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A new SAS report with research insights by IDC uncovers what’s powering the organizations winning the race to profit from their AI investments: embracing trustworthy AI measures. Organizations applying trustworthy AI practices were 15 times more likely to report strong return on investment (ROI) from their AI projects.
As identified in the second annual Data and AI Impact Report: The New Economics of Trust, organizations with the strongest governance, data quality and auditability practices consistently outperformed peers, reporting at least double the ROI from AI deployments.
“When AI works, it’s incredibly impactful,” said Bryan Harris, CTO at SAS. “However, it is well documented that state-of-the-art agents can have error rates that exceed 25% on complex tasks – which is unacceptable in high-stakes decision-making. In order to achieve accuracy and repeatability, organizations must embed domain expertise into agentic workflows, while keeping people at the center of governance and oversight. Organizations that do this successfully will close the trust gap and gain a competitive advantage in the market with AI.”
“As AI becomes more autonomous, organizations face a new challenge: maintaining confidence in systems people don’t fully understand,” said Chris Marshall, Vice President at IDC. “Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully.”
Local findings point to rapid progress in AI governance, with the UAE’s Trustworthiness Index climbing 30.2 points to 65.7 in 2026, above the global benchmark. “This progress reflects how quickly organizations in the UAE have moved from AI ambition to adoption; The UAE AI Strategy and the Dubai AI Roadmap pushed governance deployment into government and the private sector at an unprecedented pace. This capability is in response to a top-down deployment mandate, which explains why so much of it arrived at once” said Michel Ghorayeb, Managing Director, SAS UAE.
The report’s findings span three themes:
AI that can’t explain itself is a major business liability
Researchers found that employees are increasingly hesitant to rely on systems that cannot provide correct outputs or explain how decisions were reached. As AI gains autonomy, explainability becomes increasingly important.
The report also explored a major hurdle to success in AI adoption: when employees’ lack of trust in AI decisions leads them to override and make manual corrections. This only perpetuates the AI trustworthiness deficit, and can cost organizations time, productivity and profitability. When AI decision-making is only as good as the data it’s based on, building a strong data foundation becomes pivotal for organizations looking to reduce override rates.
Key findings:
- 97.2% of users override AI-generated recommendations in at least some cases.
- The number one reason employees decided to override AI, regardless of whether its output was considered correct, was when the AI could not provide an explanation behind its decision.
- Trust falls from 76% for generative AI to 66% for agentic AI, highlighting growing concerns as AI systems gain more autonomy.
This challenge is particularly evident in the UAE, where insufficient explanation is the leading reason for overriding AI recommendations, cited by 41.1% of respondents.
Trustworthy AI practices drive business success
The report exposes a widening ROI divide, suggesting organizations gaining the most value from AI are not necessarily deploying different technologies but managing AI differently.
Key findings:
- Organizations investing in trustworthy AI measures are 15 times more likely to report strong or high ROI on their AI projects (62% vs. 4%).
- Organizations with the strongest trustworthy AI practices realize 1.85 times greater gains across 13 different business outcomes, including revenue growth, cost savings and customer experience.
- 85% of these AI leaders with trustworthy practices are increasing their investment in this area by more than 10% this year, actively widening the performance gap.
Too many organizations are losing time and money to weak data foundations
Many organizations are deploying AI on severely underdeveloped or outdated data and data infrastructure, limiting their ability to govern AI effectively and realize value.
Key findings:
Only 17
5% of enterprises have a fully optimized data infrastructure mature enough for the demands of agentic AI, which negatively impacts performance. Organizations with an optimized data foundation are four times more likely to expect strong ROI from AI projects, and six times more likely to mandate the data quality and explainability controls necessary to build trust. Data Quality and Governance has emerged as the leading reliability priority among UAE organizations, rising to 62.2% in 2026. Yet only 13.5% mandate data-quality processes for every AI project, highlighting a gap between awareness and consistent implementation.
Take a deeper dive
The findings are based on a global survey of 2,699 decision-makers with knowledge of or influence over their company’s data and AI initiatives. The survey was conducted across 28 countries and four focus industries: banking, insurance, life sciences and the public sector. The report highlights industry use cases and findings that demonstrate how leaders in each of these industries around the globe are approaching AI.
Key findings:
- Banking leaders are going beyond compliance, treating robust AI governance as a competitive advantage and operational necessity – 85% of AI leader banks have established governance frameworks, compared to just 29% of laggards.
- Forty-one percent of public sector leaders are increasing trustworthy AI investment by more than 20% in the year ahead, which is as fast as the most ambitious organizations across any industry.
- 23% of life sciences organizations have scaled AI company-wide – the highest of any industry.
Explore study findings and access the full report here.
What makes AI trustworthy?
Trustworthy AI is artificial intelligence designed to be reliable, fair, secure, up to regulatory standards, and able to clearly show how it arrived at a decision. Users and decision-makers at all levels within an organization must be able to hold an AI system to a pre-determined chain of accountability for incorrect or missing AI output. Any AI system must also be governed and proven to be in compliance with clear rules.
What makes an organization a trustworthy AI leader?
Within the study, organizations were scored out of 100 against five dimensions of trustworthy AI. The report’s trustworthy AI leaders were organizations with an average total score of 80 or higher.
Each organization was scored across the following five trustworthy AI criteria.
- Data quality and governance.
- Model governance and oversight.
- Explainability and fairness.
- Responsible AI policy.
- Audit and accountability.