Thai companies are moving from experimental AI projects to serious business investments, with expectations that their returns will nearly double over the next two years. According to a new study by SAP and Oxford Economics surveying 200 business leaders in Thailand, the average Thai company expects to achieve an 18% return on AI investment this year, growing to 35% by 2028. However, significant obstacles around data quality, workforce readiness, and governance oversight could prevent organizations from realizing these ambitious targets.

What's Driving Thailand's AI Investment Acceleration?

Thai businesses are planning to spend an average of US$18.3 million on AI this year, below the global average of US$28 million. Yet investment is expected to grow by 44% over the next two years, signaling a major shift in how seriously companies view AI as a strategic priority. Nearly a quarter of all tasks (24%) in the average Thai business are already enabled by AI today, a figure projected to increase to 42% within two years.

One emerging technology capturing significant attention is agentic AI, which refers to autonomous AI systems that can perform complex tasks with minimal human intervention. Nearly 8 in 10 Thai businesses (78%) see agentic AI as having moderate to very high potential to transform their organizations. Expected returns from agentic AI alone are projected to reach US$8.5 million per company over the next two years.

"Thai businesses are moving from AI experimentation toward execution, and we are beginning to see that momentum reflected in growing returns. The next opportunity is to connect AI with the data, processes and governance that enable businesses to scale AI responsibly and turn adoption into lasting business value," noted Kulwipa Piyawattanametha, Managing Director, SAP Indochina.

Kulwipa Piyawattanametha, Managing Director, SAP Indochina

Why Are Data, Skills, and Governance Creating Bottlenecks?

Despite optimism about AI returns, Thai organizations face three interconnected challenges that could limit their ability to scale AI effectively. While 71% of Thai businesses report satisfaction with their current AI ROI, 55% acknowledge that their AI implementation is not yet delivering its full potential.

The most immediate obstacle is data readiness. Although 57% of Thai businesses claim they are currently data-ready for AI, 70% identify data quality or availability issues as a significant barrier to achieving greater returns. The impact is tangible: 79% of businesses experience low-quality AI outputs at least occasionally, causing rework, delays, or backlogs.

Workforce challenges compound the problem. Some 76% of Thai businesses are not convinced that company-led upskilling programs are keeping pace with the rapid evolution of AI tools. While 99% expect AI to have some impact on workforce planning, 81% are not convinced that existing roles are changing quickly enough to fully take advantage of AI-enabled workflows.

Governance represents perhaps the most critical gap. Only around 1 in 10 Thai businesses say their skills (13%) or their processes and frameworks (14%) are fully ready to govern AI effectively. This governance weakness is particularly concerning given that 74% of Thai businesses report that employees use third-party AI tools at least occasionally without formal approval or oversight. Among the reported impacts, 63% cite inconsistent or inaccurate outputs, and 62% report data leakage or intellectual property exposure.

How to Build a Foundation for Responsible AI Scaling

• Establish dedicated AI leadership: Less than half of Thai companies (40%) have a dedicated AI leader responsible for AI adoption, and only 30% have leadership KPIs specifically tied to AI performance. Organizations should assign clear accountability for AI strategy and outcomes.
• Invest in data infrastructure and quality: With 70% of businesses citing data quality or availability as a barrier, companies must prioritize data governance, cleansing, and integration before expanding AI deployments across the enterprise.
• Implement formal AI governance frameworks: Only 14% of Thai businesses have processes and frameworks fully ready to govern AI. Organizations should establish approval workflows, agent registries, and human-in-the-loop processes before scaling agentic AI deployments.
• Align workforce development with AI evolution: Since 76% of businesses doubt their upskilling efforts are keeping pace with AI tools, companies should create continuous learning programs that help employees adapt to AI-enabled workflows and new role requirements.
• Create oversight for shadow AI use: With 74% of businesses reporting unapproved third-party AI tool use, organizations should establish clear policies, approval processes, and monitoring to prevent data leakage and ensure consistent outputs.

What Do Agentic AI Readiness Gaps Reveal About Enterprise Maturity?

Agentic AI represents the next frontier of AI value creation, yet Thai businesses are largely unprepared for this transition. Only 2% of Thai businesses say they are fully prepared for agentic AI, while the majority report being either partially prepared or not prepared at all.

The governance challenges are particularly acute. Today, 40% of Thai companies do not have a human-in-the-loop process for agentic workflows, 40% lack a standard approval process for agents, and only 39% maintain a registry of the agents deployed across their business. These gaps are critical because nearly two-thirds of Thai businesses (65%) either agree or are unsure whether they are deploying agents faster than they can govern them.

"Realising real value from AI is not going to be easy because it demands a new approach. Businesses in Thailand, large and small, will need to connect AI to the data and processes that run their organisations, and make sure it has the context and governance to drive trusted results. That's what we call the Autonomous Enterprise. This isn't a technical change; it's a human one. Because you can only achieve real value if agents, processes, and people work as one," concluded Kulwipa Piyawattanametha.

Kulwipa Piyawattanametha, Managing Director, SAP Indochina

The research reveals a critical insight: Thai businesses are caught between ambition and readiness. While nearly half of organizations (47%) still rely on fragmented, siloed AI deployments rather than enterprise-wide adoption, the expectation of nearly doubling ROI in two years suggests that companies believe they can close these gaps quickly. Success will depend on whether organizations can simultaneously address data quality, build governance frameworks, upskill their workforce, and establish clear leadership accountability for AI outcomes. The window for moving from experimentation to execution is narrowing, and the companies that move fastest on governance and data infrastructure may pull ahead of their competitors.