The Philippines is pioneering a service-sector transformation powered by artificial intelligence (AI), according to the Philippine Private Capital Report 2026 by Foxmont Capital Partners. This shift carries powerful implications for Southeast Asia, where services dominate employment, but productivity gains remain uneven.

By embedding AI across processes and emphasising system-driven operations, the Philippines aims to transition from job-led growth to value-led productivity growth, a strategic imperative for the whole region.

Breaking the labour-intensity mould

Historically, sectors such as retail, IT-business process management (IT‑BPM), logistics, and financial services across Southeast Asia — and especially in the Philippines — have scaled primarily by increasing headcount. That model has been effective at creating jobs, but it creates limits on economic efficiency because output per worker grows slowly.

Also Read: The hidden tax on Philippine SMEs: Unreliable infrastructure

Foxmont’s report highlights that nearly 70 per cent of the productivity benefit from AI-enabled customer service transformations comes from people and process changes — workflow redesign, multi‑skilling, performance incentives, and clearer process ownership — rather than technology alone. In other words, AI is most powerful when it forces firms to redesign how work gets done.

Philippine firms that align talent, incentives, and operating models around AI-enabled processes can scale output significantly without proportional labour growth. This matters not just for competitiveness, but for the quality of jobs: employees can be shifted away from repetitive tasks into higher‑value roles such as quality assurance, relationship management, analytics, and product improvement.

The IT‑BPM sector: a case study in potential

The Philippines’s IT‑BPM sector — a cornerstone of its services export economy — illustrates the transition and highlights the gap between experimentation and full transformation. While a large share of firms have introduced AI tools for specific tasks (Foxmont notes that many firms have some AI integration), only a small minority achieve high AI maturity where AI is a core, governed part of operating infrastructure rather than a set of one-off pilots.

Global firms such as JP Morgan show a roadmap: AI moved from proof-of-concept to entrenched infrastructure through governance frameworks, cross-functional adoption (product, compliance, HR, ops), and continuous measurement of outcomes. Automation of routine tasks freed talented staff to focus on strategic work, producing measurable productivity uplift. Southeast Asian IT‑BPM hubs can adopt similar governance and measurement disciplines to accelerate value capture.

For the Philippines, the prize is clear. IT‑BPM already contributes significant export revenues and employment; raising AI maturity could boost revenues per employee, improve margins, and sustain competitiveness against lower‑cost or more-automated hubs.

Reimagining retail and logistics with e-commerce and data

E-commerce platforms have already shown how digitalisation redefines productivity. In the Philippines, players like Shopee and Lazada and a growing ecosystem of logistics and payments partners have decoupled growth from physical store expansion. Digital merchants can reach millions with relatively small teams by using data-driven merchandising, demand forecasting, and automated fulfilment.

Also Read: AI leapfrog: Paving the way for an AI-first tech ecosystem in the Philippines

The stark productivity differences between online and traditional retail demonstrate the greater return on integrating AI into the retail value chain: personalised recommendations, dynamic pricing, localised inventory placement, and automated customer support all lift revenue per worker. Logistics, meanwhile, gains from route optimisation, demand smoothing, and predictive maintenance, areas where machine learning drives immediate cost reductions and service reliability.

Across Southeast Asia, similar patterns are emerging: countries with stronger digital payments, denser fulfilment networks, and better consumer data capture are able to extract larger productivity gains from AI-enabled retail and logistics.

Government, education and policy levers in the Philippines

AI-driven transformation requires coordinated policy and investments in human capital. In the Philippines, multiple levers are being exercised:

• Public agencies, industry groups, and universities are increasingly partnering to build AI talent pipelines and curriculum updates that combine technical skills with domain knowledge (customer service design, logistics operations, financial compliance).
• Upskilling programmes from both private sector firms and technical-vocational institutions emphasise multi‑skilling — blending AI supervision, data literacy, and complex problem solving — which the Foxmont report identifies as a major source of productivity gains.
• Regulatory frameworks around data protection and fintech/financial services create the legal foundation for scalable data use and cross-border services. Strong governance and clear rules are essential to attract responsible capital and institutional buyers.
• For Southeast Asia more broadly, governments that accelerate practical AI skilling, incentivise workflow redesign pilot projects with measurable KPIs, and support cloud and data‑infrastructure deployments will see faster realisation of productivity benefits.
• Financing and private capital: shifting from pilots to scale

Private capital plays a catalytic role. Venture investors, private equity, and corporate venture arms are increasingly funding startups and incumbents that embed AI into service delivery — from AI-enabled contact centres to smart logistics and lending platforms. However, the transition from pilot to enterprise-scale deployment often requires patient growth capital and operational know‑how, not only software licenses.

The Philippine Private Capital Report 2026 emphasises the need for investors to support the organisational changes that technology demands: redesigning workflows, retraining staff, and establishing governance with measurable business outcomes. Investors who fund end-to-end transformations — rather than point solutions — are more likely to unlock durable value.

Regional implications and cooperation

Southeast Asia has several structural advantages for AI-enabled services growth: a young, digitally fluent workforce; rapidly expanding urban middle classes; high mobile penetration; and growing regional trade in services. Yet the region risks lagging if firms and governments treat AI as a tactical technology rather than a strategic re-engineering tool.

Collaboration across ASEAN — in standards, cross-border data flows, skilling frameworks, and start-up ecosystems — can accelerate adoption. Shared frameworks for responsible AI, interoperable digital identities, and regional talent exchanges would lower the cost of scaling services across markets.

Moreover, investors and multinational corporations can leverage the Philippines as a testing ground for service‑sector transformation: its sizable IT‑BPM base, growing e-commerce market, and active domestic investor community make it an attractive locus for pilots that can be replicated regionally.

Risks and how to manage them

The path to value-led growth is not automatic. Key risks include:

• Skill mismatches: rapid tech adoption without serious upskilling can create displacement or hollow-job growth. Address this with coordinated industry-academia training and transition programmes.
• Uneven regional development: productivity gains concentrated in a few cities can widen domestic inequality. Policies that support regional hubs (e.g., Cebu, Davao) and remote work can distribute benefits.
• Governance gaps: weak data protection or unclear AI accountability can undermine trust. Strengthening legal frameworks and industry standards is essential.
• Capital misallocation: funding narrow pilots without change-management budgets prevents scale. Investors should mandate transformation roadmaps and outcome metrics.

Conclusion

The Philippines’ evolving experience offers a compelling narrative for Southeast Asia: AI adoption is not just about automation, but about fundamentally redesigning service value chains to sustain economic growth. When technology is paired with workflow redesign, deliberate upskilling, governance and outcome‑based investments, services can become far more productive — creating better jobs, higher firm-level value, and stronger regional competitiveness.

