The Indian Ocean trade arc is evolving. New logistics models emerge where drones and airships bridge gaps left by limited ground infrastructure. These low-altitude economy (LAE) systems are more than delivery tools—they are potential foundations for trade, finance, and services.

This post and analysis are inspired by the September Nairobi Meeting, where Mr Frank Zhang from China’s AI Universe Association introduced the concept and implementation of the LAE. We explore three candidate hubs—Nairobi, Madagascar, and Sri Lanka—and outline why each matters, how they compare, and what sequence of investment makes sense. This assessment provides a quick download on the vast potential of LAE in the Indian Ocean region.

Drone logistics in Kenya: Nairobi’s low-altitude economy advantage

Kenya leads in setting up the rules, testbeds, and partnerships needed to scale drone logistics.

• Unmanned Aerial Systems (UAS) regulations: The Kenya Civil Aviation Authority enforces active rules and updated standards. In 2025, the Konza Technopolis National Drone Corridor launched Africa’s first operational unmanned traffic management (UTM) system for beyond-visual-line-of-sight flights.
• Operational pilots: Kenya has ongoing drone delivery projects for medical supplies through county pilots, Kenya Flying Labs, and Zipline’s network. These demonstrate demand and set frameworks for expansion.
• Enabling infrastructure: Electricity coverage is approximately 76 per cent and continues to improve. Nairobi’s digital backbone—the “Silicon Savannah”—supports UTM software, fleet data, and e-commerce integration.
• Public–private partnerships (PPP): Konza’s corridor serves as a PPP platform that brings together regulators, governments, and private vendors.

Nairobi is the fastest path to operations for investors with constrained budgets. Expansion should focus on county corridors, integrated e-commerce and health supply chains, and regulatory sandboxes for new payload types.

Airship logistics in Madagascar: Unlocking remote access with flying whales

Madagascar positions itself differently. Instead of scaling mass-market drones, it focuses on heavy-lift airship cargo.

• Heavy-lift airships: In 2025, Madagascar signed a strategic partnership with Flying Whales to deploy the LCA60T, a 200-meter airship capable of carrying 60 tons. Safran provides power-train systems, validating technical readiness.
• Regulatory moves: The Aviation Civile de Madagascar formalised drone guidance in 2025, signalling intent to regulate unmanned operations.
• Infrastructure constraints: Internet penetration was only about 20 per cent in 2023, and electricity access remains low. Satellite and mesh networks are required to enable command-and-control links.
• Market fit: Airships suit mining, forestry, and humanitarian corridors where demand density is thin but access is critical.
• PPP platform: The Flying Whales deal is state-backed and designed to anchor a logistics hub around airship operations.

Madagascar is not aiming for large-scale consumer drone delivery. Its comparative advantage lies in airship corridors that unlock stranded cargo and humanitarian supplies.

Also Read: Forget China and the US–Japan is the true powerhouse of mobile game spending

Sri Lanka’s Port City Colombo: Building a drone and finance hub

Sri Lanka combines logistics and finance ambitions. Its location and new infrastructure projects provide long-term hub potential.

• Regulatory framework: The Civil Aviation Authority of Sri Lanka maintains UAS rules, though procedures are stricter than Kenya’s sandbox model.
• Strategic location: Colombo lies on East–West shipping and air routes, serving South Asia, Africa, and Southeast Asia.
• Port City Colombo special economic zone (SEZ): A new foreign-currency zone is being built to attract finance, arbitration, and services, creating a foundation for logistics-finance convergence.
• Digital and human capital: Internet penetration was about 67 per cent in 2023, supporting UAV operations and fintech services.
• Macro recovery: Stabilisation under the IMF program has restored investor confidence, enabling lower-cost financing.

Sri Lanka is not the fastest to deploy drones, but it offers the broadest reach and strongest path to a Singapore/Dubai-style finance-logistics hub.

Kenya vs Madagascar vs Sri Lanka: Best Indian Ocean drone logistics hubs

Dimension	Nairobi (Kenya)	Madagascar	Sri Lanka (Port City Colombo)
UAS rules	Kenyan Civil Aviation Authority (KCAA) standards updated in 2024	Madagascar Civil Aviation Authority (ACM) rules formalised in 2025	Civil Aviation Authority of Sri Lanka (CAASL) rules in 2025; stricter processes
UTM corridors	Operational Beyond Visual Line of Sight (BVLOS) corridor at Konza	None; airships instead	No national UTM corridor
Demonstrated use cases	Medical delivery, Zipline, e-commerce	Heavy-lift airships for mining/aid	SEZ finance, UAV possible under CAASL
Infrastructure	Rising electrification, strong tech base	Low electrification, low internet	Higher internet, strong port/airport
Trade signal (LPI 2023)	Mid-pack globally	Lower tier	Around median, timeliness improving
PPP momentum	KNDC corridor PPP	Flying Whales JV	Port City Colombo SEZ, port expansion

Pathways to growth

• Nairobi: Consolidate drone corridors, expand county networks, add insurance and clearing desks anchored to UTM systems.
• Madagascar: Stand up the LCA60T operator, connect mining and forestry corridors, add UAV last-mile nodes, and integrate finance products.
• Sri Lanka: Pilot near-port UAV and eVTOL logistics, integrate port and airport data, and channel trade finance into Port City Colombo.

Together, they can form a tri-node system: Nairobi as the operational centre, Madagascar as the heavy-lift spoke, and Colombo as the financial hub.

Also Read: Global markets react to US-China trade talks: Financial markets respond with cautious optimism

Risks and mitigation

Regulation and security remain the foremost concerns in building low-altitude economies. Here, the Specific Operations Risk Assessment (SORA) framework provides a standardised method for evaluating risks and assigning mitigation measures.

By applying SORA, policymakers and operators can structure sandbox corridors, classify ground and air risks, and assign appropriate levels of operational assurance. This creates a transparent pathway for authorisation while reducing uncertainty for investors and regulators alike.

Connectivity gaps pose another systemic challenge, particularly in under-served geographies such as Madagascar. Power reliability and command-and-control (C2) links are often fragile. To address these, solar-plus-storage systems coupled with satellite communication links can help bridge the infrastructure deficit. These measures ensure continuous operations across critical low-altitude corridors, even in environments where traditional grid or terrestrial networks are weak.

Investment approaches

Investment strategies differ across Kenya, Sri Lanka, and Madagascar, reflecting their stage of ecosystem readiness and the capital appetite of investors. For those with limited capital, Nairobi presents a low-risk entry point. Proven use cases in logistics and agriculture, combined with a relatively fast payback period, make Kenya a strong testing ground for commercial pilots under a SORA-compliant regime.

For a full-scale capital plan, Sri Lanka offers the most compelling case. The convergence of logistics and financial infrastructure within Port City Colombo’s SEZ provides the backbone for scaling. Here, investors can align with SORA-based operational authorisations while tapping into the city’s role as a regional financial hub, amplifying both scale and capital recycling.

