The Landscape of AI Adoption in Thai Businesses: One-third of organizations have already started using AI, but the gap between the “innovation leaders” and the “followers” remains wide.

Interesting Numbers: AI Adoption Is Growing Rapidly

When shifting the perspective from national policy to on-the-ground business reality, the signs are striking. A key report by Amazon Web Services (AWS) reveals that in 2024, as many as 32% of Thai businesses — approximately 600,000 companies — have already adopted AI, marking an impressive 33% growth compared to the previous year.

This aligns with findings from ETDA and NSTDA in 2024, which show that 17.8% of organizations are currently using AI, while an additional 73.3% plan to adopt it in the near future. These numbers clearly highlight a growing interest and commitment to AI adoption across the country.

 

Looking Deeper: The Reality of Shallow Integration

However, looking beyond the adoption rate reveals that AI integration within most Thai businesses remains superficial. According to AWS, 72% of AI-using businesses only apply it for basic process optimization or efficiency improvements.

Only a small portion use AI for strategic transformation—just 10% have embedded AI into the core of their business, leveraging it for strategic decision-making or new product and service development. This underscores the gap between “using AI” and “being an AI-driven organization.”

 

Stories from Three Business Groups: A Stark Readiness Divide

The data exposes a sharp contrast among three key business groups, which forms the heart of this analysis:

  1. Group 1 – Innovation Leaders (Startups): Startups are the true front-runners in high-level AI adoption. Over 50% of startups use AI in some form, and notably, 40% are currently developing AI-powered products. These are the true innovation builders.
  2. Group 2 – Cautious Giants (Large Enterprises): While 44% of large corporations use AI, their adoption is often conservative. Only 16% use AI to create new products or services, and a mere 18% have a comprehensive AI strategy across the organization. They have the resources but often lack agility.
  3. Group 3 – The Lagging Majority (SMEs): Small and medium-sized enterprises (SMEs), the backbone of Thailand’s economy, are at risk of being left behind—only 9% use AI at an advanced level. This gap highlights the economic vulnerability of the broader market.

This uneven technological adoption is creating what experts call a “K-shaped Digital Transformation”—a phenomenon where a small, tech-savvy minority (mostly startups) accelerates rapidly upward, while the majority (especially SMEs) struggles to adapt. If this trend continues, it could lead to market concentration and widening economic inequality, as AI-powered corporations pull further ahead of traditional businesses, eroding the country’s mid-tier economy.

 

Industry Case Studies: Where AI Is Making a Real Impact

To make the benefits of AI more tangible, here are examples from key Thai industries where AI is already driving transformation:

  • Finance and Banking:
    AI is used for financial data analysis, risk assessment, and facial recognition for secure transactions. KBTG stands out as a leading example, leveraging AI for risk management and enhancing customer experiences.

  • Agriculture (AgriTech):
    AI helps analyze weather patterns, manage crop fields, and optimize fertilizer use, resulting in higher yields and lower costs. The collaboration between ListenField and Kubota to develop smart agricultural machinery is a notable case study.

  • Manufacturing:
    AI is used to inspect product quality, reduce production errors, and predict maintenance needs, boosting overall efficiency.

  • Healthcare:
    The government is actively promoting AI in the health sector to improve diagnostic accuracy and position Thailand as a regional medical hub. The Medical AI Consortium has collected over 1.6 million medical images to train AI models.

 

Bridging the Gap: From Infrastructure to True Adoption

This situation also highlights a mismatch between Thailand’s advanced national AI infrastructure (as discussed in Article 1) and the shallow, fragmented adoption at the organizational level. Having world-class supercomputers and a National AI Service Platform doesn’t automatically lead to complex business applications.

The true bottlenecks are not computing power, but rather a lack of skills, strategic vision, and ready-to-use business applications. The government’s “Build it and they will come” strategy is necessary but insufficient.

The next crucial step is solving the “last mile” of AI adoption—developing user-friendly tools, providing SME consulting, and cultivating talent capable of connecting powerful AI platforms to real business problems.

Only then can Thailand move from AI adoption to becoming a truly AI-driven economy.