In banking, trust is not optional: it is everything. Although banking has invested in AI before other sectors, most entities are implementing artificial intelligence without the oversight and infrastructure necessary to earn that trust. That is the main concern revealed by the new perspectives of the banking sector in the Data and AI Impact Report: The Trust Imperative by SAS, with research from IDC.
Among the four sectors examined in the study, the banking sector outperforms government, insurance, and life sciences in both AI spending and adoption of trusted AI practices. In fact, approximately a quarter (23%) of organizations in the banking sector operate at the highest level of IDC’s ‘Trustworthy AI Index’. But even with these advantages, most institutions in the banking sector fall far short of the document’s “ideal index”, which combines high trust with a high level of reliability.
According to the report, only 11% of entities in the banking sector have truly reliable systems and trust them. On the other hand, almost half (47%) of the banking industry falls into what IDC calls the “trust dilemma”: they underutilize AI because they do not trust it enough, or they overrely on AI systems that have not been properly validated.
“On trustworthy AI, banking leads all industries in this study, and yet the fundamental readiness of most banking organizations is nowhere near where it should be,” said Stu Bradley, senior vice president of Risk, Fraud and Compliance Solutions at SAS. “About nine in ten banks have yet to fully align trust with evidence, and around one in five are still operating with siled data. Closing the gap between AI ambition and AI readiness should be a top-level priority for the entire banking sector.”
Investment increases, but foundations remain fragile
The report, based on a global, cross-industry survey of 2,375 IT and business leaders, reveals a worrying trend: in the banking sector, investment in AI capabilities is not matched by investment in the pillars of responsible innovation that make AI trustworthy. In an industry like banking, where the failure of a single model can trigger regulatory sanctions or erode consumer trust overnight, this disconnect is especially dangerous.
And the problem isn’t a lack of investment: the banking sector’s track record of AI spending outpaces all other sectors in the study. The majority of banking organizations (60%) expect growth between 4% and 20%, while a smaller group (12%) anticipate even steeper increases. Despite this momentum, the study found that significant fundamental weaknesses persist in the banking sector, including:
• Data silos. Nearly one in five banking organizations (19%) still operate with a siled data infrastructure, the worst rate among the industries analyzed in the study.
• Insufficient data foundation. A significant portion of the banking sector lacks effective data governance (45%) and/or a centralized or optimized data infrastructure (41%).
• Talent gaps. Many organizations in the banking sector (42%) are also facing a shortage of specialized AI skills.
To address these issues, more than half (52%) of banking sector entities plan to expand their AI architecture; another 43% plan to form or expand teams dedicated to AI. However, less than a third (31%) of the banking sector plans to focus on developing and fine-tuning AI models themselves. In conclusion: these barriers are neither abstract nor theoretical; They are structural within the banking sector.
“The banking industry clearly understands the potential of AI, but understanding and execution are not the same,” said Kathy Lange, research director for IDC’s AI and Automation Practice. “Without robust data architecture, governance frameworks and talent pools, banking organizations risk investing money in AI initiatives that fail to deliver a return on investment (ROI) or, worse, undermine the very trust they depend on.”
Responsible innovation, not cost savings, drives AI ROI
The report also challenges the assumption that the primary value of AI in the banking sector is cost reduction. On the contrary, the banking sector is the only one that ranks product and service innovation above process efficiency as the main source of AI-driven value.
Cross-sector ROI figures show that the banking sector is on the right track. Companies using AI to improve customer experience saw the highest return: $1.83 for every dollar invested, closely followed by companies focused on expanding market share ($1.74). Those focused on cost savings recorded lower figures: $1.54 per dollar. Additionally, banking companies that prioritized trustworthy AI were 60% more likely to double the overall return on their AI initiatives. That’s strong proof that responsible innovation is a growth accelerator that more than pays for itself in the banking sector.
Closing the gap between AI ambition and AI readiness should be a top priority for the entire banking sector
Organizations in the banking sector are also moving more aggressively than other sectors towards Agentic AI, with almost a third planning to increase investment in trustworthy AI to support more autonomous systems. But as AI systems gain greater decision-making authority within the banking sector, the consequences of weak governance become more serious.
«Regulators are watching. Customers are watching. And right now, almost half of organizations in the banking sector are using unproven AI, or are hesitant to take advantage of AI that they have already validated,” says Mónica Gutiérrez, sales director for Financial Services at SAS for Spain and Portugal. “No banking entity wants to be left behind in this highly competitive race, and cost savings alone will not keep them in it. The banking organizations that win will be those that invest in governance, explainability, transparency and solid data foundations before they scale, not after something breaks.”
