A SAS and IDC study finds Southeast Asian organisations lead global peers on measurable AI trust practices, narrowing the region’s trust gap. Stronger governance correlates with higher reported ROI, but infrastructure, data quality, and skills remain constraints.
Organisations that invest in trustworthy AI measures are 15 times more likely to report strong or high ROI.
Infrastructure maturity trails AI maturity: AI maturity rose 20.5 points in 2026 while infrastructure maturity rose only 4.1 points.
Data quality, fragmented deployments, and skill gaps (especially in Thailand) are recurring obstacles to scaling AI and realising value.
# What the study measured
# Why trust metrics matter for business
The study links trust measures to outcomes. Organisations investing in trustworthy AI measures were 15 times more likely to report strong or high returns on investment. That frames common governance activities not as compliance costs but as drivers of business value for AI deployments.
Other regional research complements this finding. For example, a SAP SE and Oxford Economics survey found 55% of 200 Thai organisations believe their AI implementation has not reached full potential. A separate McKinsey study of 330 respondents in Southeast Asia ranked unclear business benefits as the third-largest barrier to AI adoption, on par with limited budgets and data issues.
# Where progress is uneven
Improvements in trustworthiness are not matched by underlying infrastructure and organisational readiness. The study shows a 20.5-point increase in advanced AI maturity versus a 4.1-point rise in infrastructure maturity in 2026. That gap signals risk: organisations can design responsible models but still struggle to deploy them reliably at scale.
Siloed adoption remains common. The SAP/Oxford Economics research reported 47% of Thai organisations still run fragmented AI pilots rather than enterprise-wide programs. Leadership and governance indicators are weak: only 40% of Thai firms have a dedicated AI leader, 30% include AI KPIs for leadership, and 35% provide leadership training on AI risks and capabilities.
# Data and skills are recurring bottlenecks
Data readiness and output quality limit ROI. In Thailand, 57% of businesses consider themselves data-ready for AI, yet 70% identified data quality or availability as a barrier to better ROI. Seventy-nine percent report low-quality AI outputs at least occasionally, causing rework and delays.
Skills and governance maturity are underdeveloped. Seventy-six percent of Thai businesses doubt that company-led upskilling keeps pace with AI tool changes. Only 13% report full readiness in skills and 14% report full readiness in processes and frameworks to govern AI.
# Investment intent and next steps
That sequence aligns governance with execution. When governance, data readiness, and operational infrastructure improve together, organisations are better positioned to convert AI projects into measurable business returns.
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