Also Read: Echelon Philippines 2025 – Making AI work: How leaders turn AI into business value

For Southeast Asia, the strategic imperative is clear: move beyond tool adoption to system-driven transformation. The countries that do will capture the next wave of service-led prosperity; those that don’t risk being outpaced by more disciplined and value-focused competitors. The Philippines is already showing the way — and the rest of the region would do well to pay close attention.

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Companies like to call themselves global. But in many workplaces, language, nationality, and proximity to HQ still decide whose voice carries weight. If we want a more equitable digital economy, we need to stop confusing connected teams with fairly distributed power.

When people talk about equity in the digital economy, the conversation usually starts with access.

• Access to jobs.
• Access to capital.
• Access to technology.
• Access to opportunity.

Those things matter. But from my experience working across US, European, and Asian companies, I believe the bigger issue often starts earlier and closer to home.

It starts inside the workplace.

Before a company builds an inclusive product, it builds a culture. Before it expands opportunity in the market, it decides how influence works internally. And that internal system determines who gets heard, who gets trusted, who gets promoted, and who gets left behind.

That is why workplace culture is not a soft topic. It is operating infrastructure.

This matters even more now because work itself is being redesigned at scale. The World Economic Forum’s Future of Jobs Report 2025 draws on more than 1,000 employers representing over 14 million workers across 55 economies. This is not a niche HR issue. It is a global operating reality.

The corporate narrative today is that digital work has made companies flatter, faster, and more merit-based. Teams work across time zones. Meetings happen on Zoom. Workflows run through Slack, Teams, Notion, and shared documents. Translation tools continue to improve.

In theory, all of this should make work more equitable.

In practice, it often does not.

Many companies still run on an older logic: global footprint, centralised power

The people closest to headquarters usually carry an invisible advantage. Sometimes it comes from nationality. Sometimes from language. Sometimes, it comes from understanding the tone, humour, and unwritten rules of the people at the centre. Most of the time, it is a combination of all three.

This is rarely explicit. That is exactly why it survives.

It shows up in quieter ways. Whose ideas are seen as strategic. Whose pushback is read as executive maturity. Whose mistakes are treated as learning curves. Whose communication style feels “leadership-ready,” and whose feels like it still needs work.

One of the more uncomfortable truths in global companies is this: your passport and your fluency can still influence your credibility more than your judgment should.

I have seen this firsthand. I have seen people with better local market instincts, sharper commercial judgment, and stronger execution struggle to influence decisions because they did not package their views in the style most familiar to HQ. I have also seen people gain trust faster because they sounded right to the centre of power, even when their understanding of the market was thinner.

This is where the meritocracy story starts to break.

Also Read: Cybersecurity is the trust layer powering Southeast Asia’s digital economy

A company can have offices in ten countries, employees across multiple time zones, and all the right language about inclusion. But if influence still concentrates around one accent, one nationality cluster, or one headquarters culture, then it is not as global as it thinks it is.

It is simply internationally staffed.

That distinction matters.

Language inside a company is not just a communication tool. It is also a filter for power.

Google Translate can help people understand each other. It cannot equalise authority. Translation can preserve meaning, but not always force. It can enable participation, but not necessarily influence.

Research supports that distinction. A 2024 study in the Journal of World Business found that migrant professionals in multilingual organisations experience language-based discrimination in both physical and virtual workspaces. Digital work does not automatically erase old hierarchies. Sometimes it simply moves them onto new platforms.

Anyone who has worked in a multinational company has seen this dynamic. The meeting may be in English. The deck may be translated. The collaboration tools may be shared by everyone. But some voices still land more easily than others. Some accents are heard as polished. Some styles are read as senior. Others have to work twice as hard just to sound equally credible.

Too many companies still confuse fluency with competence.

And once that happens, inequality shows up everywhere: who gets visibility, who gets invited into strategic conversations, who is trusted with ambiguity, who gets stretch assignments, and who remains categorised as strong support rather than future leadership.

This is not unique to one region.

In some US companies, speed and confidence are rewarded so quickly that reflection can be mistaken for hesitation. In some European companies, process is so valued that it can quietly protect legacy gatekeepers. In some Asian companies, hierarchy runs so deep that respect can start to look a lot like silence.

Different styles. Same underlying question.

Who actually gets permission to influence?

That, to me, is the real measure of workplace equity. And it has very little to do with perks.

A company can offer flexibility, wellness programs, polished values statements, and all the language of modern culture. None of that changes much if the same profiles continue to be trusted fastest, promoted fastest, and heard most clearly.

In many global workplaces, some employees are doing two jobs at once: the actual job and the constant cultural translation required to be seen as credible. They are translating not only language, but tone, behaviour, and working style to fit the comfort zone of the dominant culture.

Also Read: Who gets to build and benefit from the digital health economy?

Invisible labour is rarely acknowledged, but it shapes careers

And this issue does not stop at HR. It shows up in product decisions, market strategy, hiring patterns, and customer understanding. A company that does not distribute influence fairly inside the organisation will struggle to understand diverse users outside it. A company that sidelines local voices in meetings will often sideline local realities in strategy. A company that centralises authority too tightly in HQ will keep mistaking proximity for insight.

So yes, the digital economy is global.

But many workplaces inside it are still operating with a much older model of power: influence travels more easily if you have the right passport, the right accent, and the right proximity to headquarters.

Until companies confront that honestly, equity will remain something they describe better than they design.

In the end, the real test of an equitable digital economy is not how many countries a company operates in. It is how fairly it distributes influence inside its own walls.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

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In a landmark event for Southeast Asia’s tech scene, Thai artificial intelligence (AI) powerhouse Amity has just raised a whopping US$100 million in its Series D funding round, the largest generative AI-focused capital raise ever in the region.

This fresh injection of cash puts Amity on a fast track to becoming a global AI contender, with ambitions to list publicly via an initial public offering (IPO) in 2027.

Also Read: Leading global from SEA: Lessons from scaling SaaS, cultures, and team from Amity Group’s journey

What does Amity do?

At its core, Amity is a Thailand-founded software and AI group that specialises in building business tools powered by AI. Its primary customers are enterprises in industries such as retail and telcos, sectors where AI can fundamentally reshape operations and customer engagement.

Through its AI Research & Application Centre (ARAC) in Singapore, Amity develops AI models tailored to specific industries and integrates them into a suite of companies under its umbrella. These companies include Amity Solutions, which focuses on agentic AI (more on that shortly); Amity Accentix for voice AI; Tollring for communications analytics; and EGG Digital for marketing and retail analytics.

