At Techsauce Global Summit 2026, the FoodTech Summit by SPACE-F brought together policymakers, researchers, global food companies, investors and founders to examine one shared challenge: how can promising food innovation move beyond the laboratory and create measurable impact in the market?

The discussions ranged from Thailand’s national FoodTech direction and the rise of the Wellness Economy to consumer-led product development, startup investment, industrial scale-up and the technologies presented by SPACE-F Batch 7. Together, the sessions offered a practical view of what the future of food requires, and why collaboration is becoming as important as the technology itself.

What is SPACE-F?

SPACE-F is Thailand’s first global FoodTech startup incubator and accelerator. Founded through the collaboration of the National Innovation Agency (NIA), Thai Union Group and Mahidol University, with partners including Thai Beverage, Nestlé and Foodland Ventures, the equity-free programme connects startups with researchers, corporations, investors, mentors and industrial infrastructure.

Since its launch, SPACE-F has supported more than 100 startups from 18 countries. The programme is built around a practical objective: helping founders turn promising science and prototypes into products that can be tested, manufactured, regulated and commercialised in real markets.

What does FoodTech mean today?

FoodTech is the application of science, engineering, biotechnology and digital technology across the food system, from agriculture and ingredient development to manufacturing, safety, logistics, retail and consumer experience.

The sessions at Techsauce Global Summit 2026 showed why the sector requires more than a strong invention. Food is physical, highly regulated and deeply cultural. A solution must work in the laboratory, survive factory-scale production, meet safety and regulatory requirements, make economic sense and, ultimately, taste good enough for consumers to choose it again.

The 2026 theme: Wellness, resilience and the path to market

SPACE-F Batch 7 brought the programme’s long-standing mission, “Feeding the World Through Innovation,” into the context of the Wellness Economy. The central question was how Thailand can build on its strengths in food production, biodiversity, research and culinary culture to create higher-value innovations for health, sustainability and food security.

Across the programme, one message appeared repeatedly: the next generation of FoodTech will be judged by evidence of demand, viable unit economics, regulatory readiness and the ability to scale through partnerships. Innovation matters, but commercial execution determines whether it reaches consumers.

Key Takeaways from the Sessions

Thailand’s FoodTech Blueprint: From Kitchen of the World to Innovation Hub of Asia

Dr. Krithpaka Boonfueng, Executive Director of NIA, positioned Thailand’s next opportunity as a shift from exporting raw materials and processed food to exporting higher-value solutions. SPACE-F provides a working model by bringing government, academia, corporations and investors into the same process, giving startups access to real customers, facilities and feedback. Batch 7 attracted 204 applications from 57 countries, reflecting Thailand’s growing role as a launchpad for global FoodTech innovation.

FoodTech as National Policy: The Wellness Economy and Thailand’s Next Economic Engine

Dr. Sriyada Palimaphan, Advisor to the Minister of Higher Education, Science, Research and Innovation, connected FoodTech with three national priorities: better health, food security and economic growth. Thailand already has agricultural resources, biodiversity and a strong export base. The opportunity is to combine these assets with science, technology and entrepreneurial speed to move research out of the laboratory and into high-value products for health-conscious consumers.

Consumer-Centric Innovation to Impact: Startup Agility Meets FMCG Scale

Dr. Allan Lim, Head of Open Innovation at Nestlé R&D Center Singapore, stressed that food innovation must begin with consumer needs rather than a technology searching for a use case. Taste remains the first test, followed by nutrition, sustainability, convenience and accessibility. Scaling then adds another layer of difficulty, including raw-material supply, quality systems, shelf life, regulation and production economics. Startups bring agility and differentiated technology, while established food companies can contribute infrastructure, market access and manufacturing experience.

Navigating the Next Wave of Food Innovation

Dr. Tunyawat Kasemsuwan described a FoodTech investment market that has moved away from capital-intensive promises and towards disciplined commercial progress. Founders need to understand unit economics, generate early revenue where possible and consider modular paths to market. Partnerships with corporations, contract manufacturers and other players in the value chain can preserve capital and reduce scale-up risk. His core advice was clear: do not build everything alone, and approach partners with a precise understanding of the value the startup can create for them.

SPACE-F Batch 7 Accelerator Demo Day

Nine Accelerator Track startups demonstrated how broad FoodTech has become:

• ComexSoft uses AI-powered retail intelligence to analyse products, pricing and market trends.
• PROTINOS develops high-protein noodles designed for preventive nutrition and muscle health.
• KINAVA converts organic waste into bioenergy and higher-value materials through hydrothermal carbonisation.
• BeNatureBioLab applies nano and microencapsulation to improve the taste, solubility and absorption of bioactive ingredients.
• Terra Bioindustries upcycles brewers’ spent grain into protein, fibre, malt extract and other food ingredients.
• Kresko RNAtech develops stable, ready-to-formulate bioactive RNA ingredients for wellness applications.
• SicPama connects restaurant ordering and customer relationship data to identify diners who influence repeat visits and group spending.
• NUCAPS uses protein-based microencapsulation to improve the stability and absorption of functional ingredients.
• AGRIFREEZE uses electromagnetic-field freezing technology to reduce food damage and preserve quality closer to fresh products.

The cohort showed that FoodTech now extends beyond new foods. It includes ingredient science, preventive nutrition, circular production, food preservation, market intelligence and digital consumer services. AGRIFREEZE received the Feeding the Future Award in the Accelerator Track, while PROTINOS won the People’s Choice Award.

From Proof of Concept to Market: What SPACE-F Partners Are Actually Building with Startups

Chris Aurand, PhD, Rahul Colaco, Victor Chen and Dr. Allan Lim, moderated by Sean Mak, explained that one successful laboratory test is rarely enough. Commercialisation usually requires a series of proof-of-concept stages covering technical performance, product integration, quality, regulation, factory trials and consumer response.

The panel identified several conditions that improve the chance of success: a clear business need, an internal corporate champion, founders who listen and adapt, evidence of demand, realistic cost and volume assumptions, and a controlled sandbox where both sides can test and learn. Many promising technologies stall because of regulation, cost or scale rather than scientific failure. Strong collaboration must therefore include clear ownership, patience and a shared path to deployment.

Programme Recap and Awards Ceremony

Assoc. Prof. Dr. Pasit Pakawatpanurut of Mahidol University closed the programme by describing the particular difficulty of scaling FoodTech. Software can be updated instantly, but food innovation must operate within biology, chemistry, thermodynamics, supply chains, regulation and human taste.

Over five months, Batch 7 founders tested more than their technologies. They examined unit economics, intellectual property, fundraising structures, Thai business culture and industrial production. Factory visits, business matching, proof-of-concept discussions and partner negotiations turned the accelerator into preparation for the conditions startups will face after Demo Day.

The Combined Takeaway

The FoodTech Summit by SPACE-F presented Thailand’s opportunity in practical terms. The country has the ingredients, production base, research capacity and culinary credibility to become a regional FoodTech hub. Capturing that opportunity depends on connecting those strengths to founders who can solve real problems and partners who can help them validate, manufacture and enter the market.

The next wave of FoodTech will not be defined by novelty alone. The strongest solutions will combine consumer relevance, scientific evidence, commercial discipline and collaboration. That is the role SPACE-F is building: a launchpad where innovation is tested against the realities of the food industry before it scales to the world.

