Write Hatem Mohamed <\/p>\n\n\n\n
A new SAS report with research insights by IDC, based on a global survey of 2,699 business and technology decision-makers across 28 countries, uncovers what\u2019s 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.<\/strong><\/p>\n\n\n\n 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 \u2013 a comparatively small market segment \u2013 consistently outperformed peers, reporting at least double the ROI from AI deployments. Fewer than one in 20 trustworthy AI \u2018laggard\u2019 organizations reported the same.<\/p>\n\n\n\n The findings carry particular weight in Egypt, which ranked first in Africa and 51st globally out of 195 countries in Oxford Insights’ 2025 Government AI Readiness Index – a climb of 14 places in a single year. The ranking reflects the momentum behind Egypt’s National AI Strategy (2025\u20132030), which targets a 7.7% AI contribution to GDP and the training of 30,000 AI specialists by 2030. As the country scales its AI ambitions across government and industry, the SAS report\u2019s findings underscore what will determine whether that growth translates into returns: not just deploying AI faster, but deploying it in ways employees and regulators can trust and explain.<\/strong><\/p>\n\n\n\n \u201cWhen AI works, it\u2019s incredibly impactful,\u201d said Bryan Harris, CTO at SAS. \u201cHowever, it is well documented that state-of-the-art agents can have error rates that exceed 25% on complex tasks \u2013 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.\u201d<\/strong><\/p>\n\n\n\n \u201cAs AI becomes more autonomous, organizations face a new challenge: maintaining confidence in systems people don’t fully understand,\u201d said Chris Marshall, Vice President at IDC. \u201cOur findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully.\u201d<\/strong><\/p>\n\n\n\n The report\u2019s findings span three themes:<\/strong><\/p>\n\n\n\n AI that can’t explain itself is a major business liability 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\u2019s based on, building a strong data foundation becomes pivotal for organizations looking to reduce override rates.<\/strong><\/p>\n\n\n\n Key findings: Trustworthy AI practices drive business success Key findings: Too many organizations are losing time and money to weak data foundations Key findings: Take a deeper dive Key findings: Explore study findings and access the full report at sas.com\/ai-impact.<\/strong><\/p>\n\n\n\n What makes AI trustworthy? What makes an organization a trustworthy AI leader? Each organization was scored across the following five trustworthy AI criteria.<\/strong><\/p>\n\n\n\n Write Hatem Mohamed A new SAS report with research insights by IDC, based on a global survey of 2,699 business and technology decision-makers across 28 countries, uncovers what\u2019s 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 […]<\/p>\n","protected":false},"author":3,"featured_media":939,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[202,229,38,5],"tags":[230],"class_list":["post-938","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-afircian","category-ai","category-economics","category-technology","tag-sas"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/posts\/938","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=938"}],"version-history":[{"count":1,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/posts\/938\/revisions"}],"predecessor-version":[{"id":940,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/posts\/938\/revisions\/940"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=\/wp\/v2\/media\/939"}],"wp:attachment":[{"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=938"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=938"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/en.alkhabr24.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=938"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Researchers found that at many organizations, employees are increasingly hesitant to rely on systems that may or may not be able to offer correct output or explain how AI arrived at a final decision. As AI gains autonomy, this liability grows, making explainability crucial for success.<\/strong><\/p>\n\n\n\n
\u2022 97.2% of users override AI-generated recommendations in at least some cases.
\u2022 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.
\u2022 Trust falls from 76% for generative AI to 66% for agentic AI, highlighting growing concerns as AI systems gain more autonomy.<\/strong><\/p>\n\n\n\n
The report exposes a widening ROI divide between organizations that prioritize trustworthy AI practices and those that do not. The findings suggest organizations gaining the most value from AI are not necessarily deploying different technologies but instead managing AI differently.<\/strong><\/p>\n\n\n\n
\u2022 Organizations investing in trustworthy AI measures are 15 times more likely to report strong or high ROI on their AI projects (62% vs. 4%).
\u2022 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.
\u2022 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.<\/strong><\/p>\n\n\n\n
Most organizations are deploying AI on severely underdeveloped or outdated data and data infrastructure. Without a strong data foundation to support crucial transparency and explainability, organizations struggle to govern AI effectively and realize value.<\/strong><\/p>\n\n\n\n
\u2022 Only 17.5% of enterprises have a fully optimized data infrastructure mature enough for the demands of agentic AI, which negatively impacts performance.
\u2022 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.<\/strong><\/p>\n\n\n\n
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.<\/strong><\/p>\n\n\n\n
\u2022 Banking leaders are going beyond compliance, treating robust AI governance as a competitive advantage and operational necessity \u2013 85% of AI leader banks have established governance frameworks, compared to just 29% of laggards.
\u2022 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.
\u2022 23% of life sciences organizations have scaled AI company-wide \u2013 the highest of any industry.<\/strong><\/p>\n\n\n\n
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.<\/strong><\/p>\n\n\n\n
Within the study, organizations were scored out of 100 against five dimensions of trustworthy AI. The report\u2019s trustworthy AI leaders were organizations with an average total score of 80 or higher.<\/strong><\/p>\n\n\n\n\n