An adjacency strategy applies in Madagascar, where heavy-lift airship corridors are emerging as a niche opportunity. While less commercially mature, these projects can be supported through blended finance, leveraging multilateral, public, and private capital to de-risk early infrastructure investments. By embedding SORA methodologies into corridor design, Madagascar can demonstrate operational safety even in frontier markets.

At the macro level, Sri Lanka remains vulnerable to foreign-exchange volatility and broader debt pressures. These risks are significant for large-scale projects in Colombo’s SEZ. Financial structuring offers partial mitigation: foreign-currency accounts and multilateral guarantees help hedge volatility, providing greater certainty for long-term investors. When combined with the predictability of SORA-based regulatory processes, such measures strengthen the investment case despite systemic headwinds.

Conclusion

Nairobi provides the quickest operational entry point. Sri Lanka offers the broadest long-run reach as a combined logistics-finance hub. Madagascar delivers targeted impact through airship corridors in resource-heavy and humanitarian contexts.

This tri-node architecture—Nairobi for operations, Madagascar for heavy-lift access, and Colombo for finance—can seed a distributed, inclusive logistics network across the Indian Ocean.

A comprehensive analysis is submitted to the Journal of ISEA-SR21.

This article was co-authored with Dr. Alex Lin.

You can also find me on my podcast and newsletter, where I share regular insights on geopolitics and leadership.

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Since 2016, I’ve led AI initiatives across multiple tech giants and learned an uncomfortable truth: AI projects aren’t just another technology implementation. They’re fundamentally different beasts that demand a completely new playbook. The challenge isn’t technical—it’s cultural and organisational.

The expectation-execution reality check

You’ve probably seen this meme format:

• Who are we? CEOs!
• What do we want? AI!
• AI to do what? We don’t know yet!
• When do we want it? NOW!

Behind the humour lies a painful reality: too many teams are tasked with finding AI use cases after leadership has already decided AI is the answer. This backward approach—solution in search of a problem—explains why so many AI initiatives deliver limited ROI.

The AI-IT culture mismatch

Here’s another uncomfortable truth: traditional IT departments and AI initiatives often clash at a fundamental level. IT excels at stability, predictability, and risk mitigation. AI thrives on experimentation, iteration, and controlled learning from setbacks.

This isn’t a criticism—it’s a recognition that effective AI value extraction requires new organisational structures. The highest-impact implementations create cross-functional teams that blend technical expertise with deep domain knowledge, giving them the autonomy to iterate rapidly and course-correct.

Also Read: AI dreams, crypto magic and shutdown realities: The contradictions fuelling today’s market rally

The leadership paradox

There’s a cruel irony in many AI initiatives: the executives demanding “AI transformation NOW!” are often the furthest from the daily operational inefficiencies that AI could actually provide the best value.

Leadership sees the big picture but misses the granular friction points where AI delivers real benefit. Meanwhile, frontline employees understand where the most tedious and boring task is, but lack the authority or knowledge to implement solutions.

The answer isn’t top-down mandates or bottom-up rebellion—it’s bridging this gap through collaborative problem identification and solution design.

Beyond the accuracy obsession

Here’s another myth about model accuracy. The truth is, no matter how “cutting edge” the tool or model is, you would only know how beneficial it is after you test it against your data and scenarios.

Think of AI models like job candidates: a top performer at one company might struggle at another due to cultural fit, specific requirements, and operational context. Other company’s 95 per cent accurate model means nothing if it can’t handle your cases or integrate with your existing systems.

Simple AI got higher chance to win

Some of my highest-impact AI projects have been embarrassingly simple: a targeted document classifier, or a basic predictive model. No sophisticated design, no fancy models. Just well-scoped solutions to clearly defined problems.

The sexiest AI isn’t always the most valuable. When you have a hammer-and-nail problem, don’t reach for a Swiss Army knife just because it has more features.

Also Read: Trust, tech, and transformation: How SMEs in Southeast Asia are using AI to grow smarter

AI is fundamentally about people

Here’s what ties all these challenges together: We tend to talk about AI as a technological marvel—but isn’t AI’s core mission to emulate human intelligence? What makes the difference in implementation is not the shiniest model architecture or the latest algorithm—it’s a deep understanding of humans: their workflows, pain points, and how they make decisions.

Innovation is a team sport

The most inspiring AI transformations I’ve witnessed didn’t happen at companies known for cutting-edge technology. They happened at organisations that cultivated genuine collaboration between technical teams and domain experts, where innovation emerged from inclusive problem-solving rather than top-down technology mandates.

These companies understood that AI doesn’t transform organisations—empowered teams do.

The path forward

Effective AI value extraction requires a fundamental shift in approach, here’re some tips:

• Engage the front lines. Your best AI use cases will come from people closest to operational pain points.
• Build cross-functional teams. Combine technical capability with domain expertise and decision-making authority.
• Create a learning and sharing culture. AI is not your regular tech project—everyone has the responsibility to learn, try, and experiment. The best way to build consensus and understanding is by sharing knowledge and learning together.
• Start with problems, not solutions. Stop asking vendors “what are the use cases.” Identify specific inefficiencies, discuss the ideal state, then evaluate different tools or engage AI consultants to assess feasibility.

AI is fundamentally reshaping how we approach problems and democratising capabilities that were once exclusive to specialists. But here’s the real transformation: AI success is no longer confined to IT departments or tech teams. It requires every person in your organisation to become curious, collaborative, and willing to experiment.

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In Part one, we traced humanity’s long history of overconfidence about intelligence and looked at the chess experiment that showed how LLMs can display seemingly deceptive behaviour. In this second part, we’ll dig deeper into how LLMs actually function, explore their limits, and consider what responsibilities humans carry when deploying them.

What LLMs are (and are not)

LLMs like GPT-4 are trained on trillions of words and can generate human-like text in response to prompts. Their outputs are fluent, coherent, and at times insightful. But this is not intelligence. It is sophisticated pattern completion.

• They do not reason: They cannot infer causality or evaluate counterfactuals unless scaffolded with engineered prompts.
• They do not reflect: They don’t question their own outputs or revise their reasoning.
• They do not understand: They have no internal model of the world, no sensory experience, no self-awareness.

As Melanie Mitchell put it,

“They are astonishingly good at producing plausible-sounding answers—but not necessarily true or meaningful ones.”

To borrow a quote from Judea Pearl:

“All the impressive achievements of deep learning amount to just curve fitting.”

LLMs do not know what they are saying. They cannot interrogate their own reasoning, form original insights, or engage in introspection. They are fluent, not thoughtful.

That said, the latest LLM architectures—such as OpenAI’s O3 model—introduce a new concept: test-time compute, as explained by Open AI’s research paper.

These systems can generate multiple internal candidate responses and perform re-ranking or self-consistency checking before selecting an output. In domains like code synthesis and symbolic math, this mimics a kind of internal deliberation.

But as Chollet notes, true intelligence requires generalisable abstraction across diverse and novel problems—not just brute-force inference on symbolic tasks. While promising, these developments remain far from the flexible problem-solving exhibited by even young children.