Follow SPACE-F for programme updates, startup stories and future opportunities:

• Facebook: SPACE-F
• LinkedIn: SPACE-F
• Website: www.space-f.co

At HUAWEI CONNECT 2026 in Shanghai, the Techsauce team had the opportunity to attend and report directly on global tech breakthroughs and innovation trends. During the opening keynote, David Wang, Deputy Chairman of the Board and Rotating Chairman at Huawei, took the stage to share his vision under the theme "Advancing the Agentic World, Building a Solid Silicon Foundation." He outlined a clear strategy focused on advancing AI infrastructure to lay a solid silicon foundation capable of supporting the full-fledged era of Agentic AI.

Wang pointed out that AI brains are advancing faster than any technological revolution in human history. Foundation model parameters are projected to surpass 100 trillion by 2030, a scale roughly equivalent to the number of synapses in the human brain.

In China alone, daily inference token consumption has already topped 500 trillion, with expectations to skyrocket into quintillions, or hundreds of millions of billions, by 2030. As a result, traditional IT infrastructure is hitting a wall, making a complete architectural overhaul necessary.

Summary of 7 Key Strategies: How Huawei is Building a Silicon Foundation to Drive the AI Era

To tackle the challenges of the Agentic AI era, Huawei has mapped out seven strategic pillars.

1. Focusing on Computing Power Driving compute power development as the core priority while maximizing real-world value from hardware.
2. SuperPoDs & SuperClusters Architecture Moving beyond individual card specs to focus on system-level innovations through SuperPoDs and SuperClusters, offering a brand-new choice for the world.
3. Open Ecosystem Support Supporting an open ecosystem by enabling native training on Huawei’s architecture for mainstream global AI models.
4. Product Intelligence Utilizing Pangu models to boost intelligence across Huawei's entire product line.
5. Flexible Compute Deployment Delivering versatile computing options, available both on-premises and via Cloud, to accelerate digital transformation across industries.
6. Ubiquitous All-Scenario Compute Distributing compute chips of all form factors, from micro-units to supercomputers, to embed AI into smartphones, autonomous vehicles, and IoT devices.
7. Next-Gen Communications Network Building next-generation communication networks to deliver seamless computing power to every person, home, and enterprise.

Why are SuperPoDs the Ultimate Answer for Massive-Scale AI Buildouts?

Imagine a data center packed with 100,000 AI chips. The biggest hurdle isn't how fast an individual chip runs, it’s how well those chips talk to each other.

In legacy server architectures, connecting massive arrays of chips leads to significant time wasted on inter-device data transfers, forfeiting over 40% of actual processing power. Put simply, it’s like bringing 100,000 brilliant minds together, only for them to waste half the day emailing back and forth, dragging down overall progress.

Huawei addresses this bottleneck with the SuperPoD concept by linking large clusters of processing chips over ultra-high-speed interconnects. Instead of chips operating in silos, the system treats the entire cluster as one cohesive, giant logical computer.

Huawei’s test results reveal that adopting SuperPoDs in large-scale setups dramatically slashes data transfer latency and boosts real-world processing efficiency (MFU) by up to 2.75 times compared to traditional server setups.

World First: Unveiling the Atlas 960E SuperPoD Powered by Optical Data Interconnects (NPO)

One of the highlights that sparked immense interest among the Techsauce team and event attendees was the debut of the Atlas 960E SuperPoD, the industry's first supercomputer to commercially implement Near-Packaged Optics (NPO).

To put it simply, standard computers transmit data through electrical copper wires. When handling massive data flows, copper wires overheat, slow down, and draw immense power. Huawei bypassed this by positioning optical engines directly alongside the processor chips, using light instead of electricity for data transmission, sharply increasing speeds while dramatically cutting down heat generation.

• Hi-ONE Engine Serving as the core optical converter, Hi-ONE is the world's first mass-production-ready optical engine featuring a built-in light source. It boasts a blistering transmission throughput of 7.2 Terabits per second per unit.
• Atlas 960E SuperPoD Specifications A single SuperPoD rack supports up to 4,096 NPU cards, delivering a staggering 8 EFLOPS of compute performance with 1 Petabyte of total High-Bandwidth Memory (HBM).
• Unmatched Efficiency and Durability Replacing traditional cabling with 5,500 Hi-ONE units eliminates the need for 48,000 legacy optical modules. This slashes power consumption by over 550 kW, equivalent to the power used by hundreds of households. Furthermore, it doubles system fault-free operating time (MTBF) and delivers an outstanding 99.8% system availability.
• Ascend Chip Roadmap (Annual Upgrades Confirmed) Ascend 960DT is a high-performance chip scheduled for delivery in Q1 2027, 9 months ahead of schedule. Ascend 960PR will follow closely with deliveries in Q3 2027, 3 months ahead of schedule. Ascend 970 and 980 are planned for launch in 2028 and 2029, respectively, targeting a 2x performance boost every year.

Introducing Peerium: The Next-Gen Architecture Enabling 1 Million Chips to Function as a Single Machine

Beyond new hardware debuts, Huawei unveiled the Peerium Computing Architecture, a next-generation computing framework designed specifically to shatter AI bottlenecks.

Traditional computing frameworks rely on a Master-Slave hierarchy where a central master node controls subordinate slave nodes, frequently causing bottlenecks under heavy AI workloads. Peerium scraps this model entirely in favor of a peer-to-peer interconnect architecture where all chips communicate as equals. This enables system orchestrations across a massive scale of 1,000,000 processing chips, making them function seamlessly as one giant supercomputer.

The critical backbone behind Peerium’s smooth execution is UnifiedBus (UB), a new high-speed data highway connecting CPUs, AI chips (NPUs), memory, and SSD storage using a single, unified protocol. By removing protocol conversion delays, data flows freely without interruption.

Complemented by the upgraded TaiShan 950 SuperPoD for general-purpose compute, the system pools 256 TB of shared memory, accelerating AI Sandbox deployment speeds by 30x and doubling Vector Search efficiency. Working alongside it is OceanStor M900, a context memory storage cluster for AI inference that uses Single-Hop Direct Access to streamline data transfers from five steps down to just one, halving latency and extending SSD lifespan by up to 16x.

Committed to Open Source: Democratizing AI for Every Person and Device

David Wang emphasized on stage that "no single company can sustain the AI world alone," highlighting Huawei's determination to foster an open, collaborative ecosystem.

This open approach is evident in the Kunpeng ecosystem, which now boasts over 4.16 million developers. Meanwhile, the openEuler operating system has secured the top market share in China’s server OS market with over 20 million installations. Notably, external developers now account for 61% of Huawei's CANN architecture ecosystem, surpassing internal developers for the first time. CANN has also earned global recognition, becoming officially downloadable as a supported accelerator backend on the PyTorch platform.

Looking ahead, Huawei is expanding AI to end-user devices by building specialized compute platforms across four key everyday scenarios: smartphones, PCs, vehicles, and smart homes. By combining Kirin and Ascend chips, Huawei is revamping HarmonyOS into a fully AI-native operating system. In parallel, heavy investments into the Qiankun intelligent driving system aim to transition the automotive industry from assisted driving toward full L4 Autonomous Driving by 2030.

