For startups, visibility has always been a survival skill. You need to win investor trust, attract talent, and convince customers to bet on you instead of an established player. In today’s environment, that challenge has a new dimension. It is no longer just about reaching people; it is about connecting with them. It is about ensuring your brand is part of the knowledge base that powers Generative AI tools, such as ChatGPT, Gemini, Copilot, and DeepSeek.

A recent Forrester survey found that 89 per cent of B2B buyers already use GenAI in their decision-making process. This technology is no longer optional. It is integrated into every stage of the buyer journey, from scanning the market to comparing vendors to validating ROI.

For startups trying to punch above their weight, this shift can either level the playing field or make you invisible. The question is: how do you ensure that when AI platforms answer, your brand is part of the story?

The media hierarchy that AI listens to

Unlike Google, AI models do not crawl the web in real time. They rely on training data and high-credibility sources. That creates a new hierarchy of influence that every startup founder should be aware of.

• Mainstream media: Outlets like BBC or The New York Times set global credibility. One strong placement here can echo in countless AI-generated answers.
• Industry publications: Coverage in places like TechCrunch or e27 defines trends and categories. For startups, this is often the most attainable and highest-leverage tier. A TechCrunch profile or an e27 founder story is more likely to be referenced in AI explanations of “emerging fintechs in Asia” than a single blog post on your site.
• Branded thought leadership: Whitepapers and founder essays, if picked up by respected platforms, become frameworks AI repeats when offering strategic advice.
• Academic and policy reports: Data-driven research remains gold. If your startup contributes to or is cited in these, you gain durable authority.
• Forums and Q&A: Communities such as Reddit or Quora shape how AI models learn conversational tone. A viral founder AMA can have more downstream influence than you think.

For startups, the takeaway is clear. Visibility is not about chasing one channel. It is about showing up across the spectrum so you influence both the authoritative and conversational layers of AI.

Also Read: The agritech challenge in Indonesia: Can AI and mobile apps enhance productivity?

Why content structure matters more than ever

Startups often have fewer resources, which means your content must work harder. AI models prefer structured, example-rich material that they can reuse. Three formats stand out.

• How-to guides: Step-by-step advice on how to solve a problem. For startups, this could be “How to set up cross-border payments in Southeast Asia.” AI picks these up for “how do I” queries, giving you authority by default.
• Frameworks and lists: Clear models, even if simple, travel well. If your startup coins a framework such as “3 ways SMEs can digitise their logistics,” AI is more likely to replicate it in answers.
• Case studies: Concrete stories with metrics. “In 90 days, our pilot customer cut costs by 25 per cent” is more valuable than aspirational messaging. It teaches AI to ground advice in evidence.

The rule for founders: if you want your insights to spread, package them in ways that machines can easily copy and humans can quickly grasp.

From SEO to GEO: Generated exposure optimisation

Startups are used to thinking about SEO. Now there is a new layer: Generated Exposure Optimisation, or GEO. This is the discipline of making sure AI platforms see, trust and repeat your story.

That means:

• Strategic placements: Secure founder bylines and expert commentary in outlets that AI training data prioritises.
• Thought leadership: Share unique insights that AI can adopt as reference points.
• Mentions in reports: Contribute data or commentary to analyst or ecosystem reports to expand your footprint.
• Answer optimisation: Position your startup in trusted sources so you appear in AI-curated responses.
• Amplification: Use your owned channels to reinforce earned placements, ensuring humans and machines keep encountering your message.

We have seen this with startups we work with. One e27 story amplified across podcasts and LinkedIn ended up cited in AI responses months later. That is reach and credibility no paid ad could replicate.

Also Read: The story of an ‘accidental entrepreneur’

The amplified value of earned media

Startups cannot outspend incumbents on ads. Earned media is the smarter path. In the AI era, its value compounds.

• Multiplier effect: One profile or byline can spawn thousands of algorithmic mentions.
• Trust transfer: AI inherits the credibility of the sources it quotes. A mention in e27 carries more weight than your own blog.
• Longevity: Paid campaigns expire. A respected article or interview can live inside AI knowledge bases for years.

For founders, this means investing in PR early is not vanity. It is infrastructure.

Where to start

Audit your assets. Do you already have:

• Practical how-to guides you can publish externally?
• Frameworks you can brand and share?
• Case studies with measurable proof points?