Simply put, Amity builds AI that understands the unique problems of particular industries (“vertical AI”) and then uses this intelligence to automate and improve tasks, from customer service to marketing and beyond.

Breaking down vertical AI

Vertical AI is all about crafting AI tools customised for specific industries or business functions, rather than generic one-size-fits-all platforms. For example, AI that knows the retail sector inside out can predict sales trends accurately and recommend optimised marketing strategies, or AI designed for telecoms can analyse massive volumes of voice data to identify service improvement opportunities.

This contrasts with horizontal AI systems, which are general-purpose and may not perform as well on industry-specific challenges. Amity’s bet on vertical AI is rooted in its belief that targeted solutions deliver more measurable and direct returns on investment for companies.

The financial boost and what Amity plans to do with it

With this fresh US$100 million round—on top of the US$60 million announced late last year—Amity’s total funding has climbed to approximately US$160 million. The company is riding a revenue run rate of over US$100 million, up more than 10 times since 2022. More than 75 per cent of its earnings before interest, taxes, depreciation and amortisation (EBITDA) came from its European operations in 2025, signalling its reach beyond Southeast Asia.

Most of the capital will be directed at scaling up three strategic pillars. First, Amity will strengthen ARAC in Singapore, expanding its team and developing more advanced vertical AI models and “agentic AI” — software that doesn’t just analyse information but autonomously executes business processes end to end. This blend of research and practical application is key to pushing the boundaries of what AI can do.

Second, it plans rapid expansion through acquisitions: aggressively buying targeted software companies in Europe and Southeast Asia to build out its portfolio and broaden its market footprint.

Also Read: Scaling smart in Southeast Asia: What startups can learn from Amity’s journey

Third, Amity intends to integrate these diverse capabilities into a cohesive ecosystem to fast-track the commercialisation and adoption of AI solutions by enterprises. The goal is clear: establish itself as a regional leader in AI with a growing global presence.

The IPO goal and what it means

With a public listing targeted for 2027, Amity will aim to capitalise on its momentum and become one of the few Thai AI companies to reach this milestone. To date, Thailand’s tech IPO landscape remains nascent, especially for AI startups. The success of Amity could pave the way for more homegrown AI firms to access public markets and grow unabated.

Thailand’s AI landscape: a region on the rise

Although Southeast Asia has lagged behind giants like the US and China in AI adoption, countries such as Singapore and Thailand are quickly ramping up their capabilities. Thailand, in particular, has seen intensified government focus and investment in AI in recent years, coupled with a growing tech talent pool.

Key drivers include Thailand’s national AI development strategy, increased cooperation between academia and industry, and the growing number of startups entering the AI space. The government’s support has streamlined AI research initiatives and incentivised digital transformation across traditional industries such as manufacturing and retail.

However, the commercialisation of AI in the country is still finding its feet. Many businesses remain in pilot or early adoption stages. Realistically, the journey from research breakthroughs to scaling AI tools that deliver tangible business value is ongoing. This context makes Amity’s trajectory even more remarkable, as it has crossed from startup to scale-up status with a clear revenue stream and a growing international footprint.

The wider implications

Amity’s funding round was led by EDBI, the investment arm of Singapore’s SG Growth Capital, alongside Singapore-based growth equity firm Asia Partners, Indonesia-based VC SMDV, and Malaysia’s CMLIM Capital. Their backing reflects growing confidence that Southeast Asia can produce leading AI companies capable of competing globally.

Yeung Chia Li, Senior Partner at EDBI, highlighted how Amity’s Singapore AI hub strengthens its capacity to meet global enterprise demand and boost AI adoption across industries. “Amity’s expansion in Singapore, including AI research, product development, and ready go-to-market capabilities, will position the company well for the future,” she said.

Also Read: AI is eating the world and startups are riding the infrastructure wave

Asia Partners’ Vorapol Supanusonti also pointed out the strength of Amity’s ‘Build, Buy, Bridge’ strategy — combining organic research, disciplined mergers and acquisitions, and integration — presenting a compelling approach to capturing the vast enterprise AI opportunity in Southeast Asia and Europe.

Asia’s AI ecosystem is maturing

With AI revolutionising every sector and region hungry for customised, scalable solutions, Amity’s rise is a clear signal that Southeast Asia’s AI ecosystem is maturing. By combining deep research, aggressive growth plans, and a commitment to tailored AI, Amity could well become the torchbearer for Thai and regional AI ambition.

It isn’t just about technology; it’s about showcasing that Southeast Asian startups can raise significant capital, develop cutting-edge AI solutions, and play on the global stage. For those watching the AI race unfold in Asia, Amity’s journey is worth following closely.

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For three decades, startups operated in an open global system. Markets were accessible, supply chains were stable, and capital moved freely. Growth depended on building a strong product and scaling across borders.

That environment is changing.

The global system is becoming more fragmented. Countries are relying less on universal institutions and more on smaller, issue-based partnerships. These arrangements are selective, flexible, and often temporary. They are driven by strategic interests, not shared rules.

This shift has created a new structure for the global economy. Instead of a single integrated system, there are now multiple overlapping networks. Growth, capital access, and market entry depend on how well a company is positioned within these networks.

This is the alliance economy.

From global markets to alliance networks

In the past, companies expanded into markets. Regulations were broadly aligned, and scaling was a commercial decision.

That is no longer the case.

Expansion is now shaped by political, regulatory, and technological constraints. Countries are forming partnerships around specific objectives such as supply chain security, technology standards, and energy access. These partnerships are not permanent. They evolve based on interests.

As a result, market access is no longer universal. It is conditional.

Companies are not just entering markets. They are entering systems defined by alliances. These systems determine how data can be used, which technologies are allowed, and how capital flows.

Growth now depends on alignment.

Infrastructure is the new control point

Power in the current system is tied to control over critical infrastructure.

Three areas matter most.

Compute determines AI capability and digital competitiveness. Energy enables industrial and digital systems to function. Semiconductors are essential inputs for nearly all modern technologies.

These are not just economic assets. They are strategic assets that determine whether a country can operate independently.

As a result, countries are prioritising control over these areas. Alliances are often formed around shared infrastructure, access to supply chains, or mutual dependencies.

For founders and investors, this shifts where value sits.

Applications capture demand. Infrastructure captures control.

The most resilient businesses will either control these layers or enable them.

Also Read: Transition climate risk: Navigating the future of sustainable real estate

Middle powers are creating new opportunities

Not all countries are aligning with major powers. Many are pursuing strategic autonomy.

Countries such as India and Indonesia are engaging with multiple partners while retaining flexibility. They participate in international initiatives but set clear limits on their involvement. This allows them to benefit from cooperation without full alignment.