All of these will be connected via next-gen networks like 5G-A/6G and 10-Gigabit optical networks, removing data silos and turning AI compute into an accessible, safe, and utility-like resource for everyone, everywhere.

Key Takeaways from Techsauce

Attending HUAWEI CONNECT 2026 made one thing abundantly clear: the next frontier of AI isn't just about who builds the fastest standalone chip. Instead, it hinges on System Architecture, the ability to connect and orchestrate millions of chips efficiently, sustainably, and seamlessly deliver intelligence to end users.

From the Peerium Architecture and optical-interconnect Atlas 960E SuperPoD to a thriving open ecosystem, Huawei is laying down a solid Silicon Foundation for the Agentic AI era, a shift set to transform everything from hyperscale data centers to autonomous vehicles and daily consumer devices.

Most organizations are already running AI pilots. What separates the ones that reach production, according to Wonderful Thailand, is how the work is designed, deployed and measured.

Speaking at Techsauce Global Summit 2026 in a session titled "Everyone's Running Agent Pilots. But Few Are Ready to Scale. Here's Why.", Ariya Banomyong, President of Wonderful Thailand, opened by asking how AI deployments in Thailand are progressing from pilots to practical use. Reflecting on the recent growth of AI, he focused on the next challenge: moving from experimentation to scale. In his view, the constraint is rarely the technology itself, but how organizations are set up to design, deploy and measure it.

Lessons from earlier technology shifts in Thailand

Ariya has watched three technology cycles arrive on schedule: internet adoption in the early 2010s, e-commerce, which many businesses embraced in earnest during the pandemic years, and the shift of audiences from broadcast to online video. In each case the technology arrived as expected, and what differed was when organizations chose to act. AI, he suggested, poses the same question again.

Why do many AI pilots remain in silos instead of moving into production?

Ariya cautioned that building separate agents within each function can make scaling harder later, since the value of AI tends to appear across functions rather than within any one of them. When each department funds its own initiative and selects its own provider, the result can be several implementations that were never designed to work together.

Scope is a second consideration. Solutions built for a sandbox are rarely ready for enterprise volumes, or for the wider set of use cases a business will want to run on the same foundation. Designing for production scale and multiple use cases from the outset, then piloting within that architecture, reduces later rebuilds. The third point was measurement: AI programs work best when tied to business outcomes defined before deployment begins.

What production looks like

Ariya illustrated the point with two client deployments. In the first, an SME lending request moved from a chat message to a booking in the bank's core system, with a relationship assistant, a lending agent and a credit reviewer sharing context along the way. The final step was completed in a legacy system with no Application Programming Interface (API) available. In the second, a single assistant handled a home broadband fault from start to finish, reading the router's status lights through the phone camera rather than passing the customer between queues.

Coordinated agents, he noted, can support smoother handoffs across complex workflows: in a bank, a dedicated agent per customer that retains context, with lending and collections agents passing work between them. On risk, he reframed the discussion around how organizations can design safeguards to reduce the risk of errors. In Wonderful's architecture, an observer agent runs in the background to check that other agents operate within policy, governance and regulatory requirements.

Results and delivery

Bank Hapoalim in Israel went live within three weeks at 88% containment, meaning roughly 880 of every 1,000 calls or chats were resolved without human involvement. Case studies on Wonderful's website cite comparable results at 80% or above.

Delivery follows two principles: Wonderful's teams work directly with clients to redesign cross-functional workflows, then transfer capability to internal teams who build and manage agents on the same platform. Deployments generally take four to eight weeks, although timing depends on workflow and integration complexity, with governance, guardrails and evaluation tooling in place from the start.

Wonderful operates in more than 35 markets and recently opened in Thailand with a local team. Founded in 2025 and headquartered in Amsterdam, the company positions its product as an enterprise AI operating system, and says the platform is designed to support enterprise AI experiences across languages and markets. In September 2026 the company announced a $550 million Series C led by Insight Partners, with Salesforce joining and Index Ventures, Bessemer Venture Partners, IVP, Vine Ventures and 9Yards Capital returning, at a $5 billion valuation. In the same month it was named Official Enterprise AI Partner of FC Bayern, a three-year agreement that begins with matchday fan services such as digital ticket support.

Ariya closed on timing rather than technology. Eight weeks from now, he asked the executives in the room, will the conversation still be about possibilities, or will agents already be running in production?

Source: Session "Everyone's Running Agent Pilots. But Few Are Ready to Scale. Here's Why." at Techsauce Global Summit 2026, Wonderful, LinkedIn Wonderful, The Wall Street Journal

Along with the win comes a prize package worth over 200,000 THB and a sponsored flight for one team representative to attend Japan's Largest Venture Summit.

The finale on the Tech Showcase Stage at Techsauce Global Summit 2026 brought together nine finalist student teams from Thailand and the Philippines, empowered by CREDO Holdings, CP AXTRA, and HUMANICA.

Judges scored each team out of 50 points across five core criteria: novelty and creativity, business model, feasibility, market potential, and team presentation. Across all three challenge tracks, the panel pushed finalists on two critical operational questions: Do you have a working prototype? and Who is actually going to pay for this?

HR and AI Challenge (Set by HUMANICA)

• Top Secret (KMUTT): Pitched Ally, an AI onboarding companion that guides new hires through IT access, HR setups, and 30/60/90-day targets. Designed to integrate directly into HUMANICA's existing enterprise client base, the team cited data showing 55% of employees lack system access on day one, proposing a price of roughly 2,000 THB per employee per month for large multinational clients.
• HRsudlhor (Chulalongkorn University): Developed Know LA-OK, an institutional knowledge retention system that maps departing employees' skills and projects to internal successors and runs AI-driven exit interviews to capture tacit knowledge, fully compliant with PDPA.
• K-Clone (Mahidol University): Pitched K-Clone (Knowledge Clone), transforming senior employees' expertise and decision-making into a "living organizational brain" that acts as an interactive AI mentor for junior staff, while matching employee skills to internal projects and plugging into HUMANICA's existing HR systems. The team estimated an 82% boost in knowledge retention, 70% higher productivity, and a 24% shorter ramp-up time, priced from 10,000 THB per month.

Retail and AI Challenge (Set by CP AXTRA)

• Gen Z 67 (Chulalongkorn University, Burapha University & King Mongkut's University of Technology Thonburi): Pitched Stock Smart, an agentic AI demand-forecasting system for CP AXTRA's inventory network that forecasts demand based on festivals, promotions and other sales data to reduce stockouts and excess inventory, and rebalances stock across stores in real time with store managers involved in decision making.
• ลดแรงแซงทางโค้ง (Srinakharinwirot University): Pitched AXTRA Rescue Network. The engine matches CP AXTRA's surplus or at-risk-of-going-unsold products with B2B buyers such as restaurants and food factories by adding items to buyers' existing orders. At the same time, B2B customers can purchase the products at special prices with no additional delivery charges.
• คิดถึงบอล (King Mongkut's University of Technology Thonburi & Chulalongkorn University): Pitched OrderGenie, which simplifies and improves the accuracy of the complex purchasing process. The AI analyzes orders, product demand, inventory, budget and product shelf life, while linking them to Makro SKUs to create the most suitable shopping cart. This helps reduce planning time, minimize stockouts and product waste, and improve cost control.