If not, build them. Then target one flagship placement this quarter. For startups, even a single strong article can be amplified by AI into enduring visibility.

Final thought

PR is your megaphone. In the AI era, it does more than win human eyeballs. It teaches the algorithms that will advise your customers, investors and partners. For startups, this is not a nice-to-have. It is how you make sure your brand is heard in the conversations that shape your future.

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The World Economic Forum’s prediction of a US$3.5 trillion decentralised physical infrastructure network, or DePIN, by 2028 is almost certainly giving investors in each and every DePIN project a warm, fuzzy feeling of self-validation. I have a hard time arguing with a trillion-dollar market cap…but most of these investors are focused on the trees and not the forest, marvelling at individual projects like Helium’s wireless network or Render’s GPU marketplace without addressing the underlying bottleneck: an entire multi-trillion-dollar physical infrastructure sector trying to bootstrap itself onto decentralised networks that are fragmented islands unto themselves.

DePIN’s existing US$27 billion market cap is less than one per cent of the expected US$3.5 trillion opportunity by 2028. And the US$11 billion total value locked across cross-chain bridges today is being exploited every few months with quarterly major hacks, over US$2.5 billion stolen since 2021. We are collectively trying to build the next interstate highway system using nothing more than rope bridges.

The fact is that DePIN, as a sector, is held back by the lack of fundamental infrastructure needed to function at scale. Right now, each individual project is like a country, building its own border controls to admit people from other networks. Consider how these projects actually work: A single transaction might involve sensor data from IoT devices on one blockchain, compute resources on another, data storage on a third, and then finally a payment settlement layer on a fourth chain. At the moment, every bridge is a choke point that requires expensive, centralised relayers, repeated O(N2) security checks, and adds latency and fees that make large-scale use cases economically infeasible.

Core Scientific pivoting from Bitcoin mining to AI infrastructure is an excellent example. A subsidiary, Core Scientific Cloud, is already generating 80 per cent gross margins by repurposing their miners for AI compute! But only because it’s a fully integrated system, 100 per cent centralised with no real dependencies on other chains. If they had to bridge that compute power with storage networks or IoT sensor data streams on separate chains, those margins evaporate.

Also Read: How to launch collaborations that grow communities: A guide for Web3 founders

Recent legislation such as the GENIUS Act aims to provide clearer regulatory frameworks for stablecoins and digital commodities. Jurisdictions that establish regulatory clarity may gain an advantage over regions still working through fragmented approaches. But before we can build that cross-chain DePIN infrastructure, three things need to happen:

First, we need to build cross-chain communication natively, not through wrapped assets or centralised relayers. The Inter-Blockchain Communication (IBC) protocol has seen over US$30 billion in volume across over 12 chains per year with zero exploits as of today’s date. Interoperability is possible on a massive scale if we build it correctly from the ground up.

Second, we need liquidity layers that unify value transfer instead of fragmenting capital across chains. The user capital efficiency of current DePIN networks is orders of magnitude worse than the traditional web because every project forces you to hold a separate wallet with its own native token just to participate. A farmer using DePIN-powered blockchain coordinated irrigation should not need to hold five different wallets and five different tokens to receive payment.

Third, standardised physical-world attestation data. Every DePIN network will have validators attesting to some piece of physical world truthiness, whether it’s Helium validators attesting to wireless coverage, or Filecoin nodes validating data storage proofs. All of that data must be readable and interoperable cross-chain, or we risk building an entire digital infrastructure industry version of the Tower of Babel.

The use cases for DePIN go far beyond speculative opportunity. McKinsey estimates that value unlock through tokenisation of real world assets could reach US$2 trillion by 2030. But DePIN is the last missing puzzle piece to actually bridging the gap between digital tokens and real world utility, but only if we build the infrastructure to support it.

Projects based in jurisdictions with clearer regulatory frameworks are better positioned to capture this value, and recent market performance reflects investor appetite for certainty. Global capital is already flowing into DePIN initiatives in regions such as the United States, where legal clarity and technical capacity offer stronger foundations for scaling.

The counterintuitive part is that DePIN doesn’t need to anoint one or two big winners. Helium doesn’t have to completely beat traditional telecom. Render doesn’t have to bankrupt AWS. Each DePIN network is competing against centralised infrastructure, not other DePIN networks. The real revolution will happen when those networks all interconnect and coordinate in a completely new way to provide emergent value that the legacy systems can never replicate.