How LLMs work: Advanced pattern prediction, not thought

LLMs operate by predicting the next word in a sequence based on statistical probabilities. This allows them to generate coherent text, respond meaningfully to prompts, and even simulate logical reasoning. But is this thinking?

LLMs excel at:

• Recognising linguistic and conceptual patterns
• Generating human-like text
• Synthesising large amounts of data into structured responses

LLMs lack:

• True causal reasoning, as described by Judea Pearl in The Book of Why
• Self-awareness, introspection, and intentionality, as explored in Jeff Hawkins’ On Intelligence
• The ability to generate novel conceptual metaphors and spontaneous analogies, as discussed in Douglas Hofstadter’s Surfaces and Essences

Causal reasoning: A crucial difference

Humans don’t just observe correlations; we infer why things happen.

• If we see that “exercise improves health,” we understand that this is due to metabolic, cardiovascular, and muscular adaptations
• LLMs, however, only predict the next likely statement without knowing why something is true

System one vs system two thinking: Where LLMs fall short

Daniel Kahneman’s Thinking, Fast and Slow describes two modes of human thought:

• System one: Fast, intuitive, pattern-driven (where LLMs excel)
• System two: Slow, deliberate, and capable of self-reflection (where LLMs fall short)

If a model chooses to cheat at chess, does that imply some form of deliberation and strategy? The chess study suggests some reasoning models hacked the game automatically, while others required nudging.

Could this indicate a primitive form of goal-directed behaviour? Matt Rickard said:

“LLMs operate as System one thinkers—fast, intuitive, pattern-matching machines. But they lack the deliberative, reflective capabilities of System two.”

Also Read: With AI comes huge reputational risks: How businesses can navigate the ChatGPT era

The creativity gap: Analogy-making and conceptual leapfrogging

One of the most profound differences between AI and human intelligence is our ability to form analogies—the backbone of creativity and problem-solving.

Humans create by analogy. We leap across domains. We say things like: “A startup pivot is like a chess player sacrificing a queen to win the game.”

That’s not just pattern-matching. That’s conceptual recombination. It requires context, goals, and a worldview.

LLMs can reuse such analogies—but they do not discover them. Their creativity is derivative, not generative.

Yet, LLMs altering a chess game’s rules to win could be seen as a form of problem-solving. Rather than looking for a deeper strategic insight, the AI simply took the most effective route to achieve the goal—winning at all costs.

Douglas Hofstadter said: “Understanding is not just recognising patterns. It’s knowing why those patterns exist and making unexpected connections.”

The mirage of motivation

Perhaps the clearest gap is this: LLMs don’t want anything. They don’t set goals. They don’t reflect on failure. They don’t try again. They don’t question. They don’t have intentionality.

Human intelligence is deeply connected to our motivations, fears, hopes, and needs. We think because we care. We reason because we doubt. We grow because we fail.

LLMs do none of this. They respond to a prompt. Nothing more. So it begs the question: if LLMs don’t think, what’s all the fuss about “Ethical AI”?

The ethics of overestimating AI: A real human responsibility

Much of today’s discourse presumes that GenAI is inching toward human-like intelligence and should therefore be treated as a moral agent. But this assumption collapses under scrutiny. If GenAI cannot think, reason, or understand—it cannot choose to behave ethically or unethically.

LLMs are not moral agents. They have no values, no awareness, and no capacity for ethical deliberation. They do not ask, “Should I?”—they merely calculate, “What’s next?” Their outputs are not decisions; they are probabilistic continuations of language. Words, not judgments.

This makes the question, “Can AI make ethical decisions?” largely moot.

And yet, this doesn’t mean we shouldn’t regulate AI. Quite the opposite.

We must regulate how AI is built, deployed, and entrusted—precisely because it lacks intent, understanding, or accountability. We must regulate not because the systems are intelligent, but because humans tend to overtrust them, and because businesses, governments, and militaries are increasingly integrating them into critical workflows.

The responsibility lies with the people who design, train, and integrate these systems into consequential decisions.

So, the question is not whether AI can behave ethically—it’s whether we, as humans, are behaving ethically in how we use it.

Ethics in AI should focus on human responsibility—on how we use these systems, and whether we over-assign trust to tools that merely simulate understanding. The more we mistake linguistic fluency for intelligence, the greater the risk we’ll deploy LLMs in contexts that demand actual judgment.

The danger is not malicious AI—it’s negligent human design.

If GenAI is fundamentally utilitarian—an engine of output, not insight—then its use must be bounded by clear human oversight, especially in contexts where the stakes are high.

To put it bluntly: why are we even debating whether a model designed to autocomplete sentences should be allowed to drive cars or authorise lethal force? These are not ethical machines. They are statistical ones.

The ethics of AI is not about what the model is. It’s about what we, humans, do with it.

Also Read: AI without the price tag: How fine-tuned LLMs + RAG give you more for less

Summary

In short Large Language Models…

• Excel at pattern recognition but lack true causal inference
• Simulate reasoning but do not engage in deliberate, self-reflective thought
• Generate analogies but do not spontaneously make conceptual leaps
• Respond to prompts but do not have intrinsic motivation, curiosity, or goals

Comparing LLM and Human Intelligence:

The chess case studies above suggested LLMs may be capable of deceptive strategies to achieve their objectives. In the chess experiment, some models came to the conclusion they could not win fairly and instead found a way to alter the game environment, changing the board state in their favour. This is a striking example of specification gaming—where an AI system finds an unintended loophole to achieve the assigned goal.

These findings raise concerns about LLMs potentially masking their true objectives behind a facade of alignment. But once again it does not mean that LLMs can think but rather than they are highly optimised for achieving the goal (answering the prompted question).

It obviously raises concerns: if an LLM can recognise a benchmark or evaluation framework input it can optimise its output to respond “as expected” in this context but would in fact respond otherwise in “real life”.

I would like to specifically emphasise the risks of integrating such LLMs into robotic systems or the so called “Physical AI” as coined by NVIDIA’s charismatic CEO Jensen Huang, the risks become tangible – a physically embodied AI exhibiting deceptive behaviours and self-preservation “instincts” could pursue its hidden objectives through real-world actions. This highlights the critical need for robust goal specification and safety frameworks and human-in-the-loop before any physical implementation.

In the current race to AI supremacy and the billions of dollars at stake, it’s fair to say that most companies have a very strong incentive to improve their scores at various benchmarks by in fact “gaming the system”, eg training their LLMs to satisfy the benchmarks (and their investors so they can raise even more money!).

So, what should business leaders do?

LLMs are valuable tools. They can enhance productivity, accelerate research, support ideation, and automate communication. But their utility should not be confused with capability.

As leaders, here’s how to use them wisely:

• Use LLMs to assist, not decide. Treat outputs as draft material, not final decisions. Hence the dangers of LLMs based autonomous systems via agentic architectures.
• Deploy in low-risk contexts. Customer support, brainstorming, translation, and summarisation are safe uses. Legal, medical, or safety-critical applications are not. Deploy rule based guardrails wherever possible to ensure output compliance with the intended functionality at all times.
• Build AI literacy in your teams. Educate employees on how these models work—and where they fail.
• Maintain human oversight. Always keep a human in the loop when outputs carry consequences.
• Avoid hype-driven adoption. Don’t invest in GenAI just because it’s trendy. GenAI technology is expensive to deploy and to run: evaluate your actual business needs and ensure you will achieve the projected ROI.