Open Challenge: Business Growth Solutions

• Ctrl_A (Grand Prize Winner): Developed YARDS, a computer vision system automating scrap steel grading and pricing upon truck arrival. Tested across 100+ site trials on tens of thousands of scrap pieces, the team reported 91.7% grading accuracy (up to 23% more accurate than human graders), charging a setup fee plus 35 THB per ton processed.
• Carethia (Rangsit University & Kasetsart University): Built a caregiving marketplace matching families (focused on special needs care) with vetted, trained caregivers via scenario-based AI interviews (Cathier Loop). Caregivers keep 100% of their earnings while the platform charges families a 15% service fee. The MVP is live with 53 caregivers onboarded and 68 test users.
• NERDS 2.0 (Far Eastern University – Institute of Technology, Philippines): The sole international finalist pitched Birdseye, an IoT and AI monitoring platform for small and mid-sized poultry farms. Combining computer vision with ammonia sensors to detect wet litter and toxic gas spikes early, the system holds a Technology Readiness Level 7 validation, verified 92.9% pixel-level accuracy in field testing, and aims to cut flock mortality from 5% to 3%, with hardware priced from 17,899 to 29,499 PHP plus a software subscription.

What's Next

The triumph of Ctrl_A and their YARDS platform underscores the power of youth-driven innovation in tackling heavy industrial inefficiencies with practical AI. A representative from Ctrl_A will now travel to attend Japan's Largest Venture Summit to connect with global investors and tech pioneers, a significant milestone proving that ASEAN's next generation of innovators is ready to make a tangible impact on the global stage.

This execution gap stems not from technical limits, but from how organizations design workflows, upskill talent, align leadership, and measure return on investment. Here are five strategic imperatives to bridge the gap between AI experimentation and measurable business performance.

1. Design Workflows Instead of Tacking AI onto Existing Processes

The first mistake many organizations make is inserting AI into legacy workflows without questioning the underlying structure. Taking the finance and accounting department as an example, we can see three distinct levels of adaptation:

Automation / Optimization (Doing the same job faster): Most companies start by using AI to read documents, match invoices, or check figures. This saves employees a bit of time, but the underlying structure and way of working remain identical.

Reengineering (Creating connected and efficient processes): Stepping up a level, AI connects procurement systems directly to payments, streamlining working capital and cross-departmental flows. While more powerful, the results remain confined within the boundaries of the finance function.

Reinvention (Reimagining both how work gets done and the value created): The true game-changer is viewing the millions of receipt and supplier data points in accounting not as mere payment paperwork, but as early warning signals for the supply chain. Powered by holistic AI analysis, the organization instantly spots delivery risks or supplier price shifts, pivoting AI from a simple time-saving tool into a complete value engine for the business.

2. Build Workforce Readiness and a Culture of Learning

Deploying technology is easy, but getting people to adopt it willingly and effectively is far harder. The root cause today is that most organizations lack genuine AI Literacy. AI is often treated as just another computer software rather than a new skill set that every employee must learn, much like reading and writing.

Furthermore, fears of role replacement create internal anxiety and silent resistance. Employees hesitate to experiment with tools they worry might one day replace them.

Organizations must urgently restructure their workforce strategy by breaking down departmental silos, such as pairing supply chain and finance teams into cross-functional units and creating a safe environment where employees understand that AI is here to enhance their capabilities, evolve their ways of working, and open opportunities to create higher-value impact.

3. Direct AI Transformation with Clear Top-Down Leadership

Without an explicit top-down mandate from senior executives, AI projects across many companies drift aimlessly. Because AI tools are accessible to everyone, immediate confusion arises at the C-suite level over ownership:

IT Departments view AI as their operational duty.

Marketing, Sales, and HR Leaders independently purchase point solutions to experiment within their own teams.

This fragmented approach leads to duplicated effort, incompatible data, and a scattered vision. Globally successful organizations like JPMorgan Chase and DBS Bank share one common trait: the CEO personally steps in to drive and direct the strategy. Senior leaders must set clear rules, establish governance frameworks (Responsible AI), and enforce Data Sovereignty so everyone works toward a unified goal rather than committing redundant, disconnected investments.

4. Allow AI Time to Learn Enterprise Context

A strange double standard exists in the corporate world: when hiring a new employee, companies routinely offer a 3-to-6-month probation window to adapt to company culture and policies. Yet with AI, leaders expect 100% flawless results from day one.

This is a critical mindset trap. Off-the-shelf AI models excel at general knowledge, but they do not understand your company's context. Most internal policies and workflows were written for human readers, so early errors are natural when feeding these materials to AI.

Organizations must respect the learning curve. They need to invest time in supplying context, business rules, and correct operational workflows, accepting initial fine-tuning phases so the AI gradually learns and matures into a context-aware enterprise capability that delivers actual results.

5. Measure AI Value Across the Entire Workflow

The final trap for executives is setting overly narrow metrics focused solely on cost cutting or saved working hours. If using AI saves an employee one hour a day, the real question is how you turn that saved hour into actual cash or profit. In reality, that is exceptionally hard to do if workflows remain fragmented. Focusing purely on small pockets of saved time rarely yields the massive returns leaders expect.

Similarly regarding operational costs: while AI usage fees have dropped significantly, selecting models based purely on unit price or conversely, deploying the most expensive model for every task, is not the answer.

Leaders must focus on Workflow Economics, the total cost and value generated across the entire process. Matching the right model to task complexity shifts the organizational mindset from using AI to cut budgets to using AI to unlock new growth opportunities for the business.

The core takeaway from this session is that as access to AI normalizes across industries, competitive advantage will not come from having the most advanced model or the largest budget. It will belong to organizations capable of using AI to redesign how work gets done at an enterprise level.

Companies that deliver real results connect technology directly to business strategy, prepare their people to work alongside AI, set clear direction from leadership, and measure value generated across entire workflows.

The essential question today is no longer "Does your organization have AI?" but "How is AI helping your organization create new value and grow in ways it never could before?"

Picture an AI system watching the surf forecast in Bali. The moment conditions line up, it finds flights and accommodation, bundles everything into a single proposal and sends it to a group chat. Once the group agrees, the AI agent can handle the rest. This was the opening scenario for Owning the Experience: CX for the Agentic Era, a session led by Tuomas Peltoniemi, Managing Director, Design & Digital Products Lead, Accenture Song Southeast Asia, at Techsauce Global Summit 2026. It captured a fundamental shift: Customers are no longer assembling the whole experience themselves. They are starting to hand parts of it to AI agents that can weigh the options, decide and act on their behalf.

Control over the customer experience is changing hands. It belonged to the brand, then moved to consumers as they searched and built their own journeys, and it is moving again, this time to AI agents, software assistants that can decide and act for the person who owns them. Peltoniemi set out five lessons for brand owners from that shift, drawing on Accenture's Consumer Pulse Research, a survey of 25,590 consumers across 16 countries conducted between 7 and 22 January 2026.

Lesson 1: The power to design the experience is moving to AI agents

None of this is unprecedented. Every era of commerce over the past two or three decades has changed the answer to who owns the customer. In the industrial era, companies decided what to sell and how to sell it, and customers picked from what was in front of them. The web put buyers in charge of assembling their own experience, at the cost of a dozen open tabs and manual research at nearly every step.

