strategies. Such platforms provide:

• Composable services: API‑driven micro‑components that let teams plug in new AI models within days, not quarters
• Unified data fabric: Clean, trusted streams feeding both agents and analysts
• Governance by design: Access controls and audit logging embedded where functionality lives

This is strategic infrastructure, not middleware. A robust platform turns isolated bots into a coordinated workforce that learns, adapts and stays auditable.

APAC spotlight: DBS Bank

A 2024 Harvard Business School case study details how Singapore‑based DBS Bank built an internal data‑and‑model hub plus a PURE (Purposeful, Unsurprising, Respectful, Explainable) responsible‑AI framework. With that foundation, DBS scaled 300‑plus AI use cases across lending, fraud and service, boosting self‑service adoption and helping lower false‑positive fraud alerts. The case exemplifies platform‑first thinking in one of EdgeVerve’s key regions.

Also Read: 88% of consumers favour human agents; AI alone fails to deliver CX satisfaction

Scaling through strategic orchestration

A platform mind‑set reframes AI from a stand‑alone tool to a capability woven through every business process. High‑performing organisations:

• Swap models without disruption: Treating language or vision models as hot‑swappable modules under existing guardrails.
• Propagate success: Templating connectors so a winning use case in one region can be cloned elsewhere with minimal recoding.
• Monitor holistically: Combining experimentation metrics and production KPIs in a single observability stack.
• Automate compliance: Making centrally defined policies inherit automatically to every new workflow.

One Asia‑Pacific conglomerate recently merged a dozen AI pilots onto a single platform. Release cycles for new virtual‑assistant features shrank from months to weeks, and CSAT climbed double digits proving orchestration, not model count, drives value.

CXO playbook: three principles for the next wave

• Think platform‑first: Invest in data fabric, API gateways and governance layers before chasing the next generative model.
• Design for empathy + autonomy: Map journeys where human touch is irreplaceable and bake those checkpoints into orchestration.
• Embed governance early: Treat explainability, lineage and compliance as design inputs, not retrospective audits.

The road to harmony

Enterprises that orchestrate human and agentic intelligence on a strong platform spine will transform customer experience from reactive support to proactive value creation. Cisco’s survey notes 81 per cent of CX leaders believe vendors that master agentic AI will carve out enduring competitive advantage.

The stakes and the opportunities couldn’t be clearer. From hype to harmony, the future belongs to those who blend scalable technology with human empathy into one coherent, governed platform

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