At the same time, they are building domestic capabilities. India is investing in AI standards, talent development, and semiconductor production to reduce reliance on external systems.

This creates a different type of market.

These countries are not just participants. They are platforms that connect multiple systems.

For companies, this means:

• Greater openness to capital
• Evolving regulatory frameworks
• Opportunities to shape new ecosystems

These environments are more complex, but they offer more room for positioning.

Implications for business strategy

The alliance economy changes how companies should approach growth.

Market access is no longer determined only by demand. It is shaped by regulatory alignment, data governance, and political considerations. Entering a market requires understanding its position within the global system.

Technology decisions are now strategic. Choices such as cloud providers, AI models, and data infrastructure affect where a company can operate. Different regions require different standards, and these standards are not always compatible.

Supply chains are also more exposed. Dependencies on specific countries or suppliers create vulnerabilities. Disruptions can occur quickly and with little warning. Companies need to build alternatives, even if this reduces efficiency.

Capital is no longer neutral. The origin of investment can influence how a company is treated in different markets. It affects partnerships, compliance requirements, and long-term access.

Growth is no longer just operational. It is structural.

Where value is moving

Value is shifting toward businesses that support the underlying structure of the system.

Infrastructure is becoming more important. Data centres, compute capacity, energy systems, and logistics sit at critical points in the value chain. These areas are less exposed to short-term volatility and more aligned with long-term demand.

There is also increasing value in companies that can operate across systems. As regulations and technology standards diverge, businesses that can bridge these differences become more valuable. This includes payments, compliance, and data integration.

Demand for sovereignty-focused technology is also rising. Governments and organisations want control over data, infrastructure, and systems. This drives demand for cybersecurity, local cloud solutions, and AI governance tools.

Emerging markets that maintain flexibility are becoming key areas for growth. Southeast Asia, India, and parts of the Middle East are positioning themselves as connectors between systems. This creates opportunities for companies that can navigate multiple environments.

Key risks

An alliance-based system introduces new risks.

Misalignment is a primary concern. Companies that conflict with local regulations or political priorities may lose access to markets.

Dependency is another risk. Relying on a single supplier, market, or technology stack increases exposure to disruption. Diversification is necessary, not optional.

Exclusion risk is increasing. Changes in alliances or policies can restrict access to markets or technologies. These changes are often outside a company’s control.

Complexity is also rising. Operating across multiple systems increases coordination costs and operational challenges. Each additional market adds new constraints.

Risk is no longer just competitive. It is structural.

Also Read: The shifting geopolitics of sustainability, energy, and climate

A practical approach

Founders need to adjust their strategy.

Diversification reduces exposure. Relying on a single market, supplier, or capital source creates risk.

Local understanding is critical. Each market has its own regulatory and political context. These factors must be assessed early.

Resilience should be built into operations. Systems need to adapt to disruptions in supply chains, regulations, and market conditions. This may require accepting higher costs.

Partnerships are increasingly important. Local partners provide access, knowledge, and credibility. In many cases, partnerships are more effective than direct expansion.

Finally, focus should be on structural trends. Infrastructure, energy, and critical technologies are aligned with long-term priorities. These areas are more likely to sustain growth.

Conclusion

The global economy is not becoming less connected. It is becoming more structured around alliances.

These alliances are shaped by infrastructure, technology, and strategic interests. They determine how markets function and how companies grow.

For founders and investors, the shift is clear.

The world is no longer a single market. It is a network of systems.

Success depends on positioning within those systems.

Companies that understand this and adapt early will be better placed to sustain growth in the years ahead.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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In this interview, e27 speaks with Bell Beh, CEO and Co-Founder of Buzz, a travel payment platform focused on building digital infrastructure for underserved cross border travellers, starting with APAC-MENA and Muslim friendly travel. As AI capabilities expand across industries, Buzz is applying vertical AI models to address the unique needs of halal travel, pilgrimage logistics, and cross-border payment experiences across APAC and MENA.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Building AI companions for Muslim travel

e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.

Bell: Buzz is a travel payment platform, specialising in building the Muslim travel infrastructure. At the core of our offering is BAE (Buzz AI Experiences), wee have built three AI travel companions, AI Tour Guide (BAE for Global Travel), first use case was launched with the Singapore Land Authority in 2024, Omani BAE, and Hajj & Umrah BAE — each designed to support different Asian travel needs from leisure to pilgrimage.

AI is crucial in delivering halal-aware recommendations, prayer-time–sensitive itineraries, multilingual assistance, and culturally relevant support across APAC,MENA and other unfamiliar territories. It also powers smart booking flows and secure cross-border payment optimization to support Muslim travellers globally.

Real-time guidance and booking integration

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Bell: Our AI Tour Guide (Global BAE) creates value by giving travellers contextual tips and recommendations in real time, then guiding them straight into booking and payment flows globally. For faith-based journeys, Hajj & Umrah BAE provides halal-aware guidance and dynamically adjusts plans around prayer times, nearby halal dining, visa requirements, and local customs. This reduces friction and uncertainty during travel, improves user confidence, increasing booking conversion, and driving higher payment transaction volume on our platform.

Also read: AI Pulse Exclusive: How CoBALT is designing AI that teams can actually trust

Prioritising domain-specific AI models

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Bell: A key decision we made was choosing to build domain-specific AI models for Muslim travel instead of relying purely on large generic LLMs. This meant slower initial scaling and higher training effort, but it ensured cultural accuracy, halal compliance, and pilgrimage-specific intelligence that generic models often miss.

We prioritised depth and trust over speed, because in faith-based travel and cross-border payments, precision and contextual relevance matter more than volume.

Conversion gains and regulatory complexity

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Bell: What worked better than expected was how quickly AI-driven personalization translated into measurable conversion and transaction uplift, especially when embedded directly into booking and payment flows. Our direct partnership with the Indonesian Hajj authority (BPKH) further validated that purpose-built AI for pilgrimage travel is not just a consumer feature, but institutional-grade infrastructure.

More challenging than anticipated was aligning AI deployment with regulatory, compliance, and cross-border payment requirements across multiple jurisdictions. In faith-based and financial contexts, accuracy, trust, and governance standards are significantly higher than typical travel use cases. Also, both travel and payment are heavily regulated, so applying licenses on both sides to reach the Agentic AI use case was challenging, especially when we are building the infrastructure layer.

AI must align with regulation and trust

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?

Bell: One lesson founders often underestimate is that AI must align with regulation and operational reality, not just product vision. In sectors like travel and payments for us at Buzz, AI cannot bypass licensing, compliance, or trust requirements — it must integrate with them.

Agentic AI may look seamless in demos, but in the real world, automation is only as strong as the regulatory rails and human oversight behind it.

