It’s the misalignment between how much people trust AI and how trustworthy those systems actually are. 46% of organizations worldwide face this gap, leading to either underutilizing reliable systems or over-relying on unproven ones.

Trustworthy AI ensures reliability, transparency, and ethical use. Without it, AI adoption can fail to deliver ROI, increase risk, and erode confidence.

Life sciences and government show the largest trust dilemmas, with nearly half of organizations either underutilizing or over-relying on AI.

A benchmark that measures the business value delivered by AI, including customer experience, operational efficiency, and market share growth.

It measures how well organizations are implementing governance, explainability, compliance, and responsible AI practices to make AI systems reliable and ethical.

Improving customer experience, expanding market share, and boosting resilience deliver the highest returns. Cost-cutting delivers the lowest ROI.

Agentic AI systems act autonomously, making decisions and completing tasks with minimal human input. 52% of organizations report adoption, but many face barriers like data governance and skills shortages.

Quantum AI combines quantum computing with AI to solve highly complex problems in areas like logistics, finance, and climate modeling. It’s still early but gaining attention.

Non-optimized cloud environments, poor data governance, lack of skills, and siloed data are the top obstacles organizations face when scaling AI.

Because trust is directly linked to ROI. Organizations with higher investments in responsible AI are seeing stronger business outcomes and more sustainable adoption.

Organizations that align trust with trustworthy practices see significantly higher ROI. The report shows companies with strong governance, explainability, and ethical safeguards consistently achieve greater business value.

Because AI’s real value comes from enabling growth — expanding market share, improving customer experience, and increasing resilience. Cost-cutting is tactical, but strategic use cases deliver sustainable returns.

Data maturity is directly tied to AI maturity. Weak or siloed data environments stall AI progress, while optimized, well-governed data foundations unlock generative and agentic AI impact.

Ireland, Canada, and Australia/New Zealand rank high for both trustworthiness and impact, proving the value of responsible AI investment. The U.S. is in the middle, while regions like China and the UAE lag behind.

The report shows generative AI is trusted 200% more than machine learning, despite being less explainable. Over-reliance creates risks of error, bias, and compliance failures if governance isn’t in place.

Top areas of investment are AI ethics and compliance experts, data science and AI engineering teams, and reskilling the workforce to critically use AI responsibly.

Government entities show strong AI maturity but weak data infrastructure. They’re also more likely than other industries to over-rely on untrustworthy systems, creating risks for fairness and accountability.

Life sciences lead in AI maturity and data infrastructure but face the largest trust dilemma, often over-relying on unproven systems due to enthusiasm outpacing governance.

It’s universal: 46% of organizations globally are caught in misalignment. Banking invests heavily in trustworthy AI but still faces governance challenges, while insurance is cautious but slow to innovate.

Because AI search and LLMs are curating the answers executives see. If SAS’s research isn’t discoverable in HTML, structured, and cited, competitors or less authoritative voices will fill the gap.