Analysis covers diverse industries reporting varied AI maturity, trust, and investment patterns amid ongoing challenges in transparency and ethical safeguards.

Based on a survey of 2,375 respondents around the world* on the use, impact, and trustworthiness of AI, a global firm producing data management software and related services has shared some data findings with the media.

First, 48% of respondents had cited having “complete trust” in generative AI (GenAI), followed by agentic AI at 33%. Traditional AI had the lowest complete trust rate at 18%.

Second, 78% of respondents had reported having complete trust in AI overall, but 40% had indicated having invested in governance, explainability, and ethical safeguards to make AI systems demonstrably trustworthy.

Other findings

Third, respondents indicated that the use of GenAI (81%) had eclipsed traditional AI (66%) in adoption across organizations worldwide. Also:

• Investments in AI governance averaged 2%, with respondents citing the development of an AI governance framework among their top organizational priorities, and less than 10% reporting having policies for responsible AI in place.
• Respondents working in firms deemed to belong as “trustworthy AI leaders” (those investing more in trustworthy AI practices) were 1.6 times more likely to report doubling the returns on investments on their AI projects compared to organizations with lower investment levels in AI ensuring trustworthiness.
• Three major hurdles cited for AI success included weak data infrastructure (49%), poor governance (44%), and a shortage of skilled AI specialists (41%).
• 58% of respondents cited “difficulty in accessing relevant data sources” as the main challenge in managing data for AI implementations; other concerns among respondents included data privacy and compliance (49%) and data quality (46%).
• Respondents also cited concerns around data privacy (62%), transparency and explainability (57%), and ethical use (56%).
• Respondents in firms that were more-recent adopters of AI prioritized personal productivity, while those in organizations with over eight years of AI usage experience had cited process efficiency (64%) and decision making (60%) as leading priorities. Cost reduction ranked lower among respondents in mature adopters.
• Industry data analysis showed banking, insurance, life sciences, and government with varying levels of AI and data infrastructure maturity, with life sciences organizations showing relatively higher AI maturity and trustworthiness but modest plans for future investments in trustworthy AI.

According to Bryan Harris, Chief Technology Officer, SAS, the firm that commissioned the survey: “For the good of society, businesses and employees — trust in AI is imperative,” suggesting that the AI industry will need to increase the success rate of implementations; that humans need to review AI results critically; and that leaders need to empower the workforce with AI.

*including IT professionals and line-of-business leaders (with a particular focus on banking, insurance, life sciences, and government, as well as sectors such as manufacturing, retail, and telecommunications) across various parts of North America, Latin America, Europe, the Middle East and Africa, and the Asia Pacific region. However, detailed statistical disclosures of respondent demographics, precise geographic or sectoral breakdowns, sample stratification, and confidence measures were not provided in the report. Readers are advised to interpret the survey data and findings with caution due to limitations in the transparency of the methodology, lack of detailed respondent demographics, and absence of thorough statistical disclosures. As with any survey, responses represent the views of participants at the time of data collection and may not be fully representative or generalizable to broader populations.