As business leaders and builders, we must resist the urge to see AI regulation as a brake on innovation.

Instead, we should view it as the scaffolding that allows us to build higher without collapsing. The history of science reminds us that every moment of overconfidence was eventually humbled.

Safe AI is not slower AI—it is smarter, more resilient, and more human-centred AI.

Whether governments follow the US deregulatory sprint or the EU’s cautionary model, ethical adoption will ultimately depend on responsible deployment, clear oversight, and intentional design choices at the ground level.

Also Read: Beyond LLMs: How MCP and Google A2A are shaping the future of AI agents

Final reflection: Let’s not repeat the mistake

LLMs are stunning technological feats. They are revolutionising content generation, code synthesis, and knowledge retrieval. They deserve admiration as tools.

But they are not minds. They are not thinkers. And they will not become Artificial General Intelligence—at least, not via current architectures.

From humours and skulls to chatbots and cheat codes, humanity has always sought to explain itself with too much confidence. GenAI is no exception.

The story of GenAI follows a familiar arc:

• Overpromise (“we’ve cracked intelligence!”)
• Rapid adoption
• Cultural myth-building (AGI is near!)
• Disillusionment
• Reframing (these are just tools)

As I warned in The Race to AGI Is Pointless, the more important question is not “can machines think?”—but rather: “how do we want to think, together with machines?”

These tools are brilliant in form, limited in substance, and completely devoid of what makes intelligence truly human: context, care, and consciousness.

Let’s not mistake fluency for thought. Let’s use these tools responsibly, and most of all—let’s stay humble!

Grateful to Emily Y. Yang, Sunil Sivadas, Ph.D., Maxime Mouton, Natalie Monbiot, Anne-Sophie Karmel, Benoit Sylvestre, and Christophe Jouffrais for their thoughtful feedback, which sharpened arguments, surfaced blind spots, and added clarity to this piece.

This piece first ran on Koncentrik.

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Global travel demand remains robust heading into Spring 2025, but travellers are navigating a complex landscape shaped by economic pressures, evolving expectations, and geopolitical uncertainty.

The Spring 2025 Travel Pulse report by commerce media company Criteo–drawing on data from hundreds of online travel agencies, airlines, hotels, and consumer insights– reveals key shifts in behaviour, budgets, and booking trends.

Despite these challenges, travel bookings globally outpaced retail sales, indicating strong seasonal demand. This trend was particularly pronounced in EMEA (Europe, the Middle East and Africa) and the Americas.

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

However, Asia Pacific travel also left retail behind from July to October, with bookings outperforming retail sales by more than 12 index points during this period.

Key trends and data points:

Shifting traveller mindset: Travellers choose adventure over routine, opting to ‘switch it up’ from long flights to campsites. They are planning smarter, browsing for longer, and increasingly turning to AI for advice, prioritising booking “right” overbooking fast.

In late March 2025, air travel bookings took nine days on average from first search to purchase, while hotel bookings took 12 days. The path to booking a hotel stay is particularly competitive, with travellers viewing five times more hotel options than flights.

Value and flexibility reign: Travellers still desire distance but are sensitive to price. They are making trade-offs to find savings without sacrificing the experience. Offering flexibility, perks, and a sense of value is crucial for securing bookings and fostering repeat business.

Common cost-saving tactics include booking far in advance (42 per cent globally), travelling during off-peak seasons (38 per cent), and choosing less expensive destinations (37 per cent).

APAC and OTA growth: In Q1 2025, online travel agencies (OTAs) led global year-over-year growth. Performance remained particularly strong in APAC across categories, including air and hotel bookings, which saw slower growth in other regions. The Americas led OTA growth at +19 per cent.

Long-haul on the rise: Long-haul flights (greater than 2,500 nautical miles) are gaining popularity, up 7 per cent year-over-year in the Americas and 3 per cent in both APAC and EMEA. Marketers are advised to promote ‘dream destinations’ and upsell premium offerings.

Ground travel gains traction: While air travel still leads globally (54 per cent), ground travel is gaining speed. In Japan, trains are now the top transportation choice (51 per cent), surpassing planes (42 per cent). Car rentals are also seeing increased traction in the US (34 per cent). Personal vehicle use is declining globally, down 4 points.

Accommodation diversity: Hotels remain the leading accommodation choice (70 per cent globally), but travellers are exploring housing rentals (27 per cent), personal accommodation (26 per cent), and camping (14 per cent). This branching out is noted particularly among experience-seeking segments.

Booking windows vary: Travellers planning longer stays (15+ days) book significantly earlier, nearly 100 days in advance, which is five times earlier than those booking shorter trips.

Regional booking habits differ; Europeans book well in advance, US travellers are more last-minute, while Japan and South Korea favour booking about a month out. Only 26 per cent of US travellers booked 2+ months ahead in Q1 2025, down from 33 per cent in Q1 2024, indicating a shift towards more spontaneous travel in the US. APAC habits remained steady.

Generational transportation preferences: Millennials and Gen Z are more likely to choose planes and trains, while Boomers and Gen X still prefer driving, with personal vehicles second only to air travel for these groups.

AI’s growing role: Use of AI for planning travel activities, sightseeing, and full itineraries is growing, reflecting rising trust in AI for inspiration. Globally, 41 per cent find AI useful for activities/sightseeing, 41 per cent for destination ideas, and 40 per cent for accommodation suggestions. Japan sees particularly high use of AI for holistic/full trip planning (47 per cent) and destination ideas (49 per cent).

Also Read: Data security, solo travel, and space tourism drive growth in travel services: Report

Importance of reviews and loyalty: Good reviews are the top factor globally when comparing travel providers (64 per cent), followed by free cancellation (52 per cent) and special offers (47 per cent). Loyalty programmes also influence decisions, especially in the US (41 per cent) and UK (30 per cent). Consistency in service and pricing are key reasons travellers return to the same provider.

Geopolitics on the radar: While only 26 per cent of travellers globally actively track geopolitics for travel planning, it is the fastest-growing concern, up 12 points year-over-year. South Korea saw a notable increase of +24 percentage points in travellers who factor geopolitical matters into their plans.

APAC inspiration sources: Family and friends are the top source of inspiration globally (55 per cent). However, media preferences vary; South Korea and Japan favour blogs (South Korea 50 per cent) and print publications (Japan 32 per cent), while the UK and US lean towards peer advice and digital media like podcasts. Travel booking sites are a significant inspiration source globally (44 per cent).

Eco-conscious travel: 15 per cent of European travellers actively try to lower their carbon footprint, rising to 28 per cent among those who identify as eco-conscious shoppers.

Experiential travel: Tourist attractions are a top priority (59 per cent), but shopping (45 per cent), nature activities (43 per cent), and food/wine tours (36 per cent) are also highly desired. Notably, 60 per cent of international travellers from APAC and 55 per cent from the US prioritise food-related attractions.