Also Read: How AI and blockchain collaborate for a transparent Web3 future

Think of a supply chain where IoT sensors track physical shipments, decentralised compute optimises the route, distributed storage acts as the immutable ledger, and smart contracts auto-execute payments — all coordinated across different blockchains, with no centralised choke points or single points of failure. It’s all technologically possible right now. The missing part is the infrastructure that connects it all.

This is an infrastructure moment. Just as the interstate highway system transformed commerce in the 20th century, a new generation of cross-chain physical-digital infrastructure is now emerging to define the next era of convergence. Recent legislative efforts, such as the CLARITY and GENIUS Acts, illustrate how regulatory frameworks can lay the foundation for growth. What is needed next is the vision to move beyond siloed DePIN projects and begin constructing the connective tissue that can bind them into a unified network.

The reason DePIN is a US$3.5 trillion opportunity isn’t because someone is going to pick three or four winner projects. It’s because each of those thousands of projects will win when we build the infrastructure to connect them all. Every nation and company that understands that is going to own a piece of the future of physical infrastructure. The future will be shaped by the regions that build scalable, interoperable infrastructure first. 

The trillion-dollar question is not if DePIN will revolutionise physical infrastructure. The question is which regions will succeed in building the cross-chain highways to capture that value. With regulatory clarity beginning to emerge in multiple markets, the race has officially begun.

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As platforms like Meta race toward fully automated ad creation — where AI builds entire campaigns from a single product image and budget — the advertising industry is entering a new phase of hyper-personalisation. This isn’t just about performance; it’s about resonance.

Emerging AI advertising technologies, from neural networks to interest and habit targeting, are evolving rapidly to help brands deliver more culturally relevant, emotionally resonant campaigns. These tools promise more than just efficiency: by analysing nuanced user behaviours and preferences, AI can tailor ads that truly connect across languages, cultures, and contexts — minimising miscommunication and boosting engagement.

Yet, there’s an inherent tension. As AI-generated content becomes more common, critics warn of a growing sameness: templated ads, generic visuals, and a lack of emotional spark — all signs of creativity being flattened by automation. The risk? A sea of blandness where no brand stands out.

But ironically, AI may also hold the key to solving this. Rather than replacing human creativity, AI’s strength lies in precision targeting — allowing brands to craft bespoke experiences for micro-audiences, and freeing up creative teams to focus on strategy, storytelling, and emotional nuance. When used right, AI doesn’t dull the message — it sharpens the delivery.

The cross-cultural advertising challenge

Too often, “localised” ads are just translated versions of global campaigns. While technically correct, they often miss the cultural mark — either failing to resonate or, worse, turning off the audience completely.

In today’s globalised digital economy, cultural context matters more than ever. According to Google and Bain’s e-Conomy SEA report, 72 per cent of Southeast Asian consumers expect brands to personalise communication based on culture, not just demographics. A well-placed emoji or influencer-style callout might work in Vietnam or Thailand, but that same message could fall flat or feel inappropriate to a CIS audience.

Meanwhile, demand from Russian-speaking consumers is rising fast. In the first half of 2024, Yango Ads data reveals there were 570 million tourism-related search queries in Russian-speaking markets. 23 per cent of “Travelling to Asia” queries focused on Thailand, with destinations like Phuket seeing surging interest. Yet many Southeast Asian hotels, retailers, and restaurants still rely on translated materials rather than culturally adapted campaigns.

The consequence: ad dollars that fail to convert because the message feels wrong.

Also Read: Storytelling: A humane way to advertise your startup

AI’s new role from translator to cultural interpreter

Newer neural network–based systems can generate dozens of ad variations in multiple formats, adapting tone and imagery to different audiences.

By leveraging large language models (LLMs), AI tools can quickly spin up many creative variations across formats. The idea is to better match audience behaviour and context, though the quality still depends on human oversight.

And for campaigns targeting Russian-speaking audiences, native language support is baked in — helping APAC brands communicate with emotional fluency, not just functional grammar.

The potential is significant, but the real value depends on how well these tools are applied:

• Early studies suggest that AI-optimised campaigns often outperform traditional approaches on both efficiency and conversions, though results vary by sector and execution.
• Compared to campaigns without any AI optimisation, they deliver 17 per cent more conversions on average.
• The system even analyses visual content — identifying which image elements attract the most attention and automatically enhancing those creatives, all while preserving brand identity.