Also read: AI Pulse Exclusive: How Explico is building AI teachers can actually rely on

Building AI through vertical use cases

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Bell: Start with a clear vertical use case, not a generic AI deployment. AI delivers real value when it is deeply embedded into a specific workflow — whether that’s booking, payments, or customer guidance — rather than layered on as a chatbot.

Treat it as a 3-year progression, and don’t expect AI to be a magic pill: Year 1 focus on data readiness, safety, and assisted experiences; Year 2 integrate AI into core operations with human-in-the-loop and compliance-by-design; Year 3 scale toward more agentic automation once regulatory rails, partnerships, and governance are proven.

In a progressive manner, solve one high-friction problem end-to-end, ensure it aligns with compliance and operational realities, and scale from there.

From tour guide to agentic travel assistant

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Bell: Over the next 12 months, we expect AI to shift from being primarily an AI Tour Guide to becoming a Agentic AI Travel Agent that can execute—within regulated boundaries—more of the Plan – Decide – Act – Execute. It‘s no longer just suggestiona/ assisting with travel itineraries. It’s execution layer for our Vertical AI trained since 2024 with various governments.

For Buzz, this means deeper integration of BAE into partner inventory and cross-border payment rails across APAC–MENA, with stronger compliance, auditability, and human-in-the-loop controls.

Across the industry, AI will move from chat-based experiences to workflow-native automation, but adoption will be paced by regulation, trust, and licensing readiness—especially in travel fintech and Muslim travel infrastructure.

Also read: AI Pulse Exclusive: How Spacely AI is bringing generative AI into spatial design workflows

Final thoughts on vertical AI

e27: Anything else you want to share with the audience?

Bell: 2026 is the year of vertical AI, choose one niche and execute well.

The rise of vertical AI in specialised sectors

This conversation highlights how vertical AI is emerging across specialised sectors where cultural context, regulatory compliance, and domain expertise are critical. In areas like Muslim travel and cross-border payments, AI systems increasingly function as infrastructure embedded into booking, guidance, and transaction workflows. As technology evolves, organisations may find that the most durable advantage comes from combining deep domain knowledge with carefully governed AI systems.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, click here.

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Mobile gaming engagement is on the rise globally, with the latest industry data pointing to a maturing market where long-term player value is fast becoming the primary metric for success — and Asia-Pacific is leading the charge.

Measurement and analytics company Adjust released its Gaming App Insights Report: 2026 Edition today, revealing that global gaming app sessions have increased year-on-year, while the paid-to-organic ratio has climbed 61 per cent. The findings signal a broader strategic shift across the industry: studios are moving away from volume-driven acquisition models and towards precision-led growth built on retention, engagement, and monetisation data.

Across regions, APAC posted the largest increase in the paid-to-organic ratio, rising 45 per cent from 2.05 to 2.97. The surge reflects intensifying competition for users in a region that continues to draw significant investment from mobile gaming studios worldwide.

Engagement across APAC remained broadly stable, with sessions per user per day edging up from 1.69 to 1.70. Several markets, however, outperformed the regional average. Japan recorded a three per cent increase in sessions, rising from 1.76 to 1.81, while Singapore and Thailand each also posted three per cent growth. Indonesia, South Korea, and Vietnam each grew by two per cent.

On retention, APAC’s Day 1 rates held steady at 20 per cent, consistent with other global regions. Japan led the region at 25 per cent, followed by Singapore at 23 per cent, Thailand and Indonesia at 21 per cent, and South Korea at 20 per cent.

Also Read: Half of APAC consumers are tired of poor-quality AI content from brands: Report

At the subgenre level, strategy games recorded the strongest session growth of any category, climbing 57 per cent year-on-year. Casual and hyper-casual games also posted solid gains, rising 37 per cent and 31 per cent, respectively. On the install side, slots, casino, and casual games led growth, up 46 per cent, 22 per cent, and 19 per cent year-on-year. Hyper-casual, RPG, simulation, and word games also recorded growth across the same period.

The report’s findings reflect a structural evolution in how studios approach gaming app growth. Rather than prioritising install volumes alone, developers and marketers are placing greater emphasis on acquiring high-value players and sustaining their engagement over time.

Tiahn Wetzler, director of marketing at Adjust, said studios are increasingly focused on retaining high-value players, optimising creatives and channels, and building ad-to-experience flows that favour sustained play over fast turnover. “Understanding where true long-term value comes from, and the ability to connect acquisition, engagement, and monetisation data for fast decisions, is now a necessity,” Wetzler said.

April Tayson, Regional Vice President for INSEAU at Adjust, described the shift in APAC as indicative of growing market sophistication. “Studios are moving beyond pure install growth and focusing on building deeper player relationships through smarter acquisition, stronger retention strategies, and better measurement across the full player journey,” Tayson said, adding that markets across Southeast Asia in particular are showing strong engagement and retention potential.

Broader trends shaping mobile gaming in 2026

Beyond session and retention data, the report identifies several wider forces reshaping the gaming app landscape this year. Gaming App Tracking Transparency opt-in rates rose to 39 per cent in the first quarter of 2026, up from 38 per cent in the same period a year prior — a modest but continued improvement in signal quality for mobile marketers.

Also Read: Data trust remains AI’s biggest bottleneck as CIOs step into broader leadership roles: Report

The report also highlights the growing influence of direct-to-consumer strategies, AI-generated creative assets, live operations, reward-driven mechanics, and cross-platform approaches as studios look to diversify how they reach and retain players.

With competition accelerating across APAC and globally, the data suggests that the studios best positioned for sustainable growth in 2026 will be those that can connect the full arc from acquisition through to long-term player value — and act on that intelligence quickly.

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The rise of agentic AI presents a profound and dual-edged challenge to the traditional consulting industry, a sector that has long thrived now showing its age. While the technology offers the potential for unprecedented efficiency and new service lines, many consulting firms are grappling with an existential dilemma: their historical value proposition, built on the back of human labour and incremental digital solutions, is becoming obsolete.

Rather than leading clients into a new era of autonomous systems, these firms risk being left behind, still operating with a “digitalisation” mindset in an “AI-native” world. The industry’s failure to fully embrace the transformative power of agentic AI, both in its own operations and in its client services, is preventing it from reaping the true rewards of this technological revolution and even underdelivering on its promises to its clients.

The outdated model and the existential threat

For decades, the business of management consulting has relied on a well-established value proposition: it combines expert insight, proprietary frameworks, and a large human workforce to produce customised problem-solving and high-quality deliverables. This formula has been highly successful, scaling over time by hiring top university graduates and billing clients based on the scope and duration of projects.