Consumers are now less willing to pay that cost in time. Many are searching less on Google and asking large language models (LLMs) such as ChatGPT or Claude to recommend products and services instead. That makes AI agents significant new intermediaries between consumers and brands, and increasingly the layer where the experience is assembled. The research suggests the shift has gone further than many brands assume. Some 74% of respondents said they would trust a personal AI agent more than their best friend to make a purchase for them, a figure Peltoniemi described on stage as strikingly high. Executives are reading it the same way, with 70% expecting AI agents to take on direct decision making in marketing and sales within three years.

Lesson 2: Customers do not delegate to AI equally

Break the research down by level of delegation, and the picture sharpens. Some 85% of respondents are open to deciding what to buy together with AI, and 74% are open to letting an AI agent carry out specific tasks such as negotiating a deal or resolving a complaint, while 9% are already willing to hand over the decision and the payment for an agent to complete on its own.

Headline figures, though, are difficult to act on without knowing where that willingness applies. Accenture built a tool for exactly that, the Delegation Dial, which maps how far consumers are prepared to hand authority to AI. At one end sit emotional, high-value categories such as skincare, beauty and travel, where people want help thinking it through but do not want the decision made for them. At the other end sit subscriptions and the weekly household shopping, repetitive enough that many have already let an AI agent run the purchase from end to end.

A millennial respondent in Hong Kong put it plainly.

'It's fine if an AI agent handles my plane tickets, but when it comes to choosing a hotel room, I want to decide for myself. The little details matter, the view, the position of the room, the feel of the room.'

More significantly still, the dial is not a fixed setting for a type of customer. It moves within one person from moment to moment. Peltoniemi took a first-time home buyer as an example. Choosing a neighborhood carries too much emotion to delegate, and few people would let an agent make an offer on a house, yet working out what is affordable and arranging loan protection are already tasks many would happily pass to an agent. For brands, the job is therefore not simply to find where their category sits on the dial, but to design experiences that hold up as the dial moves across the journey.

Lesson 3: Organizations need to design the customer journey, not the channel

When decisions are spread across so many points, organizing the work channel by channel stops being enough. Plenty of organizations still shape both the experience and the team structure around channels. Peltoniemi described a client that still runs separate teams for its mobile app and its website, even though what actually needs an owner is the journey from end to end.

Three shifts are already underway. The first is from thinking in channels to being present wherever a customer, or a customer's AI agent, chooses to make contact. The second is from building services channel by channel to what Accenture calls orchestrated intelligence, a single layer that listens, decides and connects customers to the services behind it. The third is from systems wired along fixed paths to composable capabilities, built once and made available to every channel.

What links all three is a simple principle: intelligence cannot stay locked inside a channel. An agent that makes decisions without visibility across the enterprise will produce weaker answers than it otherwise could. The goal is a customer who can come in through any door and still get a continuous experience from an agent working with the same context.

Peltoniemi illustrated what that makes possible with a banking example. If the agents behind the scenes can see the whole picture, from salary cycles to credit scores to the products a customer already holds, the bank may be able to tell a customer and their partner, in their own chat, that they are ready to buy a first home before any application is filed. The bank wins the customer without waiting, and the customer gets an answer without filling in a form. Once the loan is approved, everything that follows, from insurance to transfers and deposits, can sit in one place.

The services beyond the bank's own products are more interesting still. Open those services to agents from other companies, and the entire move-in day, from water and electricity to internet and movers, might be arranged in a single conversation with a personal AI agent, rather than provider by provider as it is today.

That is why, in Peltoniemi's view, the question he hears most often from enterprise clients is the wrong one from the start: How do we add AI to our channels? The better question is how to expose the organization's full set of capabilities for AI to use, because the task is not to bolt AI onto one channel at a time but to build a single AI layer that reaches across the enterprise.

In practice, he advises against trying to fix everything at once. Pick one customer journey and get it right; for a bank, that might be home buying. Laying the groundwork for the year ahead means standing up an orchestration layer that brings the brand's services and its partners' services together, and opening safe, well-governed access to customers' own AI agents, as Marriott has done by letting customers connect a personal agent that books rooms according to their preferences.

Lesson 4: Brands need to win both human hearts and machine evaluation

Once an organization's capabilities are legible to AI, the obvious question is what a brand still stands for. Peltoniemi's answer is that brands now have to serve human customers and the AI agents acting for them. The way through is not to choose a side, but to win the customer's heart and pass the AI agent's evaluation at the same time. These are two briefs, running at once.

The first brief is familiar ground for brand builders: emotional resonance, identity, self-expression, and the joy or connection that leads someone to choose one brand over another. It comes down to identifying the moments customers refuse to delegate to anyone, human or agent, and winning those moments.

The second brief is much newer. It is computational legibility, or how well machines can read and interpret what a brand publishes. The basic question is whether an agent can read a brand's website at all. Peltoniemi cited Oreo as an example, noting that its assessment found only 10% of agents could read the relevant information on its website. If a brand of that scale runs into the problem, he argued, many others are likely to face the same challenge.

Failing that test means dropping off the list of recommendations. A brand with no structured product data, and with certifications sitting on its site as design assets that cannot be verified, will not surface in the answers ChatGPT or Claude give consumers who now rely on them heavily. Two more factors carry as much weight: transparent pricing and a track record of fulfillment. Agents are already assessing how reliably a company delivers, which gives customer reviews real influence. Peltoniemi pointed to the partnership between OpenAI and Reddit, under which OpenAI accesses Reddit's Data API to help its tools understand and surface structured, real-time Reddit content. For brands, the practical consequence is that what customers say in public can be set against what a brand claims on its own website.

Two things can start immediately. The first is an AI discoverability audit, to establish whether agents can find the brand at all, given how many brands still block AI from collecting information on their own sites. The second is making fees, rates and eligibility criteria machine readable through a product schema.

Even so, going all in on one side is not the answer. A brand optimized for agents but empty of meaning for people will still lose wherever human judgment decides, and a brand that wins hearts but stays invisible to agents will not reach the shortlist in the first place.

Lesson 5: Future loyalty will rest on evidence and relevance

Because agents judge on evidence rather than feeling, the shift reaches the asset that brands have spent the longest building: loyalty. Peltoniemi used himself as the example. He has been buying Nike sneakers for 20 years, but if his AI agent concluded that another brand suited him better, he suspects he would take its advice. Loyalty becomes conditional, and AI now mediates the condition.

Figures from the same research back the observation. Among behaviorally loyal customers, the ones who always pick the same brand, 37% would accept an agent's recommendation and switch to a brand that fits them better. Promises a brand cannot keep are one reason an agent points elsewhere. That leaves two things to get right: keeping information consistent everywhere the brand appears, and showing up at the moments the brand genuinely fits, while being willing to step aside when it does not.

The central message from the session is that AI agents are not lowering customer expectations. They are raising the standard, because these systems can compare options, check claims and weigh outcomes across far more data than a person can, and far faster.

Brands therefore have to do two things at once: build the meaning that makes people want to choose them, and build the data and services that let AI agents discover, understand and connect to them safely.

In the era of agentic CX, the brands with the greatest advantage may not be the loudest. They will be the ones that are relevant, credible and ready to be chosen, by customers and by the AI agents acting on their behalf.

