AI-Ready Data Infrastructure: Thailand’s Next Digital Battleground

The AI revolution isn’t approaching Thailand — it’s already here. But as the country marches confidently toward Thailand 4.0 with ambitious AI initiatives under its Thailand AI Strategy (2022-2027), many companies find themselves facing immense hurdles.

It’s not that the algorithms are weak or the hardware is insufficient. Thai companies are building up their talent pool and investing heavily in shoring up their AI infrastructure, bolstered by the government’s Direct PPA (power purchase agreement) initiative. 

But one glaring issue remains: data, the lifeblood of AI. In many companies, the data infrastructure, after years of organic growth, now resembles a chaotic sprawl rather than an organized digital asset. And it is where IBM believes Thai companies need to transform to become truly AI-ready.

Understanding Thailand’s data problem

While Thailand is well-positioned as Southeast Asia’s digital hub, companies across the country are discovering a fundamental truth: computational muscle means nothing when your data pipelines are clogged with decades of unstructured information.

“Building AI-ready infrastructure isn’t just about investing in cutting-edge hardware like GPUs,” explains Nikhil Singh Kushwaha, IBM’s chief technology officer for data and AI in APAC. “It’s about establishing a robust data ecosystem where software capabilities such as data integration, governance, observability, and lineage play a central role.”

For Thai companies, the implications of not having a strong data ecosystem are particularly severe. With digital transformation already accelerating post-pandemic and the government driving its Eastern Economic Corridor (EEC) ambitions (especially with the development of the Eastern Economic Corridor of Digital or Digital Park Thailand), companies are under pressure to demonstrate AI capabilities. But most are building their AI dreams on dangerously fragmented data foundations.

The obvious solution is to deploy a more modern infrastructure designed with AI in mind. But what happens if ripping out legacy infrastructure is not an option? IBM’s collaboration with the State Bank of India (SBI) offers insights that resonate with Thailand’s situation. Rather than merely upgrading systems, SBI fundamentally reimagined its entire data architecture. This transformed fragmented financial records into a unified intelligence platform with real-time capabilities.

For Thai financial institutions facing competition from nimble fintechs and super-apps, this approach demonstrates how they can transform entrenched data silos into competitive advantages in an ecosystem where AI drives customer experience.

Join Nikhil Singh Kushwaha and Anothai Wettayakorn at the upcoming Executive Summit "Accelerate Growth With an AI-Ready Data Architecture" in Bangkok on May 15, 2025. they will join AWS executives to share insider strategies, demonstrate successful approaches to dismantling data silos, and showcase cutting-edge AI solutions particularly relevant to ASEAN markets. Register or find out more here.

Addressing the Kreng Jai of data sharing

Having a strong data ecosystem is a good start but it amounts to nothing if data is trapped in mundane, unglamorous data silos. It’s also an issue that technology alone cannot solve.

Departments frequently develop possessive relationships with their data. This hurts enterprise-wide AI deployment, with data hoarding leading to AI bias and accelerating model drift.

“Siloed data often leads to incomplete or skewed datasets,” Kushwaha warns, “which can introduce bias into AI models, resulting in unfair or unreliable outcomes.”

The impact resonates across industries. For example, Thai energy companies balancing traditional operations with renewable goals find optimizing everything from maintenance schedules to supply chains nearly impossible without integrated data. For Thailand’s booming insurance sector, fragmented data means algorithms trained on partial customer histories degrade the service experience instead of enhancing it. In the country’s manufacturing sector, particularly in the Eastern Economic Corridor, this manifests as predictive maintenance systems missing critical failure signals because operational data isn’t integrated with maintenance records.

The result isn’t just suboptimal AI — it’s potentially destructive decision-making disguised as digital intelligence, creating what Thais call "bpan-haa song chan" (problems on top of problems).