At the core of this model were human-intensive tasks: gathering vast amounts of data, writing comprehensive reports, and creating polished, visually compelling PowerPoint decks. The value a client received was, in large part, the result of this labor-intensive process, culminating in a detailed and well-supported recommendation.

This traditional model, however, is a product of an earlier wave of digital transformation, and proving to be a poor fit for the age of agentic AI. The new generation of autonomous systems poses a direct and formidable threat to this long-standing approach. As the technology matures, it is directly capable of automating tasks that once justified significant teams and multi-month budgets. Some firms are still only in the nascent stages of this transition, with their internal AI efforts lagging behind a basic copilot subscription. Some of their so-called “AI agents” are, by some accounts, little more than simple models.

Also Read: From hype to harmony: Why agentic AI needs a platform-first mind-set to redefine CX

Preliminary strategy drafts, market scans, and benchmark reports, can now be generated in a matter of hours by advanced tools, directly compressing the value chain that traditional firms have long relied on. This commoditisation of a firm’s core output is forcing the industry to confront what has been described as an “existential” shift, yet many of their reactions are perceived as tame, defensive, and out of touch. The business is no longer about human labor as the primary means of production; it is about leveraging and orchestrating autonomous systems to achieve outcomes; a pivot many consulting firms are still struggling to make.

This resistance to change is also tied to a critical, unspoken element of the traditional consulting value proposition: providing “top cover” or a “seal of approval.” Clients would pay a premium to a well-known firm, not always to improve a solution, but to gain psychological and political leverage, a scapegoat if the plan failed. This dynamic is becoming obsolete. In an increasingly AI-driven world, it is plausible that AI itself will be viewed as a superior, more data-driven decision-maker, making the need for a human “seal of approval” from a consultant far less compelling.

Failing to grasp the true potential

The struggle of traditional consultants is not just about adapting to a new technology; it is about their fundamental failure to grasp what agentic AI truly is and the transformative potential it holds. Agentic AI represents a significant evolution beyond traditional AI systems and even the latest generation of Generative AI. At its core, an agentic AI is an autonomous system that can act independently to achieve a pre-determined goal.

Unlike traditional software, which follows a rigid set of rules, or a large language model (LLM) that is reactive to a prompt, agentic AI is proactive. It can break down a complex task into sub-tasks, plan its actions, execute them, and adapt to changing conditions with minimal human oversight. This inherent “agency” is the key distinguishing factor that empowers it to operate within dynamic, unstructured environments and orchestrate end-to-end processes.

This technological shift is not simply a matter of automating tasks; it is altering the nature of work itself. The future will not be a one-for-one replacement of human workers but the emergence of a fundamentally new organisational structure: the hybrid workforce. In this model, humans are not just supervisors but “coordinators,” “designers,” and “trainers” for AI agents.

Their roles are being redefined, and performance metrics are shifting from output quantity to more nuanced measures like innovation and strategic thinking. By remaining fixated on their old models, consultants are missing the opportunity to guide clients through this fundamental shift in organisational structure, leadership, and culture. They are still selling a product from the past, while the true market has already moved on to the future.

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

The new battlefield of cybersecurity they can’t protect

The most critical failure of the traditional consulting model lies in its inability to navigate the new Cybersecurity landscape created by agentic AI. The same autonomy and adaptability that make agentic AI so transformative for business also create a new and highly complex attack surface that shatters the static assumptions of most traditional security models.

An AI agent is not a static endpoint. It is a decentralised, adaptive entity that can operate across distributed systems, accessing multiple data sources and making independent decisions. The result is a dynamic, hard-to-predict security landscape that demands a completely new approach to defense.

Because many consulting firms are still “stuck in the old digitalisation,” they are not equipped to help their clients address these new and severe risks. The vulnerabilities are not confined to a single point but are embedded in the agent’s multi-layered architecture, leaving it susceptible to a range of sophisticated attacks.

These include “poisoned sight,” where an agent ingests malicious data that skews all its decisions, and “hijacked execution,” where sophisticated prompt injection attacks trick agents into exfiltrating data. A successful attack on a single agent can persist indefinitely, quietly rewriting the agent’s “worldview” or leaking private chat history over time.

Beyond the technical vulnerabilities, the agentic AI revolution introduces specific, high-impact security challenges that business and security leaders must address, and which consultants are often unprepared to advise on:

• “Shadow AI agents”, the proliferation of unauthorised AI agents deployed autonomously by development teams or individual users without proper IT and security oversight. This creates a critical lack of visibility, making it impossible to enforce consistent security policies.
• “Black box” problem, where many agentic systems operate with decision-making processes that are not easily interpretable by humans. This creates a crisis of accountability, where organisations cannot explain why a specific action was taken, leaving them exposed to significant legal, regulatory, and reputational risks.
• The sheer volume and exponential increase in the number of AI agents pose a monumental challenge for managing and securing their unique, verifiable identities.
• The decentralised and autonomous nature of agentic AI makes traditional, perimeter-based security models obsolete. These models were built on the assumption that internal systems are inherently trustworthy, but a decentralised network of unpredictable, autonomous agents makes this assumption invalid. The absence of a Zero Trust architecture, where no agent or system is trusted by default, is not merely a best practice; it is a fundamental security imperative that many consulting firms are simply not helping their clients implement.

Also Read: Agentic AI: The next frontier in technology

Conclusion: The path not taken

The narrative that the AI boom is leaving consultants behind is not an oversimplification. It is a direct result of their own inabilities. The firms that are “left behind” are those that remain tethered to an antiquated business model focused on billing for human labor, creating commoditised deliverables, and offering superficial “top cover” to executives.

By failing to lead clients into the full scope of the agentic AI revolution, from its fundamental impact on the nature of work to its complex and dynamic security challenges, they are failing to reap its real rewards.

The future of professional services will not be defined by a choice between human and machine but by the strategic collaboration between them. The successful enterprise will be a hybrid entity where the speed, scale, and execution of agentic AI are perfectly complemented by the creativity, empathy, and strategic foresight of human leaders.

The only way for consultants to win in this new era is to move beyond the superficial and guide their clients through the full, multi-faceted revolution of agentic AI, a path many are still not on.

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Digital PR for businesses is no longer optional, it’s a strategic necessity. The internet has transformed how consumers interact with brands. Years ago, companies relied heavily on traditional channels like newspapers, magazines, and TV for exposure.

Now, the story has changed; consumers are online, attention spans are shorter, and competition for visibility is fierce. With information at their fingertips, most people now search for brands that connect with their values before making a purchase.

That’s where digital PR services and agencies come in. By leveraging online media intelligently, digital PR builds, manages, and protects a brand’s reputation while increasing visibility and credibility.

