In my decade and a half of marketing experience, I’ve witnessed how the explosion of digital tools and data has revolutionised marketing. Yet, despite all these advancements, many businesses—including those in retail, F&B, and technology—still struggle with a fundamental challenge: understanding what truly drives consumer decisions.

Halfway into 2025, most brands have yet to recognise the power of AI, beyond ChatGPT; AI is the essential driver for turning data into real marketing success.

From data overload to actionable insights

Every day, companies collect mountains of data—clicks, views, social mentions, and more. But too often, the story ends there. Many brands get stuck chasing vanity metrics, missing the deeper insights that reveal why customers act the way they do. In my experience, this is where most businesses falter: they have the data, but not the understanding.

Consider this: 84 per cent of companies in Singapore use digital channels to promote their products and services, but only 17 per cent can directly link their marketing strategies to increased revenue.

That’s far below Asia’s regional average of 41 per cent. For me, this highlights a critical gap. It’s not about having more data; it’s about making sense of it in a way that drives real, measurable results.

Why traditional marketing methods aren’t enough

Modern marketing isn’t about following the latest trends or relying on gut instinct. Success comes from making informed decisions based on a deep understanding of consumer behaviour. When businesses stick to outdated methods or surface-level analytics, they risk missing what truly drives conversions, loyalty, and growth.

How AI and data analytics are changing the game

AI and advanced analytics have become game-changers for brands. Take Coca-Cola, for example—they use AI to analyse customer preferences, buying habits, and social media trends to create hyper-targeted campaigns and new products. Amazon goes even further, applying AI for personalised recommendations, dynamic pricing, and real-time inventory forecasting. These strategies have boosted both customer satisfaction and sales.

Also Read: VC crunch hits Southeast Asia: US$129M raised in May 2025, down 70% MoM

Unlike traditional market research, which I’ve seen can be slow and expensive, AI lets us interpret vast amounts of complex data in real time. This means we can adapt marketing strategies based on what consumers are actually doing—not just what we think they might do.

What to expect from data-driven marketing solutions

As competition grows fiercer, I know that brands—my own included—expect more from their marketing investments. We need data platforms that analyse consumer behaviour across multiple touchpoints and deliver actionable insights. It’s no longer enough to track page views or clicks. I want to understand the “why” behind customer actions.

Understanding consumer psychology and predicting behaviour has become a personal focus for me as a marketer. With shifting preferences, fragmented channels, and rising expectations, I can’t afford to rely on surface-level metrics. I need to know what truly motivates my audience to engage, convert, and stay loyal.

Adopting a behaviourally informed approach is non-negotiable. We help brands go beyond performance metrics to tap into deeper insights—how customers think, what influences their decisions, and how to communicate with them more meaningfully.

Making data-driven marketing a strategic asset

For me, embracing AI-driven insights isn’t just an option—it’s a necessity. McKinsey and KPMG both highlight how data-driven marketing is about making smarter, more effective decisions that align with consumer needs. The brands I see succeeding are those leveraging these insights to improve retention, boost engagement, and use their marketing budgets more efficiently.

Future belongs to smart solutions

If you want to stay ahead of the competition and drive real, sustainable growth, investing in AI-powered, data-driven marketing is the way forward. With the right insights, you can transform your marketing strategies and build stronger, more lasting connections with your customers.

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Seeking a state of calm in the current trade storm is clearly challenging the minds of corporate strategists around the globe today, but one thing remaining constant is the focus and optimism over AI and digital capabilities.

Amid uncertainty, digital transformation remains a critical driver for business and deal strategies, with AI capabilities increasingly driving corporate acquisitions. At the same time, a shift to defensive consolidation helps companies build operational and competitive resilience.

The EY-Parthenon CEO Outlook Survey revealed unprecedented optimism in the transformative power of technology, particularly AI, from Asia-Pacific’s CEOs. In fact, 85 per cent predict that AI will be a decisive factor in establishing industry leadership in 2025. An even higher number, 87 per cent of leaders, believe that the accelerated adoption of AI and the associated up-skilling of their workforce will be crucial differentiators in the coming years.

The latest EY research on GenAI shows 90 per cent of firms surveyed have adopted AI into operations to some degree, but most remain in the early stages, and eight per cent have not integrated AI at all.  72 per cent of surveyed leaders plan to increase annual investment related to GenAI specifically, and the rush to AI challenges business leaders to seek both optimisation of adoption strategies and acquisition as a means to accelerate capabilities.

M&A outlook in flux but opportunities remain

The prevailing optimism around AI and technology coincided with an early surge in expectations in M&A deals at the start of the year. Nearly half of the CEOs surveyed (42 per cent) expressed strong confidence in investing in Capex and R&D, as they focus on leveraging technological advancements such as AI to secure competitive advantages.