This marks a broader trend in AdTech: creative is no longer just designed, it’s trained.

Context-aware targeting: not just who, but how they think

Tone is only half the equation; precision targeting is the other. Beyond demographics, effective campaigns are increasingly shaped by long-term interests (for example, wellness travel or boutique hotels) and short-term behaviours (like last-minute bookings or halal dining searches).

For example, a Phuket hotel can target users who’ve recently searched for eco-stays or who show a pattern of browsing spa retreats. An F&B brand could target Russian-speaking tourists actively seeking Japanese cuisine or healthy dining options.

The engine behind this? First-party data. Yango Ads data reveals that 33 per cent of search queries about travelling to Asia are about Thailand in Q1 2025 — more than any other country.

With this kind of behavioural insight, brands can avoid broad-stroke messaging and instead build micro-targeted creative designed for intent-rich audiences.

Also Read: Why building user communities is far better than paid advertising

The commercial payoff for APAC brands

Southeast Asia is quickly becoming a global nexus for cross-cultural consumer flows, from outbound Chinese travellers to inbound Russian tourists, Indian remote workers, and more. But most regional brands don’t have large in-house localisation teams or endless creative bandwidth.

This is where AI becomes an equaliser. It allows smaller players — boutique resorts, family-run F&B chains, or regional e-commerce sellers — to scale creative adaptation without scaling headcount.

But creative targeting isn’t the only underused growth lever. Even seemingly passive user actions, like taking a screenshot, can signal high intent. Retail and travel apps that recognise when a user captures content (like a hotel listing or restaurant menu) can turn that moment into action: prompting a share, follow-up, or even a referral.

Even passive actions, like taking a screenshot, can signal strong intent — a reminder that engagement doesn’t always look like a click. They show what users care about, not just what they tap on.

This kind of screenshot-driven engagement, when paired with AI-personalised ad creatives and behaviour-based targeting, creates a loop of contextual relevance that drives higher conversions and loyalty, especially in mobile-first, socially driven markets like Southeast Asia.

That said, AI isn’t perfect. Generated content, particularly in non-English languages, still benefits from human review. Local context and linguistic nuance can’t always be assumed — even by the smartest models.

Still, the strategic opportunity is clear: while generic automation creates a risk of sameness, smart automation enables uniqueness. When used right, AI doesn’t just make campaigns more efficient, it makes them more emotionally precise.

The next frontier

The future of AI in advertising isn’t about removing the human, it’s about empowering it. Emerging tools for creative optimisation, behavioural targeting, and even intent tracking free up marketers to focus less on churn and more on meaning: what their message feels like, how it lands, and whether it resonates across cultures.

And that might be the most human outcome of all.

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As artificial intelligence (AI) continues to transform industries, businesses must find ways to leverage its potential to stay competitive, innovate, and improve efficiency. However, integrating AI into an organisational strategy requires thoughtful planning, a clear vision, and a phased approach.

This article outlines a step-by-step guide on how organisations can successfully incorporate AI into their strategic framework to drive long-term growth and success.

Define clear objectives and goals

The first step in integrating AI is to define clear objectives that align with the organisation’s overall business goals. Without a clear understanding of why you’re implementing AI, it’s easy for efforts to become fragmented or misaligned. Consider the following questions:

• What specific problem is AI solving?
• How can AI help improve existing processes?
• What measurable outcomes do you hope to achieve, such as reducing costs, improving customer experience, or increasing productivity?

By setting clear, achievable goals, you ensure that AI implementation is focused and measurable, helping to build momentum and demonstrating value early on.

Assess your current capabilities and infrastructure

AI integration requires a robust technological foundation. Assess your organisation’s current data infrastructure, software tools, and workforce capabilities. Consider the following:

• Do you have the necessary data? AI thrives on data, so having quality, well-organised data is essential.
• Is your IT infrastructure capable of supporting AI tools, such as cloud services, machine learning platforms, or processing power?
• Does your team have the skills required to develop and manage AI systems, or will you need to invest in training or hiring new talent?

Understanding your current capabilities will help you identify gaps and prioritise investments to ensure your organisation is ready for AI integration.