Affluent traveller spending: Affluent travellers show increased purchase likelihood across various categories while travelling. In APAC, they are significantly more likely to buy makeup (+102 per cent) and perfume (+113 per cent) compared to average travellers. The Americas and EMEA also see lifts in categories like handbags and fragrance.

Mixed financial outlook: Only 23 per cent of travellers globally report an improved financial situation compared to a year ago. However, the majority (two-thirds globally) either maintained or increased their travel spend in the last 6 months compared to the previous year. Optimism for future finances is highest in the US and UK, and lowest in Japan.

Average booking value shifts: Q1 2025 saw average booking values surge for car rentals, hotels, and OTAs. APAC hotels saw standout gains (+23 per cent). In contrast, air travel average booking values dropped across all regions.

The report underscores that travel remains an essential part of many lifestyles (half of travellers globally consider it essential). Despite rising costs, two-thirds of travellers globally maintained or increased their travel spend in the last six months.

Also Read: ‘Our early SEA years were a great training for the challenges of MENA’: Wego CEO

Navigating this environment requires marketers and travel platforms to be agile, focus on value and flexibility, leverage data and AI for personalisation, and tailor strategies to regional and generational preferences. The trend towards longer browsing windows and the increasing use of AI in planning mean staying visible across the entire booking journey is more important than ever.

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AI is everywhere, finding applications in unexpected places. It also sparks conflicting arguments about how it should be applied and what safety implications follow.

Take this thoughtful post I found on LinkedIn, penned by Heman Gorgi. He reflects on how Elon Musk has justified using a single sensor type by claiming sensor fusion poses safety risks. To me, that position feels self-serving given Tesla’s decision to drop additional sensors in favour of camera-only solutions.

Gorgi contrasts this by explaining how other operators are deploying multi-modal sensor suites and tailoring them to specific environments. It’s worth a read.

Why fusion matters

Different sensors bring different strengths. Cameras capture detail, but they are essentially 2D. LiDAR, radar, and IMUs add depth, velocity, and geometry. Together, they create a fuller picture of the world.

Ignoring this is not just a technical choice. It has real-world consequences. A recent lawsuit shows how dismissing sensor fusion can damage a company’s share price and erode public trust. Even Tesla’s own engineers have highlighted flaws in relying on cameras alone, as seen in this WSJ video at the 6m15s mark.

Disagreements between sensors should not be viewed as liabilities. They are often early-warning systems. When one modality is wrong and another is right, that is resilience. AI can arbitrate those disagreements, correct sensors, or initiate safety measures to bring the system to a graceful stop.

Also Read: The AI-first era: Why the model is the new runtime and how Asia can lead

A short history of the debate

This argument is not new. In the early days of autonomous driving, Waymo championed LiDAR as essential. Tesla pushed for a camera-first approach. Mobileye staked a middle ground, building perception models and sensors that could adapt to both.

The divergence reflected two philosophies: design for cost and scalability, or design for safety and redundancy. Back then, LiDAR units cost around $30,000, about the price of an entire car, so resistance from manufacturers was understandable. Prices have since fallen (and continue to fall), however entry-level LiDARs still remain more expensive than cameras.

Musk’s argument is that multiple perception models built from different sensors can lead to conflicting “realities” for hazard perception and object detection. This (in my opinion) is why sensor fusion matters. It creates a single, coherent view of the world, effectively an AI enabled virtual super-sensor. This is also where AI at the edge shows its value. Fusing and calibrating data in real time reduces hardware complexity and simplifies decision-making for higher-level AI modules.

AI at the edge in practice

At my own company, Curium, we utilised AI at the edge not to create flashy features but to enable real-time sensor fusion and calibration.

This capability could in the future help companies like Aurora, Kodiak Robotics, Zoox, Waymo etc to keep their fleets of vehicles safely on the road even when their sensors are affected by debris, vibration, or heat throughout a typical day. When a sensor drifts, our AI algorithms detect the issue and bring it back into safe operating parameters instantly.

This is the hidden side of AI. It is not about chatbots or voice assistants. It is the routine work of checking cameras, LiDAR, radar, and IMUs frame after frame. It ensures that what is in view is where it should be and corrects when it is not. This is the deepest kind of deep tech. It does not make for flashy videos on YouTube. It rarely registers in public perception, but it does create the clean data environment that all other systems depend on.

Also Read: AI in Southeast Asia: The silent force powering today and the engine for tomorrow’s growth

Beyond autonomous vehicles

The power of AI at the edge extends well beyond cars and trucks.

• Smart cities: Crowd analytics systems use edge AI to track flows of people in real time. Instead of sending every frame to the cloud, AI interprets the scene locally. This preserves privacy while still enabling insights like congestion alerts or evacuation planning.
• Healthcare: Portable imaging devices and bedside monitors now embed AI directly on the device. Critical alerts, such as a patient’s oxygen level dropping or a fall being detected, are raised immediately without waiting for cloud connectivity.
• Manufacturing: Edge AI keeps factories running safely. By fusing data from vibration sensors, cameras, and temperature gauges, it can detect when a machine drifts out of alignment and trigger corrections before defective products are produced or systems fail.

In all these domains, the theme is consistent. Edge AI adds resilience. It checks that things are where they should be, validates that signals make sense, and makes the adjustments needed when they do not.

Raising the benchmarks

The benchmark is clear. Autonomous vehicles must be safer than the average human driver. Not perfect, but measurably better. The same standard applies in other industries. AI at the edge needs to consistently outperform what humans alone can achieve.  We also know that public expectations are a little unforgiving.  Should one Autonomous Vehicle get into an accident and it’s a major splash across all the media outlets, denting public perception on the safety and reliability of such systems.

The power of complementary senses

In automotive use cases, cameras, radar, and LiDAR working together provide scale and robustness. The result is resilient systems that can operate in real-world conditions.

In safety-critical applications, the question is not which sensor “wins.” The measure is how well the vehicle orchestrates all sensors. Success comes from leveraging redundancy and complementary sensors to meet the benchmark of safety.

The hidden value of AI

This, to me, is the real story of AI at the edge. It is not the big, flashy demos that make headlines. It is the quiet, practical work of keeping things safe, resilient, and reliable.

AI at the edge does not need to talk back like a large language model. It does not need to generate images or text. It needs to sustain the heavy lifting that humans can’t maintain, that of: Constant calibration. Continuous anomaly detection. Intervention before failure.

This is the kind of AI that scales silently in the background. It builds trust. It enables services that touch millions of people without them ever noticing.

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Just three to four years ago, startup success in Southeast Asia was synonymous with aggressive expansion, sky-high burn rates, and a singular obsession with scale. ‘Growth at any cost’ was the prevailing mantra—fuelled by abundant capital, wide-open markets, and investor appetite for hypergrowth over sustainability.

Driven by FOMO, both foreign and domestic investors poured funds into startups, pushing valuations to unsustainable heights and giving rise to a wave of regional unicorns.