Many CEOs identified M&A as a transformation accelerant in 2025, with confidence by Asia-Pacific CEOs (61 per cent) driving an even greater appetite for deals versus European and American peers.

Also Read: When the chain snaps: How tariffs are unraveling Southeast Asia’s SMEs

In response to significant trade and market disruption, a portfolio realignment is essential to counter geopolitical risk and shifting market growth potential. Trade policy and supply chain risks will now govern many investment decisions.

 CEOs need to navigate this landscape adeptly to fully harness the value of any planned transactions while integrating technology investments that reshape growth strategies.

Pockets of opportunity remain, such as in Southeast Asia. Despite risks from recent tariff announcements out of Washington, the region could see increased investment and partnerships as governments and businesses seek growth opportunities in the volatile landscape.

Businesses anticipate greater investments through joint ventures, partial ownership, and minority interests in Asian companies. This reflects their need to balance uncertainty in countries like China, Canada, Mexico, and the EU that face added complexity given implications of tariff announcements and growing trade challenges. Strategic partnerships will help businesses navigate risks and drive growth.

There are clear opportunities in markets and regions of growing domestic demand to “build” domestic supply chain ecosystems to avoid tariff risks. The major obstacle in this path is that this strategy hinges on more than procuring and manufacturing locally, but also whether the destination of finished goods can be locally contained.

Also Read: Asia’s trade turning point: How tariffs and geopolitics are redrawing supply chains

More mature markets, like Japan and Korea, face the complex challenge of consolidating and restructuring their companies’ supply chains, which have expanded globally both upstream (e.g., raw materials and production) and downstream (e.g., distribution and sales). In a tough economic climate, achieving cost efficiencies through these efforts becomes increasingly attractive.

Seeking a state of hyper-agility and resilience

Companies will view current critical trends and disruptions as either threats to their existence or opportunities. It is this difference that separates industry leaders from laggards. Today’s business landscape is shaped by disruptive forces: rapid technological advancements, including artificial intelligence (AI), climate change-driven sustainability agendas, and geopolitical tensions affecting supply chains and global operations.

Digital ecosystems expose new threats and opportunities, while remote work reshapes organisations. Rising cybersecurity risks, shifting consumer expectations, economic volatility and complex regulations demand agility. Meanwhile, emerging markets create new competitive dynamics and growth potential.

In terms of deal strategy, while uncertainty clouds the immediate future, M&A should still be leveraged as a transformation catalyst. CEOs should look to M&A, particularly in distressed times as an opportunity for accelerated transformation. Target deals will include those that align with long-term goals, such as adopting new technologies, entering new markets or strengthening competitive positioning through strategic consolidation.

CEOs clearly need to sharpen their focus on the interplay of macroeconomic, geopolitical, regulatory and technological forces. Proactively addressing these risks enables growth opportunities and helps mitigate disruption. The traditional business dashboard must factor in more diverse sources of insight and more outlier views than in the past. New indicators must be established to build a wider view of possible scenarios so that a path to operational resilience can be found.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organisation or its member firms.

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As AI adoption accelerates across Asia Pacific, the region is facing an urgent need to implement more sustainable AI practices. AI workloads – particularly those running on hyper-scale data centres – are energy-intensive. Given that Asia Pacific has accounted for the largest share of newly added data centre supply over the past decade, ensuring AI systems are sustainable is a growing priority.

AI’s environmental impact is a subject that is well-established, no longer a conversation limited to expert forums and panel discussions. Although AI technologies promise breakthroughs in healthcare, climate science, energy transition acceleration, and more, it’s crucial to address AI’s predicted environmental downsides.

At its core, AI models are trained and run on extremely powerful, energy-hungry computers that rely on electricity that largely comes from carbon-intensive sources. Left unchecked, this could lead to significant carbon emissions.

There are many ways that we can address AI sustainability, but one often overlooked lever we can pull to improve the efficiency of AI workloads is data optimisation, or “data efficiency”. AI models rely on vast pools of data to be effective, but indiscriminately dumping disorganised, irrelevant, or even duplicate data into AI models leads to systems having to do extra work processing excess information. We cannot afford to be wasteful when it comes to AI.

By optimising the data before we feed it to AI models, we can help better manage the environmental footprint of AI. This requires careful forethought and expert planning that looks for sustainability gains along the entire AI lifecycle and prioritises data efficiency when planning AI projects.