What is digital PR and why it matters

Digital PR (Public Relations) is the strategic use of online channels to promote a brand, improve its visibility, and strengthen relationships with customers and media outlets. It goes beyond press releases — encompassing content creation, influencer collaborations, SEO, and social media engagement.

Unlike traditional PR, digital PR integrates storytelling with search optimisation, helping brands reach the right audience, secure backlinks, and position themselves as industry authorities.

Benefits of digital PR for businesses

• Builds brand awareness and recognition: Digital PR campaigns help brands gain visibility across credible platforms. When your brand appears in top-tier publications, it strengthens consumer trust and establishes your authority in the industry.
• Improves brand reputation: Effective digital PR strategies help maintain a positive public image. By sharing authentic stories, responding to media coverage, and engaging audiences directly, your brand builds credibility and goodwill.
• Drives targeted traffic and leads: Securing backlinks from high-authority sites boosts your SEO performance and sends qualified traffic to your website — increasing your chances of generating leads and conversions.
• Strengthens SEO rankings: Backlinks are one of Google’s top ranking factors. Through strategic link building and media placements, digital PR enhances your search engine visibility and drives consistent organic traffic.
• Creates valuable partnerships: Collaborating with journalists, influencers, and industry leaders through PR outreach can lead to partnerships, sponsorships, and new business opportunities.

Also Read: How Category Design drives productivity and efficiency

How digital PR strengthens modern businesses

By combining brand storytelling, authority building, and digital communication, digital PR gives businesses the tools to compete in saturated markets. It not only raises awareness but also directly supports sales growth, recruitment, and investor relations.

Here’s how digital PR for businesses plays a crucial role in sustainable growth:

• Builds positive business sentiment: Working with media professionals and influencers fosters positive perception — leading to trust, loyalty, and repeat customers.
• Boosts social media engagement: Digital PR campaigns often include social media promotion, helping you engage directly with your audience, spark conversations, and build long-term relationships.
• Improves search engine rankings: With backlinks from authoritative sites, your brand ranks higher on Google — increasing organic visibility and generating more traffic that converts.
• Enhances content reach and social proof: Digital PR amplifies your most valuable content, ensuring it reaches your target audience. This not only boosts engagement but also builds trust and social proof that reinforce your credibility.
• Encourages brand advocacy: Satisfied customers often become your strongest promoters. Through PR-driven engagement, you can turn loyal customers into advocates who spread your brand organically.
• Delivers long-term value: Unlike paid ads that disappear when budgets run out, digital PR content stays online — continuously driving backlinks, referral traffic, and brand exposure over time.

Also Read: What is digital PR, and how can you develop an effective strategy?

Digital PR vs traditional PR: What’s the difference?

Modern brands need the data-driven precision of digital PR to stay visible and credible in a competitive landscape.

Conclusion

Digital PR for businesses is no longer a “nice-to-have,” it’s a must-have strategy for brand growth and sustainability. It builds authority, drives organic traffic, and keeps your business top-of-mind in a crowded online space.

When done right, digital PR doesn’t just raise awareness — it fuels long-term success by blending storytelling, search visibility, and authentic engagement.

Whether you’re a startup or an established enterprise, investing in digital PR ensures your brand remains relevant, trusted, and influential in the years ahead.

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The layoff call came without warning, shattering more than 20 years of career building in one fell swoop. One moment, I was the Senior Vice President, planning and managing a team of 20 marketers for Singapore’s consumer banking business; the next, I was packing up my desk and receiving hushed, reluctant farewells from my colleagues.

I was just shy of reaching my fourth anniversary with the company. However, countless late nights and sacrificed weekends culminated in vain after that fateful call.

I wasn’t the newest hire, nor the highest-paid employee, and certainly not the least essential. Despite my experience and efforts, my value was reduced to a number that was eliminated to improve the company’s financial position.

The shock didn’t just affect me emotionally, but left me in a mid-career existential crisis with the realisation that no amount of corporate loyalty can keep you safe from the chopping block.

How Singapore’s younger workforce is fighting back

Ironically, Singapore’s been witnessing a slow uptrend in unemployment since 2023 despite its labour market growing. The tech sector, which once promised strong growth after the pandemic, is now one of the leading industries in terms of layoffs. As a result, a climate of uncertainty and anxiety is building among professionals at all career stages. 

Young Gen Z and millennial tech professionals are slowly realising that corporate loyalty does not equate to job security, and are taking control of their career trajectories. Now, they prioritise work-life balance and career flexibility, which manifests into embracing sabbaticals, “micro-retirement” and alternative working models to maximise their overall wellbeing.

For Singapore’s tech sector, the ripple effects are starting to take shape. Traditional tech careers usually followed linear, predictable trajectories: developer to tech lead to CTO, or marketing associate to marketing manager to CMO. Now, more tech professionals are intentionally carving out multiple career paths, developing their expertise across multiple projects simultaneously.

Younger workers are demanding for more meaningful work, to see the direct impact of their efforts and alignment between their values and what employees stand for. One of the fractional talents helping my company, Glenna Fong, is one such example.

With over 15 years of experience in media, business development, and content strategy, Glenna moved away from a stable traditional corporate role to become a Web3 token co-founder and digital pet care platform partner, while still offering fractional marketing services to young tech startups.

Flexibility is the future of work 

The unprecedented adoption of “working from home” a few years ago has fundamentally altered how we look at work. As a result, what were once considered niche like remote work, project-based contracts and freelancing have evolved from mere buzzwords into viable mainstream alternatives, allowing working professionals to reassess their definition of work.

Also Read: How founders can fund their startup without sacrificing ownership

After my retrenchment, I decided to turn towards fractional work and founded Mad About Marketing Consulting (MAMC), a fractional marketing consultancy that provides tech companies with highly experienced marketing teams on a part-time basis.

I’ve been able to step in as a part-time CMO for startups, challenging my skillsets in supporting each business’ own goals while also ensuring that these businesses receive the qualified expertise needed in order to grow, scale and compete in Singapore’s increasingly competitive landscape. 

What started as a response to my personal setback has now evolved into a model that can benefit both employers and employees that seek autonomy and impact. For startups, this immensely helps keep business costs lean, tapping into strategic expertise without incurring any executive overheads. For fractional professionals, this increases the learning velocity while reducing the risk of becoming obsolete.

A need to adapt and re-assess hiring processes

So, what does this mean for tech startups who are on the hunt for tech talent? They need to re-examine how they hire and retain these workers that look for flexibility and meaning in their professional and personal lives. Startups that can redesign their organisational structure would be able to thrive in the long-run.