Choose the right AI tools and technologies

AI is not a one-size-fits-all solution, and selecting the right tools and technologies is critical for success. Depending on your objectives, you might explore various AI solutions such as:

• Machine Learning (ML): For predictive analytics, recommendations, or optimising business processes.
• Natural Language Processing (NLP): To enhance customer service with chatbots, sentiment analysis, or automated document processing.
• Robotic Process Automation (RPA): To automate repetitive tasks and free up employee time for more value-added work.
• Computer vision: To interpret and process visual data, useful in industries such as manufacturing, healthcare, or security.

Selecting the appropriate AI tools requires careful consideration of your organisation’s specific needs, industry, and available resources.

Foster a data-driven culture

AI relies heavily on data, so fostering a data-driven culture is essential for success. This involves not only collecting and storing data but ensuring that it is clean, structured, and easily accessible. Encourage departments across the organisation to make data-driven decisions, and promote collaboration between data scientists, business leaders, and domain experts.

Also Read: How is AI transforming the future of cancer diagnosis

To build a data-driven culture, invest in data literacy programs for your team and provide tools that make data analysis easier. When employees across the organisation embrace data and AI insights, the benefits of AI integration will multiply.

Start small with pilot projects

Rather than implementing AI across the entire organisation all at once, start with smaller, manageable pilot projects. A focused pilot allows you to test AI’s effectiveness on a smaller scale before committing to larger, organisation-wide changes. Pilot projects can:

• Demonstrate AI’s potential value and feasibility.
• Help identify any challenges or obstacles that need to be addressed.
• Provide insights that will inform broader AI integration efforts.

For example, you might start by using AI for customer service automation or inventory management before expanding its use to other areas of the business.

Invest in employee training and change management

Integrating AI can be a significant cultural shift for many organisations, as it may change how employees perform their tasks or even the roles they occupy. To ensure a smooth transition, invest in training programs to equip your team with the necessary skills to work with AI tools. Offer training in areas such as:

• Understanding AI concepts and their applications.
• Using AI-powered tools effectively in daily tasks.
• Analysing AI-generated insights and making data-driven decisions.

In addition to training, prioritise change management to help employees adapt to new workflows and technologies. Communicate the benefits of AI integration clearly and show how it can enhance their work, rather than replace it.

Monitor, evaluate, and optimise

Once AI solutions are deployed, ongoing monitoring and evaluation are essential to ensure their effectiveness. Establish key performance indicators (KPIs) to track the success of AI initiatives, such as cost savings, productivity improvements, or customer satisfaction scores.

AI systems also require continuous optimisation to adapt to changing conditions. Regularly review the AI models to ensure they remain accurate and relevant. This might involve updating algorithms, retraining models with new data, or incorporating feedback from users.

Ensure ethical AI use and data privacy

As AI plays an increasingly central role in decision-making, it is critical to prioritise ethical considerations. Implement policies and frameworks that ensure AI systems are transparent, fair, and unbiased. Consider the following ethical principles:

• Bias mitigation: Regularly audit AI models for biases that may result in unfair or discriminatory outcomes.
• Transparency: Make AI decisions explainable and understandable to stakeholders, especially when they impact customers or employees.
• Data privacy: Comply with data protection regulations, such as GDPR or CCPA, and ensure that AI tools handle personal data responsibly and securely.

By focusing on ethical AI practices, you help build trust with customers, employees, and other stakeholders while minimising risks associated with AI implementation.

Also Read: Unlocking a sustainable future: A new model for green building management

Scale gradually and continuously innovate

Once initial AI projects have been successfully implemented and refined, consider expanding the use of AI across other areas of the business. Scaling should be done thoughtfully, with continuous innovation and adaptation to new technological developments and business needs.

AI is a rapidly evolving field, so staying up-to-date on new advancements and opportunities is crucial. Encourage experimentation and innovation within your organisation to unlock new AI use cases that drive value.

Conclusion

Successfully integrating AI into your organisational strategy is a multifaceted endeavour that requires a clear vision, careful planning, and a willingness to evolve. By defining clear objectives, assessing current capabilities, investing in the right technologies, and fostering a data-driven culture, your organisation will be well-positioned to harness the power of AI.

Remember, the integration of AI is a continuous journey of learning, optimisation, and adaptation. With the right approach, AI can become a powerful driver of growth and innovation for your organisation.

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This article was first published on December 2, 2024

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The financial landscape of Southeast Asia (SEA) is on the cusp of a dramatic shift. By 2030, a staggering 79 per cent of the fintech narrative will be written by Millennials and Gen Z, making this a significant redefinition of what the future generation will expect from their financial partners. These generations aren’t just digital natives, but they are also value-driven, experience-focused, and deeply discerning.

