AI Adoption Rising, but Trust Gap Limits Impact

• By John K. Waters
• 10/09/25

A recent global study by IDC and SAS found that while the adoption of artificial intelligence continues to expand rapidly across industries, a misalignment between perceived trust in AI systems and their actual trustworthiness is limiting business returns.

The Data and AI Impact Report surveyed 2,375 respondents worldwide, including business and IT leaders across banking, insurance, life sciences, government, education, and other sectors. It found that 65% of organizations are now using AI, with 32% planning to adopt it within the next year. Generative AI has already overtaken traditional AI in both visibility and use, with 81% adoption compared to 66% for machine learning. Agentic AI, which allows autonomous decision-making, has reached 52% adoption, while quantum AI remains at 30% and is largely experimental.

The Trust Dilemma

Central to the findings is what the report refers to as the "trust dilemma" — a misalignment between confidence in AI systems and their actual reliability. Nearly half of organizations surveyed (46%) experience this gap. Many either underutilize reliable systems due to low confidence or, more commonly, place strong trust in unproven systems, particularly those based on generative AI. The report highlights that while 78% of organizations claim to fully trust AI, only 40% have implemented governance, explainability or ethical safeguards to justify that trust.

This mismatch has tangible financial consequences. Companies experiencing the trust dilemma report significantly lower returns on AI investments. Organizations with stronger governance frameworks and more advanced data infrastructures reported higher business impact, underscoring the connection between trust and value realization.

Investments and Priorities

Respondents indicated that investment in trustworthy AI practices is rising. Fifty-seven percent plan to moderately increase spending in this area, while 25% expect significant increases. Key areas include hiring or training experts in AI ethics and compliance, embedding responsible AI principles throughout the life cycle, and improving model explainability and fairness.

Despite this momentum, only about a quarter of organizations currently have a central group dedicated to AI governance. The report emphasizes that embedding trust as an organizational culture, rather than merely as a compliance requirement, will be crucial to achieving sustainable adoption.

Industry Variations

The survey highlights significant differences by sector.

• Banking leads in current trustworthy AI efforts, with 23% of organizations scoring at the highest maturity level, and shows the strongest plans for future investment. However, data governance and talent shortages continue to pose obstacles.
• Insurance shows lower maturity overall but places more emphasis on data governance than other industries. About 43% of insurers fall into the trust dilemma, slowing innovation but avoiding overreliance on unproven tools.
• Government organizations demonstrate strong AI maturity but weaker data infrastructure and governance. Nearly half fall into the trust dilemma, with many over-relying on systems not yet fully trustworthy.
• Life sciences leads in overall AI and data maturity but has the largest share of organizations over-relying on AI without adequate safeguards, reflecting enthusiasm that sometimes outpaces governance.
• Education trails in data infrastructure maturity, with just 7% of respondents calling their organization's infrastructure "optimized." About four in 10 respondents consider the current status of AI at their institution "functional," while 20% called it "integrated" and 11% "transformative."

Emerging Technologies

The report also notes that quantum AI, though still experimental, is gaining attention. Around 30% of respondents reported familiarity with the technology, and 25% expressed trust in it, despite limited real-world applications. The combination of quantum computing and AI is seen as holding potential for fields such as logistics, climate modeling, cybersecurity, and life sciences.

Looking Ahead

The findings suggest that organizations that focus narrowly on cost-cutting from AI achieve the lowest returns. Greater impact is reported when AI is used to enhance decision-making, process efficiency, customer experience, and resilience. As AI moves further into agentic and quantum applications, progress will depend heavily on stronger data foundations, governance, and skilled talent.

The report concludes that overcoming the trust dilemma is a prerequisite for unlocking AI's next phase of growth and ensuring that adoption translates into long-term business and societal value.

For the full report, visit the SAS site here.