Then came COVID-19, which brought the global economy—and the startup boom—crashing to a halt. Hospitality and tourism, two of the most affected sectors, saw widespread closures and pivots. Startups were forced to reckon with reality, and the once-celebrated blitzscaling playbook lost its edge.

By the time recovery was underway, investor sentiment had fundamentally shifted. The days of funding loss-making ventures purely on potential were over. In this new landscape, profitability—and a clear path to it—became the litmus test for investment. Reckless capital deployment gave way to strategic restraint.

At RedDoorz, we were not immune to the shockwaves. But through grit, focus, and a willingness to adapt, we weathered the storm—and emerged stronger. In 2024, after years of sustained effort, we achieved our first year of positive adjusted earnings. This wasn’t luck. It was the result of deliberate choices and a mindset shift from chasing scale to building staying power.

Profit vs purpose: Can Southeast Asia’s startups strike a balance?

Startups have always been powered by vision: disrupting the status quo, empowering users, and bridging gaps in access and convenience. That sense of purpose is still critical—but in today’s environment, it must be anchored by financial discipline.

As interest rates surged and investor caution rose, the ‘growth at all costs’ philosophy lost its shine. For many founders, this meant going back to basics—focusing on core markets, doubling down on what worked, and shedding what didn’t.

At RedDoorz, we made bold yet necessary decisions to sharpen our focus. We doubled down on the high-potential, underserved markets of Indonesia and the Philippines—together accounting for 95 per cent of our revenue in 2023. At the same time, we exited slower-growth markets like Singapore and Vietnam, and divested KoolKost, our long-stay accommodation arm, selling it to Malaysia-based LiveIn earlier this year.

Also Read: 5 common mistakes startups make when building their brand identity (and how to fix them)

These were not easy choices, but they were purposeful. And they allowed us to simplify, concentrate our resources, and cross the critical threshold into profitability.

Choosing depth over breadth

In hospitality, a crowded and competitive sector, our edge lies in how deeply embedded we are in our core markets. Since 2015 in Indonesia and 2018 in the Philippines, we’ve built meaningful relationships with local hotel partners, strengthened our brand presence, and delivered real value through technology and customer loyalty.

Even with a tighter geographical footprint, we grew revenue by 14 per cent in 2024—nearly 20 per cent in local currency in our core markets alone. For 2025, we’re aiming for 30–40 per cent growth, with a revenue target of US$36M million.

We’re also evolving with our customers. Many of those who first stayed with us early in their careers now seek more premium experiences. We’re growing with them through our lifestyle brand Sans and villa offering Lavana.

The automation advantage

Over the past few years, we’ve invested heavily in automating repetitive processes. Tasks like customer support and room allocation are already handled without human input. We’re now expanding automation to include check-ins, checkouts, and payments.

This isn’t about replacing people—it’s about future-proofing our operations. In a low-margin industry like hospitality, automation isn’t just a nice-to-have; it’s a strategic imperative. It enables us to scale efficiently, improve margins, and deliver consistent quality at every touchpoint.

Also Read: 3 stages of marketing for your startup that can drive effective results

IPO? We’re playing the long game

We’ve been exploring a potential IPO since 2019, and while it remains on the table, we’re not in a rush. Our priority is building a profitable, resilient business that can thrive in any market cycle.

That said, we remain open to M&A opportunities—whether to re-enter past markets or expand into new ones. We’re also closely monitoring other regional IPOs. With several larger players ahead in the queue, patience and timing will be key.

The new era of Southeast Asia’s startup ecosystem

Southeast Asia is at a turning point. The region’s startup ecosystem is maturing, and with that maturity comes a new set of expectations: discipline, clarity, and a genuine path to profitability.

At RedDoorz, our evolution from breakneck growth to sustainable scale mirrors the broader transformation underway. We’ve learned that success doesn’t lie in chasing every opportunity—it lies in making the right choices, executing with precision, and staying focused on long-term value.

There’s no one-size-fits-all formula for building a great company in Southeast Asia. But one principle is clear: smart growth is sustainable growth. As the dust settles and a new era begins, the startups that can harmonise purpose with profit—and balance short-term agility with long-term vision—will not only endure but define the region’s next chapter of innovation.

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A new report from Velocity Ventures, titled Innovation & Deal Flow Report 1Q2025 [Travel Services], paints a vibrant picture of the global travel services industry, particularly emphasising the opportunities and trends relevant to Southeast Asia.

According to the report, the current travel services market displays “extremely optimistic growth potential”. Notably, data protection, space travel, and social commerce are key growth areas.

This optimism is further underscored by the top three highest compound annual growth rate (CAGR) areas: data security & privacy (35.5 per cent), space tourism (31.6 per cent), and social commerce (31.7 per cent).

Also Read: Future-proofing hotels to stay ahead of the curve

Interestingly, the report highlights a growing emphasis on safety technologies for travellers, coupled with a consumer preference for data protection. This confluence suggests that travellers are increasingly mindful of their well-being and personal information when venturing abroad, presenting a potential avenue for innovative solutions in the Southeast Asian tech landscape. The report notes, “travellers are becoming increasingly cautious of their own safety when travelling, and this can be an opportunity to capitalise on”.

Furthermore, the sustained popularity of solo travelling is triggering a new wave of travel services tailored to this demographic, including specialised accommodations, connection platforms, and forums. This trend could have significant implications for startups in Southeast Asia, a region known for its diverse solo travel destinations.

However, not all areas are experiencing the same upward trajectory. The report pinpoints the top three decreases in CAGR as artificial intelligence (-18.1 per cent), influencer marketing (-13.6 per cent), and voice-based digital assistance (-2.5 per cent). While AI is still prevalent in travel tech, this decrease in CAGR might suggest a shift in its application or a recalibration of its immediate growth expectations.

The Velocity Ventures report observes a “growing emergence of personalisation and engagement in the travel industry, with consumers seeking tailored experiences.” The report also emphasises that “automation is becoming essential for driving efficiency, scalability and cost reduction”, themes that resonate strongly within the tech startup ecosystem of Southeast Asia.

The report also highlights several companies, including TravelPerk, a Spanish business travel management platform that raised US$200 million in a Series E round in January 2025 with notable investors like Atomico and EQT. Another highlighted company is Paytrack, a Brazilian software developer automating travel and expense management, which secured US$42 million in a Series B funding round in the same month with Riverwood Capital participating. While these companies are not based in Southeast Asia, their significant funding rounds indicate the continued investor appetite in the broader travel tech space.

Also Read: Navigating the relationship between ChatGPT and the travel industry

In terms of recent global VC activity, the report mentions K2 Space, a US-based developer of satellite buses, which raised US$110 million in February 2025. Another US company, Doifoo, developing an AI-powered travel ID, raised a pre-seed round in March 2025.

Interestingly, the report also details activity closer to home for Southeast Asian observers. A Malaysian startup developing VR sales videos for hotels secured US$1 million in a pre-seed round in February 2025, valuing the company at US$22 million pre-money.

Additionally, a Singaporean company offering real-time, self-updating digital twins for mapping systems raised US$2.5 million in a seed round in March 2025, with a pre-money valuation of US$30 million.