Source: Owning the Experience: CX for the Agentic Era, Techsauce Global Summit 2026, and Consumer Pulse Research by Accenture

This year's summit ran from 26 to 28 August 2026 at the Queen Sirikit National Convention Center (QSNCC), drawing over 350 exhibitors. Marking its third consecutive year at Techsauce Global Summit in Thailand, iFLYTEK also took home "The Sauciest AI-Driven Company" award against more than 50 competing companies. Under its "Discover Next-Gen AI" campaign, Booth D1 presented a full lineup of AI solutions from the iFLYTEK Open Platform, spanning public services, office productivity, smart living spaces, and enterprise infrastructure.

1. Communication & Public Service Solutions

• AI Translation Screen: A transparent, dual-sided AI translation screen display that enables real-time, face-to-face communication across languages. Powered by iFLYTEK Spark LLM, it features auto-language detection, multimodal noise reduction, and a virtual customer service assistant, ideal for transit counters, airports, tourist centers, and hotel front desks.
• GuideX: A digital human virtual receptionist combining hardware and software to deliver multimodal interaction through face wake-up, sound localization, gesture recognition and space sensing. Beyond answering queries, it offers real-time direction guidance to specific booths. GuideX is already deployed globally, including at Don Mueang Airport in Thailand (supporting over 30 languages), the Federal Authority for Identity, Citizenship, Customs & Port Security, SMRT in Singapore, and Safaricom customer service centers in Kenya.

2. Enterprise AI & Productivity Tools

• Stars MaaS: A Model-as-a-Service platform enabling organizations to access, manage, and fine-tune large language models with zero code across 5 steps: Data Creation,Model Selection, Fine-Tuning, Outcome Evaluation and Model Deployment. Supporting more than 50 mainstream foundation models, including iFLYTEK Spark and DeepSeek, it reduces fine-tuning time from days to hours.
• Astron Agent & Astron Workflow: Astron Agent is a cloud-based intelligent agent that analyzes intent, breaks down complex tasks, and executes multi-step workflows using internal enterprise knowledge bases. Astron Workflow provides a drag-and-drop visual interface for building business processes. Both tools integrate with leading LLMs (Spark, DeepSeek, GLM, Kimi, MiniMax), feature over 16,000 pre-built skills, and maintain cross-session project memory.
• Ocean Enterprise AI Agents: A productivity suite featuring OceanDoc (AI presentation slide generation with speaker notes), OceanNote (multilingual meeting transcription with speaker separation and action item summaries), OceanTranslate (multimodal translation across text, documents, audio, and video in 100+ languages while preserving layout), and OceanPraxis (immersive role-play training simulations with performance evaluation).

3. Smart Spaces & Enterprise Infrastructure

• WallEX: An AI agent solution for smart spaces across premium hotels, residential properties, and commercial buildings. Integrating with sensors, Nurmina intelligent ambient lighting and third-party device ecosystems, it supports international standard protocols including Matter and KNX to control lighting, climate, curtains, and security via single voice commands. WallEX is live in flagship projects across Abu Dhabi, Saudi Arabia, and Jamaica.
• All-in-One Solutions: An integrated hardware-and-software solution designed for secure on-premise deployment in government, finance, and media sectors. The unit houses an AI Hub (speech recognition for 35+ languages, TTS, translation, OCR), a Data Annotation & Training platform, and a Smart RAG Agent for internal document Q&A. The 4U model features 4 CPU cards and 8 NPU cards for high-capacity enterprise workloads.

As part of the iFLYTEK Open Platform's broader portfolio, these solutions demonstrate iFLYTEK's full-stack capabilities across models, AI development platforms, enterprise agents and integrated hardware-software applications. To accelerate its localization strategy in Thailand, iFLYTEK also signed letters of intent with Chulalongkorn University and Zense to co-develop Thai speech recognition and natural language processing technologies, marking a concrete step forward in its local partnership roadmap.

Techsauce, a platform driving the technology and business ecosystem, announced the successful conclusion of Techsauce Global Summit 2026, an international technology and business conference held from 26–28 August 2026 at the Queen Sirikit National Convention Center (QSNCC). Held under the theme “The Race to the NEXT…”, the summit welcomed attendees from 60 countries, with the highest representation from Singapore, China, Japan, Malaysia and South Korea, reflecting the platform’s role in connecting Thailand’s ecosystem with regional and global markets. 

The Summit also featured a diverse range of international pavilions from across Asia, Europe and North America, bringing together companies, startups and ecosystem partners from markets including Canada, the Czech Republic, Japan, South Korea, Malaysia, Taiwan, Hong Kong and beyond.

Across three days, the event featured 10 stages, more than 350 speakers, over 350 exhibitors, and more than 40 workshops and masterclasses spanning AI and Agentic AI, the digital economy, the future of finance, healthspan and HealthTech, food systems, and corporate innovation. The summit brought together more than 1,000 C-level executives and over 500 investors, and facilitated more than 1,500 business-matching connections.

“The NEXT” Is Thailand’s Opportunity in a New Global Order

Oranuch Lerdsuwankij CEO and Co-founder of Techsauce Media Co., Ltd., observed that the world is becoming increasingly polarised. Strategic technologies such as semiconductors and clean energy are becoming instruments of national strategy, while supply chains are seeking a “new home”. This presents an opportunity for Thailand and Southeast Asia to move beyond their traditional role as low-cost manufacturing bases and become strategic hubs connecting technology, capital, policy and business. “The NEXT is not only about advanced technologies or emerging trends. It is about opportunity,” Lerdsuwankij said. She introduced the N-E-X-T framework: New Reality, Effect, eXperiment and Together, encouraging organisations to understand change, assess its impact, test something new within 30 days, and identify the right partners.

The Summit was also joined by Mr. Chaichanok Chidchob, Minister of Digital Economy and Society, who highlighted the government’s role as an enabler of Thailand’s digital transformation and the importance of public–private collaboration in accelerating innovation. He also presented the Prime Minister’s Digital Awards 2025, recognising organisations and individuals shaping the country’s digital future.

With Thailand’s digital transformation accelerating at the national level, the conversation at Techsauce Global Summit 2026 also turned to how organisations can adapt from adopting individual technologies to fundamentally rethinking how they operate.

Becoming AI-Native Is Not About Adding Tools. It Requires Redesigning the Organisation.

Dr. Kai-Fu Lee AI expert and investor, former Google China executive, and author of AI Superpowers, said AI is driving the cost of “intelligence and action” towards near zero. Models offering comparable capabilities have become approximately 175 times less expensive in just two years, while Agentic AI can now write code, use tools and execute multi-step workflows. However, deploying many AI applications does not automatically translate into business outcomes if those systems do not understand an organisation’s real context and processes. Dr. Lee proposed building an Enterprise Universe Model that connects data through ontologies, multi-agent systems and a Boss AI. He also advocated restructuring organisations around Directly Responsible Individuals (DRIs), or “mini-CEOs”, who own outcomes end to end, while people continue to provide vision, human understanding, trust-building and accountability.

Techsauce Advances the Healthspan Economy

Healthspan remains a key agenda that Techsauce continues to advance through its platform. Opening the Healthspan stage, Mr.Pattana Promphat, Minister of Public Health, emphasised that success in healthcare should not end with helping people live longer; those added years must also be healthy, independent and self-reliant. As Thailand moves towards an ageing society and non-communicable diseases place a growing burden on patients, families and the healthcare system, healthcare must shift from treatment to prevention, and from reactive to proactive care.

