Artificial intelligence may be transforming enterprise operations at unprecedented speed, but a new study from SAS and IDC suggests that the organisations seeing the strongest returns are not simply those deploying more AI. They are the ones governing it better.

The second annual Data and AI Impact Report, titled The New Economics Of Trust, found that organisations applying what the researchers describe as trustworthy AI practices were 15 times more likely to report strong or high returns on investment from their AI initiatives.

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Governance Is Now A Performance Driver

According to the study, enterprises with the strongest governance, data quality and auditability practices reported at least twice the return on investment from AI deployments compared with other organisations. While SAS noted that these leaders still represent a relatively small segment of the market, their advantage is clear: trust is becoming a source of competitive differentiation.

At the other end of the spectrum, fewer than one in 20 organisations classified as laggards in trustworthy AI reported similarly strong returns.

“When AI works, it’s incredibly impactful,” said SAS chief technology officer Bryan Harris. “However, it is well documented that state-of-the-art agents can have error rates that exceed 25 per cent on complex tasks, which is unacceptable in high-stakes decision-making.”

Harris added that 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,” he said.

Why Trust Is Becoming A Scaling Requirement

IDC vice president Chris Marshall said the challenge is intensifying as AI systems become more autonomous. “As AI becomes more autonomous, organisations face a new challenge: maintaining confidence in systems people don’t fully understand,” he said.

“Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully,” Marshall added.

The report found that employees frequently override AI-generated recommendations, with a lack of explanation emerging as the leading reason. Researchers said users were increasingly unwilling to rely on systems when they could not determine whether the output was correct or understand how the model reached its conclusion.

That trust deficit becomes more costly as AI systems take on greater autonomy. Manual corrections can slow workflows, reduce productivity and erode profitability, even when the underlying model is technically capable of making accurate recommendations.

According to the study, 97.2 per cent of users override AI-generated recommendations in at least some cases. The most common reason, regardless of whether employees believed the output was correct, was that the system could not explain its reasoning.

Autonomy Increases Both Opportunity And Risk

The report also found that trust declines as systems become more autonomous, falling from 76 per cent for generative AI to 66 per cent for agentic AI. That drop matters because organisations are increasingly deploying AI in settings where decisions are more consequential and less transparent.

SAS and IDC said the findings point to a widening performance gap between organisations that prioritise trustworthy AI and those that do not. The difference, they argued, is not necessarily the technology itself, but how it is managed and governed.

Organisations investing in trustworthy AI measures were 15 times more likely to report strong or high returns on investment, with 62 per cent doing so compared with 4 per cent among organisations without such practices. Those with the strongest trustworthy AI capabilities also reported 1.85 times greater gains across 13 business outcomes, including revenue growth, cost savings and customer experience.

The study further found that 85 per cent of AI leaders with strong trustworthy AI practices were increasing investment in the area by more than 10 per cent in 2026, a signal that the gap may widen further.

Data Foundations Remain A Bottleneck

Beyond governance, the research highlighted weak or outdated data foundations as another major barrier to adoption. SAS and IDC said many organisations are still deploying AI on underdeveloped data infrastructure, limiting their ability to deliver the transparency and explainability required for effective oversight.

Only 17.5 per cent of enterprises were found to have a fully optimised data infrastructure mature enough to meet the demands of agentic AI. Organisations with such a foundation were four times more likely to expect strong returns from AI projects and six times more likely to require data quality and explainability controls designed to build trust.

The study was based on a global survey of 2,699 decision-makers with knowledge of or influence over their organisations’ data and AI programmes across 28 countries and four industries: banking, insurance, life sciences and the public sector.

Industry Leaders Are Treating Governance Differently

In banking, 85 per cent of AI leaders had established AI governance frameworks, compared with just 29 per cent of organisations classified as laggards. The report said leading banks are increasingly viewing AI governance as both a competitive and operational issue, not merely a compliance obligation.

In the public sector, 41 per cent of leaders said they were increasing investment in trustworthy AI by more than 20 per cent in the coming year, one of the highest rates across the survey.

Life sciences showed another important pattern: 23 per cent of organisations had scaled AI across their entire companies, the highest proportion among the four sectors studied.

What The Report Means By Trustworthy AI

SAS and IDC define trustworthy AI as artificial intelligence designed to be reliable, fair, secure and compliant with regulatory requirements, while also being able to explain how it reached a decision.

The report argues that users and decision-makers across an organisation should be able to hold an AI system to a predetermined chain of accountability when its output is incorrect or absent. In practice, that means AI cannot be treated as a black box if it is expected to support high-stakes decisions at scale.

The companies assessed organisations across five dimensions of trustworthy AI and scored them out of 100. Those with an average score of at least 80 were classified as trustworthy AI leaders.

The five dimensions were data quality and governance, model governance and oversight, explainability and fairness, responsible AI policy, and audit and accountability.

The message from the report is unmistakable: in the next phase of AI adoption, the winners may not be those experimenting the fastest, but those building the strongest foundations of trust.