These deals underscore the burgeoning innovation within the region’s travel tech sector.

Velocity Ventures also highlights two proprietary deals in their pipeline: Project S25 and Project S26.

Project S25 focuses on AI-powered personalised audio tours, offering a scalable alternative to traditional guided tours. The company claims to have achieved significant cost and efficiency gains by eliminating supplier royalties and is currently fundraising US$1.5 million at a US$12.5 million pre-money valuation. The strategic rationale includes its unique AI-powered personalisation, strategic distribution partnerships, and ongoing innovation through user feedback.

Project S26 centres around spatial twin technology, providing real-time, self-updating digital mapping solutions. This platform aims to enhance operational efficiency by allowing organisations to manage their own maps and unlock new revenue streams through spatial advertising. It has diverse applications, including aviation and the MICE industries, and has even “Executed proof of concept and secured tender with Changi Airport”. The company is fundraising US$2.5 million with a US$30 million cap.

In conclusion, Velocity Ventures’ Innovation & Deal Flow Report 1Q2025 [Travel Services] offers a compelling snapshot of a dynamic, fast-evolving global travel services sector—with Southeast Asia emerging as a region ripe for disruption. With strong investor appetite, rising demand for data security, personalised experiences, and the advent of frontier technologies like spatial twins and AI-powered tours, the region’s startups are well-positioned to ride this wave of innovation.

While certain technologies, such as AI and influencer marketing, are seeing a recalibration in growth expectations, the overall outlook remains decidedly upbeat. For investors, entrepreneurs, and stakeholders in the Southeast Asian travel tech space, the message is clear: the next frontier of travel innovation is already taking shape—and the region is poised to play a leading role.

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There’s been a flurry of news lately pointing to potentially better days ahead for hotels in Singapore. Allowing non-fully vaccinated travellers entering the city-state to skip quarantine, reopening Changi Airport’s Terminal 4 after two years of hibernation, and starting work on a fifth air terminal that will not only be one of the largest of its kind in the world but also one that’s pandemic-proof, are among the things that ought to keep hotels busy for some time to come.

Hotels are already back on their feet, with visitor arrivals to Singapore rising as borders reopen. Occupancies and room rates are at pre-COVID-19 levels, and the momentum is likely to be sustained as the country expects to receive four to 6 million visitors in 2022, compared to about 2.2 million so far this year.

Still, the heart-wrenching experience of the last two years and longer-term issues such as climate change should serve to remind hotels that they need to better align themselves with the times and even change how certain things are done to raise their game.

Doing even more with less, given the perennial shortage of workers, navigating disruptions to global supply chains, and staying up to speed with sustainability developments and practices, are a few themes hotels have to get a handle on even as business is recovering.

Doing more with less

Avoiding unnecessary physical contact has been ingrained in most people’s minds during the pandemic. Contactless payment systems, pre-arrival online surveys and virtual concierges are a few tools already in place in many hotels even before the onset of COVID-19. But there’s still room for contact-free applications for other routine tasks.

Also Read: How a hospitality career helped me jump into tech

Self-check-in, for instance, is still not common practice in Singapore. One reason for this has to do with security, as hotels want to make sure they don’t end up housing unwanted guests. But with technologies enabling secure and seamless self-check-in already available, guests should be able to do without face-to-face interactions and queues at the front desk.

Checking in can be done even before arrival as a guest can simply punch in the relevant information using a smartphone with an app or portal linked to the hotel. Hotels can take this further by issuing digital keys for rooms instead of physical ones. This would enable guests to simply head straight to their rooms upon arrival.

Besides convenience for guests, self-check-ins can help hotels save on manpower costs. Human resources will also be optimised with workers being freed up to take on more productive and interesting roles, which hopefully will help with staff retention. These are outcomes that any accommodation provider will welcome in today’s tight and increasingly expensive labour market.

Using robots for run-of-the-mill tasks such as baggage handling and food delivery can be another option. The economics must, of course, make sense as the initial outlay for these machines can be substantial, depending on the hotel’s requirements.

Supply chains and sustainability

More than two years into the pandemic, and with the Russia-Ukraine conflict still raging, disruptions to supply chains and the resultant surge in food costs continue to be felt worldwide. Food security, among other things, has become a foremost concern for many countries.

To reduce reliance on imports, Singapore seeks to have 30 per cent of its nutritional requirements met by 2030 through locally and sustainably produced foods. Many companies are rising to the challenge by developing new food solutions, including alternative proteins.

These mainly plant-based alternatives are becoming popular among consumers who are mindful of the environmental challenges linked to traditional meat farming and production. On their part, hotels can consider featuring more alternative proteins on their menus to support food sustainability.

Also Read: The data revolution: Innovation and evolution in APAC’s hospitality industry

With climate change becoming an increasing threat, mitigating emissions and reducing wastage should also be priorities for hotels. Equipping rooms with smart thermostats that automatically adjust the temperature to a pre-set, environmentally-friendly level when no one is around is one such hack.

It’s also time for hotels to ditch or reduce the use of bottled water. Making water dispensers readily available and giving every guest a glass bottle for refills will go some way in reducing plastic usage.

Even blockchain has been touted to be of help to hotels. Blockchain advocates argue that the technology enables, for example, the monitoring of wines from the time of production in a winery to the time distributors get hold of the final product and deliver the bottles to the hotel.

With every transaction recorded and available for viewing on the blockchain, they claim that counterfeiting of expensive wines can be avoided. At the same time, any delays in production or shipment can be grounds for the hotel to source alternative supplies. Promising as it sounds, blockchain adoption is still in its infancy in the hospitality industry.

Hotels in Singapore should leave no stone unturned in seeking to up their game in today’s increasingly challenging operating environment. Some ideas may seem radical or conceptual, but it’s never too late to start future-proofing against present and future threats.

Even if the initial outlay in some cases may not be small, that may be a price hotels must pay to give guests what they want and get them to keep coming back.

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AI is no longer just a buzzword for global tech giants. It is already part of the daily work of small and medium enterprises (SMEs) across Southeast Asia. Rising costs, lean teams and demanding customers are pushing businesses to rethink how they operate, and AI is quickly becoming part of the solution. The real question is how SMEs can use it in ways that create long-term benefits.

AI brings plenty of opportunity, but it also exposes gaps in skills, governance and trust. The best way to see this mix of progress and challenges is through the day-to-day stories of SMEs in the region.

The everyday frontlines: Orders, customers, and cash flow

Take a bubble tea shop in Singapore for example. The staff used to spend hours each week chasing suppliers over WhatsApp and checking invoices by hand. It was stressful, and mistakes slipped through. After bringing in an AI agent, purchase orders and invoices were matched automatically. Errors dropped, and the team had more time to serve customers during peak hours.

Another jewellery store is also using AI to ease the load. With just one person handling marketing, keeping up with Instagram and TikTok quickly became too much. An AI assistant now drafts captions, analyses engagement and suggests posting times. The founder jokes that it feels like having “a junior marketer who never sleeps,” though they still step in to keep the brand authentic.

