The State of Responsible AI in Financial Services

2026-03-16 - 06:43

Globally, artificial intelligence (AI) and technology leaders have recognized that responsible AI standards are an essential innovation enabler for achieving tangible enterprise return on investment (ROI). However, organizations are falling short on the implementation of these standards, according to a global survey conducted by Corinium in collaboration FICO. The research, which surveyed 254 C-suite AI and tech leaders in Q2 2025, found that more than half of those surveyed (56.8%) identify responsible AI standards as a leading contributor to increasing reliable and consistent ROI. This highlights that these practices are no longer viewed purely as regulatory checkboxes or ethical obligations, and are also increasingly seen as business enablers that contribute directly to financial performance. This also suggests that organizations are maturing in their AI journey. Early AI adoption often prioritizes rapid experimentation, but now that companies are scaling AI, they need predictable outcomes. Responsible AI standards likely reduce risk, prevent costly failures, and build stakeholder trust, all of which stabilize returns. AI technologies with the greatest impact on ROI, Source: 2025 State of Responsible AI in Financial Services: Unlocking Business Value at Scale, Corinium and FICO, 2026 The standards gap Despite recognizing that AI standards are critical to long-term AI benefits and ROI, few organizations have actually integrated AI development and development standards. Of the organizations surveyed, just 12.7% have fully adopted key AI development and deployment standards, such as bias mitigation, performance monitoring, and secure data handling. Security and customer experience quality are the most widely adopted AI standards, each at about 16%. In contrast, model monitoring and bias mitigation are particularly underdeveloped, with just 7% of organizations reporting full adopted. This suggests that once models go live, they often operate with minimal oversight. These gaps raise fundamental questions about AI readiness. Models rolled out without AI standards lack defined and sanctioned metrics for drift, bias, or fairness. This leaves organizations unable to substantiate claims of responsible AI deployment. Even in organizations that have made progress defining internal standards, responsible AI protocols are often confined to specific teams or projects, rather than enforced across the entire corporation. This makes AI governance both a philosophical and logistical challenge. Standards incorporated within AI operationalization processes, Source: 2025 State of Responsible AI in Financial Services: Unlocking Business Value at Scale, Corinium and FICO, 2026 Infrastructure gaps The study also unveiled infrastructure gaps. Globally, CIOs and CTOs identified the biggest barriers to scalable AI as unpredictability in system execution performance (62.02%), data storage, and processing limitations (58.1%), and gaps in real-time monitoring (36.6%). These findings suggest that organizations struggle to guarantee consistent output speeds and reliability as AI workloads grow. It also indicates that existing hardware and cloud architectures are often insufficient to handle the massive computational demands of advanced AI models. Furthermore, teams are struggling to effectively track system health or detect anomalies as they happen. Collectively, these results show that the business community is bottlenecked not by a lack of strategic vision, but by immature infrastructure. This infrastructure fails to support the stability and scale required for enterprise-grade AI deployment. Infrastructure-related barriers to scaling AI solutions from pilot to full-scale deployment, Source: 2025 State of Responsible AI in Financial Services: Unlocking Business Value at Scale, Corinium and FICO, 2026 Improving customer experience, cost savings as main catalysts The study also asked AI and tech leaders about the main catalysts for AI investments in their organizations. Respondents cited customer experience improvement (66%) as the main driver, followed by executive goals to do more with AI (63%), and increased revenue or market share (61%). These results emphasize AI’s role in unlocking new business opportunities and revenue streams. This comes as organizations globally are facing pressure to cut costs and remain competitive. 70% of respondents cited cost savings or process efficiency as a key catalyst for AI initiatives, followed by competitive pressures (65%), and the desire to leverage the benefits of disruptive technology (55%). The main catalysts of change, Source: 2025 State of Responsible AI in Financial Services: Unlocking Business Value at Scale, Corinium and FICO, 2026 Rapid adoption and early benefits Adoption of AI has surged over the past years. The 2025 McKinsey Global Survey on the state of AI reveals that 88% of nearly 2,000 respondents regularly use AI in at least one business function, a sharp rise from 78% in 2024 and just 50% in 2022. Organizations that use AI in at least one business function, Source: McKinsey and Company, Nov 2025 Organizations also reported early benefits. A majority said that their organizations’ use of AI has improved innovation, and nearly half reported improvement in customer satisfaction and competitive differentiation. Extent at which AI use has affected organizational measures over the past year, Source: McKinsey and Company, Nov 2025 The most significant cost savings from AI are occurring in software engineering, with 56% reporting decreased cost in this business unit over the past year, followed by manufacturing (56%), and IT (54%). Conversely, revenue increases are most frequently reported in use cases within marketing and sales (67%), strategy and corporate finance (65%), and product and service development (62%). Revenue increase within business units from AI use, past 12 months, by function, Source: McKinsey and Company, Nov 2025 Featured image: Edited by Fintech News Switzerland, based on image by Dmitrii Travnikov via Freepik