How to tackle data efficiency for AI workloads 

• Map out your data strategy upfront

Begin by clearly defining what data you need, where it will come from, how often it will be collected, and how it will be processed. Consider if data can be consolidated, stored using low-impact techniques, such as tape or other backup methods, or discarded if no longer necessary.  Offloading non-essential data to more energy-efficient storage methods can reduce power consumption. 

• Clean up before you start

Data efficiency in AI goes beyond just storing useful data. Data sets should be cleaned and optimised before training a model. Using raw, off-the-shelf data sets or repositories without minimising them results in unnecessary work and inefficiency. Cleaning data upfront ensures the model works more effectively and requires fewer resources.

• Get the training data set right

Data efficiency starts with an optimised data set for training, and using customer-specific data during model tuning helps further refine the model. By ensuring that data is as concise as possible from the start, you set a foundation for efficient processing throughout the entire AI lifecycle.

• Process data only once

Once data is processed for training/tuning, avoid redundant processing. Any additional training or fine-tuning should only occur on new data, minimising repeated energy-intensive operations.

Also Read: How a data-driven approach can optimise decarbonisation in the built environment

• Avoid data debt

Managing data is especially critical for AI workloads due to the massive volumes of data, including unstructured data. One of the key strategies for reducing the environmental burden of data is eliminating inaccurate, erroneous, out of date, or duplicated data. Like technical debt, data debt – where outdated or unnecessary data accumulates – can severely impact AI systems’ performance and sustainability.

• Location matters

Processing data as close to its source as possible minimises the energy required to move it. Optimising data movement reduces both the environmental and time-related costs, ensuring faster, greener AI operations.

As AI becomes integral to industries like manufacturing, logistics, and smart city initiatives, the need for more sustainable AI practices across Asia Pacific becomes more pressing. Singapore is already making strides in this area, with a focus on sustainable data centre innovation and initiatives like the . This approach is critical to ensuring AI systems can scale responsibly.

Asia Pacific’s growing dependence on AI-driven technologies presents a unique opportunity for the region to lead by example. Through initiatives that promote energy-efficient data management and more sustainable AI strategies, Singapore is positioning itself as a global leader in creating sustainable AI ecosystems.

To build a sustainable AI ecosystem in Asia Pacific, organisations must start with clean, lean data. As AI technologies become more embedded across industries, ensuring the data feeding into these models is optimised will not only help reduce energy consumption, but also foster innovation in a way that is more environmentally responsible.

For businesses in Singapore and the wider APAC region, prioritising data efficiency today will help ensure AI’s potential is fully realised without compromising the planet’s future.

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Tashi Network, a company operating as the coordination layer for intelligent systems, has closed an oversubscribed funding round, aiming to empower trustless coordination for robots and AI agents.

The round was co-led by Blockchain Founders Fund (BFF) and Exponential Science Capital (ESC). The roster of participants also included Taisu Ventures and MN Capital.

The deal also attracted high-profile industry angels, including Gabby Dizon, co-founder of Yield Guild Games, and Wei Zhou, CEO of Coins.ph and former CFO of Binance.

Also Read: Blockchain and AI copyright: A revolution in digital rights management

The capital raised will be used to support rapid network growth and the company’s upcoming token launch on the Solana ecosystem.

Based in Singapore and California, Tashi focuses on replacing centralised coordination systems with a verifiable, distributed framework that allows machines to synchronise, validate, and settle actions in real time, without relying on central servers.

The company’s technology essentially transforms coordination itself into a form of currency; something that is measurable, rewardable, and tradable across the intelligent economy.

The startup’s experienced team of serial entrepreneurs has had multiple previous successful exits, including one sale valued at US$34 million to a company listed on the New York Stock Exchange. Amar Bedi, CEO of Tashi, previously held roles at tech giants Grab, Uber, and KPMG.

“The next computing revolution will allow trustless coordination among edge devices,” stated Bedi. He emphasised that Tashi’s core consensus technology, known as Vertex, enables the offering of “trust-preserving, global-scale coordination without any servers for the first time.”

Solving the centralisation problem

Tashi was explicitly designed to address existing industry challenges, as evidenced by the recent major outages experienced by companies like AWS. Current systems, including centralised clouds and global chains, often fail to deliver the instant, local coordination required by AI, robotics, and autonomous systems.

The company addresses this foundational problem by bridging decentralised physical infrastructure network (DePIN) with a novel approach to peer-to-peer consensus.

To understand how Tashi operates, imagine a massive orchestra composed entirely of robot musicians and AI conductors. Normally, they rely on one powerful, centralised maestro (a server) to tell them when and how to play. If that maestro has a cough or disappears, the entire concert stops.

