Singapore – Organisations across Southeast Asia are making progress in building trustworthy artificial intelligence (AI) systems, with the region outperforming global benchmarks across five measures of AI trustworthiness, according to a new study from SAS and IDC.
The second annual report found that Southeast Asia’s Trustworthiness Index increased from 57.7 in 2025 to 66.5 in 2026, while its Trust Gap narrowed from 14.6 points to 6.6 points. The region was the only market in the study to exceed the global benchmark across all five trustworthiness dimensions.
The findings come as organisations across the region increase their adoption of more autonomous AI systems. However, the report identified gaps in infrastructure readiness, ongoing oversight and measurable business returns that could affect the ability of organisations to scale AI effectively.
The study found that organisations investing in trustworthy AI measures were 15 times more likely to report strong or high returns on investment (ROI). Despite this, Southeast Asia’s Impact Index remained broadly unchanged, edging down from 58.3 to 58.0.
The proportion of organisations in the region reporting strong or high ROI also declined from 36.7% to 28.7%, suggesting that improvements in AI governance and trustworthiness have not yet translated consistently into measurable business outcomes.
“When AI works, it’s incredibly impactful. However, it is well documented that state-of-the-art agents can have error rates that exceed 25% on complex tasks1 – which is unacceptable in high-stakes decision-making,” said Bryan Harris, CTO at SAS.
“In order to achieve accuracy and repeatability, organisations must embed domain expertise into agentic workflows, while keeping people at the centre of governance and oversight. Organisations that do this successfully will close the trust gap and gain a competitive advantage in the market with AI,” Harris continued.
“Organisations in Southeast Asia that invest in building genuine AI capability will be better positioned to earn trust and lead in an AI-driven market,” said Deepak Ramanathan, Vice President, Customer Advisory at SAS.
“The region’s next challenge is turning its gains in trustworthiness into measurable impact. Explainability becomes increasingly important as autonomous AI expands; through advanced analytics, we understand how AI is being utilised throughout organisations, where potential risk areas exist, and what policy says regarding deployment, to allow leaders to progress more quickly while maintaining control of what is being deployed,” Ramanathan continued.
“As AI becomes more autonomous, organisations face a new challenge: maintaining confidence in systems people don’t fully understand. Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully,” said Chris Marshall, Vice President at IDC.
Trustworthiness improves across all five dimensions
All five dimensions measured in the study improved in Southeast Asia between 2025 and 2026.
Data Quality & Governance increased by 10.7 points to 66.7, while Model Governance & Oversight rose 14 points to 64.2. Explainability & Fairness increased by 6.5 points to 63.7.
Responsible AI Policy rose 9.8 points to 71.2, while Audit & Accountability increased by 11.3 points to 70.5.
The region’s perceived trust score remained broadly stable at 73.1. The report therefore attributed the narrowing Trust Gap primarily to improvements in organisations’ capabilities rather than a reduction in expectations around AI.
Lack of context drives AI overrides
The study also found that employees are overriding AI recommendations when they lack sufficient information to understand or assess the systems’ decisions.
In Southeast Asia, insufficient explanation was cited as the leading reason for overriding an AI recommendation at 37.8%, followed by a lack of situational context at 37%.
The findings point to explainability and data quality as increasingly important considerations as AI systems take on more autonomous tasks. Manual overrides can also add time and reduce some of the productivity benefits organisations expect from AI adoption.
AI adoption is moving faster than infrastructure
The report found that AI maturity in Southeast Asia increased by 20.5 points, compared with a 4.1-point increase in infrastructure maturity.
The gap could become more significant as organisations adopt agentic AI systems, which require infrastructure capable of supporting increasingly autonomous workloads.
At the same time, organisations are planning to increase AI spending. Some 65.6% of respondents said they expect to make a small increase in AI spending over the next 12 months, while 14.8% expect a large increase.
Governance activity falls despite higher scores
The study identified a further gap between improvements in governance capabilities and ongoing oversight.
Although the region’s Audit & Accountability score increased by 11.3 points to 70.5, the proportion of organisations conducting regular AI audits or impact assessments fell from 47.3% to 21.3%.
The report suggests that organisations will need to maintain regular verification and assessment as AI systems become more autonomous, particularly where they are involved in higher-impact decisions.
The findings are based on a survey of 2,699 decision-makers across 28 countries and four industries: banking, insurance, life sciences and the public sector. The Southeast Asia results combine responses from Singapore, Malaysia and Thailand, with 122 respondents in 2026 compared with 120 in 2025.
The study also found differences in AI adoption across industries. In banking, 85% of AI leader organisations had established AI governance frameworks, compared with 29% among lagging organisations. In the public sector, 41% of leaders planned to increase trustworthy AI investment by more than 20% over the following year.
Meanwhile, 23% of life sciences organisations reported having scaled AI across the organisation, the highest proportion among the industries covered.
The findings suggest that Southeast Asia has made progress in establishing the governance, data and accountability foundations needed for more trustworthy AI, but organisations still face challenges in translating those gains into infrastructure readiness and measurable business value.