AI, genomics, biotechnology, digital health and wearable devices can help assess disease risks earlier and enable personalised care. Thailand’s strengths in medical talent, universal health coverage and the wellness industry create an opportunity for the country to become Asia’s healthspan and longevity hub. To deliver meaningful impact, innovation must be trusted, affordable and capable of reducing health inequality.

Another example of bringing deep tech into real-world use was the Chula Innovation & IP Expo 2026, organised by the Chulalongkorn University Innovation Hub (CU Innovation Hub). The expo showcased research, technologies, intellectual property and startups from Chulalongkorn University, while connecting researchers and entrepreneurs with industry, investors and international partners to open pathways for technology transfer and investment.

From Knowledge to Business Opportunity

From Thailand’s national-level opportunities to the design of AI-native organisations and the application of technology in healthcare, Techsauce Global Summit 2026 showed that “The Race to the NEXT” extends well beyond the conference stages. It took shape across the innovation exhibition, hands-on workshops, Business Matching, Meet the VCs, Executive Networking and side events held throughout Bangkok. Together, these mechanisms connected people with business challenges, capital, technology and implementation capabilities, helping ideas move towards projects, partnerships and commercial opportunities. A key takeaway from Techsauce Global Summit 2026 is that competitive advantage in the new global order does not come from adopting technology first. It comes from selecting the problems that matter, redesigning organisations and systems, experimenting quickly with purpose, and building cross-sector collaboration. For Thailand, the opportunity lies in leveraging regional strengths and connecting technology with the economy, healthcare, skills and capital, enabling the country to move from responding to change to helping shape the next playing field.

Techsauce Connects Ecosystem Voices with Thailand’s National Agenda

In his closing remarks, Professor Dr. Yodchanan Wongsawat, Deputy Prime Minister and Minister of Higher Education, Science, Research and Innovation, highlighted Techsauce’s role in connecting people, technology and opportunity. He also described the event as a forum through which the public sector can hear directly from entrepreneurs, investors and participants, translate those perspectives into policy, and help move the country forward.

“Today, Techsauce is no longer simply a conference. Techsauce is a platform, and one that Thailand should be proud of. I am ready to be a good partner to Techsauce and to listen to the conversations taking place here: what the government can build on, which policies should be adjusted, and which voices from entrepreneurs, investors and participants the public sector should hear to help move the country forward,” said Professor Dr. Yodchanan Wongsawat, Deputy Prime Minister and Minister of Higher Education, Science, Research and Innovation.

Announced at Techsauce Global Summit 2026, the partnership combines OpenAI’s frontier AI capabilities with Fellow Intelligence’s local enterprise expertise, giving Thai organizations greater support as they move from experimenting with AI to deploying it across real business workflows.

Positioning itself as OpenAI's first boutique consulting partner in Asia-Pacific, Fellow Intelligence operates as an official OpenAI reseller in Thailand. The firm delivers ChatGPT Enterprise and Education licensing alongside capability assessments, hands-on workshops, hackathons, and change management programs executed by its parent firm, Thinking Machines Data Science. Fellow Intelligence, together with Thinking Machines, reports having upskilled over 10,000 professionals and co-built more than 3,450 custom AI assistants for major clients, including CPF, CPN, the Stock Exchange of Thailand, King Power, Sansiri, and UNDP.

The partnership comes as AI adoption in Thailand continues to accelerate. Sharma revealed that Thailand now ranks among the top 20 countries globally for weekly ChatGPT and Codex usage, with local Codex adoption surging 350x in recent months. Furthermore, roughly 30% of ChatGPT classified consumer messages in Thailand are now work-related. 

“Thailand has quickly become one of the region’s most active AI markets, and we’re seeing businesses move beyond experimentation towards putting AI to work in meaningful ways,” said Sid Sharma, Head of Go to Market, ASEAN at OpenAI. “Fellow Intelligence brings deep local expertise and experience helping organizations navigate that transition. Together, we want to make it easier for more Thai businesses to access frontier AI capabilities and turn them into real outcomes.”

Data from OpenAI’s latest report revealed that "frontier firms" (organizations furthest along in AI integration) are generating roughly eight times the output of peers stuck in pilot phases. This performance gap has nearly tripled in just five months. Sharma emphasized that the true measure of AI value isn't model selection or token costs, but "useful intelligence per dollar": how reliably tasks are completed and how that value compounds over time.

"An 8x output gap sounds intimidating. It's like something that takes years to close. In our experience it isn't. Thai professionals already have a baseline fluency with ChatGPT, as the stat shows, a third of what Thais bring to ChatGPT is already work. Our people didn't wait for permission. The real gap isn't readiness, it's the distance between staff using AI quietly and organisations putting it into the workflows that matter. ChatGPT Enterprise, with proper training, is where we start. We want Thailand's frontier firms counted in the hundreds, not the handful it is today." – Kamin Phakdurong, CEO of Fellow Intelligence

For Thai enterprises, the partnership makes local adoption simple and straightforward: Thai Baht invoicing, dedicated in-market deployment support, and training from a team deeply integrated into the local ecosystem. OpenAI framed the move as a long-term commitment to the market. "We are investing right back into Thailand," Sharma stated, adding that OpenAI wants to keep building "with Thailand, within Thailand, and with developers and the ecosystem here in Bangkok."If your organization is considering about getting started with AI (whether that's ChatGPT Enterprise licensing, team training, or planning an organization-wide AI rollout), you can find out more about Fellow Intelligence at https://fellowint.com

Informal money transfer networks such as Hundi and Hawala still move roughly $300 billion a year, even though the system is more than 500 years old, has no app, no blockchain, and no technology holding it up at all. The question of how something that old still stands in a world equipped with AI, cryptocurrency, and digital payment rails is exactly the line Aswin Phlaphongphanich, Co-founder and Chief Executive Officer of DeeMoney, used to open his own session, with a footnote attached from the very start: as a fintech founder, he does not endorse anyone using this network, because it is illegal in most countries.

In his session, titled 'Unspoken Facts of Global Payments: How Fintechs Changed the Banking Game' at Techsauce Global Summit 2026, Aswin  spoke about the things people inside the payments industry know but rarely say out loud. They all point back to a single conclusion that took roughly 20 years to crystallise, which is that technology does an excellent job of moving information, but what actually moves money is trust. Each fact that follows comes from a different corner of the business, from liquidity and regulation to customer behaviour, and each one arrives at that conclusion from a new direction.

The 500-Year-Old Network No Technology Has Managed to Bring Down

What Hawala and Hundi have done better than banks, fintechs, and crypto founders over the past hundred years is understand from the outset that the real currency of moving money is not technology, but the trust between the people at either end.

Aswin admits this was not something he could see at the beginning. When DeeMoney started around 10 years ago, the thinking was to build the best app with the smoothest Know Your Customer (KYC) journey, on the assumption that customers would choose the service because of the technology. The truth he found later was the opposite.

There Is No Global Money Transfer Network, Only Local Networks Woven Together

The first misconception is about the role of technology. The next one is about the network itself, because most people believe there is one beautiful global money transfer system out there moving funds on their behalf. That system does not exist.

What does exist is a set of local payment networks, one country at a time, woven together until they can carry money across borders. Every local regulation and every local payment channel has been stitched in line by line. Even the Society for Worldwide Interbank Financial Telecommunication (SWIFT), which many take to be a global transfer network, is in reality only a messaging platform between financial institutions. Funds still have to be converted into local currency and travel through the destination country's own rails before the recipient on the other side of the world actually receives anything.

A Payments Company Is Really a Treasury and Liquidity Company

When the network in use is a weave of local systems, what gets money to its destination within minutes is not the speed of the code. Aswin says that when DeeMoney was founded, the team thought it was building a really cool payments company. What it turned out to be building was a treasury and liquidity company, because without treasury and liquidity management running 24 hours a day, seven days a week, technology means almost nothing in this industry.

The example he gave was sending money from Bangkok to the United Kingdom at three in the morning on a Saturday and expecting it to arrive at five minutes past three. What makes that possible is not technology, but someone in the treasury team pre-funding the money on Wednesday and converting it into pounds sterling on Friday, ready for a customer who turns up in the small hours of Saturday. All of it is a great deal of orchestration that has to land in perfect harmony every single time.

Moving Money Is Easy, Moving It Legally Across Borders Is the Real Work

Liquidity is only one side of the job. The heavier side is the legal framework. Aswin sums it up briefly: moving money is easy, but moving money legally across borders is extremely hard.

Cryptocurrency is the clearest example of the point. The technology can update a ledger from one side to the other in milliseconds, but the genuinely hard part is making that process compliant across multiple corridors and multiple regulators at the same time, which is why most people are still not using cryptocurrency to send money abroad in daily life.

The Winning Formula Is Multiplication, Not Addition

The question that follows is who wins this game, fintechs, banks, or both working together. The answer Aswin offers has nothing to do with the type of player, and everything to do with four components that all have to be in place: technology, compliance, liquidity, and trust.

The reason he describes it as multiplication rather than addition is that if any one of the four is zero, the entire result becomes zero immediately. All four have to work in harmony before a network can earn a customer's trust and genuinely move money.

Why Money Cannot Travel the Way a Chat Message Does

The question Aswin says he is asked most often is this: if sending a message from Bangkok to New York through a chat app takes a few seconds, why can money not travel the same way?

What most people see is the tip of the iceberg, which holds only the fee and the foreign exchange rate, after which the money is supposed to arrive. What sits below the waterline is the complexity that has to be orchestrated every day, from speed, liquidity, and pre-funding to compliance, reconciliation, handling failed payments, managing refunds, and running a customer support team seven days a week. All of it works behind the scenes so that the journey of money from one point to another looks as simple as possible to the user.

The Four Fabrics of Trust DeeMoney Has Woven Together

None of that complexity below the waterline means anything unless it ends in trust. Aswin explains that DeeMoney has woven four fabrics of trust together. The first and most important is the trust of customers. The second is the trust of the regulator, which granted the licence to perform this job on behalf of consumers. The remaining two are the banks and the fintech partners, more than 70 of them across the network, because no fintech moves money legally across borders on its own.

Seeing the business this way also changed how the business is defined. At the start, the team thought it was running a remittance company, but it learned quickly that remittance is only one product running on the highway the company had built, or what Aswin calls a trusted financial bridge between Thailand and the world. Today DeeMoney processes more than 7 million transactions worth over $5 billion a year, and Aswin states that it is the largest cross-border payments fintech in Thailand. Cross-border payments already run on this highway, while collections and stablecoins are the products lined up next.

Banks Are Built for Stability, Customers Want Freedom

When trust has to come from all four fabrics at once, the difficulty lies in the fact that each side wants something different. Aswin is emphatic that banks do their own job very well as custodians of customer funds, holding them with care, keeping them away from risk, and protecting them from fraud. From the customer's side, however, what people want is freedom and authority over their own money, and that balance is hard to strike.

The main reason is that the banking system was built over roughly a hundred years to deliver trust, stability, control, and compliance, while customer behaviour has changed drastically over the past 15 years. People who book a ride through an app and get a car within seconds, or buy something online and receive it almost immediately, expect the same of their money. Yet most bank treasuries still do not operate on US holidays and stop accepting transactions after half past three on a Friday afternoon, while customers expect money to move around the clock. The task for fintechs is to deliver both sides at once, every time.

Customers Do Not Pay to Move Money, They Pay to Remove Uncertainty

Once customer expectations shift that far, what customers are actually buying shifts with them. Aswin recalls that the team used to believe customers chose the service for a good exchange rate, for the flat 125 baht fee to send money anywhere in the world, or for speed measured in seconds. What they found instead is that customers do not pay to move money, they pay to make uncertainty go away.

Uncertainty here goes by another name, which is friction along the entire path from hitting send to the money being received. What customers want is the certainty that funds arrive every single time, without having to call a service centre to ask where the money currently is. The hard part of the job, then, is attaching that certainty to every transaction.

A Future Where Payments Disappear from View, and Customers May Not Be Human

Another question Aswin says he is asked just as often is what the future of cross-border transfers looks like now that AI, blockchain, and a long list of other technologies are all in hand. For about five years his answer was that the cost would fall to zero and everyone would move money for free. He no longer sees it that way.

What he expects instead is that the payment experience itself will gradually disappear from view, embedded into the customer journey of whatever app or platform the user already has open. Payments stop being a separate website or app and become part of the experience the user is already in, and eventually AI or a machine may carry out the transaction without the person having to think about it at all.

The picture that follows is that customers in the future may no longer be human, but a billion AI agents acting on behalf of users on the platform. Aswin admits nobody knows exactly what that will look like, but what keeps him up at night is that for the past hundred years the industry has learned only how to win human trust, while the next 10 years may be about trusting machines instead.

Stablecoins and Deposit Tokens, the Big Cost Every Fintech Is Waiting to Shed

The question and answer session after the talk circled back to the cost that sits at the heart of the business. Aswin says the single largest cost in this business is liquidity and pre-funding, and that it is truer for fintechs than for other players, because a fintech has no bank deposits and has to run entirely on its own working capital and balance sheet.

For that reason, deposit tokens and stablecoins are a game-changing moment for every fintech, if they can genuinely take pre-funding out of money movement. Aswin assesses that it is still very early in Thailand, and if he had to guess, it could be roughly 12 to 18 months before this class of technology becomes part of money transfers, while several parts of the world are already live with it, which gives Thailand something to learn from.

As for the question of machines using financial services in place of humans, Aswin answered plainly that he does not have a precise answer. The only thing he can say is that until now the industry has built technology for humans to use. The moment AI starts taking responsibility for and authorising payments on behalf of the account holder, the element of trust that anchors this entire talk changes completely, because where a person used to decide how to send money from Thailand to the United Kingdom, an AI agent may soon be the one authorising and authenticating that transfer instead.

The question left hanging since the start of the session, of why no technology has brought down a 500-year-old network, therefore has its answer in the same line Aswin closed with, that technology does a great job of moving information, but trust is what moves money. And if the future in which financial customers are not human does arrive, the industry's next task will not be shaving a few more seconds off the journey, but working out how trust gets handed from humans to machines.

Source: 'Unspoken Facts of Global Payments: How Fintechs Changed the Banking Game' session at Techsauce Global Summit 2026