Study: Only 11% of banks have cracked the code on trustworthy AI

Manal Saleh

Even as AI spending surges, few banks have established the necessary governance and guardrails – and nearly half misjudge their own AI readiness

Dubai, United Arab Emirates, 7 April 2026 – In banking, trust isn't optional – it's everything. Yet, even as banks accelerate AI investment faster than other sectors, most are deploying AI without the oversight and infrastructure needed to earn that trust. That’s the central tension revealed in new banking insights from SAS’ Data and AI Impact Report: The Trust Imperative, with research insights by IDC.

Among the four sectors examined in the study, banking outpaces government, insurance and life sciences both in AI spending and adoption of trustworthy AI practices. In fact, about one-quarter (23%) of banks operate at the highest level of IDC’s Trustworthy AI Index. But even with these advantages, most banking institutions fall far short of the report’s “ideal state,” which combines high trust with high trustworthiness. According to the report:

·        Only 11% of banks have achieved both high internal confidence in AI and AI systems that are demonstrably trustworthy.

·        Nearly half (47%) fall into what IDC calls the “trust dilemma” – either underusing reliable AI because they don’t sufficiently trust it or overrelying on AI systems that haven’t been adequately validated.

“On trustworthy AI, banking leads every sector in this study – and even so, most banks’ foundational readiness is nowhere near where it needs to be,” said Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions at SAS. “Roughly nine in 10 banks have yet to fully align trust with proof, and about one in five are still running on siloed data. Closing the gap between AI ambition and AI readiness should be a top-down priority for all banks.”

As the UAE’s Vision 2031 and wider digital transformation efforts continue to gain momentum, banks across the Middle East are increasingly adopting advanced technologies to improve efficiency, strengthen resilience, and deliver better customer experiences.

Michel Ghorayeb, Managing Director at SAS UAE, said: “Banks in the Middle East are well-positioned to build on strong foundations, with robust data, clear governance, and effective oversight enabling AI investments to scale and deliver reliable results. At the same time, prioritizing transparency and making AI decisions easier to understand will play a key role in strengthening confidence. Banks that place responsible AI at the heart of their strategy will be best positioned to drive innovation, earn trust, and create sustainable long-term value.”

Investment is rising, but foundations remain fragile

The report, based on a global, cross-industry survey of 2,375 IT and business leaders, reveals a troubling pattern: Investment in AI capabilities is not being matched by investment in the responsible innovation pillars that make AI dependable. In an industry where a single model failure can trigger regulatory penalties or erode consumer confidence overnight, that’s a dangerous disconnect.

And the problem isn’t a lack of investment: Banks’ AI spending trajectory exceeds all other sectors in the study, with most banks (60%) expecting growth between 4% and 20%. A smaller subset (12%) anticipates even steeper increases. Despite this momentum, the study found significant foundational weaknesses remain, including:

·        Data silos. Nearly one in five banks (19%) still operate with a siloed data infrastructure – the worst rate among the study’s focus industries.

·        Insufficient data foundations. A significant portion of banks lack effective data governance (45%) and/or a centralized or optimized data infrastructure (41%).

·        Talent gaps. Many banks (42%) also face shortages of specialized AI skills.

To address these issues, more than half (52%) of banks plan to expand their AI architecture; another 43% plan to form or grow dedicated AI teams. But fewer than one-third (31%) plan to focus on developing and tuning AI models themselves. The takeaway: These aren't abstract or theoretical barriers; they’re structural.

"The banking sector clearly understands AI's potential, but understanding and execution are not the same," said Kathy Lange, Research Director of the AI and Automation Practice at IDC. "Without strong data architectures, governance frameworks and talent pipelines, banks risk pouring money into AI initiatives that can't deliver ROI – or worse, that undermine the very trust they depend on."

Responsible innovation, not cost savings, drives AI ROI

The report also challenges the assumption that AI's primary value in banking is cost cutting. To the contrary, banking stands alone in ranking product and service innovation above process efficiency as the leading source of AI-driven value.

Cross-industry ROI figures show banks are onto something. Organizations using AI to improve customer experience reported the highest return – $1.83 for every dollar invested – followed closely by those centered on expanding market share ($1.74). Those focused on cost savings reported the lowest – $1.54 per dollar. Moreover, organizations that prioritized trustworthy AI were 60% more likely to report doubling overall return on their AI initiatives. That’s solid proof that responsible innovation is a growth accelerator that more than pays for itself.

Banks are also moving more decisively than other sectors toward agentic AI, with nearly one-third planning increases in trustworthy AI investment to support more autonomous systems. But as AI systems gain greater decision-making authority, the consequences of weak governance grow more significant.

"Regulators are watching. Customers are watching. And right now, nearly half of banks are using unproven AI – or hesitating to tap AI they’ve validated," said Alex Kwiatkowski, Director of Global Financial Services at SAS. “No bank wants to become an ‘also-ran’ in this highly competitive race, and cost savings alone won’t keep them in it.

“The banks that win will be ones that invest in governance, explainability, transparency and strong data foundations before they scale, not after something breaks.”

To learn more and access the full Data and AI Impact Report, published in September 2025, visit SAS.com/ai-impact.