Blended teams represent a promising model for startups. These hybrid structures allow permanent employees who know the ins and outs of the startup to collaborate effectively with fractional specialists. Successful clients that we’ve worked with focus on communication and cross-functional teamwork, ensuring that neither team or any department in the startup is functioning in silo.

Additionally, I strongly advocate for building flexibility and autonomy into the startup’s organisational DNA from day one. Those who democratise information access and focus on making communication dynamic result in working environments that value every individual contribution regardless of hierarchy, seniority or experience. 

Also Read: Southeast Asia’s travel tech boom: The startups powering a US$73B industry

Finding security and value in a world without guarantees

My last day in my corporate job seemed like an abrupt ending, when in truth marked the beginning of something more resilient and fulfilling than what any corporate role can offer.

Building a personal brand and robust professional network is your true safety net when building your fractional career. In the fractional economy, visibility creates opportunity. My first few clients at MAMC came through friends and relationships that I have cultivated in my over 20 years in marketing. Today, MAMC successfully serves multiple startups that value our team’s expertise while fuelling our growth.

For Singapore’s tech ecosystem to thrive amid global competition, both startups and professionals must embrace this shifting reality. The question isn’t wondering if alternative work models will be the future, but on how quickly the workforce can adapt to new norms of work where meaning and flexibility are valued.

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In this interview, e27 speaks with Ian about AIBYML SG’s approach to designing and deploying custom AI systems for enterprise environments. As organisations move beyond experimentation toward operational AI adoption, consulting partners increasingly play a role in bridging technology capability with governance, cost discipline, and measurable business outcomes.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Custom AI systems for operational workflows

e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.

Ian: AIBYML SG is an AI consulting and solution firm focused on designing, building and deploying custom AI systems tailored to real operational needs, from AI-native assistants and workflow automation to intelligent customer engagement and analytics.

Transforming workflows with end-to-end AI systems

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Ian: One concrete way AI is creating value for our clients is through end-to-end process transformation powered by custom AI systems, rather than standalone tools. At AIBYML SG, we begin by working closely with clients to analyse their existing “as-is” workflows – identifying operational bottlenecks, manual handoffs, and hidden cost drivers.

leverage human intelligence and LLM AI

From there, we design “to-be” processes where AI is embedded in a targeted and measurable way.

A key part of our approach is selecting the most cost-effective model and architecture for each use case pairing it with clear performance metrics, investment estimates and usage projections. This allows clients to evaluate AI initiatives like any other business project. This discipline is what turns AI from an experiment into a scalable capability with positive ROI.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

Balancing advanced models with operational sustainability

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Ian: One recurring trade-off we help clients navigate is between using the most advanced AI models available and building solutions that are more economically and operationally sustainable at scale with open source foundation models. In many early discussions, stakeholders are understandably thrilled by the latest models because of their impressive capabilities. However, in production environments, model performance is only one part of the equation – cost, latency, reliability, data governance, and integration complexity.

Another important trade-off is between speed and organisation’s readiness. Moving quickly with a proof of concept demonstrates value, while scaling too fast without process redesign, user training, and clear benefits often lead to underutilised systems.

One thing we learned is that sustainable AI adoption requires balancing technical ambition with operational maturity. The right decision is rarely about maximising model capability – it is about maximising long-term business impact.

Adoption momentum and operational uncertainties

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Ian: Looking back, what has worked better than expected is how rapidly our AI engineering team pivot suitable AI models embedded directly into their workflow design and tied to clear business outcomes. When we redesign the “to-be” process properly and define practical metrics — such as turnaround time, cost per case, or productivity uplift — adoption tends to accelerate.

What proved more challenging was managing uncertainty across cost, governance, and technology evolution. Clients understandably want clarity on short- and long-term AI investment, from model usage and infrastructure to maintenance and scaling. In practice, variable demand patterns and shifting pricing models make precise forecasting difficult, requiring scenario-based planning rather than fixed projections.

We also regularly navigate trade-offs between cost-efficiency and data governance. Stronger controls — private deployments, access management, auditability — reduce risk but increase operational overhead. At the same time, fast-moving advances in AI models make interoperability critical. Designing modular architectures adds upfront complexity, but protects long-term flexibility.

Also read: AI Pulse Exclusive: How CAWIL.AI is building industry-focused AI solutions across specialised sectors

AI exposing broken processes

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?

Ian: One lesson from our client engagements is that AI does not fix broken processes – it exposes them.  In many engagements, we are initially asked to “add AI” to improve speed and reduce cost. However, once we analyse the existing workflow, we often discover redundancy, inconsistent data, unclear objective and undocumented exceptions. If AI is layered on these inefficiencies, it only automates complexity rather than solving it.

Another challenge is that stakeholders start by asking for a single AI feature. But once users test a prototype in their real workflow, they quickly form an “I know it when I see it” understanding and they start uncovering latent needs they couldn’t articulate upfront. Prototyping is powerful precisely because it reveals these hidden requirements through hands-on use.

Treating AI as structured transformation

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Ian: “AI doesn’t fail because models aren’t smart enough — it fails because organisations aren’t ready enough. Teams that treat AI as a structured transformation, with clear metrics, cost discipline, and room for iteration, are the ones that turn experimentation into lasting ROI.”

From AI features to operating models

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Ian: As AI tools become more powerful and no-code platforms lower the barrier to building applications, our strategic focus has shifted from “building AI features” to enabling sustainable enterprise capability. Basic chatbots and workflows will continue to be commoditised. Our long-term relevance depends on helping clients solve the harder problems around governance, integration, data, cost control, and operating ownership.

As part of our next strategic initiative, we will be working with regional enterprises that have experimented with multiple off-the-shelf AI tools but now face rising usage costs, inconsistent outputs, and growing compliance concerns. Our approach is to redesign their AI architecture to be model-agnostic, introduce structured cost monitoring and governance controls, and embed AI more deeply into core workflows.

Our key pivot is towards becoming an “AI operating model” partner — combining process redesign, modular architecture, and ongoing optimisation. The takeaway is simple: in the AI era, tools will keep changing, but organisations will always need partners who can turn fast-moving technology into reliable, governable, and scalable business capability.

Also read: AI Pulse Exclusive: How GenAI Fund is accelerating enterprise AI adoption across Southeast Asia

Operationalising AI beyond experimentation

This conversation highlights a growing shift from experimenting with AI tools to building sustainable operational capability. As enterprises face rising costs, governance considerations, and integration complexity, the focus increasingly turns toward process redesign, architecture flexibility, and measurable business outcomes. Organisations that successfully operationalise AI may find that long-term advantage lies less in the models themselves and more in how effectively they embed AI into everyday workflows.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, explore more stories here.

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