Therefore, for fintechs to stay relevant, it’s no longer enough to just digitise the traditional products, but reimagine finance from a perspective that is relatable through empathy, clarity, and a deep understanding of how these users think, feel, and act.

Why innovation alone isn’t enough to close the widening empathy gap

Much of fintech innovation to date has centred on making complex financial products more accessible through technology. But despite this progress, the core challenge of an empathy gap that outlines what users say they want, and how they actually behave.

This is most evident in recent research that revealed Gen Z and Millennials seeking a “trifecta” of “money, meaning, and well-being”, or the equivalent of financial tools that support their goals, not just bank accounts. Yet one in four Gen Zs aren’t saving, investing, or insuring enough, suggesting a disconnect between their aspirations and their current financial actions.

The reality is that many offerings today still focus on features over feelings. So how do we move beyond purely transactional relationships and truly resonate with the unique needs of these generations, all while navigating an increasingly crowded market

Also Read: The fintech ‘Wild West’ in Southeast Asia is over and maybe that’s a good thing

Guided simplicity that is designed for confidence, not complexity

To bridge this empathy gap, fintechs must transform their approach from mere digitisation to profound user-centricity. It’s no longer enough to just offer educational content and hope users will piece everything together. The responsibility now lies with fintechs to embed financial literacy and confidence directly into the user experience itself.

This means embedding real-time, personalised guidance into the solution, rather than relying on passive content or dense dashboards. Smart designs that gently nudge users towards positive outcomes like showing the potential benefits of saving a little more, or illustrating trade-offs in an actionable way, have proven to be non-disruptive and less overwhelming. 

Visual clarity, bite-sized insights, and contextual suggestions can shift a user from confusion to confidence. When done right, users report not just better understanding but reduced stress and more consistency in their pursuit of their financial goals. 

Meeting the emotional need by moving from utility to experience

Today, we’re also in the ‘experience economy’ whereby Gen Z’s prioritise solutions that simplify their lives, and Millennials expect excellence in customer experience. Both expect digital products to be highly responsive to their personal context and goals. 

For fintechs, this translates into a need for agile, user-centric development that incorporates emotional design and continuous feedback. The challenge isn’t just solving current pain points, but also anticipating future issues, before it happens. Every part of the journey, from onboarding to notifications, should quietly reinforce confidence. Whether it’s a message about a successful transaction or a reminder to save, each touchpoint is an opportunity to build trust and signal progress.

Doing so would align to an emerging insight of younger users wanting to feel in control of their finances, not just track it. This means using everyday language,, and ensuring that interfaces don’t overwhelm with jargon or data overload. 

Also Read: How the global growth of fintech defies age and gender

Solidifying the relationship through trust, transparency, and alignment

Ultimately beyond the user experience and education, establishing, building and sustaining trust with users is key for longevity. For today’s users, trust is built on transparency and shared values — does it serve my long-term goals? Does it align with how I see the world? Is it honest about what it offers and genuine about wanting to help me? 

For fintechs, this means rising to the challenge of designing products that prioritise ethical clarity, not just in how fees, terms and products are explained, but in how the product itself supports the user’s broader well-being. That means using clear, everyday language, being upfront about trade-offs, and removing hidden catches. When a product says, “This is a safe place for your spare cash,” and then proves it with behaviour, it shifts the tone from persuasion to partnership. That’s when users feel informed, respected, and genuinely understood—not just marketed to.

More importantly, trust is built through consistency. When products behave the way users expect and reflect a commitment to their financial success, not just conversion metrics, is when you’ve truly convinced them to stay with you for the long term. 

So what does the future need?

Looking ahead, fintechs that lead with empathy will lead the market. In a landscape where the finish line constantly moves, the true differentiator is not just functionality, but emotional resonance. 

This requires more than feature innovation or hype. It requires a consistent culture of listening, learning and co-creation with the very users we aim to serve. It’s about recognising that financial decisions are deeply personal and shaped by context, mindset, and emotion. 

As Millennials and Gen Z continue to reshape the fintech ecosystem, success will belong to those who don’t just build for them, but build with them. In doing so, these shared experiences will not only support their wallets, but truly resonate with them and their aspirations.  

This article has not been reviewed by the Monetary Authority of Singapore.

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