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

Tashi, however, gives every single musician a small, independent mechanism to verify and synchronise their actions with everyone else instantly and securely. This means they can perform a complex symphony perfectly without any single conductor being in charge, ensuring the music never stops.

The firm is already demonstrating significant traction; it has built products targeting specific industries and currently boasts over 100 ecosystem partners and has secured early paying clients. Its DePIN already supports more than 50,000 nodes, which are run by the community.

The post Ex-Grab exec’s Tashi Network bags funding to kill AI’s centralisation problem appeared first on e27.

If you ask most people what mRNA is, they will likely scratch their heads and, if you’re lucky, draw a vague connection to Covid-19 shots. The acronym for ‘messenger ribonucleic acid’ entered the public consciousness during the pandemic as the foundation of preventive treatment against the virus.

How does it work? In simple terms, mRNA is a set of instructions your cells use to make specific proteins that help your body function and stay healthy. The Covid-19 vaccine is a well-known example of this technology in use, which essentially trains the body to combat the disease by stimulating an immune response.

Helping populations around the world protect themselves against one of the most virulent diseases to emerge in living memory is one way that physicians have used mRNA. Innorna, a biotechnology company specializing in mRNA engineering and lipid nanoparticle (LNP) delivery platforms, is advancing a potentially world’s first bivalent mRNA vaccine candidate against respiratory syncytial virus (RSV)—IN006, which targets both RSV-A and RSV-B—into clinical trials.

Innorna sees mRNA as adding flexibility to a vaccine in case of viral mutation. The bivalent nature of the vaccine means it can cover both RSV subtypes that currently exist. This design aims to provide broad-spectrum and durable protection.

A milestone in RSV prevention: IN006 completes phase 2 clinical study enrollment

In a significant step forward, Innorna has completed enrolment and vaccination in its Phase 2 clinical trial for IN006. This milestone marks a key step in the development of this innovative vaccine candidate, which is also recognized as China’s first domestically developed RSV vaccine to enter clinical trials.

The Phase 2 study is a randomized, double-blind, placebo-controlled trial conducted in China among healthy adults aged 60 and above. It is a critical step for dose optimization, broader population validation, and evaluating a booster shot for annual revaccination. This progress sets a solid foundation for subsequent Phase 3 efficacy studies.

“This Phase 2 clinical trial marks a critical step in validating IN006’s scientific hypothesis—delivering broad-spectrum, durable protection against RSV,” said Dr. Linxian Li, Founder and CEO of Innorna. “We remain committed to advancing the clinical development of this vaccine candidate efficiently. Our goal is to deliver safer, more effective RNA medicines to meet global public health needs.”

Also read: Cracking the code-switch: How a Hong Kong AI firm helps turn linguistic chaos into commercial clarity

Addressing a critical public health gap

RSV is a highly contagious virus that poses elevated risks to older adults, young children, immunocompromised individuals, and those with chronic conditions—potentially leading to pneumonia, respiratory failure, or death.

With no approved antiviral treatment and no authorized RSV vaccine in China, the need for effective prevention remains critical. IN006 represents a major step in public health innovation to fill this void.

Built on a proprietary technology platform

IN006 is built on Innorna’s proprietary pre-fusion F protein design, mRNA, and LNP platforms. Preclinical studies showed a favorable safety profile and strong humoral and cellular immune responses. In the preclinical cotton rat challenge study, IN006 provided effective protection against both RSV-A and RSV-B.

Innorna’s expertise in LNP enables it to serve as a platform licensor for vaccine and drug delivery. The company’s proprietary, rationally designed lipid library comprising over 6,000 chemically diverse ionizable lipids enables breakthroughs in mRNA vaccines and therapeutics.

Also read: Drawing the line on manual drudgery: Automation leader FJ Dynamics transforms unseen work with robotic precision

Leaping into unchartered territories to make medical miracles

Since forming Innorna six years ago, Dr. Li says the infrastructural support that Hong Kong Science and Technology Parks Corporation (HKSTP) provides has also been vital in taking the company to where it is today.

“We have received a lot of support from HKSTP, which has included both funding and other essential resources,” he explains. “They also help us connect with potential collaborators, which is very important for us because in the end, whatever groundbreaking treatments we may develop, we need to be able to commercialise them.”

In a field where preventive options are still limited, Innorna is applying big science to a pervasive viral challenge. The company’s work on IN006 aims to bring a powerful new tool to the global fight against RSV. This candidate has the potential to benefit a broad population and reshape the landscape of respiratory disease prevention.

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Featured Image Credit: HKSTP, Innorna, Canva Images

The post Rewriting the future of RSV prevention: Innorna’s bivalent mRNA vaccine accelerates toward patients appeared first on e27.