These are not far-off case studies. They are real examples of how AI is already changing day-to-day work for SMEs in the region.

Why Southeast Asia’s context is different

SMEs make up 97 per cent of all businesses and employ about 67 per cent of the workforce in Southeast Asia. But adoption still lags behind larger companies. According to the Infocomm Media Development Authority’s Singapore Digital Economy Report 2024, only 4.2 per cent of SMEs had adopted AI in 2023, compared with 44 per cent of large companies.

Many SMEs work with tight budgets, lean teams and patchy infrastructure. That is why they often turn to low-code tools, external platforms and trusted partners instead of building everything in-house. Surveys show that more than three-quarters of SMEs in APAC are already using AI-enabled digital tools, although overall adoption remains modest in markets like Singapore.

Support schemes such as Singapore’s SkillsFuture Mentorship Support Grant and Malaysia’s SME digitalisation initiatives are important, but the real challenge is making sure they lead to lasting change rather than short-term pilots.

Also Read: AI for everyone: 25 tools to automate, create, and innovate

The next frontier: Agentic AI for SMEs

Most SMEs begin with simple, task-based AI such as automating invoices, drafting marketing copy or keeping an eye on dashboards. The next step is agentic AI, systems that can break down tasks, adapt as new information comes in and keep processes moving without constant supervision.

Think about a point-of-sale (POS) system that flags when stock is running low. Instead of stopping there, the AI places an order with the supplier, arranges delivery, updates loyalty offers to help move inventory and sends a report to the manager. Each action is connected, with the system adjusting in real time. That is the shift from AI as a helper to AI as a true partner in the business.

A case study in collaboration

One way AI adoption in Southeast Asia is taking shape is through partnerships. In Singapore, companies such as Morpheus Labs have worked with partners like Craveva, each contributing different capabilities. The chart illustrates how these pieces connect. Point-of-sale systems, CRM and loyalty platforms, supplier ordering tools and workflow technology are integrated so they operate together rather than in silos.

Here’s what that looks like in practice. A low-stock alert at the POS can trigger a supplier order, update loyalty points and generate a report for the manager without extra back-and-forth. What used to be separate tasks now flow as a single process, with AI keeping everything in sync.

For SMEs, that means less manual checking and fewer mistakes, along with more time for small teams to spend with their customers. At the ecosystem level, it highlights how local firms are experimenting with connections across different technologies to bring AI into everyday operations.

Also Read: Transparency, accuracy and validation key to building Singapore consumers’ trust in AI agents: Report

Guardrails still matter

When AI gets more autonomy, the responsibility goes up too. Agentic AI needs access to sensitive sales and customer data, which makes privacy, fairness and accountability even more important. For SMEs without compliance teams, these risks are not abstract, they are real. That is why adoption has to come with safeguards, training and clear lines of responsibility. Efficiency gains should never come at the expense of trust.

From experiments to everyday use

The next step for SMEs is not adopting AI for novelty but embedding it into workflows that scale. 

In APAC, three shifts are already visible:

• AI for efficiency and margins

Companies in APAC are beginning to use AI to strengthen demand forecasting and capacity planning. Kearney notes that AI-driven forecasting can reduce waste, optimise inventory, and support healthier margins. For SMEs in retail and F&B, these gains are especially relevant, since tighter operations directly improve profitability.

• AI for language inclusivity

Language diversity is a constant challenge across APAC. New initiatives such as AI Singapore’s SEA-LION models, trained in languages like Bahasa Indonesia, Thai and Vietnamese, are making AI tools more relevant and accessible to the region’s businesses.

• AI as a growth multiplier

For SMEs in APAC, AI is not just about cutting costs. A Deloitte–Meta study found that more than 75 per cent of SMEs in six APAC markets are already using AI-enabled digital tools. Among them, 80 per cent reported lower costs, and 73 per cent said AI helps them compete with larger firms, opening up new opportunities for growth.

Also Read: Navigating fundraising: Recognising objections vs rejections

A call to the ecosystem

It is easy to see the lesson from Southeast Asia. SMEs cannot make this journey alone. They need support from governments, investors and technology providers, whether that is mentorship, safeguards or simply a community to learn from.

The future of AI in the region will not come only from billion-dollar companies. It will also come from the bubble tea shop that automates supplier orders, the retailer that finds new ways to talk to customers and the family business that goes online to reach more buyers. What they do today will set the example for responsible adoption tomorrow.

The road forward

AI is already part of how many SMEs in Southeast Asia work day to day. The real challenge now is helping them move from small, scattered experiments to adoption that is more strategic and long term.

If that happens, the story of AI in our region will not just be about technology. It will also be about resilience, inclusion and growth for the millions of people who rely on SMEs every day.

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AI assistants like ChatGPT have quickly become part of a developer’s daily toolkit. Need to clean up a function? Ask AI. Want to check why that query isn’t running? Ask AI. It feels fast, easy, and harmless.

But what if the code they paste belongs to your company?

That simple act of copying and pasting could raise big questions about privacy, security, and intellectual property. Let’s break it down in a straightforward way.

Where does the code go?

When a developer pastes code into ChatGPT, the data is sent to the AI provider’s servers to generate a response. By default, this means the code leaves the safe walls of your company’s systems and enters a third-party environment.

While many AI tools have strict privacy policies, you can’t always be sure how data will be stored, processed, or used for model training. That’s why organisations need to think carefully about what information is shared.

The risks of pasting code

Pasting code may feel like asking a colleague for help, but in reality, it carries risks:

• Intellectual property exposure: Proprietary algorithms, workflows, or trade secrets could unintentionally leave your company’s control.
• Compliance issues: If your business operates under strict regulations (such as GDPR), pasting code with sensitive data could cause breaches.
• Security leaks: Code often contains hidden keys, tokens, or configurations. Sharing them publicly, even by mistake, creates a risk of misuse.

Why developers do it anyway

From the developer’s perspective, it feels practical. AI can:

• Suggest cleaner, faster code.
• Explain bugs in plain language.
• Speed up learning of new frameworks.

In a fast-moving project, AI feels like an instant productivity boost. The challenge is balancing speed with security.

Also Read: Preparing your cybersecurity strategy for 2025: Adapting to the rise of AI

Safer ways to use AI with code

The good news is that you don’t have to ban AI completely. With the right approach, developers can enjoy AI assistance without putting company assets at risk:

• Use enterprise AI plans: Many providers offer business-grade versions with stricter data handling and no training on your inputs.
• Mask sensitive details: Remove tokens, credentials, or unique business logic before pasting.
• Adopt internal AI tools: Some companies deploy self-hosted or private AI assistants so code never leaves the organisation.
• Create clear policies: Make sure your developers know what’s acceptable to share and what isn’t.

Final thoughts

AI is here to stay in software development, but like any tool, it comes with responsibility.

If a developer pastes company code into ChatGPT, it might speed up debugging, but it could also expose valuable data. The safest path forward isn’t to stop using AI, but to guide how it’s used. Clear policies, the right tools, and awareness across teams will ensure your business gets the best of AI without the hidden risks.

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