Shell’s global implementation shows what’s possible when these silos are eliminated and that companies can do this today. Their approach, led by IBM, transcends mere technological upgrades, becoming a strategic advantage, turning dispersed assets across drilling sites, refineries, and distribution networks into an integrated computational organism.

Thailand’s edge in the AI infrastructure race

Gartner projects that by 2026, 75% of organizations will operationalize AI. For Thailand’s business landscape, this aligns perfectly with national initiatives to establish digital leadership in ASEAN.

Thai companies possess unique advantages in this competition. The country’s blend of strong regulatory frameworks combined with the flexible mindset and entrepreneurial dynamism creates fertile ground for innovation. Organizations that build adaptable, compliant data infrastructures will lead the next wave of Thai business evolution from "setakit por pieng" (sufficiency economy) to digital abundance.

SBI’s transformation offers a blueprint that Thai financial institutions can adapt. Their approach wasn’t merely technological — it was ruthlessly practical: break down data silos, implement governance frameworks that respect regulatory requirements (a particular concern with Thailand’s strict PDPA), and create ecosystems transforming historical information into competitive advantage.

The competitive edge lies in constructing systems that harmonize with Thailand's Personal Data Protection Act (PDPA), enable seamless integration across the ASEAN region, and provide the flexibility to adopt emerging AI technologies without complete redesigns.

The pivotal role of collaboration

As Thailand positions itself within ASEAN’s digital economy, successful companies will recognize that future data infrastructure isn’t about passive information management but transforming data into a dynamic, intelligent asset that drives national competitiveness.

This is where collaborative relationships with companies like IBM come into play. “Our role as a technology partner is to help organizations transform their data into a strategic asset, ready to support both current and future AI needs,” says Kushwaha.

Shell’s implementation provides a compelling vision of what can be achieved through collaboration. Their global data architecture processes complex operational data in real time, creating what amounts to a single computational organism. It demonstrates how properly structured data doesn't just improve operations — it enables capabilities previously impossible.

Similarly, IBM Thailand is looking to work with local companies to drive up AI adoption. “A new APAC AI Outlook 2025 study commissioned by IBM revealed that 42% of the organizations surveyed believe that the advancements in AI have made it more accessible. IBM Thailand is working closely with Thai organizations to help them become front-runners by removing any obstacles in their AI adoption journey,” said Anothai Wettayakorn, managing director and technology leader at IBM Thailand.

Take Siriraj Piyamaharajkarun Hospital (SiPH)’s recent collaboration with IBM as an example. It revolutionized its pathology diagnostics with computational advances and AI to support patients across Thailand and ASEAN. It leveraged IBM’s Supply Chain Industry 4.0 team in Singapore to simplify data entry using smart forms and speech-to-text technology, and integrate tissue specimen data with high-resolution slide images, SiPH integrated laboratory systems, image scanning, and central data processing. The effort significantly enhanced efficiency and accuracy in cancer diagnosis while laying the foundation for future advancements in computational pathology and AI diagnostics in Thailand and beyond.

For Thailand, the journey to AI, especially generative AI (GenAI), is only starting. According to Wettayakorn, only 5-6% of Thai organizations have adopted GenAI currently. The AI adoption journey is long, and no one is fully prepared but IBM is looking to help Thai organizations increase AI adoption to around 15% as compared to the global average of 10%, as getting actionable insights from their data will build market competitiveness.

Thailand’s AI future lies in its data

Before Thai companies jump on the AI bandwagon, they need to leverage solutions that dismantle data silos, implement governance frameworks that maintain data integrity while navigating PDPA requirements, and deploy tools to accelerate AI adoption. Those who succeed will be well-positioned to maximize their AI advantage with their data.

As the Thai business saying goes, "Khun kao song len" (where there’s a mountain, there are people mining it). In today’s digital economy, a company’s data is the mountain —  and its data infrastructure determines whether the company’s AI algorithms are mining gold or merely moving dirt.

Image credit